xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

9126af8fc4854391b11011eb26d9bf08576a47ea

100

py

Python

hooks/hook-palisades.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

1

2020-04-15T23:23:27.000Z

hooks/hook-palisades.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

null

hooks/hook-palisades.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

null

from PyInstaller.hooks.hookutils import collect_data_files datas = collect_data_files('palisades')

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913f12e6428573e4e895df5ad73f9791d08397ee

84

py

Python

core/storage/__init__.py

lehduong/Policy-gradient-credit-assignment

1d4c102964b985212874c1fe8710a8aa6ff9f328

[ "MIT" ]

8

2020-06-29T03:45:14.000Z

2022-02-21T03:41:34.000Z

core/storage/__init__.py

lehduong/Policy-gradient-credit-assignment

1d4c102964b985212874c1fe8710a8aa6ff9f328

[ "MIT" ]

null

core/storage/__init__.py

lehduong/Policy-gradient-credit-assignment

1d4c102964b985212874c1fe8710a8aa6ff9f328

[ "MIT" ]

null

from .base_storage import RolloutStorage from .cpc_storage import CPCRolloutStorage

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5

9140a4a86d1bead147dba92b72dcc1df0c9d09f0

143

py

Python

teacher/admin.py

aashutoshrathi/Student-Lifecycle-Management

c8ad67db3bde989de6d612454ace57084da272c0

[ "MIT" ]

9

2018-03-01T16:01:08.000Z

2021-02-21T16:01:29.000Z

teacher/admin.py

aashutoshrathi/Student-Lifecycle-Management

c8ad67db3bde989de6d612454ace57084da272c0

[ "MIT" ]

2

2018-03-01T18:16:11.000Z

2018-03-03T16:12:24.000Z

teacher/admin.py

aashutoshrathi/Student-Lifecycle-Management

c8ad67db3bde989de6d612454ace57084da272c0

[ "MIT" ]

1

2019-12-04T18:20:48.000Z

from django.contrib import admin from .models import Teacher, AssignedCourse admin.site.register(Teacher) admin.site.register(AssignedCourse)

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914612665bb7def562e2189f2bf65bc7489b8862

105

py

Python

pydistort/__init__.py

barsikus007/pydistort

408c28f01bc5f9240a80df6ebbece1dd60cdb087

[ "MIT" ]

null

pydistort/__init__.py

barsikus007/pydistort

408c28f01bc5f9240a80df6ebbece1dd60cdb087

[ "MIT" ]

null

pydistort/__init__.py

barsikus007/pydistort

408c28f01bc5f9240a80df6ebbece1dd60cdb087

[ "MIT" ]

null

__version__ = '0.0.1' from .image import * from .video import * from pydistort.utils.queue import Queue

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e6f3427c6126577da5bee550ee7622939ac20fc5

168

py

Python

chemml/wrapper/notebook/__init__.py

iamchetry/DataChallenge-Fall2021

fa7748c9ea2f3c0f6bde8d0b094fc75463e28f33

[ "BSD-3-Clause" ]

108

2018-03-23T20:06:03.000Z

2022-01-06T19:32:46.000Z

chemml/wrapper/notebook/__init__.py

hachmannlab/ChemML

42b152579872a57c834884596f700c76b9320280

[ "BSD-3-Clause" ]

18

2019-08-09T21:16:14.000Z

2022-02-14T21:52:06.000Z

chemml/wrapper/notebook/__init__.py

hachmannlab/ChemML

42b152579872a57c834884596f700c76b9320280

[ "BSD-3-Clause" ]

28

2018-04-28T17:07:33.000Z

2022-02-28T07:22:56.000Z

""" The 'chemml.wrapper.notebook' module contains the ipywidgets implementatoin of the GUI. """ from chemml.wrapper.notebook.main import ChemMLNotebook __all__ = []

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fc14970e6081bec3f2763dd0f85da75dfb047701

37

py

Python

swig/main.py

gmatesunny/watchcat

765ad6a22bf9c0364b8efebafd2751b91ae6f96d

[ "MIT" ]

7

2021-08-31T13:31:47.000Z

2022-03-11T21:40:27.000Z

swig/main.py

gmatesunny/watchcat

765ad6a22bf9c0364b8efebafd2751b91ae6f96d

[ "MIT" ]

null

swig/main.py

gmatesunny/watchcat

765ad6a22bf9c0364b8efebafd2751b91ae6f96d

[ "MIT" ]

null

import watchcat watchcat.TimeEvent()

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5

fc207ee91d8e49d0c5987dcd17e9eb47729d5e63

815

py

Python

cifar10-classification/utils/make_20cls_label.py

elgong/semi-supervised-DL-using-pseduo-label

2259c6ef93939f247f71a9d7f6766224120f2548

[ "MIT" ]

1

2019-11-26T12:40:05.000Z

cifar10-classification/utils/make_20cls_label.py

elgong/semi-supervised-DL-using-pseduo-label

2259c6ef93939f247f71a9d7f6766224120f2548

[ "MIT" ]

null

cifar10-classification/utils/make_20cls_label.py

elgong/semi-supervised-DL-using-pseduo-label

2259c6ef93939f247f71a9d7f6766224120f2548

[ "MIT" ]

null

import os train_path = "/home/elgong/GEL/one_shot/torch/pytorch-cifar-master/data/train" val_path = "/home/elgong/GEL/one_shot/torch/pytorch-cifar-master/data/val" train_txt = "./train.txt" val_txt = "./val.txt" class_name = [] with open(train_txt, "w") as f: for root, dic, fList in os.walk(train_path): for img in fList: cls = img.split("_")[0] if cls not in class_name: class_name.append(cls) if cls in class_name: f.write(img + "," + str(class_name.index(cls)) + "\n") with open(val_txt, "w") as f: for root, dic, fList in os.walk(val_path): for img in fList: cls = img.split("_")[0] if cls in class_name: f.write(img + "," + str(class_name.index(cls)) + "\n")

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5

fc308ffc9daef9541c0a1a00e3e998321d3b3104

155

py

Python

pdover2t/dnvgl_st_f101/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

null

pdover2t/dnvgl_st_f101/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

null

pdover2t/dnvgl_st_f101/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

1

2019-11-24T09:32:12.000Z

from .material import * from .pressure_containment import * #from .pipe_collapse import * #from .propagation_buckling import * #from .stability import *

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fc4126ec0318afafd5c0347893d85db2f967bcd9

1,427

py

Python

oscar/lib/python2.7/site-packages/phonenumbers/data/region_MV.py

bhav11esh/Oscar-Bookshelf

b48f088e2ed908b3603f2ecc63d602f81392eac4

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/phonenumbers/data/region_MV.py

bhav11esh/Oscar-Bookshelf

b48f088e2ed908b3603f2ecc63d602f81392eac4

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/phonenumbers/data/region_MV.py

bhav11esh/Oscar-Bookshelf

b48f088e2ed908b3603f2ecc63d602f81392eac4

[ "BSD-3-Clause" ]

null

"""Auto-generated file, do not edit by hand. MV metadata""" from ..phonemetadata import NumberFormat, PhoneNumberDesc, PhoneMetadata PHONE_METADATA_MV = PhoneMetadata(id='MV', country_code=960, international_prefix='0(?:0|19)', general_desc=PhoneNumberDesc(national_number_pattern='[346-8]\\d{6,9}|9(?:00\\d{7}|\\d{6})', possible_length=(7, 10)), fixed_line=PhoneNumberDesc(national_number_pattern='(?:3(?:0[0-3]|3[0-59])|6(?:[57][02468]|6[024568]|8[024689]|90))\\d{4}', example_number='6701234', possible_length=(7,)), mobile=PhoneNumberDesc(national_number_pattern='(?:46[46]|7[3-9]\\d|9[15-9]\\d)\\d{4}', example_number='7712345', possible_length=(7,)), toll_free=PhoneNumberDesc(national_number_pattern='800\\d{7}', example_number='8001234567', possible_length=(10,)), premium_rate=PhoneNumberDesc(national_number_pattern='900\\d{7}', example_number='9001234567', possible_length=(10,)), pager=PhoneNumberDesc(national_number_pattern='781\\d{4}', example_number='7812345', possible_length=(7,)), uan=PhoneNumberDesc(national_number_pattern='4[05]0\\d{4}', example_number='4001234', possible_length=(7,)), preferred_international_prefix='00', number_format=[NumberFormat(pattern='(\\d{3})(\\d{4})', format='\\1-\\2', leading_digits_pattern=['[3467]|9(?:[1-9]|0[1-9])']), NumberFormat(pattern='(\\d{3})(\\d{3})(\\d{4})', format='\\1 \\2 \\3', leading_digits_pattern=['[89]00'])])

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5da03be42c47c00c68b7861f1ff8737ca9307f95

8,573

py

Python

readthedocs/rtd_tests/tests/test_subprojects.py

nijel/readthedocs.org

4869667a3f0b56d440142187583f4bf79b9bce07

[ "MIT" ]

1

2020-08-10T01:14:08.000Z

readthedocs/rtd_tests/tests/test_subprojects.py

nijel/readthedocs.org

4869667a3f0b56d440142187583f4bf79b9bce07

[ "MIT" ]

11

2020-07-24T23:17:24.000Z

2022-03-12T00:43:42.000Z

readthedocs/rtd_tests/tests/test_subprojects.py

nijel/readthedocs.org

4869667a3f0b56d440142187583f4bf79b9bce07

[ "MIT" ]

1

2021-07-09T14:02:39.000Z

# -*- coding: utf-8 -*- import django_dynamic_fixture as fixture from unittest import mock from django.contrib.auth.models import User from django.test import TestCase from django.test.utils import override_settings from readthedocs.projects.forms import ProjectRelationshipForm from readthedocs.projects.models import Project, ProjectRelationship from readthedocs.rtd_tests.utils import create_user class SubprojectFormTests(TestCase): def test_empty_child(self): user = fixture.get(User) project = fixture.get(Project, slug='mainproject') form = ProjectRelationshipForm( {}, project=project, user=user, ) form.full_clean() self.assertEqual(len(form.errors['child']), 1) self.assertRegex( form.errors['child'][0], r'This field is required.', ) def test_nonexistent_child(self): user = fixture.get(User) project = fixture.get(Project, slug='mainproject') self.assertFalse(Project.objects.filter(pk=9999).exists()) form = ProjectRelationshipForm( {'child': 9999}, project=project, user=user, ) form.full_clean() self.assertEqual(len(form.errors['child']), 1) self.assertRegex( form.errors['child'][0], r'Select a valid choice.', ) def test_adding_subproject_fails_when_user_is_not_admin(self): user = fixture.get(User) project = fixture.get(Project, slug='mainproject') project.users.add(user) subproject = fixture.get(Project, slug='subproject') self.assertQuerysetEqual( Project.objects.for_admin_user(user), [project], transform=lambda n: n, ordered=False, ) form = ProjectRelationshipForm( {'child': subproject.pk}, project=project, user=user, ) form.full_clean() self.assertEqual(len(form.errors['child']), 1) self.assertRegex( form.errors['child'][0], r'Select a valid choice.', ) self.assertEqual( [proj_id for (proj_id, __) in form.fields['child'].choices], [''], ) def test_adding_subproject_passes_when_user_is_admin(self): user = fixture.get(User) project = fixture.get(Project, slug='mainproject') project.users.add(user) subproject = fixture.get(Project, slug='subproject') subproject.users.add(user) self.assertQuerysetEqual( Project.objects.for_admin_user(user), [project, subproject], transform=lambda n: n, ordered=False, ) form = ProjectRelationshipForm( {'child': subproject.pk}, project=project, user=user, ) form.full_clean() self.assertTrue(form.is_valid()) form.save() self.assertEqual( [r.child for r in project.subprojects.all()], [subproject], ) def test_subproject_form_cant_create_sub_sub_project(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) subsubproject = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, ) self.assertQuerysetEqual( Project.objects.for_admin_user(user), [project, subproject, subsubproject], transform=lambda n: n, ordered=False, ) form = ProjectRelationshipForm( {'child': subsubproject.pk}, project=subproject, user=user, ) # The subsubproject is valid here, as far as the child check is # concerned, but the parent check should fail. self.assertEqual( [proj_id for (proj_id, __) in form.fields['child'].choices], ['', subsubproject.pk], ) form.full_clean() self.assertEqual(len(form.errors['parent']), 1) self.assertRegex( form.errors['parent'][0], r'Subproject nesting is not supported', ) def test_excludes_existing_subprojects(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, ) self.assertQuerysetEqual( Project.objects.for_admin_user(user), [project, subproject], transform=lambda n: n, ordered=False, ) form = ProjectRelationshipForm( {'child': subproject.pk}, project=project, user=user, ) self.assertEqual( [proj_id for (proj_id, __) in form.fields['child'].choices], [''], ) def test_subproject_cant_be_subproject(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) another_project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, ) form = ProjectRelationshipForm( {'child': subproject.pk}, project=project, user=user, ) self.assertFalse(form.is_valid()) self.assertRegex( form.errors['child'][0], 'Select a valid choice', ) form = ProjectRelationshipForm( {'child': subproject.pk}, project=another_project, user=user, ) self.assertFalse(form.is_valid()) self.assertRegex( form.errors['child'][0], 'Select a valid choice', ) def test_superproject_cant_be_subproject(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) another_project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, ) form = ProjectRelationshipForm( {'child': project.pk}, project=another_project, user=user, ) self.assertFalse(form.is_valid()) self.assertRegex( form.errors['child'][0], 'Select a valid choice', ) def test_exclude_self_project_as_subproject(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) form = ProjectRelationshipForm( {'child': project.pk}, project=project, user=user, ) self.assertFalse(form.is_valid()) self.assertNotIn( project.id, [proj_id for (proj_id, __) in form.fields['child'].choices], ) def test_alias_already_exists_for_a_project(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) subproject_2 = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, alias='subproject' ) form = ProjectRelationshipForm( { 'child': subproject_2.id, 'alias': 'subproject' }, project=project, user=user, ) self.assertFalse(form.is_valid()) error_msg = 'A subproject with this alias already exists' self.assertDictEqual(form.errors, {'alias': [error_msg]}) def test_edit_only_lists_instance_project_in_child_choices(self): user = fixture.get(User) project = fixture.get(Project, users=[user]) subproject = fixture.get(Project, users=[user]) relation = fixture.get( ProjectRelationship, parent=project, child=subproject, alias='subproject' ) form = ProjectRelationshipForm( instance=relation, project=project, user=user, ) self.assertEqual( [proj_id for (proj_id, __) in form.fields['child'].choices], ['', relation.child.id], )

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py

Python

code/log_msg/admin.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

null

code/log_msg/admin.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

2

2021-03-30T13:48:40.000Z

2021-04-08T20:43:31.000Z

code/log_msg/admin.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

null

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

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68

py

Python

test/pithy/url.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

7

2019-05-04T00:51:38.000Z

2021-12-10T15:36:31.000Z

test/pithy/url.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

null

test/pithy/url.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

1

2016-07-30T22:38:08.000Z

#!/usr/bin/env python3 from utest import * from pithy.url import *

13.6

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5

5ddd62ec616c5b4a3f11a8a243faf889882ce42a

30

py

Python

tests/test_functions.py

landegt/pylabel

9d0079a1f61eb84ec9cd10fb202a9246a08576fa

[ "MIT" ]

1

2021-11-30T04:33:13.000Z

tests/test_functions.py

landegt/pylabel

9d0079a1f61eb84ec9cd10fb202a9246a08576fa

[ "MIT" ]

null

tests/test_functions.py

landegt/pylabel

9d0079a1f61eb84ec9cd10fb202a9246a08576fa

[ "MIT" ]

1

2021-12-04T13:57:45.000Z

from pylabel import functions

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Python

backend/day/admin.py

sysopmatt/py-schedule

c087b6e5ca162481394de0d5e8c7b41a74092f99

[ "MIT" ]

null

backend/day/admin.py

sysopmatt/py-schedule

c087b6e5ca162481394de0d5e8c7b41a74092f99

[ "MIT" ]

null

backend/day/admin.py

sysopmatt/py-schedule

c087b6e5ca162481394de0d5e8c7b41a74092f99

[ "MIT" ]

null

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

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b902956b0138ce6c2fa92f9214a024de6277995d

269

py

Python

core/context_processors.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

10

2015-12-18T16:41:33.000Z

2018-11-11T08:36:46.000Z

core/context_processors.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

96

2015-07-14T22:45:56.000Z

2017-07-25T19:59:48.000Z

core/context_processors.py

losolio/website

5b983e9dfaf604212aab87c51d8904ffc29527a3

[ "MIT" ]

9

2015-07-28T14:38:43.000Z

2019-01-04T17:38:42.000Z

from django.conf import settings def settings_context(request): return {'GOOGLE_ANALYTICS_PROPERTY_ID': settings.GOOGLE_ANALYTICS_PROPERTY_ID, 'IS_PRODUCTION': settings.IS_PRODUCTION, 'ADMIN_ENABLED': settings.ADMIN_ENABLED, }

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b90850da4ee1cdfc04ab5cbecf6b6b2cce6aa913

72

py

Python

keras_contrib/losses/__init__.py

rgreenblatt/keras-contrib

46fcdb9384b3bc9399c651b2b43640aa54098e64

[ "MIT" ]

1

2019-01-24T13:09:51.000Z

keras_contrib/losses/__init__.py

rgreenblatt/keras-contrib

46fcdb9384b3bc9399c651b2b43640aa54098e64

[ "MIT" ]

null

keras_contrib/losses/__init__.py

rgreenblatt/keras-contrib

46fcdb9384b3bc9399c651b2b43640aa54098e64

[ "MIT" ]

1

2018-09-03T17:53:44.000Z

from .dssim import DSSIMObjective from .jaccard import jaccard_distance

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5d141568edd1ec6c32fa03107ab21653d802f647

98

py

Python

_resource/python/platform/default/_parameterized.py

amlyj/tensorflowStudy

1e3a4b15a57d53e746fd730af540da4be471c70b

[ "MIT" ]

4

2021-06-11T09:43:32.000Z

2021-11-17T11:15:52.000Z

_resource/python/platform/default/_parameterized.py

amlyj/tensorflowStudy

1e3a4b15a57d53e746fd730af540da4be471c70b

[ "MIT" ]

null

_resource/python/platform/default/_parameterized.py

amlyj/tensorflowStudy

1e3a4b15a57d53e746fd730af540da4be471c70b

[ "MIT" ]

2

2015-11-13T21:11:49.000Z

2015-11-29T04:13:49.000Z

"""Extension to unittest to run parameterized tests.""" raise ImportError("Not implemented yet.")

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Python

harmonica/tests/test_eql_harmonic.py

RichardScottOZ/harmonica

ccb0437ea0ed528cfd144844edab98141c8d08da

[ "BSD-3-Clause" ]

null

harmonica/tests/test_eql_harmonic.py

RichardScottOZ/harmonica

ccb0437ea0ed528cfd144844edab98141c8d08da

[ "BSD-3-Clause" ]

1

2022-01-19T03:02:22.000Z

2022-01-19T20:47:19.000Z

harmonica/tests/test_eql_harmonic.py

RichardScottOZ/harmonica

ccb0437ea0ed528cfd144844edab98141c8d08da

[ "BSD-3-Clause" ]

1

2022-01-17T23:15:18.000Z

""" Test the EQLHarmonic gridder """ import warnings import pytest import numpy as np import numpy.testing as npt import verde as vd import verde.base as vdb from .. import EQLHarmonic, EQLHarmonicSpherical, point_mass_gravity from ..equivalent_layer.harmonic import greens_func_cartesian from ..equivalent_layer.utils import ( jacobian_numba, pop_extra_coords, ) from .utils import require_numba def test_pop_extra_coords(): """ Test _pop_extra_coords private function """ # Check if extra_coords is removed from kwargs kwargs = {"bla": 1, "blabla": 2, "extra_coords": 1400.0} with warnings.catch_warnings(record=True) as warn: pop_extra_coords(kwargs) assert len(warn) == 1 assert issubclass(warn[0].category, UserWarning) assert "extra_coords" not in kwargs # Check if kwargs is not touched if no extra_coords are present kwargs = {"bla": 1, "blabla": 2} pop_extra_coords(kwargs) assert kwargs == {"bla": 1, "blabla": 2} @require_numba def test_eql_harmonic_cartesian(): """ Check that predictions are reasonable when interpolating from one grid to a denser grid. Use Cartesian coordiantes. """ region = (-3e3, -1e3, 5e3, 7e3) # Build synthetic point masses points = vd.grid_coordinates(region=region, shape=(6, 6), extra_coords=-1e3) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates(region=region, shape=(40, 40), extra_coords=0) # Get synthetic data data = point_mass_gravity(coordinates, points, masses, field="g_z") # The interpolation should be perfect on the data points eql = EQLHarmonic() eql.fit(coordinates, data) npt.assert_allclose(data, eql.predict(coordinates), rtol=1e-5) # Gridding onto a denser grid should be reasonably accurate when compared # to synthetic values upward = 0 shape = (60, 60) grid = vd.grid_coordinates(region=region, shape=shape, extra_coords=upward) true = point_mass_gravity(grid, points, masses, field="g_z") npt.assert_allclose(true, eql.predict(grid), rtol=1e-3) # Test grid method grid = eql.grid(upward, shape=shape, region=region) npt.assert_allclose(true, grid.scalars, rtol=1e-3) # Test profile method point1 = (region[0], region[2]) point2 = (region[0], region[3]) profile = eql.profile(point1, point2, upward, shape[0]) true = point_mass_gravity( (profile.easting, profile.northing, profile.upward), points, masses, field="g_z" ) npt.assert_allclose(true, profile.scalars, rtol=1e-3) def test_eql_harmonic_small_data_cartesian(): """ Check predictions against synthetic data using few data points for speed Use Cartesian coordinates. """ region = (-3e3, -1e3, 5e3, 7e3) # Build synthetic point masses points = vd.grid_coordinates(region=region, shape=(6, 6), extra_coords=-1e3) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates(region=region, shape=(8, 8), extra_coords=0) # Get synthetic data data = point_mass_gravity(coordinates, points, masses, field="g_z") # The interpolation should be perfect on the data points eql = EQLHarmonic(relative_depth=500) eql.fit(coordinates, data) npt.assert_allclose(data, eql.predict(coordinates), rtol=1e-5) # Check that the proper source locations were set tmp = [i.ravel() for i in coordinates] npt.assert_allclose(tmp[:2], eql.points_[:2], rtol=1e-5) npt.assert_allclose(tmp[2] - 500, eql.points_[2], rtol=1e-5) # Gridding at higher altitude should be reasonably accurate when compared # to synthetic values upward = 20 shape = (8, 8) grid = vd.grid_coordinates(region=region, shape=shape, extra_coords=upward) true = point_mass_gravity(grid, points, masses, field="g_z") npt.assert_allclose(true, eql.predict(grid), rtol=0.08) # Test grid method grid = eql.grid(upward, shape=shape, region=region) npt.assert_allclose(true, grid.scalars, rtol=0.08) # Test profile method point1 = (region[0], region[2]) point2 = (region[0], region[3]) profile = eql.profile(point1, point2, upward, 10) true = point_mass_gravity( (profile.easting, profile.northing, profile.upward), points, masses, field="g_z" ) npt.assert_allclose(true, profile.scalars, rtol=0.05) def test_eql_harmonic_custom_points_cartesian(): """ Check that passing in custom points works and actually uses the points Use Cartesian coordinates. """ region = (-3e3, -1e3, 5e3, 7e3) # Build synthetic point masses points = vd.grid_coordinates(region=region, shape=(6, 6), extra_coords=-1e3) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates(region=region, shape=(5, 5), extra_coords=0) # Get synthetic data data = point_mass_gravity(coordinates, points, masses, field="g_z") # Pass a custom set of point sources points_custom = tuple( i.ravel() for i in vd.grid_coordinates(region=region, shape=(3, 3), extra_coords=-550) ) eql = EQLHarmonic(points=points_custom) eql.fit(coordinates, data) # Check that the proper source locations were set npt.assert_allclose(points_custom, eql.points_, rtol=1e-5) def test_eql_harmonic_scatter_not_implemented(): """ Check if scatter method raises a NotImplementedError """ eql = EQLHarmonic() with pytest.raises(NotImplementedError): eql.scatter() @pytest.mark.use_numba def test_eql_harmonic_jacobian_cartesian(): """ Test Jacobian matrix under symmetric system of point sources. Use Cartesian coordinates. """ easting, northing, upward = vd.grid_coordinates( region=[-100, 100, -100, 100], shape=(2, 2), extra_coords=0 ) points = vdb.n_1d_arrays((easting, northing, upward + 100), n=3) coordinates = vdb.n_1d_arrays((easting, northing, upward), n=3) n_points = points[0].size jacobian = np.zeros((n_points, n_points), dtype=points[0].dtype) jacobian_numba(coordinates, points, jacobian, greens_func_cartesian) # All diagonal elements must be equal diagonal = np.diag_indices(4) npt.assert_allclose(jacobian[diagonal][0], jacobian[diagonal]) # All anti-diagonal elements must be equal (elements between distant # points) anti_diagonal = (diagonal[0], diagonal[1][::-1]) npt.assert_allclose(jacobian[anti_diagonal][0], jacobian[anti_diagonal]) # All elements corresponding to nearest neighbors must be equal nearest_neighbours = np.ones((4, 4), dtype=bool) nearest_neighbours[diagonal] = False nearest_neighbours[anti_diagonal] = False npt.assert_allclose(jacobian[nearest_neighbours][0], jacobian[nearest_neighbours]) @require_numba def test_eql_harmonic_spherical(): """ Check that predictions are reasonable when interpolating from one grid to a denser grid. Use spherical coordiantes. """ region = (-70, -60, -40, -30) radius = 6400e3 # Build synthetic point masses points = vd.grid_coordinates( region=region, shape=(6, 6), extra_coords=radius - 500e3 ) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates( region=region, shape=(40, 40), extra_coords=radius ) # Get synthetic data data = point_mass_gravity( coordinates, points, masses, field="g_z", coordinate_system="spherical" ) # The interpolation should be perfect on the data points eql = EQLHarmonicSpherical(relative_depth=500e3) eql.fit(coordinates, data) npt.assert_allclose(data, eql.predict(coordinates), rtol=1e-5) # Gridding onto a denser grid should be reasonably accurate when compared # to synthetic values upward = radius shape = (60, 60) grid = vd.grid_coordinates(region=region, shape=shape, extra_coords=upward) true = point_mass_gravity( grid, points, masses, field="g_z", coordinate_system="spherical" ) npt.assert_allclose(true, eql.predict(grid), rtol=1e-3) # Test grid method grid = eql.grid(upward, shape=shape, region=region) npt.assert_allclose(true, grid.scalars, rtol=1e-3) def test_eql_harmonic_small_data_spherical(): """ Check predictions against synthetic data using few data points for speed Use spherical coordinates. """ region = (-70, -60, -40, -30) radius = 6400e3 # Build synthetic point masses points = vd.grid_coordinates( region=region, shape=(6, 6), extra_coords=radius - 500e3 ) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates(region=region, shape=(8, 8), extra_coords=radius) # Get synthetic data data = point_mass_gravity( coordinates, points, masses, field="g_z", coordinate_system="spherical" ) # The interpolation should be perfect on the data points eql = EQLHarmonicSpherical(relative_depth=500e3) eql.fit(coordinates, data) npt.assert_allclose(data, eql.predict(coordinates), rtol=1e-5) # Check that the proper source locations were set tmp = [i.ravel() for i in coordinates] npt.assert_allclose(tmp[:2], eql.points_[:2], rtol=1e-5) npt.assert_allclose(tmp[2] - 500e3, eql.points_[2], rtol=1e-5) # Gridding at higher altitude should be reasonably accurate when compared # to synthetic values upward = radius + 2e3 shape = (8, 8) grid = vd.grid_coordinates(region=region, shape=shape, extra_coords=upward) true = point_mass_gravity( grid, points, masses, field="g_z", coordinate_system="spherical" ) npt.assert_allclose(true, eql.predict(grid), rtol=0.05) # Test grid method grid = eql.grid(upward, shape=shape, region=region) npt.assert_allclose(true, grid.scalars, rtol=0.05) def test_eql_harmonic_custom_points_spherical(): """ Check that passing in custom points works and actually uses the points Use spherical coordinates. """ region = (-70, -60, -40, -30) radius = 6400e3 # Build synthetic point masses points = vd.grid_coordinates( region=region, shape=(6, 6), extra_coords=radius - 500e3 ) masses = vd.datasets.CheckerBoard(amplitude=1e13, region=region).predict(points) # Define a set of observation points coordinates = vd.grid_coordinates(region=region, shape=(5, 5), extra_coords=radius) # Get synthetic data data = point_mass_gravity( coordinates, points, masses, field="g_z", coordinate_system="spherical" ) # Pass a custom set of point sources points_custom = tuple( i.ravel() for i in vd.grid_coordinates( region=region, shape=(3, 3), extra_coords=radius - 500e3 ) ) eql = EQLHarmonicSpherical(points=points_custom) eql.fit(coordinates, data) # Check that the proper source locations were set npt.assert_allclose(points_custom, eql.points_, rtol=1e-5) def test_eql_harmonic_spherical_scatter_not_implemented(): """ Check if scatter method raises a NotImplementedError """ eql = EQLHarmonicSpherical() with pytest.raises(NotImplementedError): eql.scatter() def test_eql_harmonic_spherical_profile_not_implemented(): """ Check if scatter method raises a NotImplementedError """ eql = EQLHarmonicSpherical() with pytest.raises(NotImplementedError): eql.profile(point1=(1, 1), point2=(2, 2), size=3) def test_eql_harmonic_spherical_no_projection(): """ Check if projection is not a valid argument of grid method """ eql = EQLHarmonicSpherical() with pytest.raises(TypeError): eql.grid(upward=10, projection=lambda a, b: (a * 2, b * 2))

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py

Python

CInterface/SWIG/setup.py

Fernal73/LearnPython3

5288017c0dbf95633b84f1e6324f00dec6982d36

[ "MIT" ]

1

2021-12-17T11:03:13.000Z

CInterface/SWIG/setup.py

Fernal73/LearnPython3

5288017c0dbf95633b84f1e6324f00dec6982d36

[ "MIT" ]

1

2020-02-05T00:14:43.000Z

2020-02-06T09:22:49.000Z

CInterface/SWIG/setup.py

Fernal73/LearnPython3

5288017c0dbf95633b84f1e6324f00dec6982d36

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from distutils.core import setup, Extension setup(ext_modules=[Extension("_cos_module", sources=["cos_module.c", "cos_module.i"])])

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py

Python

models/__init__.py

Hhhhhhhhhhao/image-cartoonization

073b51656b96b069496917d212119caad7bf4728

[ "MIT" ]

null

models/__init__.py

Hhhhhhhhhhao/image-cartoonization

073b51656b96b069496917d212119caad7bf4728

[ "MIT" ]

null

models/__init__.py

Hhhhhhhhhhao/image-cartoonization

073b51656b96b069496917d212119caad7bf4728

[ "MIT" ]

null

from .generator import * from .discriminator import * from .inception import InceptionV3 from .lenet import LeNet5 from .utils import StyleEncoder, MappingNetwork, PatchSampleF from .resnet import ResNet

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py

Python

Emoji_generator.py

mryesiller/Emoji_Generator

dba2db070e478b9ff7a16babfc381a584c27975f

[ "MIT" ]

2

2022-01-28T21:59:08.000Z

2022-03-14T00:00:37.000Z

Emoji_generator.py

mryesiller/Emoji_Generator

dba2db070e478b9ff7a16babfc381a584c27975f

[ "MIT" ]

null

Emoji_generator.py

mryesiller/Emoji_Generator

dba2db070e478b9ff7a16babfc381a584c27975f

[ "MIT" ]

null

from PIL import Image import numpy as np import os from random import randint dirname = os.path.dirname(__file__) dimensions = 480, 480 #resize 24x24 to 480x480 for x in range(0,30): #Number of generated pictures f = randint(0, 1000) #Common-Rare-Epic-Legendary if f > 400: bw = (255, 255, 255) #Borders-inside bg = (255, 255, 255) #Background bc = (0, 0, 0) #Borders-outside eb = (0,0,0) #Face elif 400 >= f > 47: bw = (255, 255, 255) bg = (255, 255, 204) bc = (31, 57, 186) eb = (0,0,0) elif 47 >= f > 7: bw = (255, 255, 255) bg = (255, 255, 102) bc = (186, 31, 160) eb = (0,0,0) else: bw = (255, 255, 255) bg = (255, 51, 255) bc = (226, 144, 21) eb = (0,0,0) emoji = [ [bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc], [bc, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bc], [bc, bw, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, bg, bg, eb, eb, eb, eb, eb, eb, eb, eb, bg, bg, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bw, bc], [bc, bw, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bw, bc], [bc, bw, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bw, bc], [bc, bw, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bw, bc], [bc, bw, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bw, bc], [bc, bw, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bg, bg, bg, eb, bg, bg, bg, bg, bg, bw, bc], [bc, bw, bg, bg, bg, bg, bg, bg, eb, eb, eb, eb, eb, eb, eb, eb, bg, bg, bg, bg, bg, bg, bw, bc], [bc, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bw, bc], [bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc, bc] ] #mouth settings mouth=randint(0, 1000) if mouth > 600: #straight mouth emoji[16][9]=(0,0,0) emoji[16][10]=(0,0,0) emoji[16][11]=(0,0,0) emoji[16][12]=(0,0,0) emoji[16][13]=(0,0,0) emoji[16][14]=(0,0,0) elif 600 >= mouth > 247: #upset emoji[15][10]=(0,0,0) emoji[15][11]=(0,0,0) emoji[15][12]=(0,0,0) emoji[15][13]=(0,0,0) emoji[16][9]=(0,0,0) emoji[17][8]=(0,0,0) emoji[16][14]=(0,0,0) emoji[17][15]=(0,0,0) elif 247 >= mouth > 107: #smile emoji[17][10]=(0,0,0) emoji[17][11]=(0,0,0) emoji[17][12]=(0,0,0) emoji[17][13]=(0,0,0) emoji[16][9]=(0,0,0) emoji[15][8]=(0,0,0) emoji[16][14]=(0,0,0) emoji[15][15]=(0,0,0) elif 107 >= mouth > 17: #circle mouth emoji[18][10]=(0,0,0) emoji[18][11]=(0,0,0) emoji[18][12]=(0,0,0) emoji[18][13]=(0,0,0) emoji[17][9]=(0,0,0) emoji[16][9]=(0,0,0) emoji[17][14]=(0,0,0) emoji[16][14]=(0,0,0) emoji[15][9]=(0,0,0) emoji[14][10]=(0,0,0) emoji[14][11]=(0,0,0) emoji[14][12]=(0,0,0) emoji[14][13]=(0,0,0) emoji[15][14]=(0,0,0) else: #grin emoji[18][10]=(0,0,0) emoji[18][11]=(0,0,0) emoji[18][12]=(0,0,0) emoji[18][13]=(0,0,0) emoji[17][9]=(0,0,0) emoji[16][8]=(0,0,0) emoji[17][14]=(0,0,0) emoji[16][15]=(0,0,0) emoji[15][7]=(0,0,0) emoji[15][16]=(0,0,0) emoji[14][7]=(0,0,0) emoji[14][16]=(0,0,0) emoji[14][8]=(0,0,0) emoji[14][9]=(0,0,0) emoji[14][10]=(0,0,0) emoji[14][11]=(0,0,0) emoji[14][12]=(0,0,0) emoji[14][13]=(0,0,0) emoji[14][14]=(0,0,0) emoji[14][15]=(0,0,0) emoji[15][10]=(0,0,0) emoji[15][13]=(0,0,0) #eye settings eye=randint(0, 1000) if eye > 600: #straight eyes emoji[9][7]=(0,0,0) emoji[9][8]=(0,0,0) emoji[9][9]=(0,0,0) emoji[9][10]=(0,0,0) emoji[9][16]=(0,0,0) emoji[9][15]=(0,0,0) emoji[9][14]=(0,0,0) emoji[9][13]=(0,0,0) elif 600 >= eye > 247: #down eyes emoji[10][7]=(0,0,0) emoji[11][7]=(0,0,0) emoji[11][8]=(0,0,0) emoji[11][9]=(0,0,0) emoji[11][10]=(0,0,0) emoji[10][10]=(0,0,0) #------------------- emoji[10][13]=(0,0,0) emoji[11][13]=(0,0,0) emoji[11][14]=(0,0,0) emoji[11][15]=(0,0,0) emoji[11][16]=(0,0,0) emoji[10][16]=(0,0,0) elif 247 >= eye > 107: #single eyebrow eyes emoji[11][7]=(0,0,0) emoji[10][7]=(0,0,0) emoji[9][8]=(0,0,0) emoji[9][9]=(0,0,0) emoji[9][10]=(0,0,0) emoji[10][11]=(0,0,0) emoji[11][11]=(0,0,0) emoji[11][16]=(0,0,0) emoji[10][16]=(0,0,0) emoji[9][15]=(0,0,0) emoji[9][14]=(0,0,0) emoji[9][13]=(0,0,0) emoji[10][12]=(0,0,0) emoji[11][12]=(0,0,0) else: #circle eyes emoji[8][8]=(0,0,0) emoji[8][9]=(0,0,0) emoji[8][10]=(0,0,0) emoji[8][7]=(0,0,0) emoji[9][7]=(0,0,0) emoji[10][7]=(0,0,0) emoji[11][7]=(0,0,0) emoji[11][8]=(0,0,0) emoji[11][9]=(0,0,0) emoji[11][10]=(0,0,0) emoji[10][10]=(0,0,0) emoji[9][10]=(0,0,0) #------------------- emoji[8][13]=(0,0,0) emoji[8][14]=(0,0,0) emoji[8][15]=(0,0,0) emoji[8][16]=(0,0,0) emoji[9][13]=(0,0,0) emoji[10][13]=(0,0,0) emoji[11][13]=(0,0,0) emoji[11][14]=(0,0,0) emoji[11][15]=(0,0,0) emoji[11][16]=(0,0,0) emoji[10][16]=(0,0,0) emoji[9][16]=(0,0,0) pixels=emoji array = np.array(pixels, dtype=np.uint8) new_image = Image.fromarray(array) new_image = new_image.resize(dimensions, resample=0) imgname = dirname + '/emoji_images/' + (str(x)) + '.png' new_image.save(imgname)

36.459091

106

0.412043

1,488

8,021

2.215054

0.067204

0.373786

0.491505

0.572816

0.773058

0.698119

0.669296

0.647148

0.638653

0.624697

0

0.181051

0.349956

8,021

220

107

36.459091

0.451093

0.039646

0

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false

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null

1

0

1

0

1

0

null

0

5

537acd993ab0fefca1d2e3cf07c851893da83d25

3,142

py

Python

kakaodecrypt.test.py

voidedWarranties/kakaodecrypt

7c4ffac41d3ff5b773b4c6025cd8f9f738969fae

[ "WTFPL" ]

1

2020-02-27T05:28:08.000Z

kakaodecrypt.test.py

voidedWarranties/kakaodecrypt

7c4ffac41d3ff5b773b4c6025cd8f9f738969fae

[ "WTFPL" ]

null

kakaodecrypt.test.py

voidedWarranties/kakaodecrypt

7c4ffac41d3ff5b773b4c6025cd8f9f738969fae

[ "WTFPL" ]

null

#!/usr/bin/python3 import unittest from kakaodecrypt import KakaoDecrypt class KakaoDecryptTest(unittest.TestCase): def testGenSalt(self): zero = b'\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0' self.assertEqual(KakaoDecrypt.genSalt(-1, 5), zero) self.assertEqual(KakaoDecrypt.genSalt(0, 5), zero) self.assertEqual(KakaoDecrypt.genSalt(1234, 0), b"1234\0\0\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 1), b"1234\0\0\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 2), b"121234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 3), b"241234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 4), b"181234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 5), b"301234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 6), b"361234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 7), b"121234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 8), b"481234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 9), b"71234\0\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 10), b"351234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 11), b"401234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 12), b"171234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 13), b"231234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 14), b"291234\0\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 15), b"isabel1234\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 16), b"kale1234\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 17), b"sulli1234\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 18), b"van1234\0\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 19), b"merry1234\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 20), b"kyle1234\0\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(1234, 21), b"james1234\0\0\0\0\0\0\0") self.assertEqual(KakaoDecrypt.genSalt(216658451, 17), b"sulli216658451\0\0") self.assertRaises(ValueError, KakaoDecrypt.genSalt, 1234, 42) def testDecryptMessage(self): self.assertEqual(KakaoDecrypt.decrypt(216658451, 17, 'UHVw8VBhUhdbIFTlvdBXdA=='), 'Hey friends!') self.assertEqual(KakaoDecrypt.decrypt(240440409, 22, 'pBO6rG5DQmOOfRwyoV6nqw=='), 'ㄱㅇㄷ') self.assertEqual(KakaoDecrypt.decrypt(195847548, 24, 'IICZJO/83CXZWZhNmiWmHg=='), "It's ok") self.assertEqual(KakaoDecrypt.decrypt(1234, 1, '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'), '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00') self.assertEqual(KakaoDecrypt.decrypt(712919372478, 22, 'Ah06VZFMkDYZTdUrbrBb77fLJjvbAuv1xjrAkaLOdkQ='), b'r\x1db\x93\x9c\xd7\xe5\xe4.A') self.assertEqual(KakaoDecrypt.decrypt(283456151, 26, 'gYKexDBLvO7OwDqjD58LlQ=='), 'i have lasers') if __name__ == '__main__': unittest.main()

66.851064

251

0.706875

564

3,142

3.923759

0.177305

0.181654

0.239946

0.278355

0.605061

0.603254

0.568007

0

0.233716

0.086251

3,142

46

252

68.304348

0.537095

0.005411

0

0.45

0.323944

0.304417

0

0.8

1

0.05

false

0

0.05

0

0.125

0

null

0

1

0

1

0

1

0

null

0

1

0

5

5380e9d5be56cc48b2d63ae0c8266cf55beefd30

215

py

Python

Datasets/__init__.py

whkwls2653/Pytorch_Face_Recognition-

60f3849def589957d9080457a1a9833112a71f6c

[ "MIT" ]

62

2020-08-26T05:42:39.000Z

2022-03-31T04:25:50.000Z

Datasets/__init__.py

whkwls2653/Pytorch_Face_Recognition-

60f3849def589957d9080457a1a9833112a71f6c

[ "MIT" ]

10

2020-08-27T06:46:10.000Z

2021-09-29T03:36:07.000Z

Datasets/__init__.py

whkwls2653/Pytorch_Face_Recognition-

60f3849def589957d9080457a1a9833112a71f6c

[ "MIT" ]

13

2020-08-30T00:27:37.000Z

2021-12-09T02:56:07.000Z

from Datasets.webface import CASIA_WebFace from Datasets.lfw import LFW from Datasets.cfp import CFP_FP from Datasets.agedb import AgeDB30 from Datasets.megaface import MegaFace from Datasets.ms1m import MS_Celeb_1M

35.833333

42

0.865116

34

215

5.352941

0.441176

0.395604

0

0.020833

0.106977

215

6

43

35.833333

0.927083

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

538c86e8e58ade9bcc1328effced905d90ff7d91

125

py

Python

rateLimit.py

lpmi-13/GitHubReadmeCorpus

2f5ecef52d1a35dcd7aa41f790a2a9222be8f215

[ "MIT" ]

1

2017-06-18T09:39:19.000Z

rateLimit.py

lpmi-13/GitHubReadmeCorpus

2f5ecef52d1a35dcd7aa41f790a2a9222be8f215

[ "MIT" ]

1

2017-06-21T20:12:59.000Z

2017-07-02T14:44:26.000Z

rateLimit.py

lpmi-13/GitHubReadmeCorpus

2f5ecef52d1a35dcd7aa41f790a2a9222be8f215

[ "MIT" ]

null

def return_rate_limit(github): rate_limit = github.get_rate_limit() rate = rate_limit.rate return rate.remaining

25

40

0.744

18

125

4.833333

0.388889

0.413793

0.344828

0

0.176

125

4

41

31.25

0.84466

0

1

0.25

false

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

538dad057242140284e3e08bf453fa16d5bde06e

18,224

py

Python

pyshacl/constraints/core/property_pair_constraints.py

Martijn-Y-ai/pySHACL

ddbc11e13cc741d6ffa334089b0d18fd346f36c7

[ "Apache-2.0" ]

null

pyshacl/constraints/core/property_pair_constraints.py

Martijn-Y-ai/pySHACL

ddbc11e13cc741d6ffa334089b0d18fd346f36c7

[ "Apache-2.0" ]

null

pyshacl/constraints/core/property_pair_constraints.py

Martijn-Y-ai/pySHACL

ddbc11e13cc741d6ffa334089b0d18fd346f36c7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ https://www.w3.org/TR/shacl/#core-components-property-pairs """ from typing import Dict, List import rdflib from pyshacl.constraints.constraint_component import ConstraintComponent from pyshacl.consts import SH from pyshacl.errors import ConstraintLoadError, ReportableRuntimeError from pyshacl.pytypes import GraphLike from pyshacl.rdfutil import stringify_node SH_equals = SH.term('equals') SH_disjoint = SH.term('disjoint') SH_lessThan = SH.term('lessThan') SH_lessThanOrEquals = SH.term('lessThanOrEquals') SH_EqualsConstraintComponent = SH.term('EqualsConstraintComponent') SH_DisjointConstraintComponent = SH.term('DisjointConstraintComponent') SH_LessThanConstraintComponent = SH.term('LessThanConstraintComponent') SH_LessThanOrEqualsConstraintComponent = SH.term('LessThanOrEqualsConstraintComponent') class EqualsConstraintComponent(ConstraintComponent): """ sh:equals specifies the condition that the set of all value nodes is equal to the set of objects of the triples that have the focus node as subject and the value of sh:equals as predicate. Link: https://www.w3.org/TR/shacl/#EqualsConstraintComponent Textual Definition: For each value node that does not exist as a value of the property $equals at the focus node, there is a validation result with the value node as sh:value. For each value of the property $equals at the focus node that is not one of the value nodes, there is a validation result with the value as sh:value. """ shacl_constraint_component = SH_EqualsConstraintComponent def __init__(self, shape): super(EqualsConstraintComponent, self).__init__(shape) property_compare_set = set(self.shape.objects(SH_equals)) if len(property_compare_set) < 1: raise ConstraintLoadError( "EqualsConstraintComponent must have at least one sh:equals predicate.", "https://www.w3.org/TR/shacl/#EqualsConstraintComponent", ) self.property_compare_set = property_compare_set @classmethod def constraint_parameters(cls): return [SH_equals] @classmethod def constraint_name(cls): return "EqualsConstraintComponent" def make_generic_messages(self, datagraph: GraphLike, focus_node, value_node) -> List[rdflib.Literal]: if len(self.property_compare_set) < 2: m = "Value of {}->{} != {}".format( stringify_node(datagraph, focus_node), stringify_node(self.shape.sg.graph, next(iter(self.property_compare_set))), stringify_node(datagraph, value_node), ) else: rules = ", ".join(stringify_node(self.shape.sg.graph, p) for p in self.property_compare_set) m = "Value of {}->{} != {}".format( stringify_node(datagraph, focus_node), rules, stringify_node(datagraph, value_node) ) return [rdflib.Literal(m)] def evaluate(self, target_graph: GraphLike, focus_value_nodes: Dict, _evaluation_path: List): """ :type target_graph: rdflib.Graph :type focus_value_nodes: dict :type _evaluation_path: list """ reports = [] non_conformant = False for eq in iter(self.property_compare_set): _nc, _r = self._evaluate_property_equals(eq, target_graph, focus_value_nodes) non_conformant = non_conformant or _nc reports.extend(_r) return (not non_conformant), reports def _evaluate_property_equals(self, eq, target_graph, f_v_dict): reports = [] non_conformant = False for f, value_nodes in f_v_dict.items(): value_node_set = set(value_nodes) compare_values = set(target_graph.objects(f, eq)) value_nodes_missing = value_node_set.difference(compare_values) compare_values_missing = compare_values.difference(value_node_set) if len(value_nodes_missing) > 0 or len(compare_values_missing) > 0: non_conformant = True else: continue for value_node in value_nodes_missing: rept = self.make_v_result(target_graph, f, value_node=value_node) reports.append(rept) for compare_value in compare_values_missing: rept = self.make_v_result(target_graph, f, value_node=compare_value) reports.append(rept) return non_conformant, reports class DisjointConstraintComponent(ConstraintComponent): """ sh:disjoint specifies the condition that the set of value nodes is disjoint with the set of objects of the triples that have the focus node as subject and the value of sh:disjoint as predicate. Link: https://www.w3.org/TR/shacl/#DisjointConstraintComponent Textual Definition: For each value node that also exists as a value of the property $disjoint at the focus node, there is a validation result with the value node as sh:value. """ shacl_constraint_component = SH_DisjointConstraintComponent def __init__(self, shape): super(DisjointConstraintComponent, self).__init__(shape) property_compare_set = set(self.shape.objects(SH_disjoint)) if len(property_compare_set) < 1: raise ConstraintLoadError( "DisjointConstraintComponent must have at least one sh:disjoint predicate.", "https://www.w3.org/TR/shacl/#DisjointConstraintComponent", ) self.property_compare_set = property_compare_set @classmethod def constraint_parameters(cls): return [SH_disjoint] @classmethod def constraint_name(cls): return "DisjointConstraintComponent" def make_generic_messages(self, datagraph: GraphLike, focus_node, value_node) -> List[rdflib.Literal]: if len(self.property_compare_set) < 2: m = "Value of {}->{} == {}".format( stringify_node(datagraph, focus_node), stringify_node(self.shape.sg.graph, next(iter(self.property_compare_set))), stringify_node(datagraph, value_node), ) else: rules = ", ".join(stringify_node(self.shape.sg.graph, p) for p in self.property_compare_set) m = "Value of {}->{} == {}".format( stringify_node(datagraph, focus_node), rules, stringify_node(datagraph, value_node) ) return [rdflib.Literal(m)] def evaluate(self, target_graph: GraphLike, focus_value_nodes: Dict, _evaluation_path: List): """ :type target_graph: rdflib.Graph :type focus_value_nodes: dict :type _evaluation_path: list """ reports = [] non_conformant = False for dj in iter(self.property_compare_set): _nc, _r = self._evaluate_property_disjoint(dj, target_graph, focus_value_nodes) non_conformant = non_conformant or _nc reports.extend(_r) return (not non_conformant), reports def _evaluate_property_disjoint(self, dj, target_graph, f_v_dict): reports = [] non_conformant = False for f, value_nodes in f_v_dict.items(): value_node_set = set(value_nodes) compare_values = set(target_graph.objects(f, dj)) common_nodes = value_node_set.intersection(compare_values) if len(common_nodes) > 0: non_conformant = True else: continue for common_node in common_nodes: rept = self.make_v_result(target_graph, f, value_node=common_node) reports.append(rept) return non_conformant, reports class LessThanConstraintComponent(ConstraintComponent): """ sh:lessThan specifies the condition that each value node is smaller than all the objects of the triples that have the focus node as subject and the value of sh:lessThan as predicate. Link: https://www.w3.org/TR/shacl/#LessThanConstraintComponent Textual Definition: For each pair of value nodes and the values of the property $lessThan at the given focus node where the first value is not less than the second value (based on SPARQL's < operator) or where the two values cannot be compared, there is a validation result with the value node as sh:value. """ shacl_constraint_component = SH_LessThanConstraintComponent def __init__(self, shape): super(LessThanConstraintComponent, self).__init__(shape) property_compare_set = set(self.shape.objects(SH_lessThan)) if len(property_compare_set) < 1: raise ConstraintLoadError( "LessThanConstraintComponent must have at least one sh:lessThan predicate.", "https://www.w3.org/TR/shacl/#LessThanConstraintComponent", ) if not shape.is_property_shape: raise ConstraintLoadError( "LessThanConstraintComponent can only be present on a PropertyShape, not a NodeShape.", "https://www.w3.org/TR/shacl/#LessThanConstraintComponent", ) self.property_compare_set = property_compare_set @classmethod def constraint_parameters(cls): return [SH_lessThan] @classmethod def constraint_name(cls): return "LessThanConstraintComponent" def make_generic_messages(self, datagraph: GraphLike, focus_node, value_node) -> List[rdflib.Literal]: if len(self.property_compare_set) < 2: m = "Value of {}->{} <= {}".format( stringify_node(datagraph, focus_node), stringify_node(self.shape.sg.graph, next(iter(self.property_compare_set))), stringify_node(datagraph, value_node), ) else: rules = ", ".join(stringify_node(self.shape.sg.graph, p) for p in self.property_compare_set) m = "Value of {}->{} <= {}".format( stringify_node(datagraph, focus_node), rules, stringify_node(datagraph, value_node) ) return [rdflib.Literal(m)] def evaluate(self, target_graph: GraphLike, focus_value_nodes: Dict, _evaluation_path: List): """ :type target_graph: rdflib.Graph :type focus_value_nodes: dict :type _evaluation_path: list """ reports = [] non_conformant = False for lt in iter(self.property_compare_set): if isinstance(lt, rdflib.Literal) or isinstance(lt, rdflib.BNode): raise ReportableRuntimeError("Value of sh:lessThan MUST be a URI Identifier.") _nc, _r = self._evaluate_less_than(lt, target_graph, focus_value_nodes) non_conformant = non_conformant or _nc reports.extend(_r) return (not non_conformant), reports def _evaluate_less_than(self, lt, target_graph, f_v_dict): reports = [] non_conformant = False for f, value_nodes in f_v_dict.items(): value_node_set = set(value_nodes) compare_values = set(target_graph.objects(f, lt)) for value_node in iter(value_node_set): if isinstance(value_node, rdflib.BNode): raise ReportableRuntimeError("Cannot use sh:lessThan to compare a BlankNode.") value_is_string = False orig_value_node = value_node if isinstance(value_node, rdflib.URIRef): value_node = str(value_node) value_is_string = True elif isinstance(value_node, rdflib.Literal) and isinstance(value_node.value, str): value_node = value_node.value value_is_string = True for compare_value in compare_values: if isinstance(compare_value, rdflib.BNode): raise ReportableRuntimeError("Cannot use sh:lessThan to compare a BlankNode.") compare_is_string = False if isinstance(compare_value, rdflib.URIRef): compare_value = str(compare_value) compare_is_string = True elif isinstance(compare_value, rdflib.Literal) and isinstance(compare_value.value, str): compare_value = compare_value.value compare_is_string = True if (value_is_string and not compare_is_string) or (compare_is_string and not value_is_string): non_conformant = True elif not value_node < compare_value: non_conformant = True else: continue rept = self.make_v_result(target_graph, f, value_node=orig_value_node) reports.append(rept) return non_conformant, reports class LessThanOrEqualsConstraintComponent(ConstraintComponent): """ sh:lessThanOrEquals specifies the condition that each value node is smaller than or equal to all the objects of the triples that have the focus node as subject and the value of sh:lessThanOrEquals as predicate. Link: https://www.w3.org/TR/shacl/#LessThanOrEqualsConstraintComponent Textual Definition: For each pair of value nodes and the values of the property $lessThanOrEquals at the given focus node where the first value is not less than or equal to the second value (based on SPARQL's <= operator) or where the two values cannot be compared, there is a validation result with the value node as sh:value. """ shacl_constraint_component = SH_LessThanOrEqualsConstraintComponent def __init__(self, shape): super(LessThanOrEqualsConstraintComponent, self).__init__(shape) property_compare_set = set(self.shape.objects(SH_lessThanOrEquals)) if len(property_compare_set) < 1: raise ConstraintLoadError( "LessThanOrEqualsConstraintComponent must have at least one sh:lessThanOrEquals predicate.", "https://www.w3.org/TR/shacl/#LessThanOrEqualsConstraintComponent", ) if not shape.is_property_shape: raise ConstraintLoadError( "LessThanOrEqualsConstraintComponent can only be present on a PropertyShape, not a NodeShape.", "https://www.w3.org/TR/shacl/#LessThanOrEqualsConstraintComponent", ) self.property_compare_set = property_compare_set @classmethod def constraint_parameters(cls): return [SH_lessThanOrEquals] @classmethod def constraint_name(cls): return "LessThanOrEqualsConstraintComponent" def make_generic_messages(self, datagraph: GraphLike, focus_node, value_node) -> List[rdflib.Literal]: if len(self.property_compare_set) < 2: m = "Value of {}->{} < {}".format( stringify_node(datagraph, focus_node), stringify_node(self.shape.sg.graph, next(iter(self.property_compare_set))), stringify_node(datagraph, value_node), ) else: rules = ", ".join(stringify_node(self.shape.sg.graph, p) for p in self.property_compare_set) m = "Value of {}->{} < {}".format( stringify_node(datagraph, focus_node), rules, stringify_node(datagraph, value_node) ) return [rdflib.Literal(m)] def evaluate(self, target_graph: GraphLike, focus_value_nodes: Dict, _evaluation_path: List): """ :type target_graph: rdflib.Graph :type focus_value_nodes: dict :type _evaluation_path: list """ reports = [] non_conformant = False for lt in iter(self.property_compare_set): if isinstance(lt, rdflib.Literal) or isinstance(lt, rdflib.BNode): raise ReportableRuntimeError("Value of sh:lessThanOrEquals MUST be a URI Identifier.") _nc, _r = self._evaluate_ltoe(lt, target_graph, focus_value_nodes) non_conformant = non_conformant or _nc reports.extend(_r) return (not non_conformant), reports def _evaluate_ltoe(self, lt, target_graph, f_v_dict): reports = [] non_conformant = False for f, value_nodes in f_v_dict.items(): value_node_set = set(value_nodes) compare_values = set(target_graph.objects(f, lt)) for value_node in iter(value_node_set): if isinstance(value_node, rdflib.BNode): raise ReportableRuntimeError("Cannot use sh:lessThanOrEquals to compare a BlankNode.") value_is_string = False orig_value_node = value_node if isinstance(value_node, rdflib.URIRef): value_node = str(value_node) value_is_string = True elif isinstance(value_node, rdflib.Literal) and isinstance(value_node.value, str): value_node = value_node.value value_is_string = True for compare_value in compare_values: if isinstance(compare_value, rdflib.BNode): raise ReportableRuntimeError("Cannot use sh:lessThanOrEquals to compare a BlankNode.") compare_is_string = False if isinstance(compare_value, rdflib.URIRef): compare_value = str(compare_value) compare_is_string = True elif isinstance(compare_value, rdflib.Literal) and isinstance(compare_value.value, str): compare_value = compare_value.value compare_is_string = True if (value_is_string and not compare_is_string) or (compare_is_string and not value_is_string): non_conformant = True elif not value_node <= compare_value: non_conformant = True else: continue rept = self.make_v_result(target_graph, f, value_node=orig_value_node) reports.append(rept) return non_conformant, reports

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53e39d1a08020602570ee1c5bc546f6a213adb6e

16,401

py

Python

experiments/cifar_exp/plot_cifar_all_algo.py

jdey4/progressive-learning

410b3525ab63e1f7c32e9838460b2c9af7b9d256

[ "Apache-2.0" ]

1

2022-01-03T12:36:28.000Z

experiments/cifar_exp/plot_cifar_all_algo.py

jdey4/progressive-learning

410b3525ab63e1f7c32e9838460b2c9af7b9d256

[ "Apache-2.0" ]

null

experiments/cifar_exp/plot_cifar_all_algo.py

jdey4/progressive-learning

410b3525ab63e1f7c32e9838460b2c9af7b9d256

[ "Apache-2.0" ]

null

#%% import pickle import matplotlib.pyplot as plt from matplotlib import rcParams rcParams.update({'figure.autolayout': True}) import numpy as np import pandas as pd from itertools import product import seaborn as sns import matplotlib.gridspec as gridspec #%% def unpickle(file): with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dict def get_fte_bte(err, single_err): bte = [[] for i in range(10)] te = [[] for i in range(10)] fte = [] for i in range(10): for j in range(i,10): #print(err[j][i],j,i) bte[i].append(err[i][i]/err[j][i]) te[i].append(single_err[i]/err[j][i]) for i in range(10): fte.append(single_err[i]/err[i][i]) return fte,bte,te def calc_mean_bte(btes,task_num=10,reps=6): mean_bte = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(btes[i][j]) tmp=tmp/reps mean_bte[j].extend(tmp) return mean_bte def calc_mean_te(tes,task_num=10,reps=6): mean_te = [[] for i in range(task_num)] for j in range(task_num): tmp = 0 for i in range(reps): tmp += np.array(tes[i][j]) tmp=tmp/reps mean_te[j].extend(tmp) return mean_te def calc_mean_fte(ftes,task_num=10,reps=6): fte = np.asarray(ftes) return list(np.mean(np.asarray(fte),axis=0)) def get_error_matrix(filename): multitask_df, single_task_df = unpickle(filename) err = [[] for _ in range(10)] for ii in range(10): err[ii].extend( 1 - np.array( multitask_df[multitask_df['base_task']==ii+1]['accuracy'] ) ) single_err = 1 - np.array(single_task_df['accuracy']) return single_err, err def stratified_scatter(te_dict,axis_handle,s,color): algo = list(te_dict.keys()) total_alg = len(algo) total_points = len(te_dict[algo[0]]) pivot_points = np.arange(-.25, (total_alg+1)*1, step=1) interval = .7/(total_points-1) for algo_no,alg in enumerate(algo): for no,points in enumerate(te_dict[alg]): axis_handle.scatter( pivot_points[algo_no]+interval*no, te_dict[alg][no], s=s, c=color[algo_no] ) #%% ### MAIN HYPERPARAMS ### ntrees = 10 slots = 10 task_num = 10 shifts = 6 total_alg = 9 alg_name = ['L2N','L2F','L2F-','Prog-NN', 'DF-CNN','LwF','EWC','O-EWC','SI'] model_file_500 = ['dnn0','fixed_uf10','uf10','Prog_NN','DF_CNN', 'LwF','EWC', 'Online_EWC', 'SI'] model_file_5000 = ['dnn0','fixed_uf5000_40','uf5000_40','Prog_NN','DF_CNN', 'LwF','EWC', 'Online_EWC', 'SI'] btes_500 = [[] for i in range(total_alg)] ftes_500 = [[] for i in range(total_alg)] tes_500 = [[] for i in range(total_alg)] btes_5000 = [[] for i in range(total_alg)] ftes_5000 = [[] for i in range(total_alg)] tes_5000 = [[] for i in range(total_alg)] ######################## #%% code for 5000 samples reps = shifts for alg in range(total_alg): count = 0 te_tmp = [[] for _ in range(reps)] bte_tmp = [[] for _ in range(reps)] fte_tmp = [[] for _ in range(reps)] for shift in range(shifts): if alg < 3: filename = 'result/result/'+model_file_5000[alg]+'_'+str(shift+1)+'_0'+'.pickle' else: filename = 'benchmarking_algorthms_result/'+model_file_5000[alg]+'_'+str(shift+1)+'.pickle' multitask_df, single_task_df = unpickle(filename) single_err, err = get_error_matrix(filename) fte, bte, te = get_fte_bte(err,single_err) te_tmp[count].extend(te) bte_tmp[count].extend(bte) fte_tmp[count].extend(fte) count+=1 tes_5000[alg].extend(calc_mean_te(te_tmp,reps=reps)) btes_5000[alg].extend(calc_mean_bte(bte_tmp,reps=reps)) ftes_5000[alg].extend(calc_mean_fte(fte_tmp,reps=reps)) #%% code for 500 samples reps = slots*shifts for alg in range(total_alg): count = 0 te_tmp = [[] for _ in range(reps)] bte_tmp = [[] for _ in range(reps)] fte_tmp = [[] for _ in range(reps)] for slot in range(slots): for shift in range(shifts): if alg < 3: filename = 'result/result/'+model_file_500[alg]+'_'+str(shift+1)+'_'+str(slot)+'.pickle' else: filename = 'benchmarking_algorthms_result/'+model_file_500[alg]+'_'+str(shift+1)+'_'+str(slot)+'.pickle' multitask_df, single_task_df = unpickle(filename) single_err, err = get_error_matrix(filename) fte, bte, te = get_fte_bte(err,single_err) te_tmp[count].extend(te) bte_tmp[count].extend(bte) fte_tmp[count].extend(fte) count+=1 tes_500[alg].extend(calc_mean_te(te_tmp,reps=reps)) btes_500[alg].extend(calc_mean_bte(bte_tmp,reps=reps)) ftes_500[alg].extend(calc_mean_fte(fte_tmp,reps=reps)) #%% te_500 = {'L2N':np.zeros(10,dtype=float), 'L2F':np.zeros(10,dtype=float),'L2Fc':np.zeros(10,dtype=float), 'Prog-NN':np.zeros(10,dtype=float), 'DF-CNN':np.zeros(10,dtype=float), 'LwF':np.zeros(10,dtype=float),'EWC':np.zeros(10,dtype=float), 'Online EWC':np.zeros(10,dtype=float), 'SI':np.zeros(10,dtype=float)} for count,name in enumerate(te_500.keys()): for i in range(10): te_500[name][i] = tes_500[count][i][9-i] df_500 = pd.DataFrame.from_dict(te_500) df_500 = pd.melt(df_500,var_name='Algorithms', value_name='Transfer Efficieny') '''mean_te = {'L2N':[np.mean(te['L2N'])],'L2F':[np.mean(te['L2F'])], 'L2Fc':[np.mean(te['L2Fc'])], 'Prog-NN':[np.mean(te['Prog-NN'])],'DF-CNN':[np.mean(te['DF-CNN'])], 'LwF':[np.mean(te['LwF'])],'EWC':[np.mean(te['EWC'])], 'Online EWC':[np.mean(te['Online EWC'])], 'SI':[np.mean(te['SI'])] } mean_df = pd.DataFrame.from_dict(mean_te) mean_df = pd.melt(mean_df,var_name='Algorithms', value_name='Transfer Efficieny')''' #%% te_5000 = {'L2N':np.zeros(10,dtype=float), 'L2F':np.zeros(10,dtype=float),'L2Fc':np.zeros(10,dtype=float), 'Prog-NN':np.zeros(10,dtype=float), 'DF-CNN':np.zeros(10,dtype=float), 'LwF':np.zeros(10,dtype=float),'EWC':np.zeros(10,dtype=float), 'Online EWC':np.zeros(10,dtype=float), 'SI':np.zeros(10,dtype=float)} for count,name in enumerate(te_5000.keys()): for i in range(10): te_5000[name][i] = tes_5000[count][i][9-i] df_5000 = pd.DataFrame.from_dict(te_5000) df_5000 = pd.melt(df_5000,var_name='Algorithms', value_name='Transfer Efficieny') #%% clr = ["#00008B", "#e41a1c", "#e41a1c", "#a65628", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#CCCC00"] c = sns.color_palette(clr, n_colors=len(clr)) fontsize=24 ticksize=20 fig, ax = plt.subplots(2,2, figsize=(14.5,12)) fig.tight_layout(pad=12.0) # plt.subplots_adjust(right=0.5) for i, fte in enumerate(ftes_500): if i == 0: ax[0][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i], linewidth=3) continue if i == 1: ax[0][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i], linewidth=3) continue if i == 2: ax[0][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', linestyle='dashed', markersize=12, label=alg_name[i], linewidth=3) continue ax[0][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i]) ax[0][0].set_xticks(np.arange(1,11)) ax[0][0].set_yticks([0.9, 1, 1.1, 1.2, 1.3,1.4]) ax[0][0].set_ylim(0.85, 1.41) ax[0][0].tick_params(labelsize=ticksize) # ax[0].legend(algos, loc='upper left', fontsize=14) # ax[0].legend(algos, bbox_to_anchor=(1.2, -.2), loc=2, borderaxespad=0) ax[0][0].set_ylabel('Forward Transfer Efficiency', fontsize=fontsize) ax[0][0].set_xlabel('Number of tasks seen', fontsize=fontsize) #ax[0][0].grid(axis='x') for i in range(task_num - 1): et = np.zeros((total_alg,task_num-i)) for j in range(0,total_alg): et[j,:] = np.asarray(btes_500[j][i]) ns = np.arange(i + 1, task_num + 1) for j in range(0,total_alg): if j == 0: if i == 0: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linewidth = 3) else: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linewidth = 3) elif j == 1: if i == 0: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linewidth = 3) else: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linewidth = 3) elif j==2: if i == 0: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linestyle='dashed', linewidth = 3) else: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linestyle='dashed', linewidth = 3) else: if i == 0: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j]) else: ax[0][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j]) # ax[1].set_title(ttle, fontsize=20) ax[0][1].set_xlabel('Number of tasks seen', fontsize=fontsize) ax[0][1].set_ylabel('Backward Transfer Efficiency', fontsize=fontsize) # ax.set_ylim(0.05 - 0.01, 0.5 + 0.01) # box = ax.get_position() # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # ax[1].legend(loc='upper left', fontsize=12) #ax[0][1].legend(loc='center left', bbox_to_anchor=(1,0.5), fontsize=22) ax[0][1].set_yticks([.4,.6,.8,.9,1, 1.1,1.2]) ax[0][1].set_xticks(np.arange(1,11)) ax[0][1].set_ylim(0.85, 1.19) ax[0][1].tick_params(labelsize=ticksize) #ax[0][1].grid(axis='x') right_side = ax[0][0].spines["right"] right_side.set_visible(False) top_side = ax[0][0].spines["top"] top_side.set_visible(False) right_side = ax[0][1].spines["right"] right_side.set_visible(False) top_side = ax[0][1].spines["top"] top_side.set_visible(False) ax[0][0].hlines(1, 1,10, colors='grey', linestyles='dashed',linewidth=1.5) ax[0][1].hlines(1, 1,10, colors='grey', linestyles='dashed',linewidth=1.5) ###################################### for i, fte in enumerate(ftes_5000): if i == 0: ax[1][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i], linewidth=3) continue if i == 1: ax[1][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i], linewidth=3) continue if i == 2: ax[1][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', linestyle='dashed', markersize=12, label=alg_name[i], linewidth=3) continue ax[1][0].plot(np.arange(1,11), fte, color=clr[i], marker='.', markersize=12, label=alg_name[i]) ax[1][0].set_xticks(np.arange(1,11)) ax[1][0].set_yticks([0.9, 1, 1.1, 1.2, 1.3,1.4]) ax[1][0].set_ylim(0.85, 1.41) ax[1][0].tick_params(labelsize=ticksize) # ax[0].legend(algos, loc='upper left', fontsize=14) # ax[0].legend(algos, bbox_to_anchor=(1.2, -.2), loc=2, borderaxespad=0) ax[1][0].set_ylabel('Forward Transfer Efficiency', fontsize=fontsize) ax[1][0].set_xlabel('Number of tasks seen', fontsize=fontsize) #ax[0][0].grid(axis='x') for i in range(task_num - 1): et = np.zeros((total_alg,task_num-i)) for j in range(0,total_alg): et[j,:] = np.asarray(btes_5000[j][i]) ns = np.arange(i + 1, task_num + 1) for j in range(0,total_alg): if j == 0: if i == 0: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linewidth = 3) else: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linewidth = 3) elif j == 1: if i == 0: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linewidth = 3) else: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linewidth = 3) elif j==2: if i == 0: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j], linestyle='dashed', linewidth = 3) else: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j], linestyle='dashed', linewidth = 3) else: if i == 0: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, label = alg_name[j], color=clr[j]) else: ax[1][1].plot(ns, et[j,:], marker='.', markersize=8, color=clr[j]) # ax[1].set_title(ttle, fontsize=20) ax[1][1].set_xlabel('Number of tasks seen', fontsize=fontsize) ax[1][1].set_ylabel('Backward Transfer Efficiency', fontsize=fontsize) # ax.set_ylim(0.05 - 0.01, 0.5 + 0.01) # box = ax.get_position() # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # ax[1].legend(loc='upper left', fontsize=12) #ax[0][1].legend(loc='center left', bbox_to_anchor=(1,0.5), fontsize=22) ax[1][1].set_yticks([.4,.6,.8,.9,1, 1.1,1.2]) ax[1][1].set_xticks(np.arange(1,11)) ax[1][1].set_ylim(0.85, 1.19) ax[1][1].tick_params(labelsize=ticksize) #ax[0][1].grid(axis='x') right_side = ax[1][0].spines["right"] right_side.set_visible(False) top_side = ax[1][0].spines["top"] top_side.set_visible(False) right_side = ax[1][1].spines["right"] right_side.set_visible(False) top_side = ax[1][1].spines["top"] top_side.set_visible(False) ax[1][0].hlines(1, 1,10, colors='grey', linestyles='dashed',linewidth=1.5) ax[1][1].hlines(1, 1,10, colors='grey', linestyles='dashed',linewidth=1.5) #plt.tight_layout() #ax[0][1].legend(loc='upper center', bbox_to_anchor=(0.5, -0.3), # fancybox=True, shadow=True, ncol=3,fontsize=15) ax[0][1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=18) # lgd = fig.legend(algos, bbox_to_anchor=(1, 0.45), loc='center left', fontsize=18) plt.savefig('result/figs/benchmark.pdf', dpi=500) #%% fig, ax = plt.subplots(1,2, figsize=(12,6)) ax[0].tick_params(labelsize=22) #ax_ = sns.stripplot(x="Algorithms", y="Transfer Efficieny", data=df, palette=c, size=6, ax=ax[1][1]) ax[0].hlines(1, -1,8, colors='grey', linestyles='dashed',linewidth=1.5) #sns.boxplot(x="Algorithms", y="Transfer Efficieny", data=mean_df, palette=c, linewidth=3, ax=ax[1][1]) ax_=sns.pointplot(x="Algorithms", y="Transfer Efficieny", data=df_500, join=False, color='grey', linewidth=1.5, ci='sd',ax=ax[0]) #ax_.set_yticks([.4,.6,.8,1, 1.2,1.4]) ax_.set_xlabel('', fontsize=fontsize) ax[0].set_ylabel('Final Transfer Efficiency', fontsize=fontsize) ax_.set_xticklabels( ['L2N','L2F','L2F-','Prog-NN','DF-CNN','LwF','EWC','O-EWC','SI'], fontsize=16,rotation=45,ha="right",rotation_mode='anchor' ) stratified_scatter(te_500,ax[0],10,c) right_side = ax[0].spines["right"] right_side.set_visible(False) top_side = ax[0].spines["top"] top_side.set_visible(False) ax[0].hlines(1, 1,9, colors='grey', linestyles='dashed',linewidth=1.5) ax[1].tick_params(labelsize=22) #ax_ = sns.stripplot(x="Algorithms", y="Transfer Efficieny", data=df, palette=c, size=6, ax=ax[1][1]) ax[1].hlines(1, -1,8, colors='grey', linestyles='dashed',linewidth=1.5) #sns.boxplot(x="Algorithms", y="Transfer Efficieny", data=mean_df, palette=c, linewidth=3, ax=ax[1][1]) ax_=sns.pointplot(x="Algorithms", y="Transfer Efficieny", data=df_5000, join=False, color='grey', linewidth=1.5, ci='sd',ax=ax[1]) #ax_.set_yticks([.4,.6,.8,1, 1.2,1.4]) ax_.set_xlabel('', fontsize=fontsize) ax[1].set_ylabel('Final Transfer Efficiency', fontsize=fontsize) ax_.set_xticklabels( ['L2N','L2F','L2F-','Prog-NN','DF-CNN','LwF','EWC','O-EWC','SI'], fontsize=16,rotation=45,ha="right",rotation_mode='anchor' ) stratified_scatter(te_5000,ax[1],10,c) right_side = ax[1].spines["right"] right_side.set_visible(False) top_side = ax[1].spines["top"] top_side.set_visible(False) ax[1].hlines(1, 1,9, colors='grey', linestyles='dashed',linewidth=1.5) plt.savefig('result/figs/final_TE.pdf', dpi=500) # %%

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53f1606593a923659bc78d0d52a9dc7a073dfada

178

py

Python

hacmec/__init__.py

joernheissler/hacmec

1a28abd4211619dae85def7cc6b49cbc80e9c5c6

[ "MIT" ]

null

hacmec/__init__.py

joernheissler/hacmec

1a28abd4211619dae85def7cc6b49cbc80e9c5c6

[ "MIT" ]

null

hacmec/__init__.py

joernheissler/hacmec

1a28abd4211619dae85def7cc6b49cbc80e9c5c6

[ "MIT" ]

null

VERSION = '0.0.3' ENDPOINT_LETSENCRYPT = "https://acme-v02.api.letsencrypt.org/directory" ENDPOINT_LETSENCRYPT_STAGING = "https://acme-staging-v02.api.letsencrypt.org/directory"

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py

Python

recoverid/cities/models/__init__.py

C3-Zally/api-python

4e64ad345d59daa32e750d5f786f2185533f3b38

[ "MIT" ]

null

recoverid/cities/models/__init__.py

C3-Zally/api-python

4e64ad345d59daa32e750d5f786f2185533f3b38

[ "MIT" ]

1

2020-08-12T01:26:08.000Z

recoverid/cities/models/__init__.py

C3-Zally/api-python

4e64ad345d59daa32e750d5f786f2185533f3b38

[ "MIT" ]

null

from .city import City

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py

Python

data_pre/reg_preprocess_example/preprocess_brain35.py

norveclibalikci/easyreg-mirror

a16254733fe957cc4024923f8dce91412966a189

[ "Apache-2.0" ]

null

data_pre/reg_preprocess_example/preprocess_brain35.py

norveclibalikci/easyreg-mirror

a16254733fe957cc4024923f8dce91412966a189

[ "Apache-2.0" ]

null

data_pre/reg_preprocess_example/preprocess_brain35.py

norveclibalikci/easyreg-mirror

a16254733fe957cc4024923f8dce91412966a189

[ "Apache-2.0" ]

null

""" A demo on data augmentation and segmentation for brain dataset """ import os, sys sys.path.insert(0,os.path.abspath('..')) sys.path.insert(0,os.path.abspath('.')) sys.path.insert(0,os.path.abspath('../easyreg')) from data_pre.file_tool import get_file_list from easyreg.reg_data_utils import read_txt_into_list, write_list_into_txt from data_pre.seg_data_pool import BaseSegDataSet def find_corr_label(img_path_list,label_root_path=None,label_switch=None): get_par_folder_name = lambda x: os.path.split(os.path.split(os.path.split(x)[0])[0])[-1] fname_list = [get_par_folder_name(path) for path in img_path_list] label_path_list = [get_file_list('/playpen-raid1/Data/annotation',fname+"*"+".nii.gz")[0] for fname in fname_list] if label_root_path is not None: label_path_list = [path.replace(os.path.split(path)[0],label_root_path) for path in label_path_list] return label_path_list def get_file_name( img_path): get_par_folder_path = lambda x: os.path.split(os.path.split(x)[0])[0] file_name = os.path.split(get_par_folder_path(img_path))[-1] return file_name dataset = BaseSegDataSet(file_type_list=["T1w_acpc_dc_restore.nii.gz"]) data_path = "/playpen-raid1/Data/Brain35" output_path ='/playpen-raid1/zyshen/data/brain_35/corrected' divided_ratio = (0.6,0.1,0.3) dataset.set_data_path(data_path) dataset.find_corr_label = find_corr_label dataset.get_file_name = get_file_name dataset.set_output_path(output_path) dataset.set_divided_ratio(divided_ratio) dataset.img_after_resize = (200,240,200) dataset.prepare_data() from easyreg.aug_utils import gen_post_aug_pair_list train_file_path = "/playpen-raid1/zyshen/data/brain_35/corrected/train/file_path_list.txt" test_file_path = "/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt" train_name_path = "/playpen-raid1/zyshen/data/brain_35/corrected/train/file_name_list.txt" test_name_path = "/playpen-raid1/zyshen/data/brain_35/corrected/test/file_name_list.txt" output_file_path = "/playpen-raid1/zyshen/data/brain_35/corrected/test_aug_path_list.txt" output_name_path = "/playpen-raid1/zyshen/data/brain_35/corrected/test_aug_name_list.txt" train_path_list = read_txt_into_list(train_file_path) test_path_list = read_txt_into_list(test_file_path) train_name_list = read_txt_into_list(train_name_path) test_name_list = read_txt_into_list(test_name_path) test_img_path_list = [path[0] for path in test_path_list] test_label_path_list = [path[1] for path in test_path_list] if isinstance(train_path_list[0],list): train_img_path_list = [path[0] for path in train_path_list] train_label_path_list = [path[1] for path in train_path_list] else: train_img_path_list = train_path_list train_label_path_list = None img_pair_list, pair_name_list = gen_post_aug_pair_list(test_img_path_list,train_img_path_list, test_fname_list=test_name_list,train_fname_list=train_name_list, test_label_path_list=test_label_path_list,train_label_path_list=train_label_path_list, pair_num_limit=-1, per_num_limit=5) pair_name_list = [pair_name[1:] for pair_name in pair_name_list] write_list_into_txt(output_file_path,img_pair_list) write_list_into_txt(output_name_path,pair_name_list) train_aug_output_path = "/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train" train_aug_output_full_path = train_aug_output_path+"/aug" output_folder = "/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_k2/train" os.makedirs(output_folder,exist_ok=True) output_path = os.path.join(output_folder,"file_path_list.txt") train_aug_img_list = get_file_list(train_aug_output_full_path,"*_image.nii.gz") train_aug_label_list = [path.replace("_image.nii.gz","_label.nii.gz") for path in train_aug_img_list] img_label_path_list = [[img_path, label_path] for img_path, label_path in zip(train_aug_img_list,train_aug_label_list)] write_list_into_txt(output_path,img_label_path_list) # train_aug_output_path = "/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train_random" train_aug_output_full_path = train_aug_output_path+"/aug" output_folder = "/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_random/train" os.makedirs(output_folder,exist_ok=True) output_path = os.path.join(output_folder,"file_path_list.txt") train_aug_img_list = get_file_list(train_aug_output_full_path,"*_image.nii.gz") train_aug_label_list = [path.replace("_image.nii.gz","_label.nii.gz") for path in train_aug_img_list] img_label_path_list = [[img_path, label_path] for img_path, label_path in zip(train_aug_img_list,train_aug_label_list)] write_list_into_txt(output_path,img_label_path_list) train_aug_output_path = "/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train_bspline" train_aug_output_full_path = train_aug_output_path+"/aug" output_folder = "/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_bspline/train" os.makedirs(output_folder,exist_ok=True) output_path = os.path.join(output_folder,"file_path_list.txt") train_aug_img_list = get_file_list(train_aug_output_full_path,"*_image.nii.gz") train_aug_label_list = [path.replace("_image.nii.gz","_label.nii.gz") for path in train_aug_img_list] img_label_path_list = [[img_path, label_path] for img_path, label_path in zip(train_aug_img_list,train_aug_label_list)] write_list_into_txt(output_path,img_label_path_list) """ training phase augmentation python demo_for_data_aug.py --file_txt=/playpen-raid1/zyshen/data/brain_35/corrected/train/file_path_list.txt --name_txt=/playpen-raid1/zyshen/data/brain_35/corrected/train/file_name_list.txt --txt_format=aug_by_file --setting_folder_path=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/opt_lddmm_brain35 --task_output_path=/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train --gpu_id_list 2 3 2 3 testing phase augmentation python demo_for_data_aug.py --file_txt=/playpen-raid1/zyshen/data/brain_35/corrected/test_aug_path_list.txt --name_txt=/playpen-raid1/zyshen/data/brain_35/corrected/test_aug_name_list.txt --txt_format=aug_by_line --setting_folder_path=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/opt_lddmm_brain35_postaug --task_output_path=/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_test --gpu_id_list 0 1 2 3 0 1 2 3 training phase augmentation (random) python gen_aug_samples.py -t=/playpen-raid1/zyshen/data/brain_35/corrected/train/file_path_list.txt -as=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/rand_lddmm_brain35_random/data_aug_setting.json -ms=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/rand_lddmm_brain35_random/mermaid_nonp_settings.json -o=/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train_random/aug -g=0 testing phase augmentation (random) python gen_aug_samples.py -t=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -as=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/rand_lddmm_brain35_postaug_random/data_aug_setting.json -ms=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/rand_lddmm_brain35_random/mermaid_nonp_settings.json -o=/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_test_random/aug -g=1 training phase augmentation (bspline) python gen_aug_samples.py -t=/playpen-raid1/zyshen/data/brain_35/corrected/train/file_path_list.txt -as=/playpen-raid/zyshen/reg_clean/demo/demo_settings/data_aug/rand_bspline_brain35/data_aug_setting.json --bspline -o=/playpen-raid1/zyshen/data/brain_35/corrected/data_aug_train_bspline/aug train segmentation without aug python demo_for_seg_train.py -o /playpen-raid1/zyshen/data/brain_35 -dtn=corrected -tn=custom_seg -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_train -g=0 train segmentation with aug python demo_for_seg_train.py -o /playpen-raid1/zyshen/data/brain_35/corrected -dtn=seg_aug_train_k2 -tn=aug_seg -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_train -g=1 train segmentation with aug random python demo_for_seg_train.py -o /playpen-raid1/zyshen/data/brain_35/corrected -dtn=seg_aug_train_random -tn=aug_seg -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_train -g=2 train segmentation with aug bspline python demo_for_seg_train.py -o /playpen-raid1/zyshen/data/brain_35/corrected -dtn=seg_aug_train_bspline -tn=aug_seg -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_train -g=3 test segmentation without aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/custom_seg/checkpoints/model_best.pth.tar -o=/playpen-raid1/zyshen/data/brain_35/corrected/custom_seg_res -g=0 test segmentation with training aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_k2/aug_seg/checkpoints/epoch_150_ -o=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_k2_res_epoch150 -g=1 test segmentation with training_random aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_random/aug_seg/checkpoints/model_best.pth.tar -o=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_random_res -g=2 test segmentation with bspline aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_bspline/aug_seg/checkpoints/model_best.pth.tar -o=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_bspline_res -g=3 test segmentation with training testing aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval_aug -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_k2/aug_seg/checkpoints/epoch_150_ -o=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_and_test_res_trainedk2testk2 -g=2 test segmentation with training testing random_aug python demo_for_seg_eval.py -ts=/playpen-raid/zyshen/reg_clean/debug/brain35/seg_eval_aug_random -txt=/playpen-raid1/zyshen/data/brain_35/corrected/test/file_path_list.txt -m=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_random/aug_seg/checkpoints/model_best.pth.tar -o=/playpen-raid1/zyshen/data/brain_35/corrected/seg_aug_train_and_test_random_res -g=2 """

65.067073

428

0.830569

1,865

10,671

4.370509

0.080965

0.048092

0.103791

0.126856

0.79389

0.769722

0.743344

0.723224

0.707398

0.692553

0

0.025284

0.05857

10,671

163

429

65.466258

0.786084

0.00581

0

0.2625

0

0.219545

0.180266

0

1

0.025

false

0

0.0625

0

0.1125

0

null

0

1

0

null

0

5

54fa8aba69d224d4910f4868277e2c72af6dcc07

1,567

py

Python

Data Collection/test.py

YooInKeun/CAU_CSE_Capstone_3

51405c4bed2b55661aa0708c8acea17fe72aa701

[ "MIT" ]

6

2019-12-07T07:30:34.000Z

2022-01-20T14:26:44.000Z

Data Collection/test.py

YooInKeun/CAU_CSE_Capstone_3

51405c4bed2b55661aa0708c8acea17fe72aa701

[ "MIT" ]

9

2019-12-28T06:18:53.000Z

2022-01-13T01:54:21.000Z

Data Collection/test.py

YooInKeun/CAU_CSE_Capstone_3

51405c4bed2b55661aa0708c8acea17fe72aa701

[ "MIT" ]

1

2020-05-21T15:55:45.000Z

from urllib.request import Request, urlopen from bs4 import BeautifulSoup import json req = Request("https://www.dabangapp.com/search#/map?filters=%7B%22multi_room_type%22%3A%5B0%2C1%2C2%5D%2C%22selling_type%22%3A%5B0%2C1%2C2%5D%2C%22deposit_range%22%3A%5B0%2C999999%5D%2C%22price_range%22%3A%5B0%2C999999%5D%2C%22trade_range%22%3A%5B0%2C999999%5D%2C%22maintenance_cost_range%22%3A%5B0%2C999999%5D%2C%22include_maintenance_option1%22%3Atrue%2C%22room_size%22%3A%5B0%2C999999%5D%2C%22supply_space_range%22%3A%5B0%2C999999%5D%2C%22room_floor_multi%22%3A%5B1%2C2%2C3%2C4%2C5%2C6%2C7%2C-1%2C0%5D%2C%22division%22%3Afalse%2C%22duplex%22%3Afalse%2C%22room_type%22%3A%5B1%2C2%5D%2C%22enter_date_range%22%3A%5B0%2C999999%5D%2C%22parking_average_range%22%3A%5B0%2C999999%5D%2C%22household_num_range%22%3A%5B0%2C999999%5D%2C%22parking%22%3Afalse%2C%22animal%22%3Afalse%2C%22short_lease%22%3Afalse%2C%22full_option%22%3Afalse%2C%22built_in%22%3Afalse%2C%22elevator%22%3Afalse%2C%22balcony%22%3Afalse%2C%22loan%22%3Afalse%2C%22pano%22%3Afalse%2C%22deal_type%22%3A%5B0%2C1%5D%7D&position=%7B%22location%22%3A%5B%5B126.84998760716898%2C37.41464989903129%5D%2C%5B127.12956613898%2C37.715102046666125%5D%5D%2C%22center%22%3A%5B126.98949617689095%2C37.5649606036606%5D%2C%22zoom%22%3A9%7D&search=%7B%22id%22%3A%22%22%2C%22type%22%3A%22%22%2C%22name%22%3A%22%22%7D&tab=all", headers={'User-Agent': 'Mozilla/5.0'}) main_html = urlopen(req).read() soup = BeautifulSoup(main_html, "html.parser") print(soup) raws = soup.find_all("ul", {"class": "styled__Ul-ityzo6-5 fxRDHg"}) print(raws)

156.7

1,309

0.804722

290

1,567

4.241379

0.393103

0.061789

0.068293

0.109756

0.247967

0.220325

0.204878

0.087805

0

0.296224

0.019783

1,567

10

1,310

156.7

0.504557

0

0.111111

0.840561

0

1

0

false

0

0.333333

0

0.333333

0.222222

0

null

0

1

0

1

0

1

0

null

0

1

0

5

070091cb6032a03981c635127be648be10409990

396

py

Python

pydlm/modeler/__init__.py

dtolpin/pydlm

0016876bf6357784b161ecaa4f0798c063c54785

[ "BSD-3-Clause" ]

423

2016-09-15T06:45:26.000Z

2022-03-29T08:41:11.000Z

pydlm/modeler/__init__.py

dtolpin/pydlm

0016876bf6357784b161ecaa4f0798c063c54785

[ "BSD-3-Clause" ]

50

2016-09-14T19:45:49.000Z

2021-07-26T17:04:10.000Z

pydlm/modeler/__init__.py

dtolpin/pydlm

0016876bf6357784b161ecaa4f0798c063c54785

[ "BSD-3-Clause" ]

99

2016-09-19T08:08:41.000Z

2022-03-07T13:47:36.000Z

# this module defines the tools for modeling # __all__ = ['trends', 'seasonality', 'dynamic', 'autoReg', 'longSeason', 'builder'] # import pydlm.modeler.trends as trends # import pydlm.modeler.seasonality as seasonality # import pydlm.modeler.dynamic as dynamic # import pydlm.modeler.builder as builder # import pydlm.modeler.autoReg as autoReg # import pydlm.modeler.longSeason as longSeason

36

84

0.775253

50

396

6.06

0.36

0.217822

0.356436

0.165017

0

0.123737

396

10

85

39.6

0.873199

0.95202

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

1

0

1

0

null

0

1

0

5

072a71d75db0b0ec2d3543e74e5467cc36515ff2

48

py

Python

code/arc018_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/arc018_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/arc018_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

H,B=map(float,input().split()) print(H**2*B/1e4)

24

30

0.645833

11

48

2.818182

0.818182

0

0.06383

0.020833

48

2

31

24

0.595745

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

073d01b92cf7075e32fa92b1e9a3711372694558

262

py

Python

bitmovin_api_sdk/encoding/encodings/streams/burn_in_subtitles/dvbsub/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/encoding/encodings/streams/burn_in_subtitles/dvbsub/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/encoding/encodings/streams/burn_in_subtitles/dvbsub/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.encoding.encodings.streams.burn_in_subtitles.dvbsub.dvbsub_api import DvbsubApi from bitmovin_api_sdk.encoding.encodings.streams.burn_in_subtitles.dvbsub.burn_in_subtitle_dvb_sub_list_query_params import BurnInSubtitleDvbSubListQueryParams

87.333333

159

0.923664

36

262

6.277778

0.555556

0.079646

0.132743

0.159292

0.557522

0

0.030534

262

2

160

131

0.889764

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

074da2fca1e6ab8fdfc1fb5622ebec670d995ad1

68

py

Python

abc/abc090/abc090a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc090/abc090a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc090/abc090a.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

c = [input() for _ in range(3)] print(c[0][0] + c[1][1] + c[2][2])

17

34

0.470588

16

68

1.9375

0.625

0

0.127273

0.191176

68

3

35

22.666667

0.436364

0

1

0

false

0

0.5

1

0

1

null

0

1

0

1

0

null

0

1

0

5

4acbd9dfce233d444086e0991dca3677d10668f6

229

py

Python

typeidea/base/funtion.py

birdywings/typeidea

d7ce276a7a823b4a9d50bf57edc07e002aa08863

[ "MIT" ]

1

2018-08-28T06:26:18.000Z

typeidea/base/funtion.py

birdywings/typeidea

d7ce276a7a823b4a9d50bf57edc07e002aa08863

[ "MIT" ]

2

2020-03-10T10:21:22.000Z

2021-06-10T20:52:02.000Z

typeidea/base/funtion.py

birdywings/typeidea

d7ce276a7a823b4a9d50bf57edc07e002aa08863

[ "MIT" ]

null

# -*- coding: UTF-8 -*- def value_judge(request, *args): # 查看客户端有没有漏传字段 for i in args: if i not in request.data or request.data.get(i) == '' or request.data.get(i) is None: return False return True

25.444444

93

0.598253

35

229

3.885714

0.628571

0.242647

0.191176

0.235294

0.25

0

0.005988

0.270742

229

8

94

28.625

0.808383

0.148472

0

1

0.2

false

0

0.6

0

null

1

0

1

0

null

0

1

0

5

4ae9786ca8ffcb225ebb57110db7cc8df8a943f5

29

py

Python

frds/data/wrds/execucomp/__init__.py

mgao6767/wrds

7dca2651a181bf38c61ebde675c9f64d6c96f608

[ "MIT" ]

31

2020-06-17T13:19:12.000Z

2022-03-27T08:56:38.000Z

frds/data/wrds/execucomp/__init__.py

mgao6767/wrds

7dca2651a181bf38c61ebde675c9f64d6c96f608

[ "MIT" ]

null

frds/data/wrds/execucomp/__init__.py

mgao6767/wrds

7dca2651a181bf38c61ebde675c9f64d6c96f608

[ "MIT" ]

8

2020-06-14T15:21:51.000Z

2021-09-29T06:28:53.000Z

from .anncomp import Anncomp

14.5

28

0.827586

4

29

6

0.75

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

5

ab613c70006f712e3afdac76d87e9315e1feec69

68

py

Python

maya/python/dl_bifrost_utils/__init__.py

Mikfr83/bifrost-dl-core

a5660076f6f76cdcd95b73c63c521f6056b05123

[ "MIT" ]

55

2021-02-08T05:17:28.000Z

2022-01-28T18:04:43.000Z

maya/python/dl_bifrost_utils/__init__.py

Mikfr83/bifrost-dl-core

a5660076f6f76cdcd95b73c63c521f6056b05123

[ "MIT" ]

27

2021-02-13T08:05:46.000Z

2021-12-07T07:32:39.000Z

maya/python/dl_bifrost_utils/__init__.py

Mikfr83/bifrost-dl-core

a5660076f6f76cdcd95b73c63c521f6056b05123

[ "MIT" ]

6

2021-04-10T06:42:43.000Z

2022-02-15T07:17:56.000Z

from . import group_utils # import the ui package from . import ui

13.6

25

0.75

11

68

4.545455

0.636364

0.4

0

0.205882

68

4

26

17

0.925926

0.308824

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

db495df5f6aac6c091c0e550ebcf37eccc8aa643

83

py

Python

Lab_6/Task_4.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

1

2022-01-12T21:48:23.000Z

Lab_6/Task_4.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

null

Lab_6/Task_4.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

null

def groups_of_3(myList): return [myList[0:3],myList[3:6],myList[6:len(myList)]]

41.5

58

0.698795

16

83

3.5

0.5625

0.25

0

0.078947

0.084337

83

2

58

41.5

0.657895

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

db5a4045cb12c42a179327b9e4f4b6d30c6d831e

834

py

Python

space-age/space_age.py

ederst/exercism-python

8791f145ff4ce1a3b78ac3566fbe428ce3a3bd7b

[ "Unlicense" ]

1

2021-06-25T16:09:02.000Z

space-age/space_age.py

ederst/exercism-python

8791f145ff4ce1a3b78ac3566fbe428ce3a3bd7b

[ "Unlicense" ]

1

2021-05-17T23:45:29.000Z

2021-05-17T23:46:01.000Z

space-age/space_age.py

ederst/exercism-python

8791f145ff4ce1a3b78ac3566fbe428ce3a3bd7b

[ "Unlicense" ]

null

class SpaceAge: def __init__(self, seconds: float): self.seconds = seconds def _space_age(self, ratio: float = 1.0, ndigits: int = 2) -> float: return round(self.seconds / 31557600.0 / ratio, ndigits) def on_mercury(self) -> float: return self._space_age(0.2408467) def on_venus(self) -> float: return self._space_age(0.61519726) def on_earth(self) -> float: return self._space_age() def on_mars(self) -> float: return self._space_age(1.8808158) def on_jupiter(self) -> float: return self._space_age(11.862615) def on_saturn(self) -> float: return self._space_age(29.447498) def on_uranus(self) -> float: return self._space_age(84.016846) def on_neptune(self) -> float: return self._space_age(164.79132)

26.0625

72

0.635492

116

834

4.310345

0.327586

0.144

0.24

0.304

0.436

0.112

0

0.109873

0.247002

834

31

73

26.903226

0.686306

0

1

0.47619

false

0

0.428571

0.952381

0

null

0

1

0

null

0

1

0

1

0

5

db96a3880d6623c7a65c679c54a2181a9c18f50a

11

py

Python

login.py

LiuTongred/test

90d9b09f2c9c29143f2ca47691ccdef28010949a

[ "MIT" ]

null

login.py

LiuTongred/test

90d9b09f2c9c29143f2ca47691ccdef28010949a

[ "MIT" ]

null

login.py

LiuTongred/test

90d9b09f2c9c29143f2ca47691ccdef28010949a

[ "MIT" ]

null

num = 2222

5.5

10

0.636364

2

11

3.5

1

0

0.5

0.272727

11

1

11

0.375

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

dbdca5e84d423d50b740bd0c4363098894318379

38

py

Python

simplpy/list_/__init__.py

frankhart2018/simplpy

a9e8781f9cc8ba9578d6ec786d58e349cba9c52a

[ "MIT" ]

1

2021-02-15T11:36:47.000Z

simplpy/list_/__init__.py

frankhart2018/simplpy

a9e8781f9cc8ba9578d6ec786d58e349cba9c52a

[ "MIT" ]

null

simplpy/list_/__init__.py

frankhart2018/simplpy

a9e8781f9cc8ba9578d6ec786d58e349cba9c52a

[ "MIT" ]

null

from simplpy.list_.list_func import *

19

37

0.815789

6

38

4.833333

0.833333

0

0.105263

38

1

38

0.852941

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

915e48f7f8846a80b3fd111683669a6dfc582b29

107

py

Python

starkbank/utilitypayment/__init__.py

thalesmello/sdk-python

fe897883b5c91948e812cfaa6ac176edcf0f9290

[ "MIT" ]

null

starkbank/utilitypayment/__init__.py

thalesmello/sdk-python

fe897883b5c91948e812cfaa6ac176edcf0f9290

[ "MIT" ]

null

starkbank/utilitypayment/__init__.py

thalesmello/sdk-python

fe897883b5c91948e812cfaa6ac176edcf0f9290

[ "MIT" ]

null

from .__utilitypayment import create, get, pdf, query, delete from .log.__log import Log from . import log

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918a523cfa56dd7f4925fcb00762460b23a1f614

173

py

Python

BDProjectsSessionsMonitor/__init__.py

bond-anton/SPAdminTools

1bc12f773b8fb7b96a64348d0b807ee4807fd5a0

[ "Apache-2.0" ]

null

BDProjectsSessionsMonitor/__init__.py

bond-anton/SPAdminTools

1bc12f773b8fb7b96a64348d0b807ee4807fd5a0

[ "Apache-2.0" ]

null

BDProjectsSessionsMonitor/__init__.py

bond-anton/SPAdminTools

1bc12f773b8fb7b96a64348d0b807ee4807fd5a0

[ "Apache-2.0" ]

null

from __future__ import division, print_function from BDProjectsSessionsMonitor.Application import SPSMApplication from BDProjectsSessionsMonitor.AboutWindow import _version

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91ac180c9ec1fe60d487d0f499cfe5393071236e

10,888

py

Python

swiftbrowser/tests/test_utils.py

OLRC/django-swiftbrowser

cfc6fa44f8eeda5e66db81cce39b5340fc5a898a

[ "Apache-2.0" ]

1

2021-09-06T12:31:27.000Z

swiftbrowser/tests/test_utils.py

OLRC/django-swiftbrowser

cfc6fa44f8eeda5e66db81cce39b5340fc5a898a

[ "Apache-2.0" ]

null

swiftbrowser/tests/test_utils.py

OLRC/django-swiftbrowser

cfc6fa44f8eeda5e66db81cce39b5340fc5a898a

[ "Apache-2.0" ]

null

from django.test import TestCase import swiftbrowser.views from swiftbrowser.utils import * class SplitAclTest(TestCase): def setUp(self): # Create an empty self.expected = { "users": [], "referrers": [], "rlistings": False, "public": False, } def test_empty(self): '''When no ACL is set, the returned dictionary should have empty lists and false rlistings and public.''' acl = "" split = split_acl(acl) self.assertEqual(0, len(split["users"])) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_user_single(self): '''Test when one user is on the acl.''' acl = "tenant:user" split = split_acl(acl) self.assertEqual("tenant:user", split["users"][0]) self.assertEqual(1, len(split["users"])) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_user_multiple(self): '''Test multiple users on the acl''' acl = "tenant:user1,tenant:user2,tenant:user3,user4" split = split_acl(acl) self.assertEqual(4, len(split)) self.assertEqual("tenant:user1", split["users"][0]) self.assertEqual("tenant:user2", split["users"][1]) self.assertEqual("tenant:user3", split["users"][2]) self.assertEqual("user4", split["users"][3]) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_referrers_single(self): '''Test single referer on the acl''' acl = ".r:example.com" split = split_acl(acl) self.assertEqual(1, len(split["referrers"])) self.assertEqual("example.com", split["referrers"][0]) self.assertEqual(0, len(split["users"])) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_referrers_multiple(self): '''Test multiple referrers on acl''' acl = ".r:example.com,.r:domain.com,.r:swiftbrowser.com,.r:abc.com" split = split_acl(acl) self.assertEqual(4, len(split["referrers"])) self.assertEqual("example.com", split["referrers"][0]) self.assertEqual("domain.com", split["referrers"][1]) self.assertEqual("swiftbrowser.com", split["referrers"][2]) self.assertEqual("abc.com", split["referrers"][3]) self.assertEqual(0, len(split["users"])) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_rlisting(self): '''Test case where rlisting is set.''' acl = ".rlistings" split = split_acl(acl) self.assertTrue(split["rlistings"]) self.assertEqual(0, len(split["users"])) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["public"]) def test_public(self): '''Test when the container is set to public.''' acl = ".r:*" split = split_acl(acl) self.assertTrue(split["public"]) self.assertEqual(0, len(split["users"])) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["rlistings"]) def test_public_rlistings(self): '''Test when a container is set to public and has rlistings is set.''' acl = ".r:*,.rlistings" split = split_acl(acl) self.assertEqual(0, len(split["users"])) self.assertEqual(0, len(split["referrers"])) self.assertTrue(split["rlistings"]) self.assertTrue(split["public"]) def test_public_multiple_referrers(self): '''Test when a container is set to public and has multiple referrers. ''' acl = ".r:*,.r:domain.com,.r:abc.com" split = split_acl(acl) self.assertEqual(0, len(split["users"])) self.assertEqual(2, len(split["referrers"])) self.assertEqual("domain.com", split["referrers"][0]) self.assertEqual("abc.com", split["referrers"][1]) self.assertFalse(split["rlistings"]) self.assertTrue(split["public"]) def test_public_multiple_users(self): '''Test when a container is set to public and has multiple users.''' acl = ".r:*,tenant:user,user2,user3,tenant:user4" split = split_acl(acl) self.assertEqual(4, len(split["users"])) self.assertEqual("tenant:user", split["users"][0]) self.assertEqual("user2", split["users"][1]) self.assertEqual("user3", split["users"][2]) self.assertEqual("tenant:user4", split["users"][3]) self.assertEqual(0, len(split["referrers"])) self.assertFalse(split["rlistings"]) self.assertTrue(split["public"]) def test_rlistings_multiple_referrers(self): '''Test when a container has rlistings set and multiple referrers.''' acl = ".rlistings,.r:domain.com,.r:abc.com,.r:example.com" split = split_acl(acl) self.assertEqual(0, len(split["users"])) self.assertEqual(3, len(split["referrers"])) self.assertEqual("domain.com", split["referrers"][0]) self.assertEqual("abc.com", split["referrers"][1]) self.assertEqual("example.com", split["referrers"][2]) self.assertTrue(split["rlistings"]) self.assertFalse(split["public"]) def test_rlistings_multiple_users(self): '''Test when a container has rlistings set and multiple users.''' acl = ".rlistings,user1,user2,tenant:user3" split = split_acl(acl) self.assertEqual(3, len(split["users"])) self.assertEqual("user1", split["users"][0]) self.assertEqual("user2", split["users"][1]) self.assertEqual("tenant:user3", split["users"][2]) self.assertEqual(0, len(split["referrers"])) self.assertTrue(split["rlistings"]) self.assertFalse(split["public"]) def test_multiple_referrers_multiple_users(self): '''Test when a container has multiple referrers and multiple users.''' acl = ".r:domain.com,user1,user2,.r:abc.com,.r:swiftbrowser.com,user3" split = split_acl(acl) self.assertEqual(3, len(split["users"])) self.assertEqual("user1", split["users"][0]) self.assertEqual("user2", split["users"][1]) self.assertEqual("user3", split["users"][2]) self.assertEqual(3, len(split["referrers"])) self.assertEqual("domain.com", split["referrers"][0]) self.assertEqual("abc.com", split["referrers"][1]) self.assertEqual("swiftbrowser.com", split["referrers"][2]) self.assertFalse(split["rlistings"]) self.assertFalse(split["public"]) def test_public_rlistings_referrers_users(self): '''Test when a container has public set, rlistings set, multiple referrers and multiple users.''' acl = (".r:*,user1,.r:domain.com,user2,.rlistings," "user3,.r:domain2.com,.r:abc.com") split = split_acl(acl) self.assertEqual(3, len(split["users"])) self.assertEqual("user1", split["users"][0]) self.assertEqual("user2", split["users"][1]) self.assertEqual("user3", split["users"][2]) self.assertEqual(3, len(split["referrers"])) self.assertEqual("domain.com", split["referrers"][0]) self.assertEqual("domain2.com", split["referrers"][1]) self.assertEqual("abc.com", split["referrers"][2]) self.assertTrue(split["rlistings"]) self.assertTrue(split["public"]) class GetNonConsecutiveTest(TestCase): def test_empty(self): '''Test an empty set.''' objects = [] self.assertEqual(get_first_nonconsecutive(objects), 1) def test_segment_one_missing(self): '''Test when segment number one is missing.''' objects = ["0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 1) def test_sequential_numbers(self): '''Test a list of objects that are perfectly sequential.''' objects = ["0001", "0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 15) def test_sequential_numbers_small(self): '''Test a small list of objects that are perfectly sequential.''' objects = ["0001", "0002"] self.assertEqual(get_first_nonconsecutive(objects), 3) def test_set_size_one(self): '''Test a list with one digit.''' objects = ["0001"] self.assertEqual(get_first_nonconsecutive(objects), 2) def test_set_size_one_incorrect(self): '''Test a list with one digit that is not one.''' objects = ["0002"] self.assertEqual(get_first_nonconsecutive(objects), 1) def test_break_in_sequence_after_one(self): '''Test a set where the break in sequence is after 1.''' objects = ["0001", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 2) def test_break_in_sequence_middle(self): '''Test a break in sequence in the middle.''' objects = ["0001", "0002", "0003", "0004", "0005", "0007", "0008", "0009", "0010", "0011", "0012", "0013", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 6) def test_large_break_in_sequence_middle(self): '''Test a break in sequence in the middle.''' objects = ["0001", "0002", "0003", "0004", "0005", "0014", "0015", "0016"] self.assertEqual(get_first_nonconsecutive(objects), 6) def test_break_in_sequence_near_end(self): '''Test a break in sequence at the end.''' objects = ["0001", "0002", "0003", "0004", "0005", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 13) def test_duplicate(self): '''Test when there is a duplicate.''' objects = ["0001", "0002", "0003", "0004", "0005", "0006", "0006", "0007", "0008", "0009", "0010", "0011", "0012", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 7) def test_duplicate_break(self): '''Test when there is a duplicate and a break following immediately.''' objects = ["0001", "0002", "0003", "0004", "0005", "0006", "0006", "0008", "0009", "0010", "0011", "0012", "0014"] self.assertEqual(get_first_nonconsecutive(objects), 7)

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null

0

1

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5

91b722d3944117600c07825b9d44f869f27b23da

5,555

py

Python

tests/test_cli.py

danvalen1/waybackpy

4b61b6ecd6eb4ae2f607afcfd3309ef048dd4a32

[ "MIT" ]

null

tests/test_cli.py

danvalen1/waybackpy

4b61b6ecd6eb4ae2f607afcfd3309ef048dd4a32

[ "MIT" ]

null

tests/test_cli.py

danvalen1/waybackpy

4b61b6ecd6eb4ae2f607afcfd3309ef048dd4a32

[ "MIT" ]

null

# -*- coding: utf-8 -*- import sys import os import pytest import argparse sys.path.append("..") import waybackpy.cli as cli # noqa: E402 from waybackpy.wrapper import Url # noqa: E402 from waybackpy.__version__ import __version__ codecov_python = False if sys.version_info > (3, 7): codecov_python = True # Namespace(day=None, get=None, hour=None, minute=None, month=None, near=False, # newest=False, oldest=False, save=False, total=False, url=None, user_agent=None, version=False, year=None) if codecov_python: def test_save(): args = argparse.Namespace(user_agent=None, url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=True, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get=None) reply = cli.args_handler(args) assert "pypi.org/user/akamhy" in reply def test_oldest(): args = argparse.Namespace(user_agent=None, url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=True, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get=None) reply = cli.args_handler(args) assert "pypi.org/user/akamhy" in reply def test_newest(): args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=True, near=False, alive=False, subdomain=False, known_urls=False, get=None) reply = cli.args_handler(args) assert "pypi.org/user/akamhy" in reply def test_total_archives(): args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=True, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get=None) reply = cli.args_handler(args) assert isinstance(reply, int) def test_known_urls(): args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://akamhy.github.io", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=True, subdomain=True, known_urls=True, get=None) reply = cli.args_handler(args) assert "github" in reply def test_near(): args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=True, alive=False, subdomain=False, known_urls=False, get=None, year=2020, month=7, day=15, hour=1, minute=1) reply = cli.args_handler(args) assert "202007" in reply def test_get(): args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get="url") reply = cli.args_handler(args) assert "waybackpy" in reply args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get="oldest") reply = cli.args_handler(args) assert "waybackpy" in reply args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get="newest") reply = cli.args_handler(args) assert "waybackpy" in reply if codecov_python: args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get="save") reply = cli.args_handler(args) assert "waybackpy" in reply args = argparse.Namespace(user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/600.8.9 \ (KHTML, like Gecko) Version/8.0.8 Safari/600.8.9", url="https://pypi.org/user/akamhy/", total=False, version=False, oldest=False, save=False, newest=False, near=False, alive=False, subdomain=False, known_urls=False, get="BullShit") reply = cli.args_handler(args) assert "get the source code of the" in reply def test_args_handler(): args = argparse.Namespace(version=True) reply = cli.args_handler(args) assert ("waybackpy version %s" % (__version__)) == reply args = argparse.Namespace(url=None, version=False) reply = cli.args_handler(args) assert ("waybackpy %s" % (__version__)) in reply def test_main(): # This also tests the parse_args method in cli.py cli.main(['temp.py', '--version'])

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null

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1

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null

0

5

91ca0f2e2dac9b3a2702df7661feb474592c23db

426

py

Python

tests/mark.py

chrismaille/fastapi-debug-toolbar

76d1e78eda4a23fc2b3e3d3c978ee9d8dbf025ae

[ "BSD-3-Clause" ]

36

2021-07-22T08:11:31.000Z

2022-01-31T13:09:26.000Z

tests/mark.py

chrismaille/fastapi-debug-toolbar

76d1e78eda4a23fc2b3e3d3c978ee9d8dbf025ae

[ "BSD-3-Clause" ]

10

2021-07-21T19:39:38.000Z

2022-02-26T15:35:35.000Z

tests/mark.py

chrismaille/fastapi-debug-toolbar

76d1e78eda4a23fc2b3e3d3c978ee9d8dbf025ae

[ "BSD-3-Clause" ]

2

2021-07-28T09:55:13.000Z

2022-02-18T11:29:25.000Z

import sys import typing as t import pytest from _pytest.mark import MarkDecorator def override_settings(**settings: t.Any) -> MarkDecorator: return pytest.mark.parametrize("settings", [settings]) def override_panels(panels: t.List[str]) -> MarkDecorator: return override_settings(panels=panels) def skip_py(*version: int) -> MarkDecorator: return pytest.mark.skipif(sys.version_info < version, reason="?")

23.666667

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1

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null

0

1

0

1

0

5

91d386543140c7dc841311dd9afc4b17a964827b

1,998

py

Python

tests/test_SFJWT.py

Rehket/SalesForce-JWT

df229bb775b59fcaa9c666c73a71de8b4fe901fb

[ "MIT" ]

null

tests/test_SFJWT.py

Rehket/SalesForce-JWT

df229bb775b59fcaa9c666c73a71de8b4fe901fb

[ "MIT" ]

2

2021-05-09T17:59:51.000Z

2021-05-09T18:00:36.000Z

tests/test_SFJWT.py

Rehket/SalesForce-JWT

df229bb775b59fcaa9c666c73a71de8b4fe901fb

[ "MIT" ]

null

# Standard library imports... from unittest import mock, TestCase import responses mock_environ = { "SFDC_CONSUMER_KEY": "false", "SFDC_USERNAME": "foo", "SFDC_PRIVATE_CERT": "foo", "SFDC_PRIVATE_CERT_PATH": "foo", } # This test is broken for some reason when all the tests are run together. class TestSandboxSFDCAuth(TestCase): def test_get_sandbox_login(self): with responses.RequestsMock() as rsps: with mock.patch("SFJWT.SFJWT.jwt.encode") as encode: encode.return_value = "my_secret_string" from SFJWT.SFJWT import jwt_login rsps.add( responses.POST, "https://test.salesforce.com/services/oauth2/token", body='{"instance_url": "salesforce.com", "access_token": "my_access_token"}', status=201, content_type="application/json", ) instance_url, token = jwt_login( "consumer_id", "username", "private_key", "sandbox" ) assert instance_url == "salesforce.com" assert token == "my_access_token" def test_get_prod_login(self): with responses.RequestsMock() as rsps: with mock.patch("SFJWT.SFJWT.jwt.encode") as encode: encode.return_value = "my_secret_string" from SFJWT.SFJWT import jwt_login rsps.add( responses.POST, "https://login.salesforce.com/services/oauth2/token", body='{"instance_url": "salesforce.com", "access_token": "my_access_token"}', status=201, content_type="application/json", ) instance_url, token = jwt_login( "consumer_id", "username", "private_key", "production" ) assert instance_url == "salesforce.com" assert token == "my_access_token"

37

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false

0

0.093023

0

0.162791

0

null

0

1

0

null

0

5

37fd2af77f101a4979815b767f91cba220a69cdb

271

py

Python

angr/procedures/libc/calloc.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

6,132

2015-08-06T23:24:47.000Z

2022-03-31T21:49:34.000Z

angr/procedures/libc/calloc.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

2,272

2015-08-10T08:40:07.000Z

2022-03-31T23:46:44.000Z

angr/procedures/libc/calloc.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

1,155

2015-08-06T23:37:39.000Z

2022-03-31T05:54:11.000Z

import angr ###################################### # calloc ###################################### class calloc(angr.SimProcedure): #pylint:disable=arguments-differ def run(self, sim_nmemb, sim_size): return self.state.heap._calloc(sim_nmemb, sim_size)

24.636364

59

0.520295

27

271

5.037037

0.666667

0.117647

0.161765

0.220588

0

0.125461

271

10

60

27.1

0.57384

0.136531

0

1

0.25

false

0

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

530962108ce5a71b6bf458dcb7a328d5897148b2

6,179

py

Python

tests/pbxcli/TestPBXProjShow.py

yixiaoqingyuz/mod-pbxproj

b1ea20e0846cd8f402224a31f7ca119d2e9ff36f

[ "MIT" ]

1

2020-01-16T08:33:38.000Z

tests/pbxcli/TestPBXProjShow.py

yixiaoqingyuz/mod-pbxproj

b1ea20e0846cd8f402224a31f7ca119d2e9ff36f

[ "MIT" ]

null

tests/pbxcli/TestPBXProjShow.py

yixiaoqingyuz/mod-pbxproj

b1ea20e0846cd8f402224a31f7ca119d2e9ff36f

[ "MIT" ]

1

2021-01-25T05:41:03.000Z

import unittest import shutil import sys from pbxproj.pbxcli import * import pbxproj.pbxcli.pbxproj_show as pbxproj_show class PBXProjShowTest(unittest.TestCase): def setUp(self): # copy the project.pbxproj, into a file that can be used by the tests shutil.copyfile('samplescli/project.pbxproj', 'samplescli/test.pbxproj') def tearDown(self): os.remove('samplescli/test.pbxproj') sys.stdout = sys.__stdout__ def testShowAllTargetsInfo(self): args = { u'<project>': u'samplescli/test.pbxproj', u'--target': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('testUITests:', result) self.assertIn('Product name: testUITests', result) self.assertIn('Configurations: Debug, Release', result) self.assertIn('Sources (PBXSourcesBuildPhase) file count: 1', result) self.assertIn('test:', result) self.assertIn('Product name: test\n', result) self.assertIn('Configurations: Debug, Release', result) self.assertIn('Sources (PBXSourcesBuildPhase) file count: 2', result) def testShowTargetBasicInfo(self): args = { u'<project>': u'samplescli/test.pbxproj', u'--target': u'test', u'--source-files': None, u'--header-files': None, u'--resource-files': None, u'--framework-files': None, u'--configurations': None, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertNotIn('testUITests:', result) self.assertNotIn('Product name: testUITests', result) self.assertIn('test:', result) self.assertIn('Product name: test\n', result) def testShowTargetConfigurations(self): args = { u'<project>': u'samplescli/test.pbxproj', u'--target': u'test', u'--source-files': None, u'--header-files': None, u'--resource-files': None, u'--framework-files': None, u'--configurations': True, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('test:', result) self.assertIn('Product name: test\n', result) self.assertIn('Configurations: Debug, Release\n', result) def testShowTargetSources(self): args = { u'<project>': u'samplescli/test.pbxproj', u'--target': u'test', u'--source-files': True, u'--header-files': None, u'--resource-files': None, u'--framework-files': None, u'--configurations': None, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('test:', result) self.assertIn('Product name: test\n', result) self.assertIn('Sources:', result) self.assertIn('AppDelegate.swift', result) self.assertIn('ViewController.swift', result) def testShowTargetResources(self): args = { u'<project>': u'samplescli/test.pbxproj', u'--target': u'test', u'--source-files': None, u'--header-files': None, u'--resource-files': True, u'--framework-files': None, u'--configurations': None, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('test:', result) self.assertIn('Product name: test\n', result) self.assertIn('Resources:', result) self.assertIn('Assets.xcassets', result) self.assertIn('LaunchScreen.storyboard', result) self.assertIn('Main.storyboard', result) def testShowTargetHeaders(self): args = { u'<project>': u'samplescli/dependency.xcodeproj/project.pbxproj', u'--target': u'helloworld', u'--source-files': None, u'--header-files': True, u'--resource-files': None, u'--framework-files': None, u'--configurations': None, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('helloworld:', result) self.assertIn('Product name: helloworld\n', result) self.assertIn('Headers:', result) self.assertIn('doit.h', result) self.assertIn('helloworld.h', result) def testShowTargetFrameworks(self): args = { u'<project>': u'samplescli/dependency.xcodeproj/project.pbxproj', u'--target': u'helloworld', u'--source-files': None, u'--header-files': None, u'--resource-files': None, u'--framework-files': True, u'--configurations': None, u'--build-phase-files': None } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('helloworld:', result) self.assertIn('Product name: helloworld\n', result) self.assertIn('Frameworks:', result) self.assertIn('AppKit.framework', result) def testShowTargetExplicitBuildPhase(self): args = { u'<project>': u'samplescli/dependency.xcodeproj/project.pbxproj', u'--target': u'helloworld', u'--source-files': None, u'--header-files': None, u'--resource-files': None, u'--framework-files': None, u'--configurations': None, u'--build-phase-files': u'PBXFrameworksBuildPhase' } project = open_project(args) result = pbxproj_show.execute(project, args) self.assertIn('helloworld:', result) self.assertIn('Product name: helloworld\n', result) self.assertIn('Frameworks:', result) self.assertIn('AppKit.framework', result)

36.347059

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0.578573

633

6,179

5.612954

0.1406

0.124965

0.151984

0.063327

0.740501

0.735435

0.723051

0

0.000449

0.279657

6,179

169

81

36.56213

0.797798

0.010843

0

0.655405

0

0.308183

0.068412

0

0.263514

1

0.067568

false

0

0.033784

0

0.108108

0

null

0

1

0

null

0

5

5339a905f22f95ba2960bc5d3c45cf04bbb6aabb

10

py

Python

test.py

canyus70/playground

7bdfc0fda903543a159aa41f52aad85f30f6afa5

[ "MIT" ]

null

test.py

canyus70/playground

7bdfc0fda903543a159aa41f52aad85f30f6afa5

[ "MIT" ]

null

test.py

canyus70/playground

7bdfc0fda903543a159aa41f52aad85f30f6afa5

[ "MIT" ]

null

print(3)

3.333333

8

0.6

2

10

3

1

0

0.125

0.2

10

2

9

5

0.625

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5345afcbdb2a4add7fbcde378576030b4c53b31a

460

py

Python

src/yass/neuralnetwork/__init__.py

Nomow/yass

9cc5cc5c5435a664b378bba9332e5b77eb792ff8

[ "Apache-2.0" ]

null

src/yass/neuralnetwork/__init__.py

Nomow/yass

9cc5cc5c5435a664b378bba9332e5b77eb792ff8

[ "Apache-2.0" ]

null

src/yass/neuralnetwork/__init__.py

Nomow/yass

9cc5cc5c5435a664b378bba9332e5b77eb792ff8

[ "Apache-2.0" ]

null

from yass.neuralnetwork.model import KerasModel from yass.neuralnetwork.model_detector import NeuralNetDetector from yass.neuralnetwork.model_autoencoder import AutoEncoder from yass.neuralnetwork.model_triage import NeuralNetTriage from yass.neuralnetwork.apply import run_detect_triage_featurize, fix_indexes __all__ = ['NeuralNetDetector', 'NeuralNetTriage', 'run_detect_triage_featurize', 'fix_indexes', 'AutoEncoder', 'KerasModel']

46

77

0.819565

49

460

7.387755

0.367347

0.110497

0.290055

0.287293

0.187845

0

0.113043

460

9

78

51.111111

0.887255

0

0.197826

0.058696

0

1

0

false

0

0.625

0

0.625

0

null

0

1

0

1

0

null

0

1

0

1

0

5

534bb85a4fe4117db9063e5a4419a55709913dd5

151

py

Python

src/pyglottolog/admin_commands/tree2lff.py

SimonGreenhill/pyglottolog

1e0aa0cdc5ae35906c763f9219c6db9b976f8d38

[ "Apache-2.0" ]

7

2019-07-28T16:09:05.000Z

2021-09-12T20:21:55.000Z

src/pyglottolog/admin_commands/tree2lff.py

d97hah/pyglottolog

fe4c2a52d54cdcf0804b4f889598dbb9b8698dbd

[ "Apache-2.0" ]

52

2019-06-18T05:16:38.000Z

2022-02-21T11:20:02.000Z

src/pyglottolog/admin_commands/tree2lff.py

d97hah/pyglottolog

fe4c2a52d54cdcf0804b4f889598dbb9b8698dbd

[ "Apache-2.0" ]

6

2019-07-26T17:40:25.000Z

2021-12-08T00:59:38.000Z

""" Create lff.txt and dff.txt from the current languoid tree. """ from pyglottolog import lff def run(args): lff.tree2lff(args.repos, args.log)

16.777778

58

0.715232

24

151

4.5

0.75

0

0.007937

0.165563

151

8

59

18.875

0.849206

0.384106

0

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

72855937cde48d6b148a7e38497c4747fc21bb79

70

py

Python

Logistic_Map_Generator/__init__.py

SubstanceIsFormAndContent/LMAP_Generator

3798bbffa0a355eb0656e804869048acfcbc5637

[ "MIT" ]

2

2019-11-23T21:28:09.000Z

2020-02-10T23:47:20.000Z

Logistic_Map_Generator/__init__.py

SubstanceIsFormAndContent/LMAP_Generator

3798bbffa0a355eb0656e804869048acfcbc5637

[ "MIT" ]

null

Logistic_Map_Generator/__init__.py

SubstanceIsFormAndContent/LMAP_Generator

3798bbffa0a355eb0656e804869048acfcbc5637

[ "MIT" ]

null

# __init__.py from .LogisticMapGenerator import LogisticMapGenerator

17.5

54

0.857143

6

70

9.333333

0.833333

0

0.1

70

3

55

23.333333

0.888889

0.157143

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

72c48173be5f4ebd72269e6e9c1940c13425665a

141

py

Python

code/answer_3-1-49.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

1

2022-03-29T13:50:12.000Z

code/answer_3-1-49.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

code/answer_3-1-49.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

H1, W1 = map(int, input().split()) H2, W2 = map(int, input().split()) print("YES" if H1 == H2 or H1 == W2 or W1 == H2 or W1 == W2 else "NO")

35.25

70

0.553191

28

141

2.785714

0.5

0.153846

0.282051

0.410256

0

0.107143

0.205674

141

3

71

47

0.589286

0

0.035461

0

1

0

true

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

5

72c544a4f9dcc5c506b7f9372217b3feb758fcd3

819

py

Python

sports_manager/mixins.py

hbuyse/dj-sports-manager

7e32cc41347b968b4ede9ea6846de14d9504c3f9

[ "MIT" ]

null

sports_manager/mixins.py

hbuyse/dj-sports-manager

7e32cc41347b968b4ede9ea6846de14d9504c3f9

[ "MIT" ]

null

sports_manager/mixins.py

hbuyse/dj-sports-manager

7e32cc41347b968b4ede9ea6846de14d9504c3f9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Django from django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin class StaffMixin(LoginRequiredMixin, UserPassesTestMixin): """ Mixin allows you to require a user with `is_staff` set to True. """ raise_exception = True def test_func(self): return self.request.is_staff class SuperuserMixin(LoginRequiredMixin, UserPassesTestMixin): """ Mixin allows you to require a user with `is_superuser` set to True. """ raise_exception = True def test_func(self): return self.request.is_superuser class OwnerMixin(LoginRequiredMixin, UserPassesTestMixin): owner_kwargs = 'username' raise_exception = True def test_func(self): return self.request.user.username == self.kwargs.get(self.owner_kwargs)

24.818182

79

0.714286

95

819

6.031579

0.421053

0.25829

0.094241

0.109948

0.547993

0

0.00152

0.196581

819

32

80

25.59375

0.869301

0.196581

0

0.428571

0

0.01278

0

1

0.214286

false

0.285714

0.071429

0.214286

1

0

null

1

0

null

0

1

0

1

0

1

0

5

72c6c0ad298834b4519f5b56809e20963f45a9f1

46

py

Python

raw_input.py

Lana-Pa/Getting-Started-with-Python

c4822755a579b6723cc966412bd06496870d118b

[ "Apache-2.0" ]

null

raw_input.py

Lana-Pa/Getting-Started-with-Python

c4822755a579b6723cc966412bd06496870d118b

[ "Apache-2.0" ]

null

raw_input.py

Lana-Pa/Getting-Started-with-Python

c4822755a579b6723cc966412bd06496870d118b

[ "Apache-2.0" ]

null

name = raw_input("Enter") print"Hello " + name

23

25

0.695652

7

46

4.428571

0.857143

0

0.130435

46

2

26

23

0.775

0

0.234043

0

null

0

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

72ee0e065980f02069a5c9e89690609ed82d427f

64

py

Python

apps/asset/serializer/__init__.py

plsof/tabops_api

39f5d2fd5158ae0c22e43ab6ff7e2b07a68a62d8

[ "MIT" ]

1

2019-07-31T07:34:38.000Z

apps/asset/serializer/__init__.py

plsof/tabops_api

39f5d2fd5158ae0c22e43ab6ff7e2b07a68a62d8

[ "MIT" ]

9

2019-12-05T00:39:29.000Z

2022-02-10T14:13:29.000Z

apps/asset/serializer/__init__.py

plsof/tabops_api

39f5d2fd5158ae0c22e43ab6ff7e2b07a68a62d8

[ "MIT" ]

null

from .idc import IdcSerializer from .host import HostSerializer

21.333333

32

0.84375

8

64

6.75

0.75

0

0.125

64

2

33

32

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

5

72f3aed5564f3f31e0d58951bc12d191d0d85912

55

py

Python

IceSpringMusicPlayer/plugins/IceSpringPlaylistPlugin/__init__.py

baijifeilong/rawsteelp

425547e6e2395bf4acb62435b18b5b3a4b7ebef4

[ "MIT" ]

null

IceSpringMusicPlayer/plugins/IceSpringPlaylistPlugin/__init__.py

baijifeilong/rawsteelp

425547e6e2395bf4acb62435b18b5b3a4b7ebef4

[ "MIT" ]

null

IceSpringMusicPlayer/plugins/IceSpringPlaylistPlugin/__init__.py

baijifeilong/rawsteelp

425547e6e2395bf4acb62435b18b5b3a4b7ebef4

[ "MIT" ]

null

# Created by BaiJiFeiLong@gmail.com at 2022/1/24 17:09

27.5

54

0.763636

11

55

3.818182

1

0

0.229167

0.127273

55

1

55

0.645833

0.945455

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

5

f409abe051cdbb8b06224a0c113a401508244b20

225

py

Python

core/views.py

Bilal815/ecommerce_storee

45e61f1d865a65b4c52d74502b4fcab7ee6c1adf

[ "MIT" ]

95

2020-04-13T09:02:30.000Z

2022-03-25T14:11:34.000Z

core/views.py

Bilal815/ecommerce_api

a3d8ce7a9e1fa2528d240d5ab508afe92607c9f8

[ "MIT" ]

87

2020-02-21T17:58:56.000Z

2022-03-21T21:37:05.000Z

core/views.py

Bilal815/ecommerce_api

a3d8ce7a9e1fa2528d240d5ab508afe92607c9f8

[ "MIT" ]

33

2021-01-18T09:30:29.000Z

2022-03-30T01:31:57.000Z

from django.shortcuts import render from django.db import transaction # class Get_Host(APIView): # def post(self, request): # host = request.META.get('HTTP_USER_AGENT') # return Response({"Host": host})

25

52

0.68

29

225

5.172414

0.724138

0.133333

0

0.2

225

8

53

28.125

0.833333

0.64

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f4216304cad26c15aa2173f37638cadd00579f0e

104

py

Python

tests/fibonacci.py

ssarangi/PyVyM

f96c46e7b8d38f938345ca915c5356b4d9c86d64

[ "MIT" ]

3

2017-09-24T17:35:29.000Z

2021-02-14T21:53:03.000Z

tests/fibonacci.py

ssarangi/PyVyM

f96c46e7b8d38f938345ca915c5356b4d9c86d64

[ "MIT" ]

null

tests/fibonacci.py

ssarangi/PyVyM

f96c46e7b8d38f938345ca915c5356b4d9c86d64

[ "MIT" ]

1

2019-08-22T01:09:15.000Z

def fibonacci(n): if n == 1 or n == 2: return 1 return fibonacci(n-1) + fibonacci(n-2)

17.333333

42

0.538462

18

104

3.111111

0.444444

0.535714

0

0.070423

0.317308

104

5

43

20.8

0.71831

0

1

0.25

false

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

f45a889c89ebfa8804c4f8845d9ca62807cf02d3

147

py

Python

pybamm/models/submodels/particle/fickian/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

1

2019-10-29T19:06:04.000Z

pybamm/models/submodels/particle/fickian/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

pybamm/models/submodels/particle/fickian/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

from .base_fickian_particle import BaseModel from .fickian_many_particles import ManyParticles from .fickian_single_particle import SingleParticle

36.75

51

0.897959

18

147

7

0.611111

0.222222

0

0.081633

147

3

52

49

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

f483bf2809d647f5a04dba9e2c822fdcca1ccd3d

109

py

Python

tests/__init__.py

sonny-zhang/MyBlog

880a80c5d95f472f0301f7380addc6c31d341b70

[ "MIT" ]

null

tests/__init__.py

sonny-zhang/MyBlog

880a80c5d95f472f0301f7380addc6c31d341b70

[ "MIT" ]

null

tests/__init__.py

sonny-zhang/MyBlog

880a80c5d95f472f0301f7380addc6c31d341b70

[ "MIT" ]

null

# @Time : 2019/3/13 17:20 # @Author : sonny.zhang # @FileName : __init__.py # @github : @sonny-zhang

21.8

29

0.59633

15

109

4.066667

0.866667

0.327869

0

0.130952

0.229358

109

4

30

27.25

0.595238

0.917431

0

null

0

null

0

null

0

null

1

null

true

0

null

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0

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1

0

1

0

1

0

null

0

1

0

5

be3a323cb542283023f528c61e5eec22f86dc56d

91

py

Python

kea/test_utils/__init__.py

SmartAcoustics/Kea

5790f18dafccfc01fe9dbe98de5bb1a5ce584c56

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

3

2020-02-28T13:03:59.000Z

2020-09-20T06:33:04.000Z

kea/test_utils/__init__.py

SmartAcoustics/Kea

5790f18dafccfc01fe9dbe98de5bb1a5ce584c56

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

null

kea/test_utils/__init__.py

SmartAcoustics/Kea

5790f18dafccfc01fe9dbe98de5bb1a5ce584c56

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

3

2018-12-17T16:33:08.000Z

2020-01-21T14:10:25.000Z

from .base_test import ( KeaTestCase, KeaVivadoVHDLTestCase, KeaVivadoVerilogTestCase)

30.333333

65

0.824176

7

91

10.571429

1

0

0.120879

91

2

66

45.5

0.925

0

1

0

true

0

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0

0.5

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1

null

0

1

0

null

0

1

0

1

0

5

be5b24af3985c89a74b813828eda9445c8d8e2a7

148

py

Python

ex027.py

BrunosVieira88/Python

7dc105a62ede0b33d25c5864e892637ca71f2beb

[ "MIT" ]

null

ex027.py

BrunosVieira88/Python

7dc105a62ede0b33d25c5864e892637ca71f2beb

[ "MIT" ]

null

ex027.py

BrunosVieira88/Python

7dc105a62ede0b33d25c5864e892637ca71f2beb

[ "MIT" ]

null

nome=str(input('digite seu nome ')).split() print('seu primeiro nome é {}'.format(nome[0])) print('seu ultimo nome é {}'.format(nome[len(nome)-1]))

37

55

0.668919

25

148

3.96

0.56

0.161616

0.222222

0.30303

0

0.014925

0.094595

148

4

55

37

0.723881

0

0.389262

0

1

0

false

0

0.666667

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null

0

1

0

1

0

null

0

1

0

5

fe36672a9b224dcaf2163d5f4ebec01e9c9c9140

27

py

Python

tda/contrib/__init__.py

zhangted/tda-api

1169c87129b80c120217d420e4996a439c5903dc

[ "MIT" ]

986

2020-04-14T21:50:03.000Z

2022-03-29T19:09:31.000Z

tda/contrib/__init__.py

zhangted/tda-api

1169c87129b80c120217d420e4996a439c5903dc

[ "MIT" ]

243

2020-04-26T14:05:34.000Z

2022-03-12T13:02:51.000Z

tda/contrib/__init__.py

zhangted/tda-api

1169c87129b80c120217d420e4996a439c5903dc

[ "MIT" ]

286

2020-04-14T22:17:04.000Z

2022-03-27T07:30:15.000Z

from . import orders, util

13.5

26

0.740741

4

27

5

1

0

0.185185

27

1

27

0.909091

0

1

0

true

0

1

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

5

fe374344bbfcdbbd759190959389ffcbfcc889cc

2,266

py

Python

slowquant/numerical/numForce.py

Melisius/Hartree-Fock

46bf811dfcf217ce0c37ddec77d34ef00da769c3

[ "BSD-3-Clause" ]

8

2019-12-05T16:02:56.000Z

2022-03-31T17:20:46.000Z

slowquant/numerical/numForce.py

erikkjellgren/SlowQuant

46bf811dfcf217ce0c37ddec77d34ef00da769c3

[ "BSD-3-Clause" ]

1

2017-05-31T23:48:28.000Z

2017-05-31T23:49:21.000Z

slowquant/numerical/numForce.py

Melisius/Hartree-Fock

46bf811dfcf217ce0c37ddec77d34ef00da769c3

[ "BSD-3-Clause" ]

7

2019-11-11T22:42:31.000Z

2021-12-30T20:30:42.000Z

import slowquant.hartreefock.runHartreeFock as HF import numpy as np import slowquant.basissets.BasisSet as BS import slowquant.molecularintegrals.runMolecularIntegrals as MI def nForce(input, set, results, print_time='No', print_scf='Yes'): dX = np.zeros(len(input)) dY = np.zeros(len(input)) dZ = np.zeros(len(input)) for j in range(1, len(input)): input[j,1] += 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,1] -= 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) xplus = results['HFenergy'] input[j,1] -= 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,1] += 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) xminus = results['HFenergy'] input[j,2] += 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,2] -= 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) yplus = results['HFenergy'] input[j,2] -= 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,2] += 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) yminus = results['HFenergy'] input[j,3] += 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,3] -= 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) zplus = results['HFenergy'] input[j,3] -= 10**-6 basis = BS.bassiset(input, set['basisset']) results = MI.runIntegrals(input, basis, set, results) input[j,3] += 10**-6 results = HF.runHartreeFock(input, set, results, print_SCF=print_scf) zminus = results['HFenergy'] dX[j] = (xplus-xminus)/(2*10**-6) dY[j] = (yplus-yminus)/(2*10**-6) dZ[j] = (zplus-zminus)/(2*10**-6) return dX, dY, dZ

41.962963

77

0.595322

296

2,266

4.510135

0.165541

0.033708

0.078652

0.104869

0.701124

0

0.035819

0.248455

2,266

54

78

41.962963

0.748092

0

0.612245

0

0.044552

0

1

0.020408

false

0

0.081633

0

0.122449

0.142857

0

null

0

1

0

null

0

5

fe43128c571f4ca6541a6dae01ecf3bfb2c8f296

5,816

py

Python

app/api/v1/routes/routes.py

Mik3y-F/sendIT-api

868c4f50424e258ef978a2541c8379fe5b9195f7

[ "MIT" ]

null

app/api/v1/routes/routes.py

Mik3y-F/sendIT-api

868c4f50424e258ef978a2541c8379fe5b9195f7

[ "MIT" ]

null

app/api/v1/routes/routes.py

Mik3y-F/sendIT-api

868c4f50424e258ef978a2541c8379fe5b9195f7

[ "MIT" ]

null

from dicttoxml import dicttoxml from flask import Blueprint, request, jsonify from ..models.models import User, Parcel api = Blueprint('api', __name__) @api.route('/parcels/', methods=['GET', 'POST']) def parcels(): if request.method == "POST": # POST # Gets name argument variable from url name = request.args.get('name') user_id = request.args.get('sender') user_location = request.args.get('location') destination = request.args.get('dest') parc_weight = request.args.get('parc_weight') if name: parcel = Parcel( name=name, senderId=user_id, delivered=False, presentLocation=user_location, pickupLocation=None, destination=destination, parcelWeight=parc_weight ) parcel.save() response = { 'parcelId': parcel.id, 'parcelName': parcel.name, 'sender': User.query.filter(User.id == user_id).first().name, 'delivered': parcel.delivered, 'presentLocation': parcel.presentLocation, 'pickupLocation': parcel.pickupLocation, 'destination': parcel.destination, 'parcelWeight': parcel.parcelWeight } response = dicttoxml(response, custom_root='test', attr_type=False) # response.status_code = 201 return response else: # GET all_parcels = Parcel.get_all() results = [] for parcel in all_parcels: obj = { 'parcelId': parcel.id, 'parcelName': parcel.name, 'sender': User.query.filter(User.id == parcel.senderId).first().name, 'delivered': parcel.delivered, 'presentLocation': parcel.presentLocation, 'pickupLocation': parcel.pickupLocation, 'destination': parcel.destination, 'parcelWeight': parcel.parcelWeight } results.append(obj) response = dicttoxml(results, custom_root='test', attr_type=False) # response.status_code = 200 return response @api.route('/parcels/<int:parcel_id>/', methods=['GET']) def get_specific_parcel(parcel_id): # GET parcel = Parcel.query.filter(Parcel.id==parcel_id).first_or_404() results = [] obj = { 'parcelId': parcel.id, 'parcelName': parcel.name, 'sender': User.query.filter(User.id == Parcel.senderId).first().name, 'delivered': parcel.delivered, 'presentLocation': parcel.presentLocation, 'pickupLocation': parcel.pickupLocation, 'destination': parcel.destination, 'parcelWeight': parcel.parcelWeight } results.append(obj) response = dicttoxml(results, custom_root='test', attr_type=False) # response.status_code = 200 return response @api.route('/users/<int:user_id>/parcels/', methods=['GET']) def get_user_parcels(user_id): parcels = Parcel.query.filter(Parcel.senderId==user_id) results = [] for parcel in parcels: obj = { 'parcelId': parcel.id, 'parcelName': parcel.name, 'sender': User.query.filter(User.id == user_id).first().name, 'delivered': parcel.delivered, 'presentLocation': parcel.presentLocation, 'pickupLocation': parcel.pickupLocation, 'destination': parcel.destination, 'parcelWeight': parcel.parcelWeight } results.append(obj) response = dicttoxml(results, custom_root='test', attr_type=False) # response.status_code = 200 return response @api.route('/parcels/<int:parcel_id>/cancel', methods=['PUT']) def cancel_parcel(parcel_id): parcel = Parcel.query.filter(Parcel.senderId==parcel_id).first_or_404() results = [] obj = { 'parcelId': parcel.id, 'parcelName': parcel.name, 'sender': User.query.filter(User.id == parcel.id).first().name, 'delivered': parcel.delivered, 'presentLocation': parcel.presentLocation, 'pickupLocation': parcel.pickupLocation, 'destination': parcel.destination, 'parcelWeight': parcel.parcelWeight } parcel.delete() results.append(obj) response = dicttoxml(results, custom_root='test', attr_type=False) response.status_code = 200 return response @api.route('/users/', methods=['GET', 'POST']) def users(): if request.method == "POST": # POST # Gets name argument variable from url name = request.args.get('name') email = request.args.get('email') phone = request.args.get('phone') if name: user = User( name=name, email=email, mobileNo=phone, isAdmin=0 ) user.save() response = { 'userId': user.id, 'userName': user.name, 'email': user.email, 'mobileNo': user.mobileNo, } response = dicttoxml(response, custom_root='test', attr_type=False) # response.status_code = 201 return response else: # GET all_users = User.get_all() results = [] for user in all_users: obj = { 'userId': user.id, 'userName': user.name, 'email': user.email, 'mobileNo': user.mobileNo, } results.append(obj) response = dicttoxml(results, custom_root='test', attr_type=False) # response.status_code = 200 return response

29.673469

85

0.566025

554

5,816

5.837545

0.140794

0.025974

0.034632

0.038961

0.741806

0.711812

0

0.007026

0.314821

5,816

196

86

29.673469

0.804517

0.044188

0

0.609929

0

0.124234

0.015326

0

1

0.035461

false

0

0.021277

0

0.106383

0.014184

0

null

0

1

0

null

0

5

fe505f617ec49460bfba58577cd08491bd2d14b0

14,953

py

Python

mkt/api/tests/test_authorization.py

spasovski/zamboni

c7f4714029e3b2dc918ddfc2103f8e051193c14d

[ "BSD-3-Clause" ]

1

2021-07-29T00:51:09.000Z

mkt/api/tests/test_authorization.py

imclab/olympia

35bc9c484e384bafab520ca8b5d5b0f8da5b62c0

[ "BSD-3-Clause" ]

null

mkt/api/tests/test_authorization.py

imclab/olympia

35bc9c484e384bafab520ca8b5d5b0f8da5b62c0

[ "BSD-3-Clause" ]

null

from django.contrib.auth.models import AnonymousUser, User from rest_framework.permissions import AllowAny, BasePermission from mock import Mock from nose.tools import eq_, ok_ from test_utils import RequestFactory from amo.tests import TestCase from users.models import UserProfile from mkt.api.authorization import (AllowAppOwner, AllowNone, AllowOwner, AllowRelatedAppOwner, AllowReadOnlyIfPublic, AllowSelf, AnyOf, ByHttpMethod, flag, GroupPermission, switch) from mkt.site.fixtures import fixture from mkt.webapps.models import Webapp class TestWaffle(TestCase): def setUp(self): super(TestWaffle, self).setUp() self.request = RequestFactory().get('/') def test_waffle_flag(self): self.create_flag('foo') ok_(flag('foo')().has_permission(self.request, '')) def test_not_waffle_flag(self): ok_(not flag('foo')().has_permission(self.request, '')) def test_waffle_switch(self): self.create_switch('foo') ok_(switch('foo')().has_permission(self.request, '')) def test_not_switch_flag(self): ok_(not switch('foo')().has_permission(self.request, '')) class TestAllowSelfAuthorization(TestCase): fixtures = fixture('user_2519', 'user_999') def setUp(self): self.permission = AllowSelf() self.anonymous = AnonymousUser() self.user = User.objects.get(pk=2519) self.request = RequestFactory().get('/') self.request.user = self.anonymous self.request.amo_user = None def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user(self): self.request.user = self.user self.request.amo_user = self.request.user.get_profile() eq_(self.permission.has_permission(self.request, 'myview'), True) def test_has_object_permission_anonymous(self): eq_(self.permission.has_object_permission( self.request, 'myview', self.user), False) def test_has_object_permission_user(self): self.request.user = self.user self.request.amo_user = self.request.user.get_profile() obj = self.user eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) def test_has_object_permission_different_user(self): self.request.user = User.objects.get(pk=999) self.request.amo_user = self.request.user.get_profile() obj = self.user eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class TestAllowOwner(TestCase): fixtures = fixture('user_2519', 'user_999') def setUp(self): self.permission = AllowOwner() self.anonymous = AnonymousUser() self.user = User.objects.get(pk=2519) self.request = RequestFactory().get('/') self.request.user = self.anonymous self.request.amo_user = None def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user(self): self.request.user = self.user self.request.amo_user = self.request.user.get_profile() eq_(self.permission.has_permission(self.request, 'myview'), True) def test_has_object_permission_user(self): self.request.user = self.user self.request.amo_user = self.request.user.get_profile() obj = Mock() obj.user = self.user eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) def test_has_object_permission_different_user(self): self.request.user = User.objects.get(pk=999) self.request.amo_user = self.request.user.get_profile() obj = Mock() obj.user = self.user eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class PartialFailPermission(BasePermission): def has_object_permission(self, request, view, obj): return False class FailPartialPermission(BasePermission): def has_permission(self, request, view): return False class TestAnyOf(TestCase): def test_has_permission(self): request = RequestFactory().get('/') ok_(AnyOf(AllowNone, AllowAny)().has_permission( request, 'myview')) ok_(AnyOf(AllowAny, AllowNone)().has_permission( request, 'myview')) def test_has_permission_fail(self): request = RequestFactory().get('/') ok_(not AnyOf(AllowNone, AllowNone)().has_permission( request, 'myview')) def test_has_object_permission(self): request = RequestFactory().get('/') ok_(AnyOf(AllowNone, AllowAny )().has_object_permission(request, 'myview', None)) ok_(AnyOf(AllowAny, AllowNone )().has_object_permission(request, 'myview', None)) def test_has_object_permission_fail(self): request = RequestFactory().get('/') ok_(not AnyOf(AllowNone, AllowNone )().has_object_permission(request, 'myview', None)) def test_has_object_permission_partial_fail(self): request = RequestFactory().get('/') ok_(not AnyOf(FailPartialPermission, PartialFailPermission )().has_object_permission(request, 'myview', None)) class TestAllowNone(TestCase): def setUp(self): self.permission = AllowNone() self.anonymous = AnonymousUser() self.user = User() self.request = RequestFactory().get('/') self.request.user = self.anonymous self.request.amo_user = None def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user(self): self.request.user = Mock() self.request_amo_user = Mock() eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_object_permission_anonymous(self): obj = Mock() eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) def test_has_object_permission_user(self): self.request.user = Mock() self.request_amo_user = Mock() obj = Mock() eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class TestAllowAppOwner(TestCase): fixtures = fixture('user_2519', 'webapp_337141') def setUp(self): self.app = Webapp.objects.get(pk=337141) self.permission = AllowAppOwner() self.anonymous = AnonymousUser() self.owner = self.app.authors.all()[0] self.request = RequestFactory().get('/') self.request.user = self.anonymous self.request.amo_user = None def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user(self): self.request.user = self.owner.user self.request.amo_user = self.owner eq_(self.permission.has_permission(self.request, 'myview'), True) def test_has_object_permission_user(self): self.request.user = self.owner.user self.request.amo_user = self.owner obj = self.app eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) def test_has_object_permission_different_user(self): self.request.user = User.objects.get(pk=2519) self.request.amo_user = self.request.user.get_profile() obj = self.app eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) def test_has_object_permission_anonymous(self): obj = self.app eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class TestAllowRelatedAppOwner(TestCase): fixtures = fixture('user_2519', 'webapp_337141') def setUp(self): self.app = Webapp.objects.get(pk=337141) self.permission = AllowRelatedAppOwner() self.anonymous = AnonymousUser() self.owner = self.app.authors.all()[0] self.request = RequestFactory().get('/') self.request.user = self.anonymous self.request.amo_user = None def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user(self): self.request.user = self.owner.user self.request.amo_user = self.owner eq_(self.permission.has_permission(self.request, 'myview'), True) def test_has_object_permission_user(self): self.request.user = self.owner.user self.request.amo_user = self.owner obj = Mock() obj.addon = self.app eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) def test_has_object_permission_different_user(self): self.request.user = User.objects.get(pk=2519) self.request.amo_user = self.request.user.get_profile() obj = Mock() obj.addon = self.app eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class TestAllowReadOnlyIfPublic(TestCase): def setUp(self): self.permission = AllowReadOnlyIfPublic() self.anonymous = AnonymousUser() self.request_factory = RequestFactory() # 'patch' is missing because it's absent from RequestFactory in # django < 1.5. Usually we don't special case 'put' vs 'patch' in # permissions code though, so it's fine. self.unsafe_methods = ('post', 'put', 'delete') self.safe_methods = ('get', 'options', 'head') def _request(self, verb): request = getattr(self.request_factory, verb)('/') request.user = self.anonymous request.amo_user = None return request def test_has_permission(self): for verb in self.safe_methods: eq_(self.permission.has_permission(self._request(verb), 'myview'), True) for verb in self.unsafe_methods: eq_(self.permission.has_permission(self._request(verb), 'myview'), False) def test_has_object_permission_public(self): obj = Mock() obj.is_public.return_value = True for verb in self.safe_methods: eq_(self.permission.has_object_permission(self._request(verb), 'myview', obj), True) for verb in self.unsafe_methods: eq_(self.permission.has_object_permission(self._request(verb), 'myview', obj), False) def test_has_object_permission_not_public(self): obj = Mock() obj.is_public.return_value = False for verb in (self.unsafe_methods + self.safe_methods): eq_(self.permission.has_object_permission(self._request(verb), 'myview', obj), False) class TestGroupPermission(TestCase): fixtures = fixture('user_2519') def setUp(self): self.permission = GroupPermission('Drinkers', 'Beer') self.obj = Mock() self.profile = UserProfile.objects.get(pk=2519) self.anonymous = AnonymousUser() self.request = RequestFactory().get('/') self.request.user = self.anonymous def test_has_permission_user_without(self): self.request.user = self.profile.user self.request.amo_user = self.profile self.request.groups = self.profile.groups.all() self.grant_permission(self.profile, 'Drinkers:Scotch') eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_permission_user_with(self): self.request.user = self.profile.user self.request.amo_user = self.profile self.request.groups = self.profile.groups.all() self.grant_permission(self.profile, 'Drinkers:Beer') eq_(self.permission.has_permission(self.request, 'myview'), True) def test_has_permission_anonymous(self): eq_(self.permission.has_permission(self.request, 'myview'), False) def test_has_object_permission_user_without(self): self.request.user = self.profile.user self.request.amo_user = self.profile self.request.groups = self.profile.groups.all() self.grant_permission(self.profile, 'Drinkers:Scotch') obj = Mock() eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) def test_has_object_permission_user_with(self): self.request.user = self.profile.user self.request.amo_user = self.profile self.request.groups = self.profile.groups.all() self.grant_permission(self.profile, 'Drinkers:Beer') obj = Mock() eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) def test_has_object_permission_anonymous(self): obj = Mock() eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) class TestByHttpMethodPermission(TestCase): def setUp(self): self.get_permission = Mock self.patch_permission = Mock self.post_permission = Mock self.put_permission = Mock self.permission = ByHttpMethod({ 'get': self.get_permission, }) self.set_permission_mock('get', True) def set_permission_mock(self, method, value): mock = self.permission.method_permissions[method] mock.has_permission.return_value = value def set_object_permission_mock(self, method, value): mock = self.permission.method_permissions[method] mock.has_object_permission.return_value = value def test_get(self): self.request = RequestFactory().get('/') eq_(self.permission.has_permission(self.request, 'myview'), True) self.set_permission_mock('get', False) eq_(self.permission.has_permission(self.request, 'myview'), False) def test_get_obj(self): obj = Mock() self.request = RequestFactory().get('/') self.set_object_permission_mock('get', True) eq_(self.permission.has_object_permission(self.request, 'myview', obj), True) self.set_object_permission_mock('get', False) eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) def test_missing_method(self): self.request = RequestFactory().post('/') eq_(self.permission.has_permission(self.request, 'myview'), False) obj = Mock() self.request = RequestFactory().post('/') eq_(self.permission.has_object_permission(self.request, 'myview', obj), False) self.request = RequestFactory().options('/') eq_(self.permission.has_permission(self.request, 'myview'), False)

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0.079265

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0.106565

0.080347

0.810234

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0

null

0

1

0

1

0

null

0

5

fe59735f3fbc8036ba1f1c2a99d0922e52a92b11

47

py

Python

2022/BaekJoon/11720.py

dongdong97/TIL

22fab3bc5509ac46510071cb6a7ce390fd4df75a

[ "MIT" ]

null

2022/BaekJoon/11720.py

dongdong97/TIL

22fab3bc5509ac46510071cb6a7ce390fd4df75a

[ "MIT" ]

null

2022/BaekJoon/11720.py

dongdong97/TIL

22fab3bc5509ac46510071cb6a7ce390fd4df75a

[ "MIT" ]

null

a = int(input()) print(sum(map(int,input())))

11.75

28

0.595745

8

47

3.5

0.75

0.571429

0

0.106383

47

3

29

15.666667

0.666667

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

5

fe8e76865a32060552cfde5f5007bd730c3d4617

921

py

Python

app/main.py

sayyamsachdev/leaninindia2.0

45aef47fc9115413b4ff5e326d38db108ab184fc

[ "MIT" ]

null

app/main.py

sayyamsachdev/leaninindia2.0

45aef47fc9115413b4ff5e326d38db108ab184fc

[ "MIT" ]

null

app/main.py

sayyamsachdev/leaninindia2.0

45aef47fc9115413b4ff5e326d38db108ab184fc

[ "MIT" ]

null

from flask import Flask, render_template, jsonify, request import json as JSON from flask_bower import Bower app = Flask(__name__) Bower(app) @app.route("/") def index(): return render_template("index.html") @app.route("/about", methods = ["GET"]) def render_team(): return render_template("index.html") @app.route("/circles", methods = ["GET"]) def render_circles(): return render_template("index.html") @app.route("/events", methods = ["GET"]) def render_events(): return render_template("events.html") @app.route("/awarenees", methods = ["GET"]) def render_awareness(): return render_template("index.html") @app.route("/blog", methods = ["GET"]) def render_blog(): return render_template("index.html") @app.route("/contact-us", methods = ["GET"]) def render_contact_us(): return render_template("index.html") @app.route('/<path:p>') def ui(p): return render_template("index.html") app.run(debug=True)

23.025

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0.710098

127

921

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0.103149

921

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null

0

1

0

null

0

1

0

1

0

5

fe94eceefdc4766f7b5421ef417c657bcf291fd0

293

py

Python

audio_separation/__init__.py

SAGNIKMJR/move2hear-active-AV-separation

3c6887aeb94b2a07983469bfd517ca277bd4124a

[ "MIT" ]

8

2021-10-05T08:03:32.000Z

2022-02-22T07:08:19.000Z

audio_separation/__init__.py

SAGNIKMJR/move2hear-active-AV-separation

3c6887aeb94b2a07983469bfd517ca277bd4124a

[ "MIT" ]

1

2021-12-02T00:21:48.000Z

2021-12-28T19:07:14.000Z

audio_separation/__init__.py

SAGNIKMJR/move2hear-active-AV-separation

3c6887aeb94b2a07983469bfd517ca277bd4124a

[ "MIT" ]

null

from audio_separation.rl.ppo.ppo_trainer import PPOTrainer, RolloutStoragePol, RolloutStorageSep from audio_separation.pretrain.passive.passive_trainer import PassiveTrainer __all__ = ["BaseTrainer", "BaseRLTrainer", "PPOTrainer", "RolloutStoragePol", "RolloutStorageSep", "PassiveTrainer"]

48.833333

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293

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0.160338

0

0.068259

293

5

117

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0.868132

0

0.279863

0

1

0

false

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0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

5

22b23b21f8d6616bd04a7a64373464cc32a07519

155

py

Python

docs/en/docs_src/get_updates/get_updates_package.py

AliRn76/rubika-bot

203da2e585f03d6b2cef96cbd7a68b471e010db7

[ "MIT" ]

1

2022-03-30T10:33:33.000Z

docs/fa/docs_src/get_updates/get_updates_package.py

AliRn76/rubika-bot

203da2e585f03d6b2cef96cbd7a68b471e010db7

[ "MIT" ]

null

docs/fa/docs_src/get_updates/get_updates_package.py

AliRn76/rubika-bot

203da2e585f03d6b2cef96cbd7a68b471e010db7

[ "MIT" ]

null

from rubika_bot.requests import get_updates from rubika_bot.models import Update updates, _ = get_updates( token='SUPER_SECRET_TOKEN', limit=10, )

22.142857

43

0.774194

22

155

5.136364

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0.176991

0.230089

0

0.015152

0.148387

155

7

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22.142857

0.840909

0

0.115385

0

1

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true

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1

0

1

0

null

0

1

0

1

0

5

22fa3bd9ce0189b4f75b75b24b9aea58eb338e87

72

py

Python

hordak/models/__init__.py

PetrDlouhy/django-hordak

71c141928c5a2cc102bcfd710d7bdf17093933c9

[ "MIT" ]

2

2016-09-05T08:58:53.000Z

2016-09-26T10:49:07.000Z

hordak/models/__init__.py

PetrDlouhy/django-hordak

71c141928c5a2cc102bcfd710d7bdf17093933c9

[ "MIT" ]

3

2016-11-06T13:14:29.000Z

2016-11-06T13:57:58.000Z

hordak/models/__init__.py

waldocollective/django-hordak

dc9b8e5008954ca0f4b089d89348e7dec4301f65

[ "MIT" ]

null

from .core import * # noqa from .statement_csv_import import * # noqa

24

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72

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0

1

0

null

0

1

0

1

0

5

fe0288cd5be80df45449a3d577648d814ebf2a42

13,622

py

Python

protos/gen/python/protos/public/monitoring/DataMonitoringService_pb2_grpc.py

stefan-petrov-toptal/modeldb

a8a9b9da6ed964c91351230b2f0d2703c75794de

[ "Apache-2.0" ]

835

2017-02-08T20:14:24.000Z

2020-03-12T17:37:49.000Z

protos/gen/python/protos/public/monitoring/DataMonitoringService_pb2_grpc.py

stefan-petrov-toptal/modeldb

a8a9b9da6ed964c91351230b2f0d2703c75794de

[ "Apache-2.0" ]

651

2019-04-18T12:55:07.000Z

2022-03-31T23:45:09.000Z

protos/gen/python/protos/public/monitoring/DataMonitoringService_pb2_grpc.py

stefan-petrov-toptal/modeldb

a8a9b9da6ed964c91351230b2f0d2703c75794de

[ "Apache-2.0" ]

170

2017-02-13T14:49:22.000Z

2020-02-19T17:59:12.000Z

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from ..monitoring import DataMonitoringService_pb2 as monitoring_dot_DataMonitoringService__pb2 class DataMonitoringServiceStub(object): """Service definitions """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.createMonitoredEntity = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/createMonitoredEntity', request_serializer=monitoring_dot_DataMonitoringService__pb2.CreateMonitoredEntityRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.CreateMonitoredEntityRequest.Response.FromString, ) self.updateMonitoredEntity = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/updateMonitoredEntity', request_serializer=monitoring_dot_DataMonitoringService__pb2.UpdateMonitoredEntityRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.UpdateMonitoredEntityRequest.Response.FromString, ) self.findMonitoredEntity = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/findMonitoredEntity', request_serializer=monitoring_dot_DataMonitoringService__pb2.FindMonitoredEntityRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.FindMonitoredEntityRequest.Response.FromString, ) self.deleteMonitoredEntity = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/deleteMonitoredEntity', request_serializer=monitoring_dot_DataMonitoringService__pb2.DeleteMonitoredEntityRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.DeleteMonitoredEntityRequest.Response.FromString, ) self.getProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/getProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerRequest.Response.FromString, ) self.createProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/createProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.CreateProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.CreateProfilerRequest.Response.FromString, ) self.updateProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/updateProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.UpdateProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.UpdateProfilerRequest.Response.FromString, ) self.listProfilers = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/listProfilers', request_serializer=monitoring_dot_DataMonitoringService__pb2.ListProfilersRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.ListProfilersRequest.Response.FromString, ) self.deleteProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/deleteProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.DeleteProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.DeleteProfilerRequest.Response.FromString, ) self.getProfilerStatus = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/getProfilerStatus', request_serializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerStatusRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerStatusRequest.Response.FromString, ) self.findProfilersForMonitoredEntity = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/findProfilersForMonitoredEntity', request_serializer=monitoring_dot_DataMonitoringService__pb2.FindProfilersForMonitoredEntityRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.FindProfilersForMonitoredEntityRequest.Response.FromString, ) self.enableProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/enableProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.EnableProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.EnableProfilerRequest.Response.FromString, ) self.disableProfiler = channel.unary_unary( '/ai.verta.monitoring.DataMonitoringService/disableProfiler', request_serializer=monitoring_dot_DataMonitoringService__pb2.DisableProfilerRequest.SerializeToString, response_deserializer=monitoring_dot_DataMonitoringService__pb2.DisableProfilerRequest.Response.FromString, ) class DataMonitoringServiceServicer(object): """Service definitions """ def createMonitoredEntity(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def updateMonitoredEntity(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def findMonitoredEntity(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def deleteMonitoredEntity(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def getProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def createProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def updateProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def listProfilers(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def deleteProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def getProfilerStatus(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def findProfilersForMonitoredEntity(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def enableProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def disableProfiler(self, request, context): # missing associated documentation comment in .proto file pass context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_DataMonitoringServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'createMonitoredEntity': grpc.unary_unary_rpc_method_handler( servicer.createMonitoredEntity, request_deserializer=monitoring_dot_DataMonitoringService__pb2.CreateMonitoredEntityRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.CreateMonitoredEntityRequest.Response.SerializeToString, ), 'updateMonitoredEntity': grpc.unary_unary_rpc_method_handler( servicer.updateMonitoredEntity, request_deserializer=monitoring_dot_DataMonitoringService__pb2.UpdateMonitoredEntityRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.UpdateMonitoredEntityRequest.Response.SerializeToString, ), 'findMonitoredEntity': grpc.unary_unary_rpc_method_handler( servicer.findMonitoredEntity, request_deserializer=monitoring_dot_DataMonitoringService__pb2.FindMonitoredEntityRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.FindMonitoredEntityRequest.Response.SerializeToString, ), 'deleteMonitoredEntity': grpc.unary_unary_rpc_method_handler( servicer.deleteMonitoredEntity, request_deserializer=monitoring_dot_DataMonitoringService__pb2.DeleteMonitoredEntityRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.DeleteMonitoredEntityRequest.Response.SerializeToString, ), 'getProfiler': grpc.unary_unary_rpc_method_handler( servicer.getProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerRequest.Response.SerializeToString, ), 'createProfiler': grpc.unary_unary_rpc_method_handler( servicer.createProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.CreateProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.CreateProfilerRequest.Response.SerializeToString, ), 'updateProfiler': grpc.unary_unary_rpc_method_handler( servicer.updateProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.UpdateProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.UpdateProfilerRequest.Response.SerializeToString, ), 'listProfilers': grpc.unary_unary_rpc_method_handler( servicer.listProfilers, request_deserializer=monitoring_dot_DataMonitoringService__pb2.ListProfilersRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.ListProfilersRequest.Response.SerializeToString, ), 'deleteProfiler': grpc.unary_unary_rpc_method_handler( servicer.deleteProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.DeleteProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.DeleteProfilerRequest.Response.SerializeToString, ), 'getProfilerStatus': grpc.unary_unary_rpc_method_handler( servicer.getProfilerStatus, request_deserializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerStatusRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.GetProfilerStatusRequest.Response.SerializeToString, ), 'findProfilersForMonitoredEntity': grpc.unary_unary_rpc_method_handler( servicer.findProfilersForMonitoredEntity, request_deserializer=monitoring_dot_DataMonitoringService__pb2.FindProfilersForMonitoredEntityRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.FindProfilersForMonitoredEntityRequest.Response.SerializeToString, ), 'enableProfiler': grpc.unary_unary_rpc_method_handler( servicer.enableProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.EnableProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.EnableProfilerRequest.Response.SerializeToString, ), 'disableProfiler': grpc.unary_unary_rpc_method_handler( servicer.disableProfiler, request_deserializer=monitoring_dot_DataMonitoringService__pb2.DisableProfilerRequest.FromString, response_serializer=monitoring_dot_DataMonitoringService__pb2.DisableProfilerRequest.Response.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'ai.verta.monitoring.DataMonitoringService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))

54.270916

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13,622

9.114484

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0.171472

0.186602

0.804358

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0.241222

0

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5

fe065fde30eae1593305d8b80ea065d6f6da2ca7

77

py

Python

Python 101/Chapter 2/String_Formatting_4.py

enemy123456789/Python-101-Notes

aafd38826f18b3af11d5ce4c16d29bbf3de915cd

[ "Apache-2.0" ]

null

Python 101/Chapter 2/String_Formatting_4.py

enemy123456789/Python-101-Notes

aafd38826f18b3af11d5ce4c16d29bbf3de915cd

[ "Apache-2.0" ]

null

Python 101/Chapter 2/String_Formatting_4.py

enemy123456789/Python-101-Notes

aafd38826f18b3af11d5ce4c16d29bbf3de915cd

[ "Apache-2.0" ]

null

print("%(lang)s is fun!" % {"lang":"test"}) #Output """ Python is fun! """

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null

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5

a3ad105e291566b132b2a5f3b369487748aa1efd

40

py

Python

mw/__init__.py

he7d3r/Mediawiki-Utilities

717c30f8e74fa1d9975900b16bc7dff53fe9deb2

[ "MIT" ]

null

mw/__init__.py

he7d3r/Mediawiki-Utilities

717c30f8e74fa1d9975900b16bc7dff53fe9deb2

[ "MIT" ]

null

mw/__init__.py

he7d3r/Mediawiki-Utilities

717c30f8e74fa1d9975900b16bc7dff53fe9deb2

[ "MIT" ]

null

from .types import Timestamp, Namespace

20

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a3ba84ec1b0260ff7d51086309ce170aac667a2e

229

py

Python

example/vault/write.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

2,293

2015-08-14T22:39:31.000Z

2022-03-31T12:44:49.000Z

example/vault/write.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

676

2015-08-20T19:29:56.000Z

2022-03-31T13:45:51.000Z

example/vault/write.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

255

2015-12-02T21:16:33.000Z

2022-03-20T22:03:46.000Z

from dynaconf import settings from dynaconf.loaders import vault_loader vault_loader.write(settings, {"SECRET": "vault_works"}) with settings.using_env("dev"): vault_loader.write(settings, {"SECRET": "vault_works_in_dev"})

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5

a3cd6a56b82bcf0ca093a5e3977186d5f8d93876

78

py

Python

5 Star Python/Arithmetic Operator.py

TheCodeAlpha26/Hackerrank-Demystified

03713a8f3a05e5d6dfed6f6808b06340558e2310

[ "Apache-2.0" ]

6

2021-04-26T17:09:54.000Z

2021-07-08T17:36:16.000Z

5 Star Python/Arithmetic Operator.py

TheCodeAlpha26/Hackerrank-Demystified

03713a8f3a05e5d6dfed6f6808b06340558e2310

[ "Apache-2.0" ]

null

5 Star Python/Arithmetic Operator.py

TheCodeAlpha26/Hackerrank-Demystified

03713a8f3a05e5d6dfed6f6808b06340558e2310

[ "Apache-2.0" ]

null

a = int(input()) b = int(input()) print(str(a+b)+"\n"+str(a-b)+"\n"+str(a*b))

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1

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null

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5

4a33cb52c86f5e5addffa7248d8dcd5fa38d6550

168

py

Python

python/tinyusdz/UsdGeom/__init__.py

GermanAizek/tinyusdz

42358383f363143ad8dd512939a4851902d4f339

[ "MIT" ]

159

2020-04-14T15:59:35.000Z

2022-03-31T14:19:05.000Z

python/tinyusdz/UsdGeom/__init__.py

GermanAizek/tinyusdz

42358383f363143ad8dd512939a4851902d4f339

[ "MIT" ]

16

2020-05-21T06:00:40.000Z

2022-02-26T08:50:33.000Z

python/tinyusdz/UsdGeom/__init__.py

GermanAizek/tinyusdz

42358383f363143ad8dd512939a4851902d4f339

[ "MIT" ]

8

2020-07-01T04:13:42.000Z

2022-01-30T17:50:52.000Z

from . import Tokens def SetStageUpAxis(cls, stage: Stage, axis: Tokens): assert axis == Tokens.x or axis == Tokens.y or axis == Tokens.z stage.upAxis = axis

24

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1

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null

0

1

0

5

4a63c57d312ddc1d32c108936d0096513f0c5bb1

36,502

py

Python

tensorflow_graphics/rendering/opengl/tests/math_test.py

jackd/graphics

736b99a3306e302674a9b7599e3e2857b85fdb74

[ "Apache-2.0" ]

null

tensorflow_graphics/rendering/opengl/tests/math_test.py

jackd/graphics

736b99a3306e302674a9b7599e3e2857b85fdb74

[ "Apache-2.0" ]

null

tensorflow_graphics/rendering/opengl/tests/math_test.py

jackd/graphics

736b99a3306e302674a9b7599e3e2857b85fdb74

[ "Apache-2.0" ]

1

2020-04-11T10:37:36.000Z

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for OpenGL math routines.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import math from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.rendering.opengl import math as glm from tensorflow_graphics.util import test_case class MathTest(test_case.TestCase): def test_perspective_right_handed_preset(self): """Tests that perspective_right_handed generates expected results..""" vertical_field_of_view = ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,)) aspect_ratio = ((1.5,), (1.1,)) near = ((1.0,), (1.2,)) far = ((10.0,), (5.0,)) pred = glm.perspective_right_handed(vertical_field_of_view, aspect_ratio, near, far) gt = (((1.15470052, 0.0, 0.0, 0.0), (0.0, 1.73205066, 0.0, 0.0), (0.0, 0.0, -1.22222221, -2.22222233), (0.0, 0.0, -1.0, 0.0)), ((1.9495517, 0.0, 0.0, 0.0), (0.0, 2.14450693, 0.0, 0.0), (0.0, 0.0, -1.63157892, -3.15789485), (0.0, 0.0, -1.0, 0.0))) self.assertAllClose(pred, gt) @parameterized.parameters( ((1,), (1,), (1,), (1,)), ((None, 1), (None, 1), (None, 1), (None, 1)), ((None, 3, 1), (None, 3, 1), (None, 3, 1), (None, 3, 1)), ) def test_perspective_right_handed_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.perspective_right_handed, shapes) @parameterized.parameters( ("Not all batch dimensions are identical", (1,), (3, 1), (3, 1), (3, 1)), ("Not all batch dimensions are identical", (3, 1), (None, 3, 1), (3, 1), (3, 1)), ) def test_perspective_right_handed_shape_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.perspective_right_handed, error_msg, shapes) @parameterized.parameters( ((1.0,), (1.0,), np.random.uniform(-1.0, 0.0, size=(1,)).astype(np.float32), (1.0,)), ((1.0,), (1.0,), (0.0,), (1.0,)), ((1.0,), np.random.uniform(-1.0, 0.0, size=(1,)).astype(np.float32), (0.1,), (1.0,)), ((1.0,), (0.0,), (0.1,), (1.0,)), ((1.0,), (1.0,), np.random.uniform(1.0, 2.0, size=(1,)).astype(np.float32), np.random.uniform(0.1, 0.5, size=(1,)).astype(np.float32)), ((1.0,), (1.0,), (0.1,), (0.1,)), (np.random.uniform(-math.pi, 0.0, size=(1,)).astype(np.float32), (1.0,), (0.1,), (1.0,)), (np.random.uniform(math.pi, 2.0 * math.pi, size=(1,)).astype(np.float32), (1.0,), (0.1,), (1.0,)), ((0.0,), (1.0,), (0.1,), (1.0,)), ((math.pi,), (1.0,), (0.1,), (1.0,)), ) def test_perspective_right_handed_valid_range_exception_raised( self, vertical_field_of_view, aspect_ratio, near, far): """Tests that an exception is raised with out of bounds values.""" with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.perspective_right_handed(vertical_field_of_view, aspect_ratio, near, far)) def test_perspective_right_handed_cross_jacobian_preset(self): """Tests the Jacobian of perspective_right_handed.""" vertical_field_of_view_init = np.array((1.0,)) aspect_ratio_init = np.array((1.0,)) near_init = np.array((1.0,)) far_init = np.array((10.0,)) self.assert_jacobian_is_correct_fn( glm.perspective_right_handed, [vertical_field_of_view_init, aspect_ratio_init, near_init, far_init]) def test_perspective_right_handed_cross_jacobian_random(self): """Tests the Jacobian of perspective_right_handed.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() eps = np.finfo(np.float64).eps vertical_field_of_view_init = np.random.uniform( eps, math.pi - eps, size=tensor_shape + [1]) aspect_ratio_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) near_init = np.random.uniform(eps, 10.0, size=tensor_shape + [1]) far_init = np.random.uniform(10 + eps, 100.0, size=tensor_shape + [1]) self.assert_jacobian_is_correct_fn( glm.perspective_right_handed, [vertical_field_of_view_init, aspect_ratio_init, near_init, far_init]) def test_look_at_right_handed_preset(self): """Tests that look_at_right_handed generates expected results..""" camera_position = ((0.0, 0.0, 0.0), (0.1, 0.2, 0.3)) look_at = ((0.0, 0.0, 1.0), (0.4, 0.5, 0.6)) up_vector = ((0.0, 1.0, 0.0), (0.7, 0.8, 0.9)) pred = glm.look_at_right_handed(camera_position, look_at, up_vector) gt = (((-1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, -1.0, 0.0), (0.0, 0.0, 0.0, 1.0)), ((4.08248186e-01, -8.16496551e-01, 4.08248395e-01, -2.98023224e-08), (-7.07106888e-01, 1.19209290e-07, 7.07106769e-01, -1.41421378e-01), (-5.77350318e-01, -5.77350318e-01, -5.77350318e-01, 3.46410215e-01), (0.0, 0.0, 0.0, 1.0))) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (3,), (3,)), ((None, 3), (None, 3), (None, 3)), ((None, 2, 3), (None, 2, 3), (None, 2, 3)), ) def test_look_at_right_handed_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.look_at_right_handed, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (1,)), ("Not all batch dimensions are identical", (3,), (3, 3), (3, 3)), ) def test_look_at_right_handed_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.look_at_right_handed, error_msg, shapes) def test_look_at_right_handed_jacobian_preset(self): """Tests the Jacobian of look_at_right_handed.""" camera_position_init = np.array(((0.0, 0.0, 0.0), (0.1, 0.2, 0.3))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.4, 0.5, 0.6))) up_vector_init = np.array(((0.0, 1.0, 0.0), (0.7, 0.8, 0.9))) self.assert_jacobian_is_correct_fn( glm.look_at_right_handed, [camera_position_init, look_at_init, up_vector_init]) def test_look_at_right_handed_jacobian_random(self): """Tests the Jacobian of look_at_right_handed.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() camera_position_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) up_vector_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn( glm.look_at_right_handed, [camera_position_init, look_at_init, up_vector_init]) def test_model_to_eye_preset(self): """Tests that model_to_eye generates expected results..""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) camera_position = ((0.0, 0.0, 0.0), (0.1, 0.2, 0.3)) look_at = ((0.0, 0.0, 1.0), (0.4, 0.5, 0.6)) up_vector = ((0.0, 1.0, 0.0), (0.7, 0.8, 0.9)) pred = glm.model_to_eye(point, camera_position, look_at, up_vector) gt = ((-2.0, 3.0, -4.0), (2.08616257e-07, 1.27279234, -6.58179379)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (3,), (3,), (3,)), ((None, 3), (None, 3), (None, 3), (None, 3)), ((100, 3), (3,), (3,), (3,)), ((None, 1, 3), (None, 2, 3), (None, 2, 3), (None, 2, 3)), ) def test_model_to_eye_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_eye, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (2,)), ("Not all batch dimensions are identical", (3,), (2, 3), (3, 3), (3, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3), (3, 3), (3, 3), (3, 3)), ) def test_model_to_eye_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.model_to_eye, error_msg, shapes) def test_model_to_eye_jacobian_preset(self): """Tests the Jacobian of model_to_eye.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.1, 0.2, 0.3))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.4, 0.5, 0.6))) up_vector_init = np.array(((0.0, 1.0, 0.0), (0.7, 0.8, 0.9))) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_model_to_eye_jacobian_random(self): """Tests the Jacobian of model_to_eye.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) up_vector_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_eye_to_clip_preset(self): """Tests that eye_to_clip generates expected results.""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) vertical_field_of_view = ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,)) aspect_ratio = ((1.5,), (1.6,)) near_plane = ((1.0,), (2.0,)) far_plane = ((10.0,), (11.0,)) pred = glm.eye_to_clip(point, vertical_field_of_view, aspect_ratio, near_plane, far_plane) gt = ((2.30940104, 5.19615173, -7.11111116, -4.0), (4.02095032, 8.57802773, -12.11111069, -5.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (1,), (1,), (1,), (1,)), ((None, 3), (None, 1), (None, 1), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 1), (None, 5, 1), (None, 5, 1), (None, 5, 1)), ) def test_eye_to_clip_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.eye_to_clip, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (1,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (1,), (2,)), ("Not all batch dimensions are broadcast-compatible", (3, 3), (2, 1), (1,), (1,), (1,)), ) def test_eye_to_clip_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.eye_to_clip, error_msg, shapes) def test_eye_to_clip_jacobian_preset(self): """Tests the Jacobian of eye_to_clip.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,))) aspect_ratio_init = np.array(((1.5,), (1.6,))) near_init = np.array(((1.0,), (2.0,))) far_init = np.array(((10.0,), (11.0,))) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=1e-5) def test_eye_to_clip_jacobian_random(self): """Tests the Jacobian of eye_to_clip.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) eps = np.finfo(np.float64).eps vertical_field_of_view_init = np.random.uniform( eps, math.pi - eps, size=tensor_shape + [1]) aspect_ratio_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) near_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(eps, 10.0, size=tensor_shape + [1]) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=5e-06) def test_clip_to_ndc_preset(self): """Tests that clip_to_ndc generates expected results.""" point = ((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0)) pred = glm.clip_to_ndc(point) gt = ((2.0, 4.0, 8.0), (4.0, 8.0, 16.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((4,)), ((None, 4),), ((None, 5, 4),), ) def test_clip_to_ndc_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.clip_to_ndc, shapes) def test_clip_to_ndc_exception_raised(self): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( glm.clip_to_ndc, "must have exactly 4 dimensions in axis -1", ((2,),)) def test_clip_to_ndc_jacobian_preset(self): """Tests the Jacobian of clip_to_ndc.""" point_init = np.array(((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0))) self.assert_jacobian_is_correct_fn(glm.clip_to_ndc, [point_init]) def test_clip_to_ndc_jacobian_random(self): """Tests the Jacobian of clip_to_ndc.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [4]) self.assert_jacobian_is_correct_fn(glm.clip_to_ndc, [point_init]) def test_ndc_to_screen_preset(self): """Tests that ndc_to_screen generates expected results.""" point = ((1.1, 2.2, 3.3), (5.1, 5.2, 5.3)) lower_left_corner = ((6.4, 4.8), (0.0, 0.0)) screen_dimensions = ((640.0, 480.0), (300.0, 400.0)) near = ((1.0,), (11.0,)) far = ((10.0,), (100.0,)) pred = glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far) gt = ((678.40002441, 772.79998779, 20.34999847), (915.0, 1240.0, 291.3500061)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (2,), (2,), (1,), (1,)), ((None, 3), (None, 2), (None, 2), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 2), (None, 5, 2), (None, 5, 1), (None, 5, 1)), ) def test_ndc_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.ndc_to_screen, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (2,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (2,), (3,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (1,), (3,)), ("Not all batch dimensions are identical", (3,), (2, 2), (3, 2), (3, 1), (3, 1)), ("Not all batch dimensions are broadcast-compatible", (4, 3), (3, 2), (3, 2), (3, 1), (3, 1)), ) def test_ndc_to_screen_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.ndc_to_screen, error_msg, shapes) def test_ndc_to_screen_exception_near_raised(self): """Tests that an exception is raised when `near` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(-1.0, 0.0, size=(1,)) far = np.random.uniform(1.0, 2.0, size=(1,)) with self.subTest("negative_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, np.array((0.0,)), far)) def test_ndc_to_screen_exception_far_raised(self): """Tests that an exception is raised if `far` is not greater than `near`.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(-1.0, 0.0, size=(1,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) def test_ndc_to_screen_exception_screen_dimensions_raised(self): """Tests that an exception is raised when `screen_dimensions` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(-1.0, 0.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(0.1, 1.0, size=(1,)) with self.subTest("negative_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, np.array((0.0, 0.0)), near, far)) def test_ndc_to_screen_jacobian_preset(self): """Tests the Jacobian of ndc_to_screen.""" point_init = np.array(((1.1, 2.2, 3.3), (5.1, 5.2, 5.3))) lower_left_corner_init = np.array(((6.4, 4.8), (0.0, 0.0))) screen_dimensions_init = np.array(((640.0, 480.0), (300.0, 400.0))) near_init = np.array(((1.0,), (11.0,))) far_init = np.array(((10.0,), (100.0,))) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_ndc_to_screen_jacobian_random(self): """Tests the Jacobian of ndc_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 1.0, 1000.0, size=tensor_shape + [2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=(1,)) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_model_to_screen_preset(self): """Tests that model_to_screen generates expected results.""" point_world_space = ((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1)) camera_position = ((0.0, 0.0, 0.0), (0.4, -0.8, 0.1)) camera_up = ((0.0, 1.0, 0.0), (0.0, 0.0, 1.0)) look_at = ((0.0, 0.0, 1.0), (0.0, 1.0, 0.0)) vertical_field_of_view = ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,)) lower_left_corner = ((0.0, 0.0), (10.0, 20.0)) screen_dimensions = ((501.0, 501.0), (400.0, 600.0)) near = ((0.01,), (1.0,)) far = ((4.0,), (3.0,)) pred_screen, pred_w = glm.model_to_screen(point_world_space, camera_position, look_at, camera_up, vertical_field_of_view, screen_dimensions, near, far, lower_left_corner) gt_screen = ((-13.23016357, 599.30444336, 4.00215721), (98.07017517, -95.40383911, 3.1234405)) gt_w = ((5.1,), (3.42247,)) self.assertAllClose(pred_screen, gt_screen, atol=1e-5, rtol=1e-5) self.assertAllClose(pred_w, gt_w) @parameterized.parameters( ((3,), (3,), (3,), (3,), (1,), (2,), (1,), (1,), (2,)), ((640, 480, 3), (3,), (3,), (3,), (1,), (2,), (1,), (1,), (2,)), ((None, 3), (None, 3), (None, 3), (None, 3), (None, 1), (None, 2), (None, 1), (None, 1), (None, 2)), ((3,), (None, 1, 3), (None, 1, 3), (None, 1, 3), (None, 1, 1), (None, 1, 2), (None, 1, 1), (None, 1, 1), (None, 1, 2)), ) def test_model_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_screen, shapes) @parameterized.parameters( ("point_model_space must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (2,), (3,), (3,), (3,)), ("camera_position must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (2,), (3,), (3,)), ("look_at must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (2,), (3,)), ("up_vector must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (2,)), ("vertical_field_of_view must have exactly 1 dimensions in axis -1", (1.0, 1.0), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("screen_dimensions must have exactly 2 dimensions in axis -1", (1.0,), (1.0,), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("near must have exactly 1 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0, 1.0), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("far must have exactly 1 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0, 2.0), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("lower_left_corner must have exactly 2 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0,), (3,), (3,), (3,), (3,)), ("Not all batch dimensions are broadcast-compatible", ((1.0,), (1.0,)), ((1.0, 1.0), (1.0, 1.0)), ((1.0,), (1.0,)), ((2.0,), (2.0,)), ((0.0, 0.0), (0.0, 0.0)), (5, 3), (2, 3), (2, 3), (2, 3)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (2, 3), (3,), (3,)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (2, 3), (3,)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (2, 3)), ("Not all batch dimensions are identical", ((1.0,),), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("Not all batch dimensions are identical", (1.0,), ((1.0, 1.0),), (1.0,), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), ((1.0,),), (2.0,), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), (1.0,), ((2.0,),), (0.0, 0.0), (3,), (3,), (3,), (3,)), ("Not all batch dimensions are identical", (1.0,), (1.0, 1.0), (1.0,), (2.0,), ((0.0, 0.0),), (3,), (3,), (3,), (3,)), ) def test_model_to_screen_exception_raised(self, error_msg, vertical_field_of_view, screen_dimensions, near, far, lower_left_corner, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( func=glm.model_to_screen, error_msg=error_msg, shapes=shapes, vertical_field_of_view=vertical_field_of_view, screen_dimensions=screen_dimensions, near=near, far=far, lower_left_corner=lower_left_corner) def test_model_to_screen_jacobian_preset(self): """Tests the Jacobian of model_to_screen.""" point_world_space_init = np.array(((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.4, -0.8, 0.1))) camera_up_init = np.array(((0.0, 1.0, 0.0), (0.0, 0.0, 1.0))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.0, 1.0, 0.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,))) lower_left_corner_init = np.array(((0.0, 0.0), (10.0, 20.0))) screen_dimensions_init = np.array(((501.0, 501.0), (400.0, 600.0))) near_init = np.array(((0.01,), (1.0,))) far_init = np.array(((4.0,), (3.0,))) args = [ point_world_space_init, camera_position_init, look_at_init, camera_up_init, vertical_field_of_view_init, screen_dimensions_init, near_init, far_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args) with self.subTest(name="jacobian_w"): partial_fn = functools.partial( glm.model_to_screen, lower_left_corner=lower_left_corner_init) self.assert_jacobian_is_correct_fn(lambda *args: partial_fn(*args)[1], args[:-1]) def test_model_to_screen_jacobian_random(self): """Tests the Jacobian of model_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_world_space_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) camera_up_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [2]) near_init = np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) args = [ point_world_space_init, camera_position_init, look_at_init, camera_up_init, vertical_field_of_view_init, screen_dimensions_init, near_init, far_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args) with self.subTest(name="jacobian_w"): partial_fn = functools.partial( glm.model_to_screen, lower_left_corner=lower_left_corner_init) self.assert_jacobian_is_correct_fn(lambda *args: partial_fn(*args)[1], args[:-1]) def test_perspective_correct_interpolation_preset(self): """Tests that perspective_correct_interpolation generates expected results.""" camera_origin = np.array((0.0, 0.0, 0.0)) camera_up = np.array((0.0, 1.0, 0.0)) look_at = np.array((0.0, 0.0, 1.0)) fov = np.array((90.0 * np.math.pi / 180.0,)) bottom_left = np.array((0.0, 0.0)) image_size = np.array((501.0, 501.0)) near_plane = np.array((0.01,)) far_plane = np.array((10.0,)) batch_size = np.random.randint(5) triangle_x_y = np.random.uniform(-10.0, 10.0, (batch_size, 3, 2)) triangle_z = np.random.uniform(2.0, 10.0, (batch_size, 3, 1)) triangles = np.concatenate((triangle_x_y, triangle_z), axis=-1) # Builds barycentric weights. barycentric_weights = np.random.uniform(size=(batch_size, 3)) barycentric_weights = barycentric_weights / np.sum( barycentric_weights, axis=-1, keepdims=True) # Barycentric interpolation of vertex positions. convex_combination = np.einsum("ba, bac -> bc", barycentric_weights, triangles) # Computes where those points project in screen coordinates. pixel_position, _ = glm.model_to_screen(convex_combination, camera_origin, look_at, camera_up, fov, image_size, near_plane, far_plane, bottom_left) # Builds attributes. num_pixels = pixel_position.shape[0] attribute_size = np.random.randint(10) attributes = np.random.uniform(size=(num_pixels, 3, attribute_size)) prediction = glm.perspective_correct_interpolation( triangles, attributes, pixel_position[..., 0:2], camera_origin, look_at, camera_up, fov, image_size, near_plane, far_plane, bottom_left) groundtruth = np.einsum("ba, bac -> bc", barycentric_weights, attributes) self.assertAllClose(prediction, groundtruth) @parameterized.parameters( ((500, 400, 3, 3), (3, 7), (2,), (3,), (3,), (3,), (1,), (2,), (1,), (1,), (2,)), ((3, 3), (3, 7), (2,), (3,), (3,), (3,), (1,), (2,), (1,), (1,), (2,)), ((None, 3, 3), (None, 3, 7), (None, 2), (None, 3), (None, 3), (None, 3), (None, 1), (None, 2), (None, 1), (None, 1), (None, 2)), ) def test_perspective_correct_interpolation_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.perspective_correct_interpolation, shapes) @parameterized.parameters( ("point_model_space must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 2), (3, 7), (2,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -2", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (2, 3), (3, 7), (2,), (3,), (3,), (3,)), ("attribute must have exactly 3 dimensions in axis -2", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (2, 7), (2,), (3,), (3,), (3,)), ("must have exactly 2 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (1,), (3,), (3,), (3,)), ("camera_position must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (4,), (3,), (3,)), ("look_at must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (1,), (3,)), ("up_vector must have exactly 3 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (3,), (2,)), ("vertical_field_of_view must have exactly 1 dimensions in axis -1", (1.0, 1.0), (1.0, 1.0), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (3,), (3,)), ("screen_dimensions must have exactly 2 dimensions in axis -1", (1.0,), (1.0,), (1.0,), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (3,), (3,)), ("near must have exactly 1 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0, 1.0), (2.0,), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (3,), (3,)), ("far must have exactly 1 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0, 2.0), (0.0, 0.0), (3, 3), (3, 7), (2,), (3,), (3,), (3,)), ("lower_left_corner must have exactly 2 dimensions in axis -1", (1.0,), (1.0, 1.0), (1.0,), (2.0,), (0.0,), (3, 3), (3, 7), (2,), (3,), (3,), (3,)), ) def test_perspective_correct_interpolation_exception_raised( self, error_msg, vertical_field_of_view, screen_dimensions, near, far, lower_left_corner, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( func=glm.perspective_correct_interpolation, error_msg=error_msg, shapes=shapes, vertical_field_of_view=vertical_field_of_view, screen_dimensions=screen_dimensions, near=near, far=far, lower_left_corner=lower_left_corner) def test_perspective_correct_interpolation_jacobian_preset(self): """Tests the Jacobian of perspective_correct_interpolation.""" vertices_init = np.tile( ((-0.2857143, 0.2857143, 5.0), (0.2857143, 0.2857143, 0.5), (0.0, -0.2857143, 1.0)), (2, 1, 1)) attributes_init = np.tile( (((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))), (2, 1, 1)) pixel_position_init = np.array(((125.5, 375.5), (250.5, 250.5))) camera_position_init = np.tile((0.0, 0.0, 0.0), (2, 3, 1)) look_at_init = np.tile((0.0, 0.0, 1.0), (2, 3, 1)) up_vector_init = np.tile((0.0, 1.0, 0.0), (2, 3, 1)) vertical_field_of_view_init = np.tile((1.0471975511965976,), (2, 3, 1)) screen_dimensions_init = np.tile((501.0, 501.0), (2, 3, 1)) near_init = np.tile((0.01,), (2, 3, 1)) far_init = np.tile((10.0,), (2, 3, 1)) lower_left_corner_init = np.tile((0.0, 0.0), (2, 3, 1)) self.assert_jacobian_is_correct_fn(glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, camera_position_init, look_at_init, up_vector_init, vertical_field_of_view_init, screen_dimensions_init, near_init, far_init, lower_left_corner_init ]) def test_perspective_correct_interpolation_jacobian_random(self): """Tests the Jacobian of perspective_correct_interpolation.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() vertices_init = np.random.uniform(size=tensor_shape + [3, 3]) num_attributes = np.random.randint(1, 10) attributes_init = np.random.uniform(size=tensor_shape + [3, num_attributes]) pixel_position_init = np.random.uniform(size=tensor_shape + [2]) camera_position_init = np.random.uniform(size=tensor_shape + [3, 3]) look_at_init = np.random.uniform(size=tensor_shape + [3, 3]) up_vector_init = np.random.uniform(size=tensor_shape + [3, 3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) screen_dimensions_init = np.random.uniform( 1.0, 10.0, size=tensor_shape + [3, 2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [3, 1]) far_init = near_init + np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [3, 2]) self.assert_jacobian_is_correct_fn(glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, camera_position_init, look_at_init, up_vector_init, vertical_field_of_view_init, screen_dimensions_init, near_init, far_init, lower_left_corner_init ]) if __name__ == "__main__": test_case.main()

46.677749

94

0.594598

5,783

36,502

3.549023

0.055508

0.035568

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0.032937

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0.716186

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null

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null

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5

4a65c999ba3027fe1616c4445a8c053ddc244f71

112

py

Python

python/testData/inspections/PyUnresolvedReferencesInspection3K/PreferImportedModuleOverNamespacePackage/a.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyUnresolvedReferencesInspection3K/PreferImportedModuleOverNamespacePackage/a.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyUnresolvedReferencesInspection3K/PreferImportedModuleOverNamespacePackage/a.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import c print(c.A().foo()) print(c.<warning descr="Cannot find reference 'b' in 'c.py'">b</warning>.A().foo())

28

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112

3.428571

0.619048

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0.089286

112

4

83

28

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0.309735

0

null

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0.333333

null

0.666667

1

0

null

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null

0

1

0

1

0

1

0

5

4aa728600a94fa4e0f719b37efe33a9625f121b5

2,707

py

Python

tests/calculators/test_non_working_days_in_ago.py

FrappucinoGithub/school_meal_forecast_xgboost

dc2411765fb78f92c7f8e2af3b8afda88d58347e

[ "MIT" ]

6

2020-12-15T09:31:02.000Z

2021-12-12T09:42:05.000Z

tests/calculators/test_non_working_days_in_ago.py

fBedecarrats/school_meal_forecast_xgboost

ebb10a8395b9b8158685953b030e664337cf20e0

[ "MIT" ]

2

2021-12-12T09:57:38.000Z

2022-01-27T22:01:22.000Z

tests/calculators/test_non_working_days_in_ago.py

fBedecarrats/school_meal_forecast_xgboost

ebb10a8395b9b8158685953b030e664337cf20e0

[ "MIT" ]

3

2021-02-25T07:49:31.000Z

2022-01-10T09:57:39.000Z

#!/usr/bin/python3 import unittest import pandas as pd import app.calculators as calc class TestNonWorkingDaysInAgo(unittest.TestCase): # pylint: disable=too-many-statements def test_add_feature_non_working_days_in_ago(self): dtf = pd.DataFrame({ 'index_date': ["2017-09-04", "2017-09-05", "2019-05-07", "2019-05-08", "2019-07-15"], 'date_col': ["2017-09-04", "2017-09-05", "2019-05-07", "2019-05-08", "2019-07-15"]}) dtf.set_index('index_date', inplace=True) train_dtf = calc.add_feature_non_working_days_in_ago(dtf.copy(), 'date_col', "%Y-%m-%d", "tests/data") print(train_dtf) self.assertTrue('nom_jour_ferie' in train_dtf) self.assertTrue('non_working_in' in train_dtf) self.assertTrue('non_working_ago' in train_dtf) self.assertEqual(train_dtf.shape, (5, 4)) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['date_col'].iloc[0], '2017-09-04') self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_in'].iloc[0], 58) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_ago'].iloc[0], 0) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['date_col'].iloc[1], '2017-09-05') self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_in'].iloc[1], 57) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_ago'].iloc[1], 0) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['date_col'].iloc[2], '2019-05-07') self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_in'].iloc[2], 1) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_ago'].iloc[2], 6) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['date_col'].iloc[3], '2019-07-15') # latest day in the dataset, thus next non_working_in cannot be computed self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_in'].iloc[3], 0) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "jour_ouvre"]['non_working_ago'].iloc[3], 1) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "Victoire des alliés"]['date_col'].iloc[0], '2019-05-08') self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "Victoire des alliés"]['non_working_in'].iloc[0], 0) self.assertEqual(train_dtf[train_dtf['nom_jour_ferie'] == "Victoire des alliés"]['non_working_ago'].iloc[0], 0) if __name__ == '__main__': unittest.main()

58.847826

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0.112084

0.214828

0.718039

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60.155556

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false

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0

1

0

1

0

null

0

1

0

5

4376d914ca001b7bf891fdff8239c6c45bbf171b

18,066

py

Python

dl4s/CGRNN/CGRNN.py

liu2231665/Project-dl4s

615d504caf6f05b676be1c25621d2dd94e41ec54

[ "MIT" ]

null

dl4s/CGRNN/CGRNN.py

liu2231665/Project-dl4s

615d504caf6f05b676be1c25621d2dd94e41ec54

[ "MIT" ]

null

dl4s/CGRNN/CGRNN.py

liu2231665/Project-dl4s

615d504caf6f05b676be1c25621d2dd94e41ec54

[ "MIT" ]

null

"""######################################################################### Author: Yingru Liu Institute: Stony Brook University Descriptions: the file contains the model description of CGRNN. ----2017.11.15 #########################################################################""" from .utility import configCGRNN, CGCell from dl4s.cores.tools import BernoulliNLL from dl4s.cores.model import _model import tensorflow as tf import numpy as np """######################################################################### Class: _CGRNN - the hyper abstraction of the CGRNN. #########################################################################""" class _CGRNN(_model, object): """######################################################################### __init__:the initialization function. input: Config - configuration class in ./utility. output: None. #########################################################################""" def __init__( self, config=configCGRNN() ): # Check the froward recurrent dimension configuration. if config.dimRec == []: raise (ValueError('The forward recurrent structure is empty!')) super().__init__(config) with self._graph.as_default(): # <scalar> the steps of Gibbs sampling. self._gibbs = config.Gibbs # <scalar> the number of samples of AIS. self._aisRun = config.aisRun # <scalar> the number of intermediate proposal distributions of AIS. self._aisLevel = config.aisLevel # <scalar> dimensions of input frame. self._dimInput = config.dimIN # <scalar> dimensions of stochastic states. self._dimState = config.dimState # <scalar list> the size of forward recurrent hidden layers. self._dimRec = config.dimRec # <scalar list> the size of feed-forward hidden layers. self._dimMlp = config.dimMlp # <string> the mode. self._mode = config.mode self.VAE = None """######################################################################### Class: binCGRNN - the CGRNN mode for binary input. #########################################################################""" class binCGRNN(_CGRNN, object): """######################################################################### __init__:the initialization function. input: Config - configuration class in ./utility. VAE - if a well trained VAE is provided. Using NVIL to estimate the upper bound of the partition function. output: None. #########################################################################""" def __init__( self, config=configCGRNN(), VAE=None ): super().__init__(config) """build the graph""" with self._graph.as_default(): self.Cell = CGCell(config, inputType='binary') state = self.Cell.zero_state(tf.shape(self.x)[0], dtype=tf.float32) (self.newV, self.newH, self.newS, self.muV, self.muH, self.muS, bvt, bht), _ = \ tf.nn.dynamic_rnn(self.Cell, self.x, initial_state=state) # update the RBM's bias with bvt & bht. self.Cell.RBM._bh = bht self.Cell.RBM._bv = bvt # one step sample. muV0, muH0, muS0 = self.Cell.RBM.GibbsSampling(self.x, k=1)[-3:] # add the tensor computation of extracted feature. self._outputs = muV0 self._feature = muH0 self._sparse_feature = muH0 * muS0 # the training loss is per bits. Loss = self.Cell.RBM.ComputeLoss(V=self.x, samplesteps=config.Gibbs) self._loss = BernoulliNLL(self.x, self.muV) self._params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) self._train_step = self._optimizer.minimize(Loss) # Define the components to evaluate the partition function by whether NVIL or AIS. if VAE is None: self._logZ = self.Cell.RBM.AIS(self._aisRun, self._aisLevel, tf.shape(self.x)[0], tf.shape(self.x)[1]) self._nll = tf.reduce_mean(self.Cell.RBM.FreeEnergy(self.x) + self._logZ) #self._nll = self._logZ #self._nll = self.Cell.RBM.FreeEnergy(self.x) self.VAE = VAE else: self._logZ = self._NVIL_VAE(VAE) # X, logPz_X, logPx_Z, logPz, VAE.x self.xx = tf.placeholder(dtype='float32', shape=[None, None, None, config.dimIN]) self.FEofSample = self.Cell.RBM.FreeEnergy(self.xx) self.FEofInput = self.Cell.RBM.FreeEnergy(self.x) self.VAE = VAE """define the process to generate samples.""" state = self.Cell.zero_state(1, dtype=tf.float32) x_ = tf.zeros((1, self._dimInput), dtype='float32') # TensorArray to save the output of the generating. gen_operator = tf.TensorArray(tf.float32, self.sampleLen) # condition and body of while loop (input: i-iteration, xx-RNN input, ss-RNN state) i = tf.constant(0) cond = lambda i, xx, ss, array: tf.less(i, self.sampleLen) # def body(i, xx, ss, array): ii = i + 1 (new_xx, _, _, _, _, _, _, _), new_ss = self.Cell(xx, ss, gibbs=1) new_array = array.write(i, new_xx) return ii, new_xx, new_ss, new_array gen_operator = tf.while_loop(cond, body, [i, x_, state, gen_operator])[-1] self._gen_operator = gen_operator.concat() # self._runSession() """######################################################################### _NVIL_VAE: generate the graph to compute the NVIL upper bound of log Partition function by a well-trained VAE. input: VAE - the well-trained VAE(SRNN/VRNN). output: the upper boundLogZ. #########################################################################""" def _NVIL_VAE(self, VAE): # get the marginal and conditional distribution of the VAE. probs = VAE._dec Px_Z = tf.distributions.Bernoulli(probs=probs, dtype=tf.float32) mu, std = VAE._enc Pz_X = tf.distributions.Normal(loc=mu, scale=std) mu, std = VAE._prior Pz = tf.distributions.Normal(loc=mu, scale=std) # generate the samples. X = Px_Z.sample() logPz_X = tf.reduce_sum(Pz_X.log_prob(VAE._Z), axis=[-1]) # shape = [batch, steps] # logPx_Z = tf.reduce_prod(Px_Z.log_prob(X), axis=[-1]) logPx_Z = tf.reduce_sum( (1 - X) * tf.log(tf.maximum(tf.minimum(1.0, 1 - probs), 1e-32)) + X * tf.log(tf.maximum(tf.minimum(1.0, probs), 1e-32)), axis=[-1]) # shape = [runs, batch, steps] logPz = tf.reduce_sum(Pz.log_prob(VAE._Z), axis=[-1]) return X, logPz_X, logPx_Z, logPz, VAE.x """######################################################################### ais_function: compute the approximated negative log-likelihood with partition function computed by annealed importance sampling. input: input - numerical input. output: the negative log-likelihood value. #########################################################################""" def ais_function(self, input): with self._graph.as_default(): if self.VAE is None: loss_value = self._sess.run(self._nll, feed_dict={self.x: input}) else: loss_value = [] X = [] logPz_X = [] logPx_Z = [] logPz = [] for i in range(self._aisRun): Xi, logPz_Xi, logPx_Zi, logPzi = self.VAE._sess.run(self._logZ[0:-1], feed_dict={self._logZ[-1]: input}) X.append(Xi) logPz_X.append(logPz_Xi) logPx_Z.append(np.nan_to_num(logPx_Zi)) logPz.append(logPzi) # shape = [runs, batch, steps] X = np.asarray(X, dtype=np.float64) logPz_X = np.asarray(logPz_X, dtype=np.float64) logPx_Z = np.asarray(logPx_Z, dtype=np.float64) logPz = np.asarray(logPz, dtype=np.float64) FEofSample = self._sess.run(self.FEofSample, feed_dict={self.xx: X, self.x: input}) FEofSample = np.cast[np.float64](FEofSample) logTerm = 2 * (-FEofSample + logPz_X - logPx_Z - logPz) / 1000 #self._dimInput r_ais = np.mean(np.exp(logTerm), axis=0) logZ = 0.5 * (np.log(r_ais+1e-38)) FEofInput = self._sess.run(self.FEofInput, feed_dict={self.x: input}) FEofInput = np.cast[np.float64](FEofInput) loss_value.append(np.mean(FEofInput + logZ * 1000))#self._dimInput)) loss_value = np.asarray(loss_value).mean() return loss_value # TODO" """######################################################################### Class: gaussCGRNN - the CGRNN mode for continuous input. #########################################################################""" class gaussCGRNN(_CGRNN, object): """######################################################################### __init__:the initialization function. input: Config - configuration class in ./utility. VAE - if a well trained VAE is provided. Using NVIL to estimate the upper bound of the partition function. output: None. #########################################################################""" def __init__( self, config, VAE=None ): super().__init__(config) """build the graph""" with self._graph.as_default(): self.Cell = CGCell(config, inputType='continuous') state = self.Cell.zero_state(tf.shape(self.x)[0], dtype=tf.float32) (self.newV, self.newH, self.newS, self.muV, self.muH, self.muS, self.bvt, self.bht, self.gamma), _ = tf.nn.dynamic_rnn(self.Cell, self.x, initial_state=state) # update the RBM's bias with bvt & bht, gamma. self.Cell.RBM._bh = self.bht self.Cell.RBM._bv = self.bvt self.Cell.RBM._gamma = self.gamma # one step sample. muV0, muH0, muS0 = self.Cell.RBM.GibbsSampling(self.x, k=1)[-3:] # add the tensor computation of extracted feature. self._outputs = muV0 self._feature = muH0 self._sparse_feature = muH0 * muS0 # the training loss is per frame. Loss = self.Cell.RBM.ComputeLoss(V=self.x, samplesteps=config.Gibbs) # define the monitor. monitor = tf.reduce_sum((self.x - self.muV) ** 2, axis=-1) self._loss = tf.sqrt(tf.reduce_mean(monitor)) self._params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) self._train_step = self._optimizer.minimize(Loss) # add the computation of precision and covariance matrix of ssRBM. newH = tf.expand_dims(self.newH, axis=2) W = tf.expand_dims(tf.expand_dims(self.Cell.RBM._W, axis=0), axis=0) term1 = newH * W / (self.Cell.RBM._alpha + 1e-8) term1 = tf.tensordot(term1, self.Cell.RBM._W, [[-1], [-1]]) Cv_sh = 1 / (tf.expand_dims(self.Cell.RBM._gamma, axis=2) + tf.tensordot(newH, self.Cell.RBM._phi, [[-1], [0]]) + 1e-8) term2 = Cv_sh * tf.eye(self._dimInput, batch_shape=[1, 1]) self.PreV_h = term2 + term1 self.CovV_h = tf.matrix_inverse(self.PreV_h) # if VAE is None: self._logZ = self.Cell.RBM.AIS(self._aisRun, self._aisLevel, tf.shape(self.x)[0], tf.shape(self.x)[1]) self._nll = tf.reduce_mean(self.Cell.RBM.FreeEnergy(self.x) + self._logZ) self.VAE = VAE else: self._logZ = self._NVIL_VAE(VAE) # X, logPz_X, logPx_Z, logPz, VAE.x self.xx = tf.placeholder(dtype='float32', shape=[None, None, None, config.dimIN]) self.FEofSample = self.Cell.RBM.FreeEnergy(self.xx) self.FEofInput = self.Cell.RBM.FreeEnergy(self.x) self.VAE = VAE """define the process to generate samples.""" state = self.Cell.zero_state(1, dtype=tf.float32) x_ = tf.zeros((1, self._dimInput), dtype='float32') # TensorArray to save the output of the generating. gen_operator = tf.TensorArray(tf.float32, self.sampleLen) # condition and body of while loop (input: i-iteration, xx-RNN input, ss-RNN state) i = tf.constant(0) cond = lambda i, xx, ss, array: tf.less(i, self.sampleLen) # def body(i, xx, ss, array): ii = i + 1 (new_xx, _, _, _, _, _, _, _, _), new_ss = self.Cell(xx, ss, gibbs=1) new_array = array.write(i, new_xx) return ii, new_xx, new_ss, new_array gen_operator = tf.while_loop(cond, body, [i, x_, state, gen_operator])[-1] self._gen_operator = gen_operator.concat() # self._runSession() """######################################################################### ais_function: compute the approximated negative log-likelihood with partition function computed by annealed importance sampling. input: input - numerical input. output: the negative log-likelihood value. #########################################################################""" def ais_function(self, input): with self._graph.as_default(): if self.VAE is None: loss_value = self._sess.run(self._nll, feed_dict={self.x: input}) else: loss_value = [] X = [] logPz_X = [] logPx_Z = [] logPz = [] for i in range(self._aisRun): Xi, logPz_Xi, logPx_Zi, logPzi = self.VAE._sess.run(self._logZ[0:-1], feed_dict={self._logZ[-1]: input}) X.append(Xi) logPz_X.append(np.nan_to_num(logPz_Xi)) logPx_Z.append(np.nan_to_num(logPx_Zi)) logPz.append(np.nan_to_num(logPzi)) # shape = [runs, batch, steps] X = np.asarray(X, dtype=np.float64) logPz_X = np.asarray(logPz_X, dtype=np.float64) logPx_Z = np.asarray(logPx_Z, dtype=np.float64) logPz = np.asarray(logPz, dtype=np.float64) FEofSample = self._sess.run(self.FEofSample, feed_dict={self.xx: X, self.x: input}) FEofSample = np.cast[np.float64](FEofSample) logTerm = 2 * (-FEofSample + logPz_X - logPx_Z - logPz) / 1000 # self._dimInput r_ais = np.mean(np.exp(logTerm), axis=0) logZ = 0.5 * (np.log(r_ais + 1e-38)) FEofInput = self._sess.run(self.FEofInput, feed_dict={self.x: input}) FEofInput = np.cast[np.float64](FEofInput) loss_value.append(np.mean(FEofInput + logZ * 1000)) # self._dimInput)) loss_value = np.asarray(loss_value).mean() return loss_value """######################################################################### _NVIL_VAE: generate the graph to compute the NVIL upper bound of log Partition function by a well-trained VAE. input: VAE - the well-trained VAE(SRNN/VRNN). output: the upper boundLogZ. #########################################################################""" def _NVIL_VAE(self, VAE): # get the marginal and conditional distribution of the VAE. mu, std = VAE._dec Px_Z = tf.distributions.Normal(loc=mu, scale=std) mu1, std1 = VAE._enc Pz_X = tf.distributions.Normal(loc=mu1, scale=std1) mu, std = VAE._prior Pz = tf.distributions.Normal(loc=mu, scale=std) # generate the samples. X = Px_Z.sample() logPz_X = tf.reduce_sum(Pz_X.log_prob(VAE._Z), axis=[-1]) # shape = [batch, steps] logPx_Z = tf.reduce_sum(Px_Z.log_prob(X), axis=[-1]) logPz = tf.reduce_sum(Pz.log_prob(VAE._Z), axis=[-1]) return X, logPz_X, logPx_Z, logPz, VAE.x """######################################################################### cov_function: compute the covariance matrix Cv_h. input: input - numerical input. output: covariance matrix Cv_h. #########################################################################""" def cov_function(self, input): with self._graph.as_default(): return self._sess.run(self.CovV_h, feed_dict={self.x: input}) """######################################################################### pre_function: compute the precision matrix Cv_h^{-1}. input: input - numerical input. output: precision matrix Cv_h^{-1}. #########################################################################""" def pre_function(self, input): with self._graph.as_default(): return self._sess.run(self.PreV_h, feed_dict={self.x: input})

52.517442

132

0.496457

2,014

18,066

4.282522

0.149454

0.030609

0.029333

0.01113

0.791768

0.763014

0.749449

0.734957

0.727304

0.718261

0

0.013857

0.296967

18,066

343

133

52.670554

0.665223

0.163954

0

0.680365

0

0.007216

0

0.002915

0

1

0.050228

false

0

0.022831

0

0.123288

0

null

0

1

0

null

0

5

438c7596524513b6f5670e8c76aee46370387b73

44

py

Python

application/flicket_admin/scripts/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

application/flicket_admin/scripts/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

application/flicket_admin/scripts/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

#! usr/bin/python3 # -*- coding: utf8 -*- #

11

22

0.522727

5

44

4.6

1

0

0.055556

0.181818

44

3

23

14.666667

0.583333

0.863636

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

43b031f1eca4348d81d92e84e0d56dd356e8d071

52

py

Python

demo3.py

xinbaolai/we-are-a-team

27c8f55e85171a984fb1d86519f59889a065b05f

[ "Apache-2.0" ]

null

demo3.py

xinbaolai/we-are-a-team

27c8f55e85171a984fb1d86519f59889a065b05f

[ "Apache-2.0" ]

null

demo3.py

xinbaolai/we-are-a-team

27c8f55e85171a984fb1d86519f59889a065b05f

[ "Apache-2.0" ]

null

print("aaaaaaaaaaa") # aaaaaaaaaaaaaaaaaaaaaaaaaaaaa

26

31

0.865385

3

52

15

1

0

0.038462

52

2

31

26

0.9

0.557692

0

0.5

0

1

0

true

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

43b2bb59051229391be6c756bbab9a9435d21340

61

py

Python

ex/24 (14).py

Time2003/lr7

b47edaf11ced014022764b6c5edef34e4c107c0b

[ "MIT" ]

null

ex/24 (14).py

Time2003/lr7

b47edaf11ced014022764b6c5edef34e4c107c0b

[ "MIT" ]

null

ex/24 (14).py

Time2003/lr7

b47edaf11ced014022764b6c5edef34e4c107c0b

[ "MIT" ]

null

list_1 = [1, 2, 3] list_2 = [4, 5, 6] печать(list_1 + list_2)

20.333333

23

0.590164

15

61

2.133333

0.533333

0.3125

0

0.204082

0.196721

61

3

23

20.333333

0.44898

0

1

0

false

0

1

0

null

1

0

1

0

1

0

1

0

null

0

5

43c1e1436a273de9761fb418a69acb4083f66610

48

py

Python

tests/admin_scripts/custom_templates/project_template/additional_dir/additional_file.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

61,676

2015-01-01T00:05:13.000Z

2022-03-31T20:37:54.000Z

tests/admin_scripts/custom_templates/project_template/additional_dir/additional_file.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

8,884

2015-01-01T00:12:05.000Z

2022-03-31T19:53:11.000Z

tests/admin_scripts/custom_templates/project_template/additional_dir/additional_file.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

33,143

2015-01-01T02:04:52.000Z

2022-03-31T19:42:46.000Z

# some file for {{ project_name }} test project

24

47

0.708333

7

48

4.714286

0.857143

0

0.1875

48

1

48

0.846154

0.9375

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

43d74a652bd2a097c24aea08f097fbb7b43e735e

68,535

py

Python

UGI/UGInfos_Latin.py

protimient/Glyphs-Scripts

0481ea01153844667cff8cfa3fad97c33af09956

[ "Apache-2.0" ]

2

2021-02-12T20:36:29.000Z

2021-11-03T08:04:01.000Z

UGI/UGInfos_Latin.py

protimient/Glyphs-Scripts

0481ea01153844667cff8cfa3fad97c33af09956

[ "Apache-2.0" ]

null

UGI/UGInfos_Latin.py

protimient/Glyphs-Scripts

0481ea01153844667cff8cfa3fad97c33af09956

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from unifiedglyphinfo import CollectedGlyphInfos, xpos, ypos def collect_infos(infos_dict): return infos_dict.update(ugi.unified_infos) ugi = CollectedGlyphInfos() x = ugi('A') x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.capHeight) x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('V flip_horizontal flip_vertical') x = ugi('AE') x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.capHeight) x.addKerning(left='AE', right='E') x.addMetrics(left='AE', right='E') x.addRecipe('A decompose', 'E decompose') x = ugi('AEacute') x.addKerning(left='AE', right='E') x.addMetrics(left='AE', right='E') x = ugi('Aacute') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Abreve') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Abreveacute') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'brevecomb_acutecomb') x = ugi('Abrevedotbelow') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'brevecomb', 'dotbelowcomb') x = ugi('Abrevegrave') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'brevecomb_gravecomb') x = ugi('Abrevehookabove') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'brevecomb_hookabovecomb') x = ugi('Abrevetilde') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'brevecomb_tildecomb') x = ugi('Acaron') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Acircumflex') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Acircumflexacute') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'circumflexcomb_acutecomb') x = ugi('Acircumflexdotbelow') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'circumflexcomb', 'dotbelowcomb') x = ugi('Acircumflexgrave') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'circumflexcomb_gravecomb') x = ugi('Acircumflexhookabove') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'circumflexcomb_hookabovecomb') x = ugi('Acircumflextilde') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'circumflexcomb_tildecomb') x = ugi('Adieresis') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Adotbelow') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Agrave') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Ahookabove') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Alpha') x.addKerning(left='O', right='H') x = ugi('Alpha-latin') x.addRecipe('D flip_vertical flip_horizontal decompose', 'I decompose') x = ugi('Alphaturned-latin') x.addRecipe('Alpha-latin flip_vertical flip_horizontal') x = ugi('Amacron') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Aogonek') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Aring') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Aringacute') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x.addRecipe('A', 'ringcomb', 'acutecomb') x = ugi('Atilde') x.addKerning(left='A', right='A') x.addMetrics(left='A', right='A') x = ugi('Aturned') x.addKerning(left='V', right='V') x.addRecipe('A flip_horizontal flip_vertical') x = ugi('B') x.addKerning(left='H', right='B') x.addMetrics(left='H', right='B') x = ugi('Bhook') x.addKerning(left='Bhook', right='O') x.addRecipe('B', '_part.Hookleft') x = ugi('Bsmall') x.addKerning(left='n', right='Bsmall') x.addRecipe('ve-cy') x = ugi('C') x.addAnchor('bottom', position_x=xpos.apex_bottom) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.capHeight) x.addKerning(left='O', right='C') x.addMetrics(left='O', right='C') x = ugi('Cacute') x.addKerning(left='O', right='C') x.addMetrics(left='C', right='C') x = ugi('Ccaron') x.addKerning(left='O', right='C') x.addMetrics(left='C', right='C') x = ugi('Ccedilla') x.addKerning(left='O', right='C') x.addMetrics(left='C', right='C') x = ugi('Ccircumflex') x.addKerning(left='O', right='C') x.addMetrics(left='C', right='C') x = ugi('Cdotaccent') x.addKerning(left='O', right='C') x.addMetrics(left='C', right='C') x = ugi('Chook') x.addKerning(left='O', right='C') x.addRecipe('C', '_part.Hook') x = ugi('D') x.addKerning(left='H', right='O') x.addMetrics(left='H', right='O') x = ugi('Dafrican') x.addKerning(left='H', right='O') x.addRecipe('Eth') x = ugi('Dcaron') x.addKerning(left='H', right='O') x.addMetrics(left='D', right='D') x = ugi('Dcroat') x.addKerning(left='Eth', right='O') x.addMetrics(left='Eth', right='D') x.addRecipe('Eth') x = ugi('Dhook') x.addKerning(left='Bhook', right='O') x.addRecipe('D', '_part.Hookleft') x = ugi('Dsmall') x.addKerning(left='n', right='o') x.addMetrics(left='n', right='o') x = ugi('E') x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='H', right='E') x.addMetrics(left='H', right='E') x.addRecipe('_part.stem', '_part.bar', '_part.bar', '_part.bar') x = ugi('Eacute') x.addKerning(left='H', right='E') x.addMetrics(left='E', right='E') x = ugi('Ebreve') x.addKerning(left='H', right='E') x.addMetrics(left='E', right='E') x = ugi('Ecaron') x.addKerning(left='H', right='E') x.addMetrics(left='E', right='E') x = ugi('Ecircumflex') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Ecircumflexacute') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x.addRecipe('E', 'circumflexcomb_acutecomb') x = ugi('Ecircumflexdotbelow') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x.addRecipe('E', 'circumflexcomb', 'dotbelowcomb') x = ugi('Ecircumflexgrave') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x.addRecipe('E', 'circumflexcomb_gravecomb') x = ugi('Ecircumflexhookabove') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x.addRecipe('E', 'circumflexcomb_hookabovecomb') x = ugi('Ecircumflextilde') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x.addRecipe('E', 'circumflexcomb_tildecomb') x = ugi('Edieresis') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Edotaccent') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Edotbelow') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Egrave') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Ehookabove') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Emacron') x.addKerning(left='H', right='E') x.addMetrics(left='E', width='E') x = ugi('Eng') x.addKerning(left='H', right='N') x.addMetrics(left='H', right='N') x.addRecipe('jdotless decompose', 'N decompose') x = ugi('Eogonek') x.addKerning(left='H', right='E') x.addMetrics(left='E', right='E') x = ugi('Eopen') x.addKerning(left='S', right='C') x.addRecipe('Ze-cy flip_horizontal decompose') x = ugi('EreversedOpen') x.addKerning(left='S', right='B') x.addRecipe('Ze-cy') x = ugi('Esh') x.addKerning(left='X', right='E') x.addRecipe('Sigma') x = ugi('Eth') x.addBuildString(u'önghljóðuðust') x.addKerning(left='Eth', right='O') x.addMetrics(left='Eth', right='D') x.addRecipe('D', '_part.bar') x.addRecipe('D', 'macroncomb decompose') x = ugi('Etilde') x.addKerning(left='H', right='E') x.addMetrics(left='H', right='E') x = ugi('Ezh') x.addKerning(left='Ezh', right='Germandbls') x.addRecipe('ezh decompose') x = ugi('F') x.addAnchor('#bar', position_x=xpos.stem_bottom_center, position_y=ypos.height_25) x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='H', right='F') x.addMetrics(left='H', right='F') x.addRecipe('E decompose') x = ugi('G') x.addAnchor('bottom', position_x=xpos.apex_bottom) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.capHeight) x.addKerning(left='O', right='G') x.addMetrics(left='O', right='G') x = ugi('Gacute') x.addKerning(left='O', right='G') x = ugi('Gammaafrican') x.addKerning(left='V', right='V') x.addRecipe('gamma-latin decompose') x = ugi('Gbreve') x.addKerning(left='O', right='G') x.addMetrics(left='G', right='G') x = ugi('Gcircumflex') x.addKerning(left='O', right='G') x.addMetrics(left='G', right='G') x = ugi('Gcommaaccent') x.addKerning(left='O', right='G') x.addMetrics(left='G', right='G') x = ugi('Gdotaccent') x.addKerning(left='O', right='G') x.addMetrics(left='G', right='G') x = ugi('Germandbls') x.addKerning(left='Germandbls', right='Germandbls') x.addMetrics(left='Germandbls', right='Germandbls') x.addRecipe('I decompose', 'S decompose') x = ugi('Ghook') x.addKerning(left='O', right='G') x.addRecipe('G', '_part.hook') x = ugi('Glottalstop') x.addRecipe('glottalstop decompose') x = ugi('Gscript') x.addKerning(left='O', right='H') x.addRecipe('Alpha-latin decompose', 'gsingle decompose') x = ugi('Gsmall') x.addKerning(left='o', right='Gsmall') x = ugi('Gsmallhook') x.addKerning(left='o', right='dhook') x.addMetrics(left='Gsmall', right='dhook') x = ugi('H') x.addAnchor('#bar', position_x=xpos.outline_center, position_y=ypos.height_75) x.addKerning(left='H', right='H') x.addMetrics(left='H', right='H') x.addRecipe('_part.stem', '_part.bar', '_part.stem') x = ugi('Hbar') x.addKerning(left='H', right='H') x.addMetrics(left='H', right='H') x.addRecipe('H', '_part.bar') x.addRecipe('H', 'macroncomb decompose') x = ugi('Hcircumflex') x.addKerning(left='H', right='H') x.addMetrics(left='H', right='H') x = ugi('Hhook') x.addKerning(left='Bhook', right='H') x.addRecipe('H', '_part.Hookleft') x = ugi('Hsmall') x.addKerning(left='n', right='u') x.addRecipe('en-cy') x = ugi('Hturned') x.addKerning(left='Hturned', right='H') x = ugi('I') x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addAnchor('topleft', position_x=xpos.outline_left, position_y=ypos.capHeight) x.addKerning(left='H', right='H') x.addMetrics(left='H', right='H') x.addRecipe('_part.stem') x = ugi('IJ') x.addKerning(left='H', right='J') x.addMetrics(left='H', right='J') x = ugi('Iacute') x.addKerning(left='H', right='H') x.addMetrics(left='I', width='I') x = ugi('Ibreve') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Icaron') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Icircumflex') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Idieresis') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Idotaccent') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Idotbelow') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Igrave') x.addKerning(left='H', right='H') x.addMetrics(width='I', right='I') x = ugi('Ihookabove') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Imacron') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Iogonek') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('Iotaafrican') x.addKerning(left='H', right='Iotaafrican') x.addRecipe('iota decompose') x = ugi('Ismall') x.addKerning(left='Ismall', right='Ismall') x = ugi('Istroke') x.addKerning(left='Eth', right='Istroke') x.addRecipe('I', '_part.bar') x = ugi('Itilde') x.addKerning(left='H', right='H') x.addMetrics(left='I', right='I') x = ugi('J') x.addAnchor('bottom', position_x=xpos.apex_bottom, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.capHeight) x.addKerning(left='J', right='J') x.addMetrics(left='J', right='J') x = ugi('Jcircumflex') x.addKerning(left='J', right='J') x.addMetrics(left='J', right='J') x = ugi('Jcrossedtail') x.addKerning(left='J', right='Jcrossedtail') x.addRecipe('J decompose', 'jcrossedtail decompose') x = ugi('K') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.base_line) x.addKerning(left='H', right='K') x.addMetrics(left='H', right='K') x = ugi('Kcommaaccent') x.addKerning(left='H', right='K') x.addMetrics(left='K', right='K') x = ugi('Khook') x.addRecipe('K decompose', '_part.Hook decompose') x = ugi('Kturned') x.addKerning(left='X', right='H') x.addRecipe('K flip_horizontal flip_vertical') x.addRecipe('K flip_vertical flip_horizontal') x = ugi('L') x.addAnchor('#dot', position_x=xpos.width_75, position_y=ypos.outline_middle) x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.capHeight) x.addAnchor('topright', position_x=xpos.stem_top_right, position_y=ypos.capHeight) x.addKerning(left='H', right='L') x.addMetrics(left='H', right='L') x.addRecipe('E decompose') x = ugi('Lacute') x.addKerning(left='H', right='L') x.addMetrics(left='L', right='L') x = ugi('Lbelt') x.addKerning(left='Lbelt', right='L') x.addRecipe('L', 'lbelt decompose') x = ugi('Lcaron') x.addKerning(left='H', right='L') x.addMetrics(left='L', right='L') x = ugi('Lcommaaccent') x.addKerning(left='H', right='L') x.addMetrics(left='L', right='L') x = ugi('Ldot') x.addKerning(left='H', right='L') x.addMetrics(left='L', right='L') x.addRecipe('L', 'dotaccent') x.addRecipe('L', 'periodcentered.loclCAT') x = ugi('Lmiddletilde') x.addKerning(left='Eth', right='L') x.addRecipe('L', '_part.tilde') x = ugi('Lslash') x.addKerning(left='H', right='L') x.addMetrics(left='Lslash', right='L') x.addRecipe('macroncomb decompose', 'L') x = ugi('Lsmall') x.addKerning(left='n', right='Lsmall') x = ugi('M') x.addKerning(left='H', right='H') x.addMetrics(left='H', right='H') x = ugi('Mhook') x.addKerning(left='H', right='H') x.addRecipe('M', '_part.hook flip_horizontal flip_vertical') x = ugi('Mturned') x.addKerning(left='U', right='H') x.addRecipe('u decompose', 'I decompose') x = ugi('N') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='H', right='N') x.addMetrics(left='H', right='N') x = ugi('Nacute') x.addKerning(left='H', right='N') x.addMetrics(left='N', right='N') x = ugi('Napostrophe') x.addMetrics(left='quoteright', right='N') x = ugi('Ncaron') x.addKerning(left='H', right='N') x.addMetrics(left='N', right='N') x = ugi('Ncommaaccent') x.addKerning(left='H', right='N') x.addMetrics(left='N', right='N') x = ugi('Nhookleft') x.addKerning(left='H', right='H') x.addRecipe('N', '_part.hook flip_horizontal flip_vertical') x = ugi('Nlongrightleg') x.addKerning(left='H', right='H') x.addRecipe('Shha-cy decompose') x = ugi('Nsmall') x.addKerning(left='n', right='u') x = ugi('Ntilde') x.addKerning(left='H', right='N') x.addMetrics(left='N', right='N') x = ugi('O') x.addAnchor('#center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('#topleft', position_x=xpos.outline_left, position_y=ypos.capHeight) x.addAnchor('#topright', position_x=xpos.outline_right, position_y=ypos.capHeight) x.addAnchor('bottom', position_x=xpos.apex_bottom, position_y=ypos.base_line) x.addAnchor('center', suppress_auto=True) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.capHeight) x.addAnchor('topleft', suppress_auto=True) x.addAnchor('topright', suppress_auto=True) x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('OE') x.addKerning(left='O', right='E') x.addMetrics(left='O', right='E') x.addRecipe('O decompose', 'E') x = ugi('OEsmall') x.addKerning(left='o', right='OEsmall') x = ugi('Oacute') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Obreve') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ocaron') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ocenteredtilde') x.addKerning(left='O', right='O') x.addRecipe('Obarred-cy') x = ugi('Ocircumflex') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ocircumflexacute') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x.addRecipe('O', 'circumflexcomb_acutecomb') x = ugi('Ocircumflexdotbelow') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x.addRecipe('O', 'circumflexcomb', 'dotbelowcomb') x = ugi('Ocircumflexgrave') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x.addRecipe('O', 'circumflexcomb_gravecomb') x = ugi('Ocircumflexhookabove') x.addKerning(left='O', right='O') x.addMetrics(left='O', width='O') x.addRecipe('O', 'circumflexcomb_hookabovecomb') x = ugi('Ocircumflextilde') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x.addRecipe('O', 'circumflexcomb_tildecomb') x = ugi('Odieresis') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Odotbelow') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ograve') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ohookabove') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Ohorn') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x = ugi('Ohornacute') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x.addRecipe('Ohorn', 'acutecomb') x.addRecipe('Ohorn', 'acutecomb.case') x = ugi('Ohorndotbelow') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x.addRecipe('Ohorn', 'dotbelowcomb') x.addRecipe('Ohorn', 'dotbelowcomb.case') x = ugi('Ohorngrave') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x.addRecipe('Ohorn', 'gravecomb') x.addRecipe('Ohorn', 'gravecomb.case') x = ugi('Ohornhookabove') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x.addRecipe('Ohorn', 'hookabovecomb') x.addRecipe('Ohorn', 'hookabovecomb.case') x = ugi('Ohorntilde') x.addKerning(left='O', right='Ohorn') x.addMetrics(left='O', right='Ohorn') x.addRecipe('Ohorn', 'tildecomb') x.addRecipe('Ohorn', 'tildecomb.case') x = ugi('Ohungarumlaut') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Omacron') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Oopen') x.addKerning(left='Oopen', right='O') x.addRecipe('C flip_horizontal flip_vertical') x = ugi('Oslash') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x.addRecipe('O', 'slash decompose') x = ugi('Oslashacute') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Otilde') x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('P') x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addKerning(left='H', right='P') x.addMetrics(left='H', right='P') x = ugi('Phook') x.addKerning(left='Bhook', right='P') x.addRecipe('P', '_part.Hookleft') x = ugi('Q') x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.capHeight) x.addKerning(left='O', right='O') x.addMetrics(left='O', right='O') x = ugi('Qhooktail') x.addKerning(left='O', right='H') x.addRecipe('Alpha-latin', '_part.Hook flip_vertical') x = ugi('R') x.addKerning(left='H', right='R') x.addMetrics(left='H', right='R') x = ugi('Racute') x.addKerning(left='H', right='R') x.addMetrics(left='R', right='R') x = ugi('Rcaron') x.addKerning(left='H', right='R') x.addMetrics(left='R', right='R') x = ugi('Rcommaaccent') x.addKerning(left='H', right='R') x.addMetrics(left='R', right='R') x = ugi('Rsmall') x.addKerning(left='n', right='Rsmall') x = ugi('Rsmallinverted') x.addKerning(left='n', right='Rsmallinverted') x.addRecipe('Rsmall') x = ugi('Rtail') x.addKerning(left='H', right='R') x.addRecipe('R', '_part.Hook flip_vertical') x = ugi('S') x.addAnchor('#center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('bottom', position_x=xpos.apex_bottom, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.capHeight) x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('Sacute') x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('Scaron') x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('Scedilla') x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('Schwa') x.addKerning(left='O', right='O') x.addMetrics(left='Schwa', right='O') x.addRecipe('G decompose flip_horizontal') x.addRecipe('O decompose', 'two decompose', 'H decompose', italic=True) x.addRecipe('Schwa-cy') x.addRecipe('schwa decompose') x = ugi('Scircumflex') x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('Scommaaccent') x.addKerning(left='S', right='S') x.addMetrics(left='S', right='S') x = ugi('T') x.addAnchor('#center', position_x=xpos.stem_bottom_center, position_y=ypos.outline_middle) x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center) x.addKerning(left='T', right='T') x.addMetrics(left='T', right='T') x.addRecipe('_part.stem', '_part.bar') x = ugi('Tbar') x.addKerning(left='T', right='F') x.addMetrics(left='T', right='T') x.addRecipe('T', '_part.bar') x = ugi('Tcaron') x.addKerning(left='T', right='T') x.addMetrics(left='T', right='T') x = ugi('Tcedilla') x.addKerning(left='T', right='T') x.addMetrics(left='T', right='T') x.addRecipe('T', 'cedillacomb') x = ugi('Tcommaaccent') x.addKerning(left='T', right='T') x.addMetrics(left='T', right='T') x = ugi('Thook') x.addKerning(left='Bhook', right='T') x.addRecipe('T decompose', '_part.Hookleft decompose') x = ugi('Thorn') x.addKerning(left='H', right='Thorn') x.addMetrics(left='H', right='Thorn') x.addRecipe('P decompose', 'I decompose') x = ugi('Tretroflexhook') x.addKerning(left='T', right='T') x.addRecipe('T', '_part.Hook flip_vertical') x = ugi('Tturned') x.addKerning(left='Tturned', right='L') x.addRecipe('T flip_vertical flip_horizontal') x = ugi('U') x.addAnchor('bottom', position_x=xpos.apex_bottom, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Uacute') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Ubar') x.addKerning(left='U', right='U') x.addRecipe('U', '_part.bar') x = ugi('Ubreve') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Ucaron') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Ucircumflex') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udieresis') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udieresisacute') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udieresiscaron') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udieresisgrave') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udieresismacron') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Udotbelow') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Ugrave') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Uhookabove') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Uhorn') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x = ugi('Uhornacute') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x.addRecipe('Uhorn', 'acutecomb') x.addRecipe('Uhorn', 'acutecomb.case') x = ugi('Uhorndotbelow') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x.addRecipe('Uhorn', 'dotbelowcomb') x.addRecipe('Uhorn', 'dotbelowcomb.case') x = ugi('Uhorngrave') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x.addRecipe('Uhorn', 'gravecomb') x.addRecipe('Uhorn', 'gravecomb.case') x = ugi('Uhornhookabove') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x.addRecipe('Uhorn', 'hookabovecomb') x.addRecipe('Uhorn', 'hookabovecomb.case') x = ugi('Uhorntilde') x.addKerning(left='U', right='Uhorn') x.addMetrics(left='U', right='Uhorn') x.addRecipe('Uhorn', 'tildecomb') x.addRecipe('Uhorn', 'tildecomb.case') x = ugi('Uhungarumlaut') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Umacron') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Uogonek') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Upsilonafrican') x.addKerning(left='O', right='O') x.addRecipe('Omega flip_vertical flip_horizontal') x = ugi('Uring') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('Usmall') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='=|u') x.addRecipe('u decompose') x = ugi('Utilde') x.addKerning(left='U', right='U') x.addMetrics(left='U', right='U') x = ugi('V') x.addKerning(left='V', right='V') x.addMetrics(left='V', right='V') x = ugi('Vhook') x.addKerning(left='U', right='U') x.addRecipe('vhook decompose', 'U decompose') x = ugi('Vturned') x.addKerning(left='A', right='A') x.addRecipe('V flip_vertical flip_horizontal') x = ugi('W') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='V', right='V') x.addMetrics(left='V', right='V') x = ugi('Wacute') x.addKerning(left='V', right='V') x.addMetrics(left='W', right='W') x = ugi('Wcircumflex') x.addKerning(left='V', right='V') x.addMetrics(left='W', right='W') x = ugi('Wdieresis') x.addKerning(left='V', right='V') x.addMetrics(left='W', right='W') x = ugi('Wgrave') x.addKerning(left='V', right='V') x.addMetrics(left='W', right='W') x = ugi('Whook') x.addKerning(left='V', right='V') x.addRecipe('W decompose', 'Khook decompose') x = ugi('X') x.addKerning(left='X', right='K') x.addMetrics(left='=|X', right='=K*1.05') x = ugi('Y') x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.capHeight) x.addKerning(left='Y', right='Y') x.addMetrics(left='V', right='V') x = ugi('Yacute') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Ycircumflex') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Ydieresis') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Ydotbelow') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Ygrave') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Yhookabove') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Ysmall') x.addKerning(left='v', right='v') x = ugi('Ytilde') x.addKerning(left='Y', right='Y') x.addMetrics(left='Y', right='Y') x = ugi('Z') x.addKerning(left='Z', right='Z') x.addMetrics(left='Z', right='Z') x = ugi('Zacute') x.addKerning(left='Z', right='Z') x.addMetrics(left='Z', right='Z') x = ugi('Zcaron') x.addKerning(left='Z', right='Z') x.addMetrics(left='Z', right='Z') x = ugi('Zdotaccent') x.addKerning(left='Z', right='Z') x.addMetrics(left='Z', right='Z') x = ugi('Zstroke') x.addMetrics(left='Z', right='Z') # # -------------------------------- # # Lowercase # # -------------------------------- # x = ugi('a') x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addKerning(left='a', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', right='a', italic_left='o', italic_right='u') x = ugi('aacute') x.addKerning(left='a', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('abreve') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('abreveacute') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'brevecomb_acutecomb') x = ugi('abrevedotbelow') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'brevecomb', 'dotbelowcomb') x = ugi('abrevegrave') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'brevecomb_gravecomb') x = ugi('abrevehookabove') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'brevecomb_hookabovecomb') x = ugi('abrevetilde') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'brevecomb_tildecomb') x = ugi('acaron') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('acircumflex') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('acircumflexacute') x.addKerning(left='abreve', right=None, italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'circumflexcomb_acutecomb') x = ugi('acircumflexdotbelow') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'circumflexcomb', 'dotbelowcomb') x = ugi('acircumflexgrave') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'circumflexcomb_gravecomb') x = ugi('acircumflexhookabove') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'circumflexcomb_hookabovecomb') x = ugi('acircumflextilde') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x.addRecipe('a', 'circumflexcomb_tildecomb') x = ugi('acutegraveacutecomb') x.addMetrics(left='=50', right='=50') x.addRecipe('graveacutegravecomb flip_horizontal') x = ugi('acutemacroncomb') x.addMetrics(left='=50', right='=50') x.addRecipe('macrongravecomb flip_horizontal') x = ugi('adieresis') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('adotbelow') x.addKerning(left='a', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('ae') x.addKerning(left='a', right='e', italic_left='o') x.addMetrics(left='a', right='e', italic_left='o') x.addRecipe('a decompose', 'e decompose') x = ugi('aeacute') x.addKerning(left='a', right='e', italic_left='o') x.addMetrics(left='ae', width='ae', italic_left='o') x = ugi('agrave') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('ahookabove') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('alpha') x.addKerning(left='o', right='u') x = ugi('alpha-latin') x.addRecipe('alpha') x = ugi('alphaturned') x.addKerning(left='n', right='o') x = ugi('alphaturned-latin') x.addRecipe('alpha') x = ugi('amacron') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('aogonek') x.addKerning(left='a', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('aring') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('aringacute') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('atilde') x.addKerning(left='abreve', right='a', italic_left='o', italic_right='u') x.addMetrics(left='a', width='a') x = ugi('aturned') x.addKerning(left='u', right='e') x.addRecipe('a flip_vertical') x = ugi('b') x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.ascender) x.addKerning(left='b', right='o') x.addMetrics(left='b', right='o') x = ugi('bhook') x.addKerning(left='b', right='o') x = ugi('bilabialclick') x.addKerning(left='bilabialclick', right='O') x.addRecipe('O', 'dotaccentcomb') x = ugi('brevebelowcomb') x.addMetrics(left='brevecomb', right='brevecomb') x = ugi('bridgebelowcomb') x.addRecipe('minusbelowcomb decompose') x = ugi('bridgeinvertedbelowcomb') x.addRecipe('bridgebelowcomb flip_vertical') x = ugi('c') x.addAnchor('bottom', position_x=xpos.apex_bottom) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.xHeight) x.addKerning(left='o', right='c') x.addMetrics(left='o', right='c') x = ugi('cacute') x.addKerning(left='o', right='c') x.addMetrics(left='c', width='c') x = ugi('ccaron') x.addKerning(left='o', right='c') x.addMetrics(left='c', width='c') x = ugi('ccedilla') x.addKerning(left='o', right='c') x.addMetrics(left='c', width='c') x = ugi('ccircumflex') x.addKerning(left='o', right='c') x.addMetrics(left='c', width='c') x = ugi('ccurl') x.addKerning(left='o', right='c') x = ugi('cdotaccent') x.addKerning(left='o', right='c') x.addMetrics(left='c', width='c') x = ugi('chook') x.addKerning(left='o', right='dhook') x.addMetrics(left='c', right='dhook') x.addRecipe('c', '_part.hook') x = ugi('clickalveolar') x.addKerning(left='clickalveolar', right='clickalveolar') x.addRecipe('clickdental', '_part.bar', '_part.bar') x = ugi('clickdental') x.addKerning(left='clickdental', right='clickdental') x.addRecipe('bar decompose') x = ugi('clicklateral') x.addKerning(left='clickdental', right='clickdental') x.addRecipe('clickdental', 'clickdental') x = ugi('clickretroflex') x.addKerning(left='h', right='d') x.addRecipe('exclam') x = ugi('closeup') x.addMetrics(left='undertie', right='undertie') x.addRecipe('undertie', 'breveinverteddoublecomb') x = ugi('colontriangularhalfmod') x.addRecipe('periodcentered decompose') x = ugi('colontriangularmod') x.addRecipe('colontriangularhalfmod', 'colontriangularhalfmod flip_vertical') x = ugi('d') x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.ascender) x.addAnchor('topright', position_x=xpos.stem_top_right, position_y=ypos.ascender) x.addKerning(left='o', right='d') x.addMetrics(left='d', right='d') x = ugi('dblarchinvertedbelowcomb') x.addMetrics(left='seagullbelowcomb', right='seagullbelowcomb') x.addRecipe('seagullbelowcomb flip_vertical flip_horizontal') x = ugi('dblverticalbar') x.addRecipe('bar', 'bar') x = ugi('dcaron') x.addKerning(left='o', right='dcaron') x.addMetrics(left='d', width='d') x = ugi('dcroat') x.addKerning(left='o', right='d') x.addMetrics(left='d', right='d') x.addRecipe('d', '_part.bar') x.addRecipe('d', 'macroncomb decompose') x = ugi('dezh') x.addKerning(left='o', right='ezh') x.addRecipe('d', 'ezh') x = ugi('dhook') x.addKerning(left='o', right='dhook') x.addMetrics(left='d') x.addRecipe('d decompose', '_part.hook') x = ugi('dhookandtail') x.addKerning(left='o', right='dhook') x.addMetrics(left='d', right='dhook') x.addRecipe('dhook', '_part.hook') x = ugi('downtackbelowcomb') x.addRecipe('uptackbelowcomb flip_vertical flip_horizontal') x = ugi('downtackmod') x.addRecipe('uptackmod flip_vertical') x = ugi('dtail') x.addKerning(left='o', right='dtail') x.addRecipe('d', '_part.hook') x = ugi('dzaltone') x.addKerning(left='o', right='z') x.addMetrics(left='d', right='z') x.addRecipe('d', 'z') x = ugi('dzcurl') x.addKerning(left='d', right='zcurl') x.addMetrics(left='d', right='zcurl') x.addRecipe('d', 'zcurl') x = ugi('e') x.addAnchor('bottom', position_x=xpos.apex_bottom) x.addAnchor('ogonek', position_x=xpos.width_75, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.apex_top) x.addKerning(left='o', right='e') x.addMetrics(left='o', right='e') x = ugi('eacute') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('ebreve') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('ecaron') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('ecircumflex') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('ecircumflexacute') x.addKerning(left='o', right=None) x.addMetrics(left='e', width='e') x.addRecipe('e', 'circumflexcomb_acutecomb') x = ugi('ecircumflexdotbelow') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x.addRecipe('e', 'circumflexcomb', 'dotbelowcomb') x = ugi('ecircumflexgrave') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x.addRecipe('e', 'circumflexcomb_gravecomb') x = ugi('ecircumflexhookabove') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x.addRecipe('e', 'circumflexcomb_hookabovecomb') x = ugi('ecircumflextilde') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x.addRecipe('e', 'circumflexcomb_tildecomb') x = ugi('edieresis') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('edotaccent') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('edotbelow') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('egrave') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('ehookabove') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('emacron') x.addKerning(left='o', right='e') x.addMetrics(left='e', width='e') x = ugi('eng') x.addKerning(left='n', right='j') x.addMetrics(left='n', right='j') x.addRecipe('jdotless decompose', 'n decompose') x = ugi('eogonek') x.addKerning(left='o', right='e') x.addMetrics(left='e', right='e') x = ugi('eopen') x.addKerning(left='s', right='c') x.addRecipe('ze-cy decompose') x = ugi('eopenreversed') x.addKerning(left='eopenreversed', right='Bsmall') x.addRecipe('ze-cy') x = ugi('eopenreversedclosed') x.addKerning(left='o', right='Bsmall') x.addRecipe('ze-cy decompose') x = ugi('eopenreversedhook') x.addKerning(left='eopenreversed', right='eopenreversedhook') x.addRecipe('eopenreversed', '_part.hook') x = ugi('ereversed') x.addKerning(left='o', right='o') x.addRecipe('e decompose') x = ugi('esh') x.addKerning(left='j', right='dhook') x.addMetrics(right='dhook') x.addRecipe('f decompose') x = ugi('eth') x.addKerning(left='eth', right='eth') x.addMetrics(left='eth', right='eth') x = ugi('etilde') x.addKerning(left='o', right='e') x.addMetrics(left='e', right='e') x = ugi('ezh') x.addKerning(left='ezh', right='ezh') x = ugi('f') x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.ascender) x.addKerning(left='f', right='f') x.addMetrics(left='f', right='f') x = ugi('f_f') x.addKerning(left='f', right='f') x.addRecipe('f', 'f') x = ugi('f_f_i') x.addKerning(left='f', right='i') x.addRecipe('f', 'f', 'i') x = ugi('f_f_l') x.addKerning(left='f', right='d') x.addRecipe('f', 'f', 'l') x = ugi('f_i') x.addKerning(left='f', right='i') x.addRecipe('fi') x = ugi('f_l') x.addKerning(left='f', right='d') x.addRecipe('fl') x = ugi('fi') x.addKerning(left='f', right='i') x.addRecipe('f_i') x = ugi('fl') x.addKerning(left='f', right='d') x.addRecipe('f_l') x = ugi('g') x.addKerning(left='g', right='g') x.addMetrics(left='g', right='g') x = ugi('gacute') x.addKerning(left='g', right='g') x = ugi('gamma') x.addKerning(left='v', right='v') x = ugi('gamma-latin') x.addRecipe('v decompose') x = ugi('gbreve') x.addKerning(left='g', right='g') x.addMetrics(left='g', right='g') x = ugi('gcircumflex') x.addKerning(left='g', right='g') x.addMetrics(left='g', right='g') x = ugi('gcommaaccent') x.addKerning(left='g', right='g') x.addMetrics(left='g', right='g') x.addRecipe('g', 'commaturnedabovecomb') x = ugi('gdotaccent') x.addKerning(left='g', right='g') x.addMetrics(left='g', right='g') x = ugi('germandbls') x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.ascender) x.addKerning(left='f', right='germandbls') x.addMetrics(left='f', right='germandbls') x.addRecipe('f decompose', 's decompose') x = ugi('ghook') x.addKerning(left='o', right='dhook') x.addMetrics(left='g', right='dhook') x.addRecipe('gsingle', '_part.hook') x = ugi('glottalstop') x.addKerning(left='glottalstop', right='glottalstop') x.addRecipe('question decompose') x = ugi('glottalstopreversed') x.addKerning(left='glottalstopreversed', right='glottalstopreversed') x.addRecipe('glottalstop flip_horizontal') x = ugi('glottalstopsmall') x.addRecipe('glottalstop decompose') x = ugi('glottalstopstroke') x.addKerning(left='glottalstopstroke', right='glottalstopstroke') x.addRecipe('glottalstop', '_part.bar') x = ugi('glottalstopstrokereversed') x.addKerning(left='glottalstopstrokereversed', right='glottalstopstrokereversed') x.addRecipe('glottalstopreversed', '_part.bar') x = ugi('graveacutegravecomb') x.addMetrics(left='=50', right='=50') x.addRecipe('caron decompose', 'circumflex decompose') x = ugi('gravemacroncomb') x.addMetrics(left='=50', right='=50') x.addRecipe('macronacutecomb flip_horizontal') x = ugi('gsingle') x.addKerning(left='o', right='u') x.addRecipe('q decompose', 'y decompose') x = ugi('h') x.addKerning(left='h', right='n') x.addMetrics(left='h', right='n') x = ugi('hbar') x.addKerning(left='h', right='n') x.addMetrics(width='h', right='h') x.addRecipe('h', '_part.bar') x.addRecipe('h', 'macroncomb decompose') x = ugi('hcircumflex') x.addKerning(left='h', right='n') x.addMetrics(width='h', right='h') x = ugi('henghook') x.addKerning(left='j', right='n') x.addRecipe('hhook', '_part.hook') x = ugi('hhook') x.addKerning(left='h', right='n') x.addRecipe('n', '_part.hook') x = ugi('hturned') x.addKerning(left='u', right='q') x.addRecipe('h flip_horizontal flip_vertical') x = ugi('hv') x.addKerning(left='h', right='vhook') x.addMetrics(left='h', right='vhook') x.addRecipe('h decompose', 'vhook decompose') x = ugi('i') x.addAnchor('bottom', position_x=xpos.stem_bottom_center) x.addAnchor('ogonek', position_x='xpos.stem_bottom_right') x.addKerning(left='i', right='i') x.addMetrics(left='i', right='i') x = ugi('iacute') x.addKerning(left='i', right='i') x.addMetrics(left='i', right='i') x = ugi('ibreve') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('icaron') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('icircumflex') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('idieresis') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('idotaccent') x.addKerning(left='i', right='i') x.addMetrics(left='i', right='i') x = ugi('idotbelow') x.addKerning(left='i', right='i') x.addMetrics(left='i', width='i') x = ugi('idotless') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('ogonek', suppress_auto=True) x.addAnchor('top', position_x=xpos.outline_center) x.addKerning(left='n', right='u') x.addMetrics(left='i', width='i') x.addRecipe('i decompose') x = ugi('igrave') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('ihookabove') x.addKerning(left='i', right='i') x.addMetrics(left='i', width='i') x = ugi('ij') x.addKerning(left='i', right='j') x.addMetrics(left='i', right='j') x = ugi('imacron') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('iogonek') x.addKerning(left='i', right='i') x.addMetrics(width='i') x.addRecipe('i', 'ogonekcomb') x = ugi('istroke') x.addKerning(left='i', right='i') x.addRecipe('i', '_part.bar') x = ugi('itilde') x.addKerning(left='idieresis', right='i') x.addMetrics(left='idieresis', right='i') x = ugi('j') x.addKerning(left='j', right='j') x.addMetrics(left='j', right='j') x = ugi('jcircumflex') x.addKerning(left='jcircumflex', right='j') x.addMetrics(left='jcircumflex', right='j') x = ugi('jcrossedtail') x.addKerning(left='jcrossedtail', right='jcrossedtail') x.addRecipe('j decompose') x = ugi('jdotless') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.outline_bottom) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.xHeight) x.addKerning(left='j', right='j') x.addKerning(left='p', right='q') x.addMetrics(left='j', width='j') x.addRecipe('j decompose') x = ugi('jdotlessstroke') x.addKerning(left='jdotlessstroke', right='istroke') x.addRecipe('jdotless', '_part.bar') x = ugi('jdotlessstrokehook') x.addKerning(left='jdotlessstroke', right='dhook') x.addRecipe('jdotlessstroke', '_part.hook') x = ugi('k') x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.ascender) x.addKerning(left='h', right='k') x.addMetrics(left='h', right='k') x = ugi('kcommaaccent') x.addKerning(left='h', right='k') x.addMetrics(left='k', right='k') x = ugi('kgreenlandic') x.addKerning(left='n', right='k') x.addMetrics(left='n', right='k') x.addRecipe('k decompose') x = ugi('khook') x.addKerning(left='h', right='k') x.addRecipe('kgreenlandic', '_part.hook') x = ugi('kturned') x.addKerning(left='x', right='q') x.addRecipe('k flip_horizontal flip_vertical') x = ugi('l') x.addAnchor('#dot', position_x='xpos.RSB', position_y=ypos.outline_middle) x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center) x.addAnchor('topright', position_x=xpos.stem_top_right) x.addKerning(left='h', right='d') x.addMetrics(left='h', right='d') x = ugi('lacute') x.addKerning(left='h', right='d') x.addMetrics(left='l', right='l') x = ugi('lambdastroke') x.addKerning(left='vturned', right='vturned') x.addMetrics(left='lambda', right='lambda') x.addRecipe('lambda', 'eth decompose') x = ugi('lbar') x.addKerning(left='lslash', right='lslash') x.addMetrics(left='lslash', right='lslash') x.addRecipe('l', '_part.bar') x = ugi('lbelt') x.addKerning(left='lslash', right='lslash') x.addRecipe('l', 'zcurl decompose') x = ugi('lcaron') x.addKerning(left='h', right='dcaron') x.addMetrics(left='l', right='dcaron') x = ugi('lcommaaccent') x.addKerning(left='h', right='d') x.addMetrics(left='l', right='l') x = ugi('ldot') x.addKerning(left='h', right='ldot') x.addMetrics(left='l', right='ldot') x.addRecipe('l', 'dotaccent') x.addRecipe('l', 'periodcentered.loclCAT') x = ugi('leftangleabovecomb') x.addRecipe('lefttackbelowcomb decompose') x = ugi('lefttackbelowcomb') x.addRecipe('uptackmod decompose') x = ugi('lezh') x.addKerning(left='h', right='ezh') x.addRecipe('l', 'ezh') x = ugi('lhookretroflex') x.addKerning(left='h', right='dtail') x.addRecipe('l', '_part.hook') x = ugi('lmiddletilde') x.addKerning(left='lslash', right='lslash') x.addRecipe('l', '_part.tilde') x = ugi('longs') x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.ascender) x.addKerning(left='f', right='f') x.addMetrics(left='f', right='f') x.addRecipe('f decompose') x = ugi('lslash') x.addKerning(left='lslash', right='lslash') x.addMetrics(left='lslash', right='lslash') x.addRecipe('macroncomb decompose', 'l') x = ugi('m') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.xHeight) x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('macronacutecomb') x.addMetrics(left='=50', right='=50') x.addRecipe('macron decompose', 'acute decompose') x = ugi('macronbelowcomb') x.addMetrics(left='macroncomb', right='macroncomb') x = ugi('macrongravecomb') x.addMetrics(left='=50', right='=50') x.addRecipe('macroncomb decompose', 'gravecomb decompose') x = ugi('mhook') x.addKerning(left='n', right='j') x.addRecipe('m', '_part.hook flip_horizontal flip_vertical') x = ugi('minusbelowcomb') x.addRecipe('lefttackbelowcomb', 'lefttackbelowcomb flip_horizontal') x = ugi('minusmod') x.addMetrics(left='plus', right='plus') x.addRecipe('macron') x = ugi('mlonglegturned') x.addKerning(left='u', right='q') x.addRecipe('m flip_horizontal flip_vertical', '_part.stem flip_horizontal flip_vertical') x = ugi('mpalatalhook') x.addKerning(left='n', right='n') x.addRecipe('m', '_part.hook flip_vertical flip_horizontal') x = ugi('mturned') x.addKerning(left='u', right='u') x.addRecipe('m flip_horizontal flip_vertical') x = ugi('n') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.xHeight) x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('n.subs') x.addRecipe('nmod') x = ugi('nacute') x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('napostrophe') x.addKerning(left=None, right='n') x.addMetrics(left=None, right='n') x.addRecipe('quoteright', 'n') x = ugi('ncaron') x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('ncommaaccent') x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('nhookleft') x.addKerning(left='j', right='n') x.addRecipe('n', '_part.hook flip_horizontal flip_vertical') x = ugi('nhookretroflex') x.addKerning(left='n', right='nhookretroflex') x.addRecipe('n', '_part.hook flip_vertical') x = ugi('nmod') x.addRecipe('n.sups') x = ugi('ntilde') x.addKerning(left='n', right='n') x.addMetrics(left='n', right='n') x = ugi('o') x.addKerning(left='o', right='o') x.addMetrics(left='o', right='o') x = ugi('oacute') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('obarred') x.addKerning(left='o', right='o') x.addRecipe('obarred-cy') x = ugi('obreve') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('ocaron') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('ocircumflex') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('ocircumflexacute') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x.addRecipe('o', 'circumflexcomb_acutecomb') x = ugi('ocircumflexdotbelow') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x.addRecipe('o', 'circumflexcomb', 'dotbelowcomb') x = ugi('ocircumflexgrave') x.addKerning(left=None, right='o') x.addMetrics(right='o', width='o') x.addRecipe('o', 'circumflexcomb_gravecomb') x = ugi('ocircumflexhookabove') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x.addRecipe('o', 'circumflexcomb_hookabovecomb') x = ugi('ocircumflextilde') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x.addRecipe('o', 'circumflexcomb_tildecomb') x = ugi('odieresis') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('odotbelow') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('oe') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.xHeight) x.addKerning(left='o', right='e') x.addMetrics(left='o', right='e') x.addRecipe('o', 'e') x = ugi('ograve') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('ohookabove') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('ohorn') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x = ugi('ohornacute') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x.addRecipe('ohorn', 'acutecomb') x = ugi('ohorndotbelow') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x.addRecipe('ohorn', 'dotbelowcomb') x = ugi('ohorngrave') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x.addRecipe('ohorn', 'gravecomb') x = ugi('ohornhookabove') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x.addRecipe('ohorn', 'hookabovecomb') x = ugi('ohorntilde') x.addKerning(left='o', right='ohorn') x.addMetrics(left='o', width='ohorn') x.addRecipe('ohorn', 'tildecomb') x = ugi('ohungarumlaut') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('omacron') x.addKerning(left='o', right='o') x.addMetrics(left='o', width='o') x = ugi('oopen') x.addKerning(left='eopenreversed', right='o') x.addRecipe('c flip_horizontal flip_vertical') x = ugi('oslash') x.addKerning(left='o', right='o') x.addMetrics(left='o', right='o') x.addRecipe('o', 'slash decompose') x = ugi('oslashacute') x.addKerning(left='o', right='o') x.addMetrics(left='oslash', right='oslash') x = ugi('otilde') x.addKerning(left='o', right='o') x.addMetrics(left='o', right='o') x = ugi('p') x.addKerning(left='p', right='o') x.addMetrics(left='p', right='o') x = ugi('phi') x.addKerning(left='o', right='o') x = ugi('phi-latin') x.addRecipe('phi') x = ugi('plusbelowcomb') x.addRecipe('lefttackbelowcomb decompose', 'lefttackbelowcomb flip_vertical') x = ugi('plusmod') x.addMetrics(left='plus', right='plus') x.addRecipe('plusbelowcomb') x = ugi('q') x.addKerning(left='o', right='q') x.addMetrics(left='o', right='q') x = ugi('r') x.addAnchor('bottom', position_x=xpos.stem_bottom_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.xHeight) x.addKerning(left='n', right='r') x.addMetrics(left='n', right='r') x = ugi('racute') x.addKerning(left='n', right='r') x.addMetrics(left='r', right='r') x = ugi('ramshorn') x.addKerning(left='ramshorn', right='ramshorn') x.addRecipe('gamma-latin decompose') x = ugi('rcaron') x.addKerning(left='rcaron', right='r') x.addMetrics(left='r', right='r') x = ugi('rcommaaccent') x.addKerning(left='n', right='r') x.addMetrics(left='r', right='r') x = ugi('rfishhook') x.addKerning(left='s', right='r') x.addRecipe('_part.stem', '_part.hook') x = ugi('rhook') x.addKerning(left='n', right='r') x.addRecipe('r', '_part.hook flip_vertical') x = ugi('rhookturned') x.addKerning(left='rturned', right='rhookturned') x.addRecipe('rturned', '_part.hook flip_vertical') x = ugi('rhotichookmod') x.addRecipe('eopenreversedhook decompose') x = ugi('righttackbelowcomb') x.addRecipe('lefttackbelowcomb flip_horizontal') x = ugi('ringhalfleftbelowcomb') x.addRecipe('ringhalfrightbelowcomb flip_horizontal') x = ugi('ringhalfrightbelowcomb') x.addRecipe('brevecomb') x = ugi('rlonglegturned') x.addKerning(left='rturned', right='d') x.addRecipe('rturned', '_part.stem flip_horizontal flip_vertical') x = ugi('rturned') x.addKerning(left='rturned', right='u') x.addRecipe('r flip_horizontal flip_vertical') x = ugi('s') x.addAnchor('bottom', position_x=xpos.apex_bottom) x.addAnchor('top', position_x=xpos.apex_top) x.addKerning(left='s', right='s') x.addMetrics(left='s', right='s') x = ugi('s_t') x.addRecipe('s', 't') x = ugi('sacute') x.addKerning(left='s', right='s') x.addMetrics(left='s', right='s') x = ugi('scaron') x.addKerning(left='s', right='s') x.addMetrics(left='scaron', right='s') x = ugi('scedilla') x.addKerning(left='s', right='s') x.addMetrics(left='s', right='s') x = ugi('schwa') x.addKerning(left='o', right='o') x.addMetrics(left='o', right='o') x.addRecipe('e flip_vertical flip_horizontal') x = ugi('schwahook') x.addKerning(left='o', right='eopenreversedhook') x.addRecipe('schwa decompose', 'eopenreversedhook decompose') x = ugi('scircumflex') x.addKerning(left='s', right='s') x.addMetrics(left='s', right='s') x = ugi('scommaaccent') x.addKerning(left='s', right='s') x.addMetrics(left='s', right='s') x = ugi('seagullbelowcomb') x.addRecipe('brevecomb decompose', 'brevecomb decompose') x = ugi('shook') x.addKerning(left='s', right='s') x.addRecipe('s', '_part.hook flip_vertical') x = ugi('squarebelowcomb') x.addRecipe('bridgebelowcomb', 'bridgebelowcomb flip_vertical') x = ugi('t') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.outline_top) x.addAnchor('topright', position_x=xpos.stem_top_right, position_y=ypos.ascender) x.addKerning(left='t', right='t') x.addMetrics(left='t', right='t') x = ugi('tbar') x.addKerning(left='t', right='t') x.addMetrics(left='t', right='t') x.addRecipe('t', '_part.bar') x.addRecipe('t', 'macroncomb decompose') x = ugi('tcaron') x.addKerning(left='t', right='tcaron') x.addMetrics(left='t', right='t') x = ugi('tccurl') x.addKerning(left='t', right='c') x.addMetrics(left='t', right='ccurl') x.addRecipe('t decompose', 'ccurl decompose') x = ugi('tcedilla') x.addKerning(left='t', right='t') x.addMetrics(left='t', right='t') x.addRecipe('t', 'cedillacomb') x = ugi('tcommaaccent') x.addKerning(left='t', right='t') x.addMetrics(left='t', right='t') x = ugi('tesh') x.addKerning(left='t', right='dhook') x.addMetrics(left='t', right='esh') x.addRecipe('t', 'esh') x = ugi('thorn') x.addKerning(left='b', right='o') x.addMetrics(left='b', right='o') x.addRecipe('p decompose', 'l decompose') x = ugi('tildeoverlaycomb') x.addRecipe('asciitilde decompose', 'z') x = ugi('tonebarextrahighmod') x.addRecipe('plus decompose') x = ugi('tonebarextralowmod') x.addRecipe('tonebarextrahighmod flip_vertical') x = ugi('tonebarhighmod') x.addRecipe('tonebarextrahighmod decompose') x = ugi('tonebarlowmod') x.addRecipe('tonebarhighmod flip_vertical') x = ugi('tonebarmidmod') x.addRecipe('tonebarextrahighmod decompose') x = ugi('tretroflexhook') x.addKerning(left='t', right='tretroflexhook') x.addRecipe('t decompose') x = ugi('ts') x.addKerning(left='t', right='s') x.addMetrics(left='t', right='s') x.addRecipe('t', 's') x = ugi('u') x.addAnchor('bottom', position_x=xpos.outline_center) x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.xHeight) x.addAnchor('topright', position_x=xpos.stem_top_right, position_y=ypos.xHeight) x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('uacute') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('ubar') x.addKerning(left='istroke', right='istroke') x.addRecipe('u', '_part.bar') x = ugi('ubreve') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('ucaron') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('ucircumflex') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udieresis') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udieresisacute') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udieresiscaron') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udieresisgrave') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udieresismacron') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('udotbelow') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('ugrave') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('uhookabove') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('uhorn') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='ohorn') x = ugi('uhornacute') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='uhorn') x.addRecipe('uhorn', 'acutecomb') x = ugi('uhorndotbelow') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='uhorn') x.addRecipe('uhorn', 'dotbelowcomb') x = ugi('uhorngrave') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='uhorn') x.addRecipe('uhorn', 'gravecomb') x = ugi('uhornhookabove') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='uhorn') x.addRecipe('uhorn', 'hookabovecomb') x = ugi('uhorntilde') x.addKerning(left='u', right='ohorn') x.addMetrics(left='u', right='uhorn') x.addRecipe('uhorn', 'tildecomb') x = ugi('uhungarumlaut') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('umacron') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('undertie') x.addRecipe('parenleft') x = ugi('uni2C70') x.addKerning(left='H', right='O') x = ugi('uogonek') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('upsilon') x.addKerning(left='upsilon-latin', right='upsilon-latin') x = ugi('uptackbelowcomb') x.addRecipe('lefttackbelowcomb decompose') x = ugi('uptackmod') x.addMetrics(left='plus', right='plus') x.addRecipe('plus decompose') x = ugi('uring') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('utilde') x.addKerning(left='u', right='u') x.addMetrics(left='u', right='u') x = ugi('v') x.addKerning(left='v', right='v') x.addMetrics(left='v', right='v') x = ugi('verticallinebelowcomb') x.addRecipe('macroncomb decompose') x = ugi('verticallinelowmod') x.addRecipe('verticallinemod') x = ugi('verticallinemod') x.addRecipe('verticallinebelowcomb') x = ugi('vhook') x.addKerning(left='u', right='vhook') x.addRecipe('u decompose', '_part.hook flip_horizontal') x = ugi('vrighthook') x.addKerning(left='v', right='r') x.addMetrics(left='v', right='r') x.addRecipe('v decompose', '_part.hook decompose') x = ugi('vturned') x.addKerning(left='vturned', right='vturned') x.addRecipe('v flip_horizontal flip_vertical') x.addRecipe('v') x = ugi('w') x.addKerning(left='v', right='v') x.addMetrics(left='v', right='v') x = ugi('wacute') x.addKerning(left='v', right='v') x.addMetrics(left='w', right='w') x = ugi('wcircumflex') x.addKerning(left='v', right='v') x.addMetrics(left='w', right='w') x = ugi('wdieresis') x.addKerning(left='v', right='v') x.addMetrics(left='w', right='w') x = ugi('wgrave') x.addKerning(left='v', right='v') x.addMetrics(left='w', right='w') x = ugi('wturned') x.addKerning(left='vturned', right='vturned') x.addRecipe('w flip_horizontal flip_vertical') x = ugi('x') x.addKerning(left='x', right='k') x.addMetrics(left='x', right='x') x = ugi('xdotaccent') x.addMetrics(left='x', right='x') x = ugi('y') x.addAnchor('bottom', position_x=xpos.width_75, position_y=ypos.base_line) x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='v', right='v', italic_left='y', italic_right='y') x = ugi('yacute') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('ycircumflex') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('ydieresis') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('ydotbelow') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', width='y') x = ugi('ygrave') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('yhookabove') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('ytilde') x.addKerning(left='v', right='v', italic_left='y', italic_right='y') x.addMetrics(left='y', right='y', italic_left='y', italic_right='y') x = ugi('yturned') x.addKerning(left='vturned', right='vturned') x.addRecipe('y flip_horizontal flip_vertical') x = ugi('z') x.addKerning(left='z', right='z') x.addMetrics(left='z', right='z') x = ugi('zacute') x.addKerning(left='z', right='z') x.addMetrics(left='z', right='z') x = ugi('zcaron') x.addKerning(left='z', right='z') x.addMetrics(left='z', right='z') x = ugi('zcurl') x.addKerning(left='z', right='zcurl') x.addRecipe('z decompose', 'ccurl decompose') x = ugi('zdotaccent') x.addKerning(left='z', right='z') x.addMetrics(left='z', right='z') x = ugi('zretroflexhook') x.addKerning(left='z', right='rhookturned') x.addRecipe('z', '_part.hook flip_vertical') x = ugi('zstroke') x.addMetrics(left='z', right='z') x = ugi('a.sc') x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.smallcapHeight) x = ugi('ae.sc') x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.smallcapHeight) x.addRecipe('a.sc decompose', 'e.sc decompose') x.addBuildString('/A/AE/E/a.sc/ae.sc/e.sc') x = ugi('dcroat.sc') x.addRecipe('eth.sc') x = ugi('e.sc') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.base_line) x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x = ugi('eng.sc') x.addRecipe('jdotless decompose', 'n.sc decompose') x.addBuildString('/N/Eng/J/n.sc/eng.sc/j.sc') x = ugi('f.sc') x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x = ugi('g.salt.sc') x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.outline_top) x = ugi('g.sc') x.addAnchor('top', position_x=xpos.apex_top, position_y=ypos.smallcapHeight) x = ugi('germandbls.sc') x.addRecipe('s.sc', 's.sc') x = ugi('h.sc') x.addAnchor('#bottomright', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('#topleft', position_x=xpos.outline_left, position_y=ypos.smallcapHeight) x.addAnchor('#topright', position_x=xpos.outline_right, position_y=ypos.smallcapHeight) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x.addAnchor('topleft', suppress_auto=True) x = ugi('i.sc') x.addAnchor('ogonek', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('top', position_x=xpos.stem_top_center, position_y=ypos.smallcapHeight) x = ugi('k.sc') x.addAnchor('#bottomright', position_x=xpos.outline_right, position_y=ypos.base_line) x.addAnchor('#topleft', position_x=xpos.outline_left, position_y=ypos.smallcapHeight) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x.addAnchor('topleft', suppress_auto=True) x = ugi('napostrophe.sc') x.addMetrics(left='quoteright', right='n.sc') x = ugi('o.sc') x.addAnchor('#center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('center', suppress_auto=True) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x = ugi('oe.sc') x.addRecipe('o.sc decompose', 'e.sc') x.addBuildString('/O/OE/E/o.sc/oe.sc/e.sc') x = ugi('t.sc') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.outline_bottom) x.addAnchor('center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.outline_top) x = ugi('tcedilla.sc') x.addRecipe('t.sc', 'cedillacomb') x = ugi('thorn.sc') x.addRecipe('p.sc decompose', 'i.sc decompose') x.addBuildString('/P/Thorn/thorn.sc/p.sc') x = ugi('u.sc') x.addAnchor('bottom', position_x=xpos.outline_center, position_y=ypos.base_line) x.addAnchor('center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.outline_top) x = ugi('y.sc') x.addAnchor('#center', position_x=xpos.outline_center, position_y=ypos.outline_middle) x.addAnchor('top', position_x=xpos.outline_center, position_y=ypos.smallcapHeight) x.addAnchor('topleft', position_x=xpos.outline_left, position_y=ypos.smallcapHeight) # # -------------------------------- # # Accents Marks # # -------------------------------- # x = ugi('dotaccentcomb') x.addRecipe('dieresiscomb decompose') x = ugi('dieresisbelowcomb') x.addRecipe('dieresiscomb accent_bottom') x = ugi('brevebelowcomb') x.addRecipe('brevecomb accent_bottom') x = ugi('macronbelowcomb') x.addRecipe('macroncomb accent_bottom')

26.761031

90

0.683432

10,623

68,535

4.344535

0.043114

0.051309

0.166407

0.041255

0.856886

0.807267

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0.725841

0.674402

0.649983

0

0.00065

0.078952

68,535

2,560

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26.771484

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0

null

0

1

0

1

0

null

0

5

78e0db8d4e431aff800d3aa98bdef481cb8145b8

72

py

Python

python/cuXfilter/charts/core/__init__.py

AjayThorve/cuxfilter

537ff67de80439a43e0bad7373558f5e25dcb112

[ "Apache-2.0" ]

2

2019-03-06T02:10:05.000Z

2020-05-06T06:33:02.000Z

python/cuXfilter/charts/core/__init__.py

AjayThorve/cuxfilter

537ff67de80439a43e0bad7373558f5e25dcb112

[ "Apache-2.0" ]

null

python/cuXfilter/charts/core/__init__.py

AjayThorve/cuxfilter

537ff67de80439a43e0bad7373558f5e25dcb112

[ "Apache-2.0" ]

null

# from .core_chart import BaseChart from .core_widget import BaseWidget

24

35

0.833333

10

72

5.8

0.7

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0

0.125

72

2

36

0.920635

0.458333

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true

0

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null

0

1

0

1

0

5

60704c449624cf87e45044e11e5810c0b66d5d98

123

py

Python

coolPro/app/module2/print_funs.py

airwindow/Python-Standard-Project

f975350b8eb05466198ae7e548b7ad63837fbd36

[ "Apache-2.0" ]

null

coolPro/app/module2/print_funs.py

airwindow/Python-Standard-Project

f975350b8eb05466198ae7e548b7ad63837fbd36

[ "Apache-2.0" ]

null

coolPro/app/module2/print_funs.py

airwindow/Python-Standard-Project

f975350b8eb05466198ae7e548b7ad63837fbd36

[ "Apache-2.0" ]

null

def print_sth(s): print('print from module2: {}'.format(s)) if __name__ == "__main__": s = 'test in main' print_sth(s)

17.571429

42

0.650407

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123

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123

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17.571429

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null

1

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1

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null

0

1

0

5

6070e70603f7f80316ee8ceb1586ac237d30fa16

8,782

py

Python

cakechat/dialog_model/inference/tests/sampling.py

sketscripter/emotional-chatbot-cakechat

470df58a2206a0ea38b6bed53b20cbc63bd3de24

[ "Apache-2.0" ]

1,608

2018-01-31T15:22:29.000Z

2022-03-30T19:59:16.000Z

cakechat/dialog_model/inference/tests/sampling.py

GaelicThunder/cakechat

844507281b30d81b3fe3674895fe27826dba8438

[ "Apache-2.0" ]

60

2018-02-01T11:45:51.000Z

2019-11-13T10:35:59.000Z

cakechat/dialog_model/inference/tests/sampling.py

GaelicThunder/cakechat

844507281b30d81b3fe3674895fe27826dba8438

[ "Apache-2.0" ]

690

2018-01-31T17:57:19.000Z

2022-03-30T07:07:41.000Z

import os import sys import unittest import keras.backend as K import numpy as np from scipy.stats import binom sys.path.append( os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))) from cakechat.dialog_model.inference.candidates.sampling import TokenSampler from cakechat.config import REPETITION_PENALIZE_COEFFICIENT, RANDOM_SEED np.random.seed(seed=RANDOM_SEED) # Type I error rate: probability that a test will fail even though everything is OK # The lower the probability is the more inaccurate (in terms of Type II error) the test becomes. # This is independent probability for every test in the TestCase. _CONFIDENCE_LEVEL = 1e-6 # Number of samples for monte-carlo estimation or probabilities. # The bigger number of sample is, the more accurate tests become _SAMPLES_NUM = 10000 class TestSampling(unittest.TestCase): def test_sample_list(self): # Error rate is p(token1) + p(token2) = conf_level / 2 + conf_level / 2 = conf_level: probs = [_CONFIDENCE_LEVEL / 2, _CONFIDENCE_LEVEL / 2, 1 - _CONFIDENCE_LEVEL] token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) expected_token_ids = np.array([2]) actual_token_ids = token_sampler.sample(probs, sample_idx=0) self.assertEqual(expected_token_ids, actual_token_ids) def test_sample_ndarray(self): # Error rate is p(token1) + p(token2) = conf_level / 2 + conf_level / 2 = conf_level probs = np.array([_CONFIDENCE_LEVEL / 2, _CONFIDENCE_LEVEL / 2, 1 - _CONFIDENCE_LEVEL], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) expected_token_ids = np.array([2]) actual_token_ids = token_sampler.sample(probs, sample_idx=0) self.assertEqual(expected_token_ids, actual_token_ids) def test_sample_probs(self): probs = [0.3, 0.6, 0.1] token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) adjusted_confidence_level = _CONFIDENCE_LEVEL / len(probs) # bonferroni correction confidence_intervals = [binom.interval(1 - adjusted_confidence_level, _SAMPLES_NUM, p) for p in probs] est_probs_from, est_probs_to = zip(*confidence_intervals) samples = np.array([token_sampler.sample(probs, 0) for _ in range(_SAMPLES_NUM)]) counts = {val: np.sum(samples == val) for val in np.unique(samples)} for i, _ in enumerate(probs): self.assertLessEqual(counts[i], est_probs_to[i]) self.assertGreaterEqual(counts[i], est_probs_from[i]) def test_sample_with_zeros(self): probs = np.array([1.0, 0, 0], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) expected_token_ids = np.array([0]) actual_token_ids = token_sampler.sample(probs, sample_idx=0) self.assertEqual(expected_token_ids, actual_token_ids) def test_sample_banned_tokens(self): eps = _CONFIDENCE_LEVEL * 0.3 # Here we multiply the confidence level by 0.3 so that after removal of banned token and renormalization # the probability of an error remains equal to _CONFIDENCE_LEVEL value. probs = np.array([0.7, 0.3 - eps, eps], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[0], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) expected_token_ids = np.array([1]) actual_token_ids = token_sampler.sample(probs, sample_idx=0) self.assertEqual(expected_token_ids, actual_token_ids) def test_sample_banned_tokens_2(self): eps = 1e-6 probs = np.array([1.0 - eps, eps, 0], dtype=K.floatx()) token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[0], non_penalizable_tokens_ids=range(len(probs)), repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) # Token #1 has to be returned even though its probability is really small expected_token_ids = np.array([1]) actual_token_ids = token_sampler.sample(probs, sample_idx=0) self.assertEqual(expected_token_ids, actual_token_ids) def test_repetition_penalization(self): probs = [0.5, 0.5] actual_num_nonequal_pairs = 0 for _ in range(_SAMPLES_NUM): token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=[], repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) first_token = token_sampler.sample(probs, sample_idx=0) second_token = token_sampler.sample(probs, sample_idx=0) actual_num_nonequal_pairs += int(first_token != second_token) # P(first != second) = P(first=0, second=1) + P(first=1, second=0) = # = 0.5 * 0.5 * r / (0.5 + 0.5 * r) + 0.5 * 0.5 * r / (0.5 + 0.5 * r) = r / (1 + r) expected_nonequal_pair_rate = REPETITION_PENALIZE_COEFFICIENT / (1 + REPETITION_PENALIZE_COEFFICIENT) expected_nonequal_pair_rate_from, expected_nonequal_pair_rate_to = \ binom.interval(1 - _CONFIDENCE_LEVEL, _SAMPLES_NUM, expected_nonequal_pair_rate) self.assertLessEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_to) self.assertGreaterEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_from) def test_nonpenalizable_tokens(self): probs = [0.5, 0.5] actual_num_nonequal_pairs = 0 samples_generated = 0 while samples_generated < _SAMPLES_NUM: token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=[0], repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) first_token = token_sampler.sample(probs, sample_idx=0) if first_token == 0: samples_generated += 1 second_token = token_sampler.sample(probs, sample_idx=0) actual_num_nonequal_pairs += (first_token != second_token) # When we don't penalize for token#0, P(first != second | first=0) = P(second=1 | first=0) = 0.5 expected_nonequal_pair_rate = 0.5 expected_nonequal_pair_rate_from, expected_nonequal_pair_rate_to = binom.interval( 1 - _CONFIDENCE_LEVEL, _SAMPLES_NUM, expected_nonequal_pair_rate) self.assertLessEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_to) self.assertGreaterEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_from) def test_nonpenalizable_tokens_2(self): probs = [0.5, 0.5] actual_num_nonequal_pairs = 0 samples_generated = 0 while samples_generated < _SAMPLES_NUM: token_sampler = TokenSampler( batch_size=1, banned_tokens_ids=[], non_penalizable_tokens_ids=[1], repetition_penalization_coefficient=REPETITION_PENALIZE_COEFFICIENT) first_token = token_sampler.sample(probs, sample_idx=0) if first_token == 0: samples_generated += 1 second_token = token_sampler.sample(probs, sample_idx=0) actual_num_nonequal_pairs += (first_token != second_token) # When we penalize for token#0, P(first != second | first=0) = P(second=1 | first=0) = 0.5 * r / (0.5 + 0.5 * r) = r / (1 + r) expected_nonequal_pair_rate = REPETITION_PENALIZE_COEFFICIENT / (1 + REPETITION_PENALIZE_COEFFICIENT) expected_nonequal_pair_rate_from, expected_nonequal_pair_rate_to = binom.interval( 1 - _CONFIDENCE_LEVEL, _SAMPLES_NUM, expected_nonequal_pair_rate) self.assertLessEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_to) self.assertGreaterEqual(actual_num_nonequal_pairs, expected_nonequal_pair_rate_from) if __name__ == '__main__': unittest.main()

47.47027

134

0.685151

1,115

8,782

5.038565

0.150673

0.044856

0.06408

0.076896

0.739231

0.734247

0.724101

0.707013

0

0.021314

0.230699

8,782

184

135

47.728261

0.810243

0.132772

0

0.642857

0

0.001053

0

0.092857

1

0.064286

false

0

0.057143

0

0.128571

0

null

0

1

0

null

0

5

60eabdaede4a0a3f186c4178a0edb20e19aa9d93

919

py

Python

prescience/labelling/__init__.py

grockious/bounded-prescience

cc1278fb4c077f67c611ef8ac00b00f0f2c4f433

[ "BSD-3-Clause" ]

1

2021-01-26T12:17:12.000Z

prescience/labelling/__init__.py

grockious/bounded-prescience

cc1278fb4c077f67c611ef8ac00b00f0f2c4f433

[ "BSD-3-Clause" ]

null

prescience/labelling/__init__.py

grockious/bounded-prescience

cc1278fb4c077f67c611ef8ac00b00f0f2c4f433

[ "BSD-3-Clause" ]

2

2021-01-26T11:19:01.000Z

2021-03-19T10:18:13.000Z

from prescience.labelling.Labeller import Labeller from prescience.labelling.properties import get_property from prescience.labelling.Freeway import Hit from prescience.labelling.Death import Death from prescience.labelling.Assault import Overheat from prescience.labelling.Below_Reward import Below_Reward from prescience.labelling.Bowling import No_Hit from prescience.labelling.Bowling import No_Strike from prescience.labelling.DoubleDunk import Out_Of_Bounds from prescience.labelling.DoubleDunk import Shoot_Bf_Clear from prescience.labelling.Seaquest import Early_Surface from prescience.labelling.Seaquest import Out_Of_Oxygen from prescience.labelling.InstantNegativeReward import Instant_Negative_Reward from prescience.labelling.Frostbite import Freezing from prescience.labelling.Gravitar import Fuel from prescience.labelling.Hero import Dynamite from prescience.labelling.KungFuMaster import Energy_Loss

48.368421

78

0.887922

117

919

6.846154

0.350427

0.297129

0.48814

0.064919

0.284644

0.094881

0

0.075082

919

18

79

51.055556

0.942353

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

60f34e774b0d6b2e49455d4416b4005f13149e47

61

py

Python

python/LsstPwrCtrlCore/__init__.py

slaclab/lsst-pwr-ctrl-core

e37c38e2c55f0f0ee0f4b3691a93e2e3115f14e6

[ "BSD-3-Clause-LBNL" ]

null

python/LsstPwrCtrlCore/__init__.py

slaclab/lsst-pwr-ctrl-core

e37c38e2c55f0f0ee0f4b3691a93e2e3115f14e6

[ "BSD-3-Clause-LBNL" ]

3

2018-04-04T05:39:39.000Z

2018-07-09T19:48:49.000Z

python/LsstPwrCtrlCore/__init__.py

slaclab/lsst-pwr-ctrl-core

e37c38e2c55f0f0ee0f4b3691a93e2e3115f14e6

[ "BSD-3-Clause-LBNL" ]

1

2020-12-12T23:00:48.000Z

#!/usr/bin/env python from LsstPwrCtrlCore._core import *

20.333333

38

0.737705

8

61

5.5

1

0

0.147541

61

2

39

30.5

0.846154

0.327869

0

1

0

true

0

1

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0

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null

0

1

0

1

0

1

0

5

60fbdc350a85db1da17caf617ac7e1782a5ce36d

187

py

Python

test.py

Adolph-Anthony/study-reptile

d4ce637a25fba88bafb7f967b1813107792deab0

[ "MIT" ]

null

test.py

Adolph-Anthony/study-reptile

d4ce637a25fba88bafb7f967b1813107792deab0

[ "MIT" ]

null

test.py

Adolph-Anthony/study-reptile

d4ce637a25fba88bafb7f967b1813107792deab0

[ "MIT" ]

null

import requests 'https://github.com/Adolph-Anthony' ''' 这样输入用户名密码访问 ''' r = requests.get('https://github.com/Adolph-Anthony', auth=('Adolph-Anthony', 'xujing518333')) print(r.status_code)

26.714286

94

0.73262

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88039fb2439cd25a5b0402eb23f897011f8a10b8

209

py

Python

scicopia_tools/analyzers/__init__.py

pikatech/Scicopia-tools

0e19d694adeae862e3db92779d204e4944cc47bc

[ "MIT" ]

null

scicopia_tools/analyzers/__init__.py

pikatech/Scicopia-tools

0e19d694adeae862e3db92779d204e4944cc47bc

[ "MIT" ]

null

scicopia_tools/analyzers/__init__.py

pikatech/Scicopia-tools

0e19d694adeae862e3db92779d204e4944cc47bc

[ "MIT" ]

1

2021-06-18T16:00:35.000Z

from typing import Any, Dict class Analyzer: def __init__(self) -> None: pass def process(self, text: str) -> Dict[str, Any]: return {} def release_resources(self): pass

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71878945872280fd57eabb5f850237bebed1775d

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py

Python

autoran/oailte/epc/__init__.py

samiemostafavi/oai-ran-docker

cfaf8adbfdd1d3ed3f33388db74a43f17681f1d1

[ "MIT" ]

null

autoran/oailte/epc/__init__.py

samiemostafavi/oai-ran-docker

cfaf8adbfdd1d3ed3f33388db74a43f17681f1d1

[ "MIT" ]

1

2022-02-25T13:09:34.000Z

autoran/oailte/epc/__init__.py

samiemostafavi/oai-ran-docker

cfaf8adbfdd1d3ed3f33388db74a43f17681f1d1

[ "MIT" ]

null

from .EPC import Cassandra, HSS, MME, SPGWU, SPGWC, EvolvedPacketCore from .EPCRouter import CoreRouter

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71916609d9ca18d58e2a772a7d463fce867fce22

117

py

Python

alpyca_launch/src/alpyca/launch/__init__.py

arturmiller/alpyca

207eae92ebcdd593b7953ecb6ad4816215ecb516

[ "MIT" ]

3

2018-12-04T18:40:36.000Z

2019-01-13T12:01:19.000Z

alpyca_launch/src/alpyca/launch/__init__.py

alpyca/alpyca

207eae92ebcdd593b7953ecb6ad4816215ecb516

[ "MIT" ]

4

2019-01-21T19:50:56.000Z

2019-02-02T06:32:11.000Z

alpyca_launch/src/alpyca/launch/__init__.py

alpyca/alpyca

207eae92ebcdd593b7953ecb6ad4816215ecb516

[ "MIT" ]

null

from launch import Launch, ParsingException from master import Master from node import Node from runner import Runner

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5

71c1e9f99f9864670fc3b18c6ad27a1e0229f8f3

244

py

Python

src/compressario/compression_algorithms/__init__.py

ieaves/compressario

fd20ae36b283b119085f158c9fd0fb0e6f9f0242

[ "MIT" ]

null

src/compressario/compression_algorithms/__init__.py

ieaves/compressario

fd20ae36b283b119085f158c9fd0fb0e6f9f0242

[ "MIT" ]

null

src/compressario/compression_algorithms/__init__.py

ieaves/compressario

fd20ae36b283b119085f158c9fd0fb0e6f9f0242

[ "MIT" ]

null

from compressario.compression_algorithms import type_compressions from compressario.compression_algorithms.type_compressions import ( compress_float, compress_integer, compress_complex, compress_object, compress_datetime, )

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5

e07ba6e1a7821ae410823c5384434f389e5adaa7

129

py

Python

code/playground/gopro_keepalive.py

manavjain99/oscar_buggy

b5dab0848f8667c9515bcfb078730cd0c4060000

[ "MIT" ]

3

2020-08-27T14:25:14.000Z

2020-11-13T13:13:41.000Z

code/playground/gopro_keepalive.py

manavjain99/oscar_buggy

b5dab0848f8667c9515bcfb078730cd0c4060000

[ "MIT" ]

null

code/playground/gopro_keepalive.py

manavjain99/oscar_buggy

b5dab0848f8667c9515bcfb078730cd0c4060000

[ "MIT" ]

null

from goprocam import GoProCamera from goprocam import constants gopro = GoProCamera.GoPro() gopro.stream("udp://127.0.0.1:10000")

32.25

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129

5.368421

0.631579

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129

4

37

32.25

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null

1

0

1

0

null

0

1

0

5

e083faaec3780fd98a0dc60a6e17c56a35980f0c

4,288

py

Python

onnx/backend/test/case/model/gradient.py

L-Net-1992/onnx

acc127219b45bc27b0180b1fdc08299eac81b167

[ "Apache-2.0" ]

1

2022-03-04T03:29:37.000Z

onnx/backend/test/case/model/gradient.py

alsj213/onnx

35092895d9bf3592e58f4710d098f8131afef259

[ "Apache-2.0" ]

null

onnx/backend/test/case/model/gradient.py

alsj213/onnx

35092895d9bf3592e58f4710d098f8131afef259

[ "Apache-2.0" ]

1

2022-03-27T19:17:02.000Z

# SPDX-License-Identifier: Apache-2.0 import numpy as np # type: ignore import onnx from onnx.defs import ONNX_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN from ..base import Base from . import expect class Gradient(Base): @staticmethod def export_gradient_scalar_add() -> None: add_node = onnx.helper.make_node('Add', ['a', 'b'], ['c'], name='my_add') gradient_node = onnx.helper.make_node( 'Gradient', ['a', 'b'], ['dc_da', 'dc_db'], name='my_gradient', domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=['a', 'b'], y='c') a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # dc / da = d(a+b) / da = 1 dc_da = np.array(1).astype(np.float32) # db / db = d(a+b) / db = 1 dc_db = np.array(1).astype(np.float32) graph = onnx.helper.make_graph( nodes=[add_node, gradient_node], name='GradientOfAdd', inputs=[ onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT, [])], outputs=[ onnx.helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('dc_da', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('dc_db', onnx.TensorProto.FLOAT, [])]) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)] model = onnx.helper.make_model( graph, producer_name='backend-test', opset_imports=opsets) expect(model, inputs=[a, b], outputs=[c, dc_da, dc_db], name='test_gradient_of_add') @staticmethod def export_gradient_scalar_add_and_mul() -> None: add_node = onnx.helper.make_node('Add', ['a', 'b'], ['c'], name='my_add') mul_node = onnx.helper.make_node('Mul', ['c', 'a'], ['d'], name='my_mul') gradient_node = onnx.helper.make_node( 'Gradient', ['a', 'b'], ['dd_da', 'dd_db'], name='my_gradient', domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=['a', 'b'], y='d') a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # d = a * c = a * (a + b) d = a * c # dd / da = d(a*a+a*b) / da = 2 * a + b dd_da = (2 * a + b).astype(np.float32) # dd / db = d(a*a+a*b) / db = a dd_db = a graph = onnx.helper.make_graph( nodes=[add_node, mul_node, gradient_node], name='GradientOfTwoOperators', inputs=[ onnx.helper.make_tensor_value_info('a', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT, [])], outputs=[ onnx.helper.make_tensor_value_info('d', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('dd_da', onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info('dd_db', onnx.TensorProto.FLOAT, [])]) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)] model = onnx.helper.make_model(graph, producer_name='backend-test', opset_imports=opsets) expect(model, inputs=[a, b], outputs=[d, dd_da, dd_db], name='test_gradient_of_add_and_mul')

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