xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

b5205edd8aab4d0aa6260c04b7e15a833d448e5d

298

py

Python

challenges/fifo-animal-shelter/pets.py

jeremyCtown/data-structures-and-algorithms

d4ba8741f858fb5298f8ce560240373fb7742e20

[ "MIT" ]

null

challenges/fifo-animal-shelter/pets.py

jeremyCtown/data-structures-and-algorithms

d4ba8741f858fb5298f8ce560240373fb7742e20

[ "MIT" ]

null

challenges/fifo-animal-shelter/pets.py

jeremyCtown/data-structures-and-algorithms

d4ba8741f858fb5298f8ce560240373fb7742e20

[ "MIT" ]

null

class Dog: """ Creates dog object """ def __init__(self): self.val = 'dog' def __repr__(self): return self.val class Cat: """ Creates cat object """ def __init__(self): self.val = 'cat' def __repr__(self): return self.val

14.190476

24

0.516779

34

298

4.058824

0.323529

0.202899

0.188406

0.246377

0.695652

0

0.362416

298

20

25

14.9

0.726316

0.124161

0

0.6

0

0.026087

0

1

0.4

false

0

0.2

0.8

0

null

1

0

null

0

1

0

1

0

6

b576d5aece893c6e7258835bd199ea37f39fabca

6,685

py

Python

ckanext/satreasury/tests/test_plugin.py

OpenUpSA/ckanext-satreasury

b6e71009ee5fdfe8cc477304b10536b5af614de6

[ "MIT" ]

1

2019-09-08T08:56:54.000Z

ckanext/satreasury/tests/test_plugin.py

vulekamali/ckanext-satreasury

b6e71009ee5fdfe8cc477304b10536b5af614de6

[ "MIT" ]

3

2020-03-24T17:05:46.000Z

2021-02-02T22:01:44.000Z

ckanext/satreasury/tests/test_plugin.py

vulekamali/ckanext-satreasury

b6e71009ee5fdfe8cc477304b10536b5af614de6

[ "MIT" ]

1

2019-05-30T19:30:22.000Z

import json import unittest from functools import partial import ckan.model as model import responses from ckanext.satreasury.plugin import SATreasuryDatasetPlugin from mock import MagicMock, Mock, PropertyMock, patch TRAVIS_ENDPOINT = "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal" TRAVIS_COMMIT_MESSAGE = 'Rebuild with new/modified dataset' TRAVIS_WEB_URL = "https://travis-ci.org/vulekamali/static-budget-portal/builds/" class TestNotifyMethod(unittest.TestCase): @responses.activate def setUp(self): self.entity = Mock(spec=model.Package) self.entity.owner_org = PropertyMock(return_value=True) self.plugin = SATreasuryDatasetPlugin() flash_success_patch = patch( 'ckanext.satreasury.plugin.ckan_helpers.flash_success') self.flash_success_mock = flash_success_patch.start() flash_error_patch = patch( 'ckanext.satreasury.plugin.ckan_helpers.flash_error') self.flash_error_mock = flash_error_patch.start() self.addCleanup(flash_success_patch.stop) @patch( 'ckanext.satreasury.plugin.travis.build_trigger_enabled', return_value=True) def test_notify_already_building(self, build_trigger_enabled_mock): with responses.RequestsMock() as rsps: rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/builds", json={ 'builds': [ { 'id': 535878234, 'commit': { 'message': TRAVIS_COMMIT_MESSAGE }, }]}, status=200, content_type='application/json') self.plugin.notify(self.entity, None) message = "vulekamali will be updated in less than an hour. <a href='https://travis-ci.org/vulekamali/static-budget-portal/builds/535878234' >Check progress of the update process.</a>" self.flash_success_mock.assert_called_with( message, allow_html=True) @patch( 'ckanext.satreasury.plugin.travis.build_trigger_enabled', return_value=True) def test_notify_build_triggered(self, build_trigger_enabled_mock): with responses.RequestsMock() as rsps: rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/builds", json={ 'builds': []}, status=200, content_type='application/json') rsps.add( responses.POST, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/requests", json={ 'request': { 'id': 12345}}, status=200, content_type='application/json') rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/request/12345", json={ 'builds': [ { 'commit': { 'message': TRAVIS_COMMIT_MESSAGE}, 'id': 535878234, }]}, status=200, content_type='application/json') self.plugin.notify(self.entity, None) message = "vulekamali will be updated in less than an hour. <a href='https://travis-ci.org/vulekamali/static-budget-portal/builds/535878234' >Check progress of the update process.</a>" self.flash_success_mock.assert_called_with( message, allow_html=True) @patch( 'ckanext.satreasury.plugin.travis.build_trigger_enabled', return_value=True) def test_notify_build_request_but_no_build(self, build_trigger_enabled_mock): with responses.RequestsMock() as rsps: rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/builds", json={ 'builds': []}, status=200, content_type='application/json') rsps.add( responses.POST, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/requests", json={ 'request': { 'id': 12345}}, status=200, content_type='application/json') rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/request/12345", json={ 'builds': []}, status=200, content_type='application/json') self.plugin.notify(self.entity, None) message = "vulekamali will be updated in less than an hour. <a href='https://travis-ci.org/vulekamali/static-budget-portal/builds/' >Check progress of the update process.</a>" self.flash_success_mock.assert_called_with( message, allow_html=True) @patch( 'ckanext.satreasury.plugin.travis.build_trigger_enabled', return_value=True) def test_notify_build_trigger_errored(self, build_trigger_enabled_mock): with responses.RequestsMock() as rsps: rsps.add( responses.GET, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/builds", json={ 'builds': []}, status=200, content_type='application/json') rsps.add( responses.POST, "https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/requests", json={ 'request': { 'id': 12345}}, status=500, content_type='application/json') self.plugin.notify(self.entity, None) message = 'An error occurred when updating the static site data. Technical details: 500 Server Error: Internal Server Error for url: https://api.travis-ci.org/repo/vulekamali%2Fstatic-budget-portal/requests' self.flash_error_mock.assert_called_with(message) @patch( 'ckanext.satreasury.plugin.travis.build_trigger_enabled', return_value=False) def test_notify_build_not_enabled(self, build_trigger_enabled_mock): self.plugin.notify(self.entity, None) self.assertTrue(True)

43.129032

219

0.57457

680

6,685

5.491176

0.179412

0.032137

0.044189

0.047134

0.778254

0.746652

0.738618

0.712373

0.698982

0

0.022522

0.322513

6,685

154

220

43.409091

0.801943

0

0.723404

0

0.092199

0.318325

0.055647

0

0.035461

1

0.042553

false

0

0.049645

0

0.099291

0

null

0

1

0

1

0

null

0

6

a97015a85173cf78e85bed10e73b68dc69502a9d

78

py

Python

pydemic/report/__init__.py

GCES-Pydemic/pydemic

f221aa16e6a32ed1303fa11ebf8a357643f683d5

[ "MIT" ]

null

pydemic/report/__init__.py

GCES-Pydemic/pydemic

f221aa16e6a32ed1303fa11ebf8a357643f683d5

[ "MIT" ]

null

pydemic/report/__init__.py

GCES-Pydemic/pydemic

f221aa16e6a32ed1303fa11ebf8a357643f683d5

[ "MIT" ]

null

from .report_group import GroupReport from .report_single import SingleReport

26

39

0.871795

10

78

6.6

0.7

0.30303

0

0.102564

78

2

40

39

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

a9939846090c5322d4926d75f10b1fc68c18dada

153

py

Python

{{cookiecutter.repo_name}}/{{cookiecutter.package_name}}/{{cookiecutter.package_name}}.py

numengo/cc-py-setup

392dfb85acb9052bf48586b9be98fc1f591d8991

[ "ISC", "Apache-2.0", "MIT" ]

3

2018-02-16T17:10:15.000Z

2018-03-01T19:38:54.000Z

{{cookiecutter.repo_name}}/{{cookiecutter.package_name}}/{{cookiecutter.package_name}}.py

numengo/cc-py-setup

392dfb85acb9052bf48586b9be98fc1f591d8991

[ "ISC", "Apache-2.0", "MIT" ]

null

{{cookiecutter.repo_name}}/{{cookiecutter.package_name}}/{{cookiecutter.package_name}}.py

numengo/cc-py-setup

392dfb85acb9052bf48586b9be98fc1f591d8991

[ "ISC", "Apache-2.0", "MIT" ]

null

# -*- coding: utf-8 -*- """Main module {{cookiecutter.project_name}} """ from __future__ import absolute_import from __future__ import unicode_literals

25.5

48

0.751634

18

153

5.777778

0.777778

0.192308

0.307692

0

0.007407

0.117647

153

5

49

30.6

0.762963

0.424837

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

8d3e794674c7c132a4877a4a375649bf2399c45b

2,639

py

Python

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

1

2021-05-24T10:08:51.000Z

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.keras.applications namespace. """ from __future__ import print_function as _print_function import sys as _sys from keras.api._v2.keras.applications import densenet from keras.api._v2.keras.applications import efficientnet from keras.api._v2.keras.applications import imagenet_utils from keras.api._v2.keras.applications import inception_resnet_v2 from keras.api._v2.keras.applications import inception_v3 from keras.api._v2.keras.applications import mobilenet from keras.api._v2.keras.applications import mobilenet_v2 from keras.api._v2.keras.applications import mobilenet_v3 from keras.api._v2.keras.applications import nasnet from keras.api._v2.keras.applications import resnet from keras.api._v2.keras.applications import resnet50 from keras.api._v2.keras.applications import resnet_v2 from keras.api._v2.keras.applications import vgg16 from keras.api._v2.keras.applications import vgg19 from keras.api._v2.keras.applications import xception from keras.applications.densenet import DenseNet121 from keras.applications.densenet import DenseNet169 from keras.applications.densenet import DenseNet201 from keras.applications.efficientnet import EfficientNetB0 from keras.applications.efficientnet import EfficientNetB1 from keras.applications.efficientnet import EfficientNetB2 from keras.applications.efficientnet import EfficientNetB3 from keras.applications.efficientnet import EfficientNetB4 from keras.applications.efficientnet import EfficientNetB5 from keras.applications.efficientnet import EfficientNetB6 from keras.applications.efficientnet import EfficientNetB7 from keras.applications.inception_resnet_v2 import InceptionResNetV2 from keras.applications.inception_v3 import InceptionV3 from keras.applications.mobilenet import MobileNet from keras.applications.mobilenet_v2 import MobileNetV2 from keras.applications.mobilenet_v3 import MobileNetV3Large from keras.applications.mobilenet_v3 import MobileNetV3Small from keras.applications.nasnet import NASNetLarge from keras.applications.nasnet import NASNetMobile from keras.applications.resnet import ResNet101 from keras.applications.resnet import ResNet152 from keras.applications.resnet import ResNet50 from keras.applications.resnet_v2 import ResNet101V2 from keras.applications.resnet_v2 import ResNet152V2 from keras.applications.resnet_v2 import ResNet50V2 from keras.applications.vgg16 import VGG16 from keras.applications.vgg19 import VGG19 from keras.applications.xception import Xception del _print_function

47.981818

82

0.869269

346

2,639

6.50578

0.196532

0.332297

0.261217

0.093292

0.617948

0.359396

0.278987

0.180364

0.039982

0

0.034142

0.078818

2,639

54

83

48.87037

0.891814

0.065934

0

1

0

1

0

true

0

0.978261

0

0.978261

0.043478

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

8d6c38dd172b4fa935c4b931081b7a40d9bc40a8

6,045

py

Python

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

null

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

null

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

1

2020-12-23T16:45:20.000Z

#!/usr/bin/python import sys from pyspark import SparkContext from shutil import rmtree import os.path as path def generar(line): array = [] array.append(line[0]) array.append(line[1]) aux = line[2] ini = 2 fin = 18 if aux != "I" and aux != "D" and aux != "N": aux = line[3] ini = 3 fin = 19 array.append(aux) ini+=1 for i in range(ini, fin): if line[i] == '': array.append("0") else: array.append(line[i]) return array if len(sys.argv) > 1: if path.exists("output"): rmtree("output") sc = SparkContext() localidad = sys.argv[1] localidadRDD = sc.textFile("Gasolineras.csv") localidadRDD = localidadRDD.map(lambda line: line.encode("ascii", "ignore")) localidadRDD = localidadRDD.map(lambda rows: rows.split(",")) localidadRDD = localidadRDD.filter(lambda rows: localidad == rows[5]) localidadRDD = localidadRDD.map(lambda rows: (rows[5], rows[7], rows[8], rows[9],rows[10], rows[11], rows[12], rows[13], rows[14], rows[15], rows[16], rows[17], rows[18], rows[19], rows[20], rows[21], rows[22], rows[23], rows[24])) datosRDD = localidadRDD.map(generar) if datosRDD.isEmpty(): result = sc.parallelize("0") result.saveAsTextFile("output") else: precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[5])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasolina_95.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[6])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasoleo_a.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[7])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasoleo_b.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[8])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_bioetanol.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[9])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_nuevo_gasoleo_a.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[10])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_biodiesel.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[11])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_ester_metilico.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[12])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_bioalcohol.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[13])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasolina_98.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[14])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_natural_comprimido.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[15])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_natural_licuado.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[16])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_licuados_del_petr.txt") else: print "Error no ha introducido localidad."

45.451128

235

0.643176

754

6,045

5.106101

0.151194

0.091169

0.087792

0.114805

0.778442

0.757143

0.745714

0

0.025057

0.201158

6,045

132

236

45.795455

0.77221

0.002647

0

0.455357

0

0.094061

0.080126

0

null

0

0.035714

null

0.008929

0

null

0

1

0

null

0

1

0

6

a5d187baa7ec34c04b159476ef8dc6d77a915eac

4,175

py

Python

generated/azure-cli/aro/custom.py

audevbot/autorest.devops.debug

a507fb6e2dd7826212537f27d583f203aac1c28f

[ "MIT" ]

null

generated/azure-cli/aro/custom.py

audevbot/autorest.devops.debug

a507fb6e2dd7826212537f27d583f203aac1c28f

[ "MIT" ]

null

generated/azure-cli/aro/custom.py

audevbot/autorest.devops.debug

a507fb6e2dd7826212537f27d583f203aac1c28f

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # pylint: disable=line-too-long # pylint: disable=too-many-statements # pylint: disable=too-many-lines # pylint: disable=too-many-locals # pylint: disable=unused-argument import json # module equivalent: azure_rm_openshiftmanagedcluster # URL: /subscriptions/{{ subscription_id }}/resourceGroups/{{ resource_group }}/providers/Microsoft.ContainerService/openShiftManagedClusters/{{ open_shift_managed_cluster_name }} def create_aro(cmd, client, resource_group, name, location, open_shift_version, tags=None, plan=None, network_profile=None, router_profiles=None, master_pool_profile=None, agent_pool_profiles=None, auth_profile=None): body = {} body['location'] = location # str body['tags'] = tags # dictionary body['plan'] = json.loads(plan) if isinstance(plan, str) else plan body['open_shift_version'] = open_shift_version # str body['network_profile'] = json.loads(network_profile) if isinstance(network_profile, str) else network_profile body['router_profiles'] = json.loads(router_profiles) if isinstance(router_profiles, str) else router_profiles body['master_pool_profile'] = json.loads(master_pool_profile) if isinstance(master_pool_profile, str) else master_pool_profile body['agent_pool_profiles'] = json.loads(agent_pool_profiles) if isinstance(agent_pool_profiles, str) else agent_pool_profiles body['auth_profile'] = json.loads(auth_profile) if isinstance(auth_profile, str) else auth_profile return client.create_or_update(resource_group_name=resource_group, resource_name=name, parameters=body) # module equivalent: azure_rm_openshiftmanagedcluster # URL: /subscriptions/{{ subscription_id }}/resourceGroups/{{ resource_group }}/providers/Microsoft.ContainerService/openShiftManagedClusters/{{ open_shift_managed_cluster_name }} def update_aro(cmd, client, body, resource_group, name, location, open_shift_version, tags=None, plan=None, network_profile=None, router_profiles=None, master_pool_profile=None, agent_pool_profiles=None, auth_profile=None): body = client.get(resource_group_name=resource_group, resource_name=name).as_dict() body.location = location # str body.tags = tags # dictionary body.plan = json.loads(plan) if isinstance(plan, str) else plan body.open_shift_version = open_shift_version # str body.network_profile = json.loads(network_profile) if isinstance(network_profile, str) else network_profile body.router_profiles = json.loads(router_profiles) if isinstance(router_profiles, str) else router_profiles body.master_pool_profile = json.loads(master_pool_profile) if isinstance(master_pool_profile, str) else master_pool_profile body.agent_pool_profiles = json.loads(agent_pool_profiles) if isinstance(agent_pool_profiles, str) else agent_pool_profiles body.auth_profile = json.loads(auth_profile) if isinstance(auth_profile, str) else auth_profile return client.create_or_update(resource_group_name=resource_group, resource_name=name, parameters=body) # module equivalent: azure_rm_openshiftmanagedcluster # URL: /subscriptions/{{ subscription_id }}/resourceGroups/{{ resource_group }}/providers/Microsoft.ContainerService/openShiftManagedClusters/{{ open_shift_managed_cluster_name }} def list_aro(cmd, client, resource_group): if resource_group is not None: return client.list_by_resource_group(resource_group_name=resource_group) return client.list()

55.666667

180

0.683353

469

4,175

5.793177

0.187633

0.076555

0.062569

0.036805

0.859036

0.834376

0.817446

0

0.191617

4,175

74

181

56.418919

0.805037

0.293413

0

0.461538

0

0.039944

0

1

0.057692

false

0

0.019231

0

0.153846

0

null

0

1

0

null

0

6

a5f2ce8d23f3ea07c4d73928966352c760c23c7e

47

py

Python

scripts/portal/OutElfKingRoom.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/portal/OutElfKingRoom.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/portal/OutElfKingRoom.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# 101050010 sm.warp(101050000, 7) sm.dispose()

11.75

21

0.723404

7

47

4.857143

0.857143

0

0.452381

0.106383

47

3

22

15.666667

0.357143

0.191489

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

93cf143d7b69f8a96f36f23910ce3b0b601f20d1

436

py

Python

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

2

2020-11-04T03:08:21.000Z

2020-11-05T08:14:41.000Z

"""Common imports for generated bigtableclusteradmin client library.""" # pylint:disable=wildcard-import import pkgutil from googlecloudsdk.third_party.apitools.base.py import * from googlecloudsdk.third_party.apis.bigtableclusteradmin.v1.bigtableclusteradmin_v1_client import * from googlecloudsdk.third_party.apis.bigtableclusteradmin.v1.bigtableclusteradmin_v1_messages import * __path__ = pkgutil.extend_path(__path__, __name__)

39.636364

102

0.855505

49

436

7.204082

0.510204

0.249292

0.195467

0.23796

0.464589

0

0.009852

0.068807

436

10

103

43.6

0.859606

0.222477

0

1

0

1

0

false

0

0.8

0

0.8

0

null

1

0

1

0

null

0

1

0

1

0

6

93d7075c75f515ae6f7dbc9fddf988695545df0c

2,715

py

Python

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

null

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

null

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

1

2021-10-07T20:58:56.000Z

import folium def add_lyr_google_hybrid(min_zoom, max_zoom): row = { 'link': 'https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}', 'name': 'Google Hybrid', 'attribution': 'https://www.google.com/maps', } lyr = folium.TileLayer( tiles=row['link'], attr=('<a href="{}" target="blank">{}</a>'.format(row['attribution'], row['name'])), name=row['name'], min_zoom=min_zoom, max_zoom=max_zoom, subdomains=['mt0', 'mt1', 'mt2', 'mt3'], overlay=False, control=True, show=True, ) return lyr def add_lyr_google_satellite(min_zoom, max_zoom): row = { 'link': 'https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}', 'name': 'Google Satelite', 'attribution': 'https://www.google.com/maps', } lyr = folium.TileLayer( tiles=row['link'], attr=('<a href="{}" target="blank">{}</a>'.format(row['attribution'], row['name'])), name=row['name'], min_zoom=min_zoom, max_zoom=max_zoom, subdomains=['mt0', 'mt1', 'mt2', 'mt3'], overlay=False, control=True, show=False, ) return lyr def add_lyr_google_terrain(min_zoom, max_zoom): row = { 'link': 'https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}', 'name': 'Google Terrain', 'attribution': 'https://www.google.com/maps', } lyr = folium.TileLayer( tiles=row['link'], attr=('<a href="{}" target="blank">{}</a>'.format(row['attribution'], row['name'])), name=row['name'], min_zoom=min_zoom, max_zoom=max_zoom, subdomains=['mt0', 'mt1', 'mt2', 'mt3'], overlay=False, control=True, show=False, ) return lyr def add_lyr_google_streets(min_zoom, max_zoom): row = { 'link': 'https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}', 'name': 'Google Streets', 'attribution': 'https://www.google.com/maps', } lyr = folium.TileLayer( tiles=row['link'], attr=('<a href="{}" target="blank">{}</a>'.format(row['attribution'], row['name'])), name=row['name'], min_zoom=min_zoom, max_zoom=max_zoom, subdomains=['mt0', 'mt1', 'mt2', 'mt3'], overlay=False, control=True, show=False, ) return lyr def add_lyr_cartodbpositron(min_zoom, max_zoom): lyr = folium.TileLayer( tiles='cartodbpositron', attr='Carto', name='CartoDB Positron', min_zoom=min_zoom, max_zoom=max_zoom, overlay=False, control=True, show=False, ) return lyr if __name__ == '__main__': pass

27.15

92

0.537385

336

2,715

4.1875

0.166667

0.074627

0.117271

0.099502

0.857143

0.840085

0.745558

0

0.01003

0.265562

2,715

99

93

27.424242

0.695587

0

0.666667

0

0.045977

0.279926

0.030939

0

1

0.057471

false

0.011494

0

0.126437

0

null

0

1

0

null

0

6

93e030c92ac6f8fce1b888c7a5422a8bac82faba

144

py

Python

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import division, print_function from ._mesh import * from ._openscad import * from ._threads import *

18

47

0.722222

18

144

5.333333

0.666667

0.208333

0

0.008333

0.166667

144

7

48

20.571429

0.791667

0.145833

0

1

0

true

0

1

0

1

0.25

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f55005f6eda0c8aeadd07c4aee7c84c8198766c5

11

py

Python

src/__init__.py

Peefy/StatisticalLearningMethod.Python

7324d51b58932052bc518b9e82f64b76f0c39bf0

[ "Apache-2.0" ]

1

2018-10-05T08:20:50.000Z

src/__init__.py

Peefy/StatisticalLearningMethod.Python

7324d51b58932052bc518b9e82f64b76f0c39bf0

[ "Apache-2.0" ]

null

src/__init__.py

Peefy/StatisticalLearningMethod.Python

7324d51b58932052bc518b9e82f64b76f0c39bf0

[ "Apache-2.0" ]

null

# pdf.244

3.666667

9

0.545455

2

11

3

1

0

0.375

0.272727

11

2

10

5.5

0.375

0.636364

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

f56fadc40b0f5bac091cf8c15f4a134f11cb883f

49

py

Python

sfrmaker/test/test_nhdplus_utils.py

mnfienen/sfrmaker

f3ee175c67c80df15bff509235d9a6218bfc6b0b

[ "CC0-1.0" ]

17

2015-08-15T02:20:04.000Z

2020-04-30T17:36:21.000Z

sfrmaker/test/test_nhdplus_utils.py

rfrederiksen/sfrmaker

7e66d67d6cb0ad84fbb9994402f0baaf5b3fcd01

[ "CC0-1.0" ]

15

2015-03-04T16:57:13.000Z

2020-01-14T16:29:18.000Z

sfrmaker/test/test_nhdplus_utils.py

rfrederiksen/sfrmaker

7e66d67d6cb0ad84fbb9994402f0baaf5b3fcd01

[ "CC0-1.0" ]

9

2015-08-18T14:15:07.000Z

2020-04-28T18:45:21.000Z

# TODO: add unit tests for test_nhdplus_utils.py

24.5

48

0.795918

9

49

4.111111

1

0

0.142857

49

1

49

0.880952

0.938776

0

null

0

null

0

null

0

1

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

193b0ac325ef43006df164a8a14a3f901439a20c

185

py

Python

main_app/forms.py

reeshabhkumarranjan/SocPay

ba3f3ea0b7b814e1ca40293b14f192b6d40adbbd

[ "MIT" ]

null

main_app/forms.py

reeshabhkumarranjan/SocPay

ba3f3ea0b7b814e1ca40293b14f192b6d40adbbd

[ "MIT" ]

null

main_app/forms.py

reeshabhkumarranjan/SocPay

ba3f3ea0b7b814e1ca40293b14f192b6d40adbbd

[ "MIT" ]

null

from django.forms import ModelForm # class add_money_form(ModelForm): # class Meta: # model = Transaction # fields = ['transaction_user_2', 'transaction_amount']

20.555556

63

0.675676

20

185

6

0.8

0.233333

0

0.006993

0.227027

185

8

64

23.125

0.832168

0.745946

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

195268ef4f0f6c79ac3ca7cf1356d6b54616df26

31

py

Python

src/__init__.py

abdelsamea/DeTraC

2c94d55908285fc9cbb24086da63078ee917525a

[ "MIT" ]

1

2020-09-17T14:17:50.000Z

src/__init__.py

arkkhanu/DeTraC_COVId19

ab03719b49a1a048f74f08600a6670f6757bbe60

[ "MIT" ]

null

src/__init__.py

arkkhanu/DeTraC_COVId19

ab03719b49a1a048f74f08600a6670f6757bbe60

[ "MIT" ]

1

2021-04-14T08:52:36.000Z

import tools import frameworks

10.333333

17

0.870968

4

31

6.75

0.75

0

0.129032

31

2

18

15.5

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

19618fa1e0fd69bf4ce89b6dd9ce0cf4d5bdf4a2

33

py

Python

PosPy/__init__.py

richierh/PointofSalePy

54fc11b5f167d361c75b6b1cb890c7020393d46c

[ "Apache-2.0" ]

null

PosPy/__init__.py

richierh/PointofSalePy

54fc11b5f167d361c75b6b1cb890c7020393d46c

[ "Apache-2.0" ]

null

PosPy/__init__.py

richierh/PointofSalePy

54fc11b5f167d361c75b6b1cb890c7020393d46c

[ "Apache-2.0" ]

null

#!usr/bin/python import __main__

11

16

0.787879

5

33

4.4

1

0

0.090909

33

2

17

16.5

0.733333

0.454545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5fd5ed7c8159f71250918b127471e85178346d85

31

py

Python

utils_folder/__init__.py

codevideo/Airsim_Test

0b47665c67ba674d277fc8de58739a59648bb7eb

[ "MIT" ]

null

utils_folder/__init__.py

codevideo/Airsim_Test

0b47665c67ba674d277fc8de58739a59648bb7eb

[ "MIT" ]

null

utils_folder/__init__.py

codevideo/Airsim_Test

0b47665c67ba674d277fc8de58739a59648bb7eb

[ "MIT" ]

null

from .image_generator import *

15.5

30

0.806452

4

31

6

1

0

0.129032

31

1

31

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5fee310b12b10f820f272b0dba6075da741be40b

9,817

py

Python

nexusmaker/tests/test_CognateParser.py

SimonGreenhill/NexusMaker

12591bd4217bc1ba4477845e524083c2164df91a

[ "BSD-3-Clause" ]

null

nexusmaker/tests/test_CognateParser.py

SimonGreenhill/NexusMaker

12591bd4217bc1ba4477845e524083c2164df91a

[ "BSD-3-Clause" ]

2

2017-10-12T08:59:51.000Z

2021-05-31T00:37:16.000Z

nexusmaker/tests/test_CognateParser.py

SimonGreenhill/NexusMaker

12591bd4217bc1ba4477845e524083c2164df91a

[ "BSD-3-Clause" ]

1

2017-10-12T08:59:09.000Z

import unittest from nexusmaker import CognateParser class TestCognateParser(unittest.TestCase): def test_simple(self): self.assertEqual(CognateParser().parse_cognate('1'), ['1']) self.assertEqual(CognateParser().parse_cognate('10'), ['10']) self.assertEqual(CognateParser().parse_cognate('100'), ['100']) self.assertEqual(CognateParser().parse_cognate('111'), ['111']) def test_subset(self): self.assertEqual(CognateParser().parse_cognate('1,2'), ['1', '2']) self.assertEqual(CognateParser().parse_cognate('1 , 2'), ['1', '2']) def test_dubious(self): self.assertEqual(CognateParser().parse_cognate('1?'), ['u_1']) self.assertEqual(CognateParser().parse_cognate('?'), ['u_1']) def test_dubious_subset(self): self.assertEqual(CognateParser().parse_cognate('1, 2?'), ['1']) self.assertEqual(CognateParser().parse_cognate('1?, 2'), ['2']) self.assertEqual(CognateParser().parse_cognate('91?, 42'), ['42']) self.assertEqual(CognateParser().parse_cognate('?, 31'), ['31']) # note that both of these are dubious, should become a unique # state instead self.assertEqual(CognateParser().parse_cognate('1?, 2?'), ['u_1']) def test_bad_entries_uniques(self): # coded as x self.assertEqual(CognateParser(uniques=True).parse_cognate('X'), ['u_1']) self.assertEqual(CognateParser(uniques=True).parse_cognate('x'), ['u_1']) def test_bad_entries_in_forms_uniques(self): # coded as x self.assertEqual(CognateParser(uniques=True).parse_cognate('X20'), ['u_1']) self.assertEqual(CognateParser(uniques=True).parse_cognate('x20'), ['u_1']) def test_bad_entries_nouniques(self): self.assertEqual(CognateParser(uniques=False).parse_cognate('X'), []) self.assertEqual(CognateParser(uniques=False).parse_cognate('x'), []) def test_s_entries_uniques(self): # entries that are in the wrong word (e.g. you sg. not you pl.) self.assertEqual(CognateParser(uniques=True).parse_cognate('s'), ['u_1']) def test_s_entries_nouniques(self): # entries that are in the wrong word (e.g. you sg. not you pl.) self.assertEqual(CognateParser(uniques=False).parse_cognate('s'), []) def test_empty_entries(self): with self.assertRaises(ValueError): CognateParser(uniques=False).parse_cognate(',,') def test_is_unique_cognateset(self): self.assertEqual(CognateParser().is_unique_cognateset('u_1'), True) self.assertEqual( CognateParser().is_unique_cognateset('u_1', labelled=False), True ) self.assertEqual( CognateParser().is_unique_cognateset('hand_u_1', labelled=False), False ) self.assertEqual( CognateParser().is_unique_cognateset('hand_u_1', labelled=True), True ) def test_add_unique(self): CP = CognateParser() self.assertEqual(CP.parse_cognate(''), ['u_1']) self.assertEqual(CP.parse_cognate(''), ['u_2']) self.assertEqual(CP.parse_cognate(''), ['u_3']) self.assertEqual(CP.parse_cognate(''), ['u_4']) def test_no_uniques(self): CP = CognateParser(uniques=False) self.assertEqual(CP.parse_cognate(''), []) self.assertEqual(CP.parse_cognate(''), []) self.assertEqual(CP.parse_cognate(''), []) def test_dubious_with_no_strict(self): self.assertEqual(CognateParser(strict=False).parse_cognate('1?'), ['1']) self.assertEqual(CognateParser(strict=False).parse_cognate('1, 2?'), ['1', '2']) def test_null(self): self.assertEqual(CognateParser().parse_cognate(None), ['u_1']) def test_bad_cog_int(self): with self.assertRaises(ValueError): CognateParser().parse_cognate(1) def test_complicated_strict_unique(self): CP = CognateParser(strict=True, uniques=True) # # 3. right # Maori katau 5, 40 # Maori matau 5 # South Island Maori tika self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40']) self.assertEqual(CP.parse_cognate('5'), ['5']) self.assertEqual(CP.parse_cognate(''), ['u_1']) # # 8. turn # Maori huri 15 # South Island Maori tahuli 15 # South Island Maori tahuri to turn, to turn around 15 self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) # # 20. to know # Maori moohio 52 # South Island Maori matau 1 # South Island Maori mohio to know 52 # South Island Maori ara to know, to awake self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate('1'), ['1']) self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate(''), ["u_2"]) # # 36: to spit # Maori tuha 19, 34? # South Island Maori huare 18 # South Island Maori tuha to expectorate, to spit 19, 34? self.assertEqual(CP.parse_cognate('19, 34?'), ['19']) self.assertEqual(CP.parse_cognate('18'), ['18']) self.assertEqual(CP.parse_cognate('19, 34?'), ['19']) def test_complicated_nostrict_unique(self): CP = CognateParser(strict=False, uniques=True) # # 3. right # Maori katau 5, 40 # Maori matau 5 # South Island Maori tika self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40']) self.assertEqual(CP.parse_cognate('5'), ['5']) self.assertEqual(CP.parse_cognate(''), ['u_1']) # # 8. turn # Maori huri 15 # South Island Maori tahuli 15 # South Island Maori tahuri to turn, to turn around 15 self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) # # 20. to know # Maori moohio 52 # South Island Maori matau 1 # South Island Maori mohio to know 52 # South Island Maori ara to know, to awake self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate('1'), ['1']) self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate(''), ["u_2"]) # # 36: to spit # Maori tuha 19, 34? # South Island Maori huare 18 # South Island Maori tuha to expectorate, to spit 19, 34? self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34']) self.assertEqual(CP.parse_cognate('18'), ['18']) self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34']) def test_complicated_nostrict_nounique(self): CP = CognateParser(strict=False, uniques=False) # # 3. right # Maori katau 5, 40 # Maori matau 5 # South Island Maori tika self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40']) self.assertEqual(CP.parse_cognate('5'), ['5']) self.assertEqual(CP.parse_cognate(''), []) # # 8. turn # Maori huri 15 # South Island Maori tahuli 15 # South Island Maori tahuri to turn, to turn around 15 self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) self.assertEqual(CP.parse_cognate('15'), ['15']) # # 20. to know # Maori moohio 52 # South Island Maori matau 1 # South Island Maori mohio to know 52 # South Island Maori ara to know, to awake self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate('1'), ['1']) self.assertEqual(CP.parse_cognate('52'), ['52']) self.assertEqual(CP.parse_cognate(''), []) # # 36: to spit # Maori tuha 19, 34? # South Island Maori huare 18 # South Island Maori tuha to expectorate, to spit 19, 34? self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34']) self.assertEqual(CP.parse_cognate('18'), ['18']) self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34']) def test_complicated_with_slash(self): CP = CognateParser(strict=True, uniques=True) self.assertEqual(CP.parse_cognate('53/54'), ['53', '54']) def test_combined_cognate(self): self.assertEqual(CognateParser().parse_cognate('1a'), ['1', '1a']) self.assertEqual(CognateParser().parse_cognate('2b'), ['2', '2b']) self.assertEqual(CognateParser().parse_cognate('3az'), ['3', '3az']) self.assertEqual(CognateParser().parse_cognate('45c'), ['45', '45c']) self.assertEqual(CognateParser().parse_cognate('1a,2b'), ['1', '1a', '2', '2b']) def test_trailing_dash(self): with self.assertRaises(ValueError): CognateParser().parse_cognate('1-') def test_semicolon(self): with self.assertRaises(ValueError): CognateParser().parse_cognate('2, 63; 87') def test_sorting(self): self.assertEqual(CognateParser(sort=True).parse_cognate('2, 1'), ['1', '2']) self.assertEqual(CognateParser(sort=False).parse_cognate('2, 1'), ['2', '1'])

44.220721

88

0.581237

1,143

9,817

4.843395

0.112861

0.222182

0.144328

0.186777

0.864342

0.801481

0.719653

0.651734

0.582731

0.574603

0

0.049889

0.266986

9,817

221

89

44.420814

0.719427

0.192727

0

0.418605

0

0.051687

0

0.666667

1

0.186047

false

0

0.015504

0

0.209302

0

null

1

0

1

0

null

0

1

0

6

2717b6d6d414bb9ece4ff0a008bac03ea5586ef2

27

py

Python

api/web/__init__.py

AutoCoinDCF/NEW_API

f4abc48fff907a0785372b941afcd67e62eec825

[ "Apache-2.0" ]

null

api/web/__init__.py

AutoCoinDCF/NEW_API

f4abc48fff907a0785372b941afcd67e62eec825

[ "Apache-2.0" ]

null

api/web/__init__.py

AutoCoinDCF/NEW_API

f4abc48fff907a0785372b941afcd67e62eec825

[ "Apache-2.0" ]

null

from .webapi import WebAPI

13.5

26

0.814815

4

27

5.5

0.75

0

0.148148

27

1

27

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2726aff9726f405364127c08f5f1c2bf93aa5997

91

py

Python

src/rocket/stage2/src/errors/src/SensorOverload.py

proballstar/atlas

6e4eb36b7e43e750dbb281c2051439198c82f296

[ "MIT" ]

null

src/rocket/stage2/src/errors/src/SensorOverload.py

proballstar/atlas

6e4eb36b7e43e750dbb281c2051439198c82f296

[ "MIT" ]

null

src/rocket/stage2/src/errors/src/SensorOverload.py

proballstar/atlas

6e4eb36b7e43e750dbb281c2051439198c82f296

[ "MIT" ]

null

def init(): # @TODO(aaronhma): UPDATE def catchErrors(): # @TODO(aaronhma): UPDATE

18.2

29

0.626374

10

91

5.7

0.6

0.421053

0.631579

0

0.197802

91

5

30

18.2

0.780822

0.516484

0

0.2

0

null

0

null

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

2775121ab7502b6919cf78437931035cd8b7a2d9

158

py

Python

src/onegov/user/auth/clients/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/user/auth/clients/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/user/auth/clients/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

from onegov.user.auth.clients.kerberos import KerberosClient from onegov.user.auth.clients.ldap import LDAPClient __all__ = ('KerberosClient', 'LDAPClient')

31.6

60

0.816456

19

158

6.578947

0.578947

0.16

0.224

0.288

0.4

0

0.082278

158

4

61

39.5

0.862069

0

0.151899

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

27abe035638fda37c09ec1990dca44e2161d8667

30

py

Python

onepassword/__init__.py

jemmyw/1pass

8dbfa5e062ce08e26c5619dbdb2b27323e5b3dc9

[ "MIT" ]

1

2016-11-14T22:16:48.000Z

onepassword/__init__.py

elliotchance/1pass

4bd45a52476c410c6e5b51f90fd46cbdd436807f

[ "MIT" ]

null

onepassword/__init__.py

elliotchance/1pass

4bd45a52476c410c6e5b51f90fd46cbdd436807f

[ "MIT" ]

null

from keychain import Keychain

15

29

0.866667

4

30

6.5

0.75

0

0.133333

30

1

30

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fd9dfebdc3992ce7b8a849f7cc5d3d65ff09d8f0

352

py

Python

src/tweetvalidator/data_processing/__init__.py

JoshuaGRubin/AI-Tweet-Validator

c011b08163cfd322c637c2296dab7ae41a64ae71

[ "MIT" ]

5

2019-06-27T02:18:08.000Z

2020-03-09T22:03:09.000Z

src/tweetvalidator/data_processing/__init__.py

JoshuaGRubin/AI-Tweet-Validator

c011b08163cfd322c637c2296dab7ae41a64ae71

[ "MIT" ]

11

2020-01-28T22:50:34.000Z

2022-02-10T00:31:21.000Z

src/tweetvalidator/data_processing/__init__.py

JoshuaGRubin/AI-Text-Validator

c011b08163cfd322c637c2296dab7ae41a64ae71

[ "MIT" ]

1

2020-02-21T03:38:12.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from .filter_tweets import filter_tweets_from_directories from .filter_tweets import filter_tweets_from_files from .download_tweets import get_tweets_by_user from .embed_tweets import SentenceEncoder from .embed_tweets import embed_tweets_from_file from .embed_tweets import embed_tweets_from_directories

39.111111

57

0.855114

52

352

5.384615

0.384615

0.257143

0.160714

0.225

0.528571

0

0.006231

0.088068

352

8

58

44

0.866044

0.122159

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

8bf7c2002c8b113a9de4b7623d703ed3f154d1fb

118

py

Python

code/super_minitaur/script/lpmslib/lputils.py

buenos-dan/quadrupedal_robot

605054c027e20b83e347f2aa175c03c965e72983

[ "MIT" ]

5

2019-03-22T06:39:42.000Z

2021-07-27T13:56:45.000Z

code/super_minitaur/script/lpmslib/lputils.py

buenos-dan/quadrupedal_robot

605054c027e20b83e347f2aa175c03c965e72983

[ "MIT" ]

null

code/super_minitaur/script/lpmslib/lputils.py

buenos-dan/quadrupedal_robot

605054c027e20b83e347f2aa175c03c965e72983

[ "MIT" ]

2

2021-02-16T09:52:04.000Z

2021-11-30T12:12:55.000Z

#helpers def logd(tag, msg): print "[Debug-"+tag+"]", msg def loge(tag, msg): print "[Error-"+tag+"]", msg

13.111111

32

0.550847

17

118

3.823529

0.529412

0.369231

0.338462

0

0.20339

118

8

33

14.75

0.691489

0.059322

0

0.148148

0

null

0

null

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e300c54c781958b660c0d153f40329e21fe52fd9

6,539

py

Python

three_d_resnet_builder/builder.py

thauptmann/3D-ResNet-for-Keras

ac1b8b3d0032c9af832cc945bc57a63106366e54

[ "MIT" ]

4

2021-05-23T09:30:40.000Z

2021-12-29T16:14:46.000Z

three_d_resnet_builder/builder.py

thauptmann/3D-ResNet-for-Keras

ac1b8b3d0032c9af832cc945bc57a63106366e54

[ "MIT" ]

3

2021-06-24T09:26:58.000Z

2022-01-06T11:01:59.000Z

three_d_resnet_builder/builder.py

thauptmann/3D-ResNet-for-Keras

ac1b8b3d0032c9af832cc945bc57a63106366e54

[ "MIT" ]

3

2021-06-07T18:11:34.000Z

2021-12-22T01:57:03.000Z

from . import three_D_resnet from .kernel import get_kernel_to_name def build_three_d_resnet(input_shape, output_shape, repetitions, output_activation, regularizer=None, squeeze_and_excitation=False, use_bottleneck=False, kernel_size=3, kernel_name='3D'): """Return a full customizable resnet. :param input_shape: The input shape of the network as (frames, height, width, channel) :param output_shape: The output shape. Dependant on the task of the network. :param repetitions: Define the repetitions of the Residual Blocks e.g. (2, 2, 2, 2) for ResNet-18 :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Define the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation: Activate or deactivate SE-Paths. :param use_bottleneck: Activate bottleneck layers. Recommended for networks with many layers. :param kernel_size: Set the kernel size. Don't need to be changes in almost all cases. It's just exist for customization purposes. :param kernel_name: :return: Return the built network. """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, repetitions, output_activation, regularizer, squeeze_and_excitation, use_bottleneck, kernel_size, kernel=conv_kernel) def build_three_d_resnet_18(input_shape, output_shape, output_activation, regularizer=None, squeeze_and_excitation=False, kernel_name='3D'): """Return a customizable resnet_18. :param input_shape: The input shape of the network as (frames, height, width, channel) :param output_shape: The output shape. Dependant on the task of the network. :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Defines the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation:Activate or deactivate SE-Paths. :param kernel_name: :return: The built ResNet-18 """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, output_activation, (2, 2, 2, 2), regularizer, squeeze_and_excitation, kernel=conv_kernel) def build_three_d_resnet_34(input_shape, output_shape, output_activation, regularizer=None, squeeze_and_excitation=False, kernel_name='3D'): """Return a customizable resnet_34. :param input_shape: The input shape of the network as (frames, height, width, channel) :param output_shape: The output shape. Dependant on the task of the network. :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Defines the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation:Activate or deactivate SE-Paths. :param kernel_name: :return: The built ResNet-34 """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, output_activation, (3, 4, 6, 3), regularizer, squeeze_and_excitation, kernel=conv_kernel) def build_three_d_resnet_50(input_shape, output_shape, output_activation, regularizer=None, squeeze_and_excitation=False, kernel_name='3D'): """Return a customizable resnet_50. :param input_shape: The input shape of the network as (frames, height, width, channels) :param output_shape: The output shape. Dependant on the task of the network. :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Defines the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation:Activate or deactivate SE-Paths. :param kernel_name: :return: The built ResNet-50 """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, output_activation, (3, 4, 6, 3), regularizer, squeeze_and_excitation, use_bottleneck=True, kernel=conv_kernel) def build_three_d_resnet_102(input_shape, output_shape, output_activation, regularizer=None, squeeze_and_excitation=False, kernel_name='3D'): """Return a customizable resnet_102. :param input_shape: The input shape of the network as (frames, height, width, channel) :param output_shape: The output shape. Dependant on the task of the network. :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Defines the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation:Activate or deactivate SE-Paths. :param kernel_name: :return: The built ResNet-102 """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, output_activation, (3, 4, 23, 3), regularizer, squeeze_and_excitation, use_bottleneck=True, kernel=conv_kernel) def build_three_d_resnet_152(input_shape, output_shape, output_activation, regularizer=None, squeeze_and_excitation=False, kernel_name='3D'): """ Return a customizable resnet_152 :param input_shape: The input shape of the network as (frames, height, width, channel) :param output_shape: The output shape. Dependant on the task of the network. :param output_activation: Define the used output activation. Also depends on the task of the network. :param regularizer: Defines the regularizer to use. E.g. "l1" or "l2" :param squeeze_and_excitation:Activate or deactivate SE-Paths. :param kernel_name: :return: The built ResNet-152 """ conv_kernel = get_kernel_to_name(kernel_name) return three_D_resnet.ThreeDConvolutionResNet(input_shape, output_shape, output_activation, (3, 8, 36, 3), regularizer, squeeze_and_excitation, use_bottleneck=True, kernel=conv_kernel)

57.867257

115

0.692002

850

6,539

5.097647

0.114118

0.055389

0.083083

0.058158

0.89453

0.88553

0.877914

0.840526

0

0.016663

0.238263

6,539

112

116

58.383929

0.853242

0.493348

0

0.611111

0

0.003913

0

1

0.166667

false

0

0.055556

0

0.388889

0

null

0

1

0

null

0

6

e323ed5e92eb5da83c0443afabf48a5b468396f3

176

py

Python

gd/utils/crypto/__init__.py

scottwedge/gd.py

328c9833abc949b1c9ac0eabe276bd66fead4c2c

[ "MIT" ]

null

gd/utils/crypto/__init__.py

scottwedge/gd.py

328c9833abc949b1c9ac0eabe276bd66fead4c2c

[ "MIT" ]

null

gd/utils/crypto/__init__.py

scottwedge/gd.py

328c9833abc949b1c9ac0eabe276bd66fead4c2c

[ "MIT" ]

null

"""Main module for operating on crypted/encoded strings in Geometry Dash""" from gd.utils.crypto.coders import Coder from gd.utils.crypto.xor_cipher import XORCipher as xor

44

76

0.795455

28

176

4.964286

0.785714

0.086331

0.158273

0.244604

0

0.130682

176

3

77

58.666667

0.908497

0.392045

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

e33da7e662f4c2fc76532c7c89e8edb38e2cccee

96

py

Python

venv/lib/python3.8/site-packages/filelock/_error.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/filelock/_error.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/filelock/_error.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/ab/0b/2c/7ae80e56fd2208fbee5ef317ac009972f468b5601f62f8f799f9d9279a

96

0.895833

9

96

9.555556

1

0

0.40625

0

96

1

96

0.489583

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

8b6db37566c6d60a2bd9e55330800dc0a7ad705e

8,558

py

Python

tests/test_simba.py

SIMBAChain/libsimba.py-platform

a815105a5ed84564c7eafbe01281473cebfb44e5

[ "MIT" ]

null

tests/test_simba.py

SIMBAChain/libsimba.py-platform

a815105a5ed84564c7eafbe01281473cebfb44e5

[ "MIT" ]

2

2022-02-25T05:03:13.000Z

2022-03-09T13:56:56.000Z

tests/test_simba.py

SIMBAChain/libsimba.py-platform

a815105a5ed84564c7eafbe01281473cebfb44e5

[ "MIT" ]

null

import unittest from unittest.mock import patch from libsimba.simba import Simba class TestSimba(unittest.TestCase): def setUp(self): self.simba = Simba() patcher_send = patch("libsimba.simba_request.SimbaRequest.send") patcher_init = patch("libsimba.simba_request.SimbaRequest.__init__") self.addCleanup(patcher_send.stop) self.addCleanup(patcher_init.stop) self.mock_send = patcher_send.start() self.mock_init = patcher_init.start() self.mock_init.return_value = None def test_submit_transaction_by_address(self): resp = self.simba.submit_transaction_by_address( "app_id", "contract", "identifier", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/address/identifier/method/', {}, method='POST') def test_submit_transaction_by_address_with_params(self): resp = self.simba.submit_transaction_by_address( "app_id", "contract", "identifier", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/address/identifier/method/', {"bob": "boby"}, method='POST') def test_submit_transaction_by_asset(self): resp = self.simba.submit_transaction_by_asset( "app_id", "contract", "identifier", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/asset/identifier/method/', {}, method='POST') def test_submit_transaction_by_asset_with_params(self): resp = self.simba.submit_transaction_by_asset( "app_id", "contract", "identifier", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/asset/identifier/method/', {"bob": "boby"}, method='POST') def test_submit_contract_method(self): resp = self.simba.submit_contract_method( "app_id", "contract", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/method/', {}, method='POST') def test_submit_contract_method_with_params(self): resp = self.simba.submit_contract_method( "app_id", "contract", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/contract/contract/method/', {"bob": "boby"}, method='POST') def test_submit_transaction_by_address_async(self): resp = self.simba.submit_transaction_by_address_async( "app_id", "contract", "identifier", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/address/identifier/method/', {}, method='POST') def test_submit_transaction_by_address_async_with_params(self): resp = self.simba.submit_transaction_by_address_async( "app_id", "contract", "identifier", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/address/identifier/method/', {"bob": "boby"}, method='POST') def test_submit_transaction_by_asset_async(self): resp = self.simba.submit_transaction_by_asset_async( "app_id", "contract", "identifier", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/asset/identifier/method/', {}, method='POST') def test_submit_transaction_by_asset_async_with_params(self): resp = self.simba.submit_transaction_by_asset_async( "app_id", "contract", "identifier", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/asset/identifier/method/', {"bob": "boby"}, method='POST') def test_submit_contract_method_async(self): resp = self.simba.submit_contract_method_async( "app_id", "contract", "method", {"key": "value"}, ) self.mock_send.assert_called_once_with(headers={}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/method/', {}, method='POST') def test_submit_contract_method_async_with_params(self): resp = self.simba.submit_contract_method_async( "app_id", "contract", "method", {"key": "value"}, query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}, json_payload={"key": "value"}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/async/contract/contract/method/', {"bob": "boby"}, method='POST') def test_create_contract_instance(self): resp = self.simba.create_contract_instance( "app_id", "contract", ) self.mock_send.assert_called_once_with(headers={}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/new/contract/', {}, method='POST') def test_create_contract_instance_with_params(self): resp = self.simba.create_contract_instance( "app_id", "contract", query_args={"bob": "boby"}, sender_address="0x1773", ) self.mock_send.assert_called_once_with( headers={'txn-sender': '0x1773'}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/new/contract/', {"bob": "boby"}, method='POST') def test_submit_signed_transaction(self): resp = self.simba.submit_signed_transaction( "app_id", "tnx-id", {"txn": "data"}, ) self.mock_send.assert_called_once_with(json_payload={'transaction': {'txn': 'data'}}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/transactions/tnx-id/', {}, method='POST') def test_submit_signed_transaction_with_params(self): resp = self.simba.submit_signed_transaction( "app_id", "tnx-id", {"txn": "data"}, query_args={"bob": "boby"}, ) self.mock_send.assert_called_once_with(json_payload={'transaction': {'txn': 'data'}}) self.mock_init.assert_called_once_with( '/v2/apps/app_id/transactions/tnx-id/', {'bob': 'boby'}, method='POST')

38.9

113

0.592545

950

8,558

4.991579

0.066316

0.059047

0.107971

0.134964

0.93062

0.915015

0.905947

0.872628

0.862294

0.849431

0

0.013692

0.266067

8,558

219

114

39.077626

0.741283

0

0.680203

0

0.21391

0.103565

0

0.009819

0

0.162437

1

0.086294

false

0

0.015228

0

0.106599

0

null

0

1

0

null

0

6

8b85c3050d99d93fcfb7dcf610efc1fcee13814c

6,645

py

Python

src/werdich_cfr/code_not_used/Shinichi_3D_Inception_Model.py

awerdich/werdich-cfr

39d9a7f05d0a92304a6dd60df0124068735222ad

[ "MIT" ]

null

src/werdich_cfr/code_not_used/Shinichi_3D_Inception_Model.py

awerdich/werdich-cfr

39d9a7f05d0a92304a6dd60df0124068735222ad

[ "MIT" ]

null

src/werdich_cfr/code_not_used/Shinichi_3D_Inception_Model.py

awerdich/werdich-cfr

39d9a7f05d0a92304a6dd60df0124068735222ad

[ "MIT" ]

null

def inception_module(x,filters_1x1,filters_3x3_reduce,filters_3x3,filters_5x5_reduce,filters_5x5,filters_pool_proj,trainable=True): conv_1x1 = Conv3D(filters_1x1, (1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) conv_3x3 = Conv3D(filters_3x3_reduce, (1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) conv_1x1 = BatchNormalization(scale=False,trainable=trainable)(conv_1x1) conv_3x3 = BatchNormalization(scale=False,trainable=trainable)(conv_3x3) conv_3x3 = Conv3D(filters_3x3, (3,3,3), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(conv_3x3) conv_3x3 = BatchNormalization(scale=False,trainable=trainable)(conv_3x3) conv_5x5 = Conv3D(filters_5x5_reduce, (1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) conv_5x5 = BatchNormalization(scale=False,trainable=trainable)(conv_5x5) conv_5x5 = Conv3D(filters_5x5, (3,3,3),strides=(1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(conv_5x5) conv_5x5 = BatchNormalization(scale=False,trainable=trainable)(conv_5x5) conv_5x5 = Conv3D(filters_5x5, (3,3,3),strides=(1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(conv_5x5) #conv_7x7 = BatchNormalization()(conv_5x5) #conv_7x7 = Conv3D(filters_5x5, (3,3,3),strides=(1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init)(conv_7x7) pool_proj = MaxPooling3D((3,3,3), strides=(1,1,1), padding='same')(x) pool_proj = Conv3D(filters_pool_proj, (1,1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(pool_proj) output = concatenate([conv_1x1, conv_3x3, conv_5x5, pool_proj], axis=4) return output def get_models(Inbatchsize): trainable=True #False inputEco=Input(shape=SHAPE) inputScale=Input(shape=(1,)) inputInvScale=Input(shape=(1,)) filters=64#48 x=inputEco # x=BatchNormalization()(x) x=Conv3D(filters,(7,7,7),padding='same',strides=(2,2,2),activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) x=BatchNormalization(scale=False,trainable=trainable)(x) x=MaxPooling3D(pool_size=(3,3,3),padding='same',strides=(2,2,2))(x) x=Conv3D(filters,(1,1,1),padding='same',strides=(1,1,1),activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) x=BatchNormalization(scale=False,trainable=trainable)(x) x=Conv3D(filters*3,(3,3,3),padding='same',strides=(1,1,1),activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init,trainable=trainable)(x) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=filters,filters_3x3_reduce=int(filters*1.5),filters_3x3=filters*4,filters_5x5_reduce=int(filters/4),filters_5x5=int(filters/2),filters_pool_proj=int(filters/2),trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=filters*2,filters_3x3_reduce=filters*2,filters_3x3=filters*3,filters_5x5_reduce=int(filters/2),filters_5x5=filters*3,filters_pool_proj=filters,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=MaxPooling3D(pool_size=(1,3,3),padding='same',strides=(2,2,2))(x) #(1,3,3) x=inception_module(x,filters_1x1=filters*3,filters_3x3_reduce=int(filters*1.5),filters_3x3=int(filters*3.25),filters_5x5_reduce=int(filters/4),filters_5x5=int(filters*0.75),filters_pool_proj=filters,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=int(filters*2.5),filters_3x3_reduce=int(filters*1.75),filters_3x3=int(filters*3.5),filters_5x5_reduce=int(filters*0.375),filters_5x5=filters,filters_pool_proj=filters,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=filters*2,filters_3x3_reduce=filters*2,filters_3x3=filters*4,filters_5x5_reduce=int(filters*0.375),filters_5x5=filters,filters_pool_proj=filters,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=int(filters*1.75),filters_3x3_reduce=int(filters*2.25),filters_3x3=int(filters*4.5),filters_5x5_reduce=int(filters/2),filters_5x5=filters,filters_pool_proj=filters,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=filters*4,filters_3x3_reduce=int(filters*2.5),filters_3x3=filters*5,filters_5x5_reduce=int(filters/2),filters_5x5=filters*2,filters_pool_proj=filters*2,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=MaxPooling3D((1,3,3), strides=(2,2,2))(x) #(2,3,3) padding='same' x=inception_module(x,filters_1x1=filters*4,filters_3x3_reduce=int(filters*2.5),filters_3x3=filters*5,filters_5x5_reduce=int(filters/2),filters_5x5=filters*2,filters_pool_proj=filters*2,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=inception_module(x,filters_1x1=filters*6,filters_3x3_reduce=filters*3,filters_3x3=filters*6,filters_5x5_reduce=int(filters*0.75),filters_5x5=filters*2,filters_pool_proj=filters*2,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=MaxPooling3D(pool_size=(1,3,3),trainable=trainable)(x) x=inception_module(x,filters_1x1=filters*6,filters_3x3_reduce=filters*3,filters_3x3=filters*6,filters_5x5_reduce=int(filters*0.75),filters_5x5=filters*2,filters_pool_proj=filters*2,trainable=trainable) x=BatchNormalization(scale=False,trainable=trainable)(x) x=GlobalAveragePooling3D()(x) x=Dropout(0.4)(x) x=Dense(1,activation="relu",trainable=trainable)(x) x=BatchNormalization(scale=False,trainable=trainable)(x) a=inputScale #a=Dense(256,activation='relu')(a) a= multiply([x,a]) b=inputInvScale #b=Dense(256,activation='relu')(b) b=multiply([x,b]) x=concatenate([x,a,b]) x=BatchNormalization(scale=False,trainable=trainable)(x) #x=Dense(768,activation="relu")(x) #xa=BatchNormalization(scale=False)(x) #xa=Dropout(0.2)(xa) xa=Dense(1,name="reg")(x) model=Model([inputEco,inputScale,inputInvScale],xa) return model

89.797297

224

0.78164

1,000

6,645

4.995

0.068

0.151351

0.125526

0.148148

0.836837

0.814414

0.777177

0.77037

0.760561

0.720721

0

0.060958

0.066817

6,645

74

225

89.797297

0.744557

0.063205

0

0.378788

0

0.015932

0

1

0.030303

false

0

0.060606

0

null

0

1

0

null

0

6

8bd6939b53a3043718695a488bbde929cd4e44d7

423

py

Python

akagi/contents/__init__.py

pauchan/akagi

7cf1f5a52b8f1ebfdc74a527bf6b26254f99343b

[ "MIT" ]

26

2017-05-18T11:52:04.000Z

2018-08-25T22:03:07.000Z

akagi/contents/__init__.py

pauchan/akagi

7cf1f5a52b8f1ebfdc74a527bf6b26254f99343b

[ "MIT" ]

325

2017-05-08T07:22:28.000Z

2022-03-31T15:43:18.000Z

akagi/contents/__init__.py

pauchan/akagi

7cf1f5a52b8f1ebfdc74a527bf6b26254f99343b

[ "MIT" ]

7

2017-05-02T02:06:15.000Z

2020-04-09T05:32:11.000Z

# -*- coding: utf-8 -*- from akagi.contents import s3_content from akagi.contents import local_file_content from akagi.contents import spreadsheet_content from akagi.contents import url_content from akagi.contents.s3_content import S3Content from akagi.contents.local_file_content import LocalFileContent from akagi.contents.spreadsheet_content import SpreadsheetContent from akagi.contents.url_content import URLContent

35.25

65

0.858156

57

423

6.192982

0.298246

0.203966

0.385269

0.260623

0.254958

0

0.010417

0.092199

423

11

66

38.454545

0.908854

0.049645

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

4735d1f5a8954c473193826a0ee08dd64ab9a93e

35

py

Python

Training/pylens/__init__.py

Annarien/GravitationalLenses

c2606aacc62d2534fb199f5228dc21c0ea604251

[ "MIT" ]

null

Training/pylens/__init__.py

Annarien/GravitationalLenses

c2606aacc62d2534fb199f5228dc21c0ea604251

[ "MIT" ]

null

Training/pylens/__init__.py

Annarien/GravitationalLenses

c2606aacc62d2534fb199f5228dc21c0ea604251

[ "MIT" ]

null

import massmodel,pylens,MassModels

17.5

34

0.885714

4

35

7.75

1

0

0.057143

35

1

35

0.939394

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

47391c58cb36718e353c6d24ef9b6c3dc10d34e8

230

py

Python

propertiesTemplate/Fields/FieldTester.py

HenShalom/tableTransform

ede5c9ed3ecb32f74712778a6746b03773ac28ef

[ "Apache-2.0" ]

null

propertiesTemplate/Fields/FieldTester.py

HenShalom/tableTransform

ede5c9ed3ecb32f74712778a6746b03773ac28ef

[ "Apache-2.0" ]

null

propertiesTemplate/Fields/FieldTester.py

HenShalom/tableTransform

ede5c9ed3ecb32f74712778a6746b03773ac28ef

[ "Apache-2.0" ]

2

2019-11-20T02:47:23.000Z

from propertiesTemplate.Fields.BasicField import BasicField from propertiesTemplate.Fields.ConstField import ConstField def get_field(value): if value[0] == '@': return BasicField(value) return ConstField(value)

25.555556

59

0.76087

25

230

6.96

0.52

0.252874

0.321839

0

0.005155

0.156522

230

8

60

28.75

0.891753

0

0.004348

0

1

0.166667

false

0

0.333333

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

47a0a4fbe45ee5f24ba893991497ace327ecbebb

36

py

Python

lib/jnpr/eznc/runstat/__init__.py

cro/py-junos-eznc

4c111476cc8eb7599462379ddf55743ae30bbf5c

[ "Apache-2.0", "BSD-3-Clause" ]

null

lib/jnpr/eznc/runstat/__init__.py

cro/py-junos-eznc

4c111476cc8eb7599462379ddf55743ae30bbf5c

[ "Apache-2.0", "BSD-3-Clause" ]

null

lib/jnpr/eznc/runstat/__init__.py

cro/py-junos-eznc

4c111476cc8eb7599462379ddf55743ae30bbf5c

[ "Apache-2.0", "BSD-3-Clause" ]

null

from .rsmaker import RunstatMaker

9

33

0.805556

4

36

7.25

1

0

0.166667

36

3

34

12

0.966667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9a2bcb820df0cd2448d9d527aa5328ae749fbcf6

246

py

Python

calculations.py

DikshaAGowda/Project3

675d4d80ad4b44b3a49e8962c9f85709898d0a94

[ "MIT" ]

null

calculations.py

DikshaAGowda/Project3

675d4d80ad4b44b3a49e8962c9f85709898d0a94

[ "MIT" ]

null

calculations.py

DikshaAGowda/Project3

675d4d80ad4b44b3a49e8962c9f85709898d0a94

[ "MIT" ]

null

def addition(num1, num2): return num1 + num2 def subtraction(num1, num2): return num1 - num2 def multiplication(num1, num2): return num1 * num2 def division(num1, num2): if num2 == 0: return None return num1 / num2

17.571429

31

0.642276

33

246

4.787879

0.333333

0.405063

0.35443

0.341772

0.474684

0

0.099448

0.264228

246

13

32

18.923077

0.773481

0

1

0.4

false

0

0.3

0.9

0

null

1

0

null

0

1

0

1

0

6

7bd5134da373e6ab71f1575fcac61884fd8fa7f9

41

py

Python

bot/run.py

anhhanuman/python-selenium

6dbb169282c44c50189447a1c9a303ae1a790a8b

[ "Apache-2.0" ]

null

bot/run.py

anhhanuman/python-selenium

6dbb169282c44c50189447a1c9a303ae1a790a8b

[ "Apache-2.0" ]

5

2021-09-02T13:02:25.000Z

2021-09-20T04:58:37.000Z

bot/run.py

anhhanuman/python-selenium

6dbb169282c44c50189447a1c9a303ae1a790a8b

[ "Apache-2.0" ]

null

from booking.constants import myConstant

20.5

40

0.878049

5

41

7.2

1

0

0.097561

41

1

41

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d0199dc840ae15f108dd63b1047696f29f1a9218

1,488

py

Python

tests/test_warp.py

Krande/ipygany

471355d043e3952ac68052613135fd5a5ee3a41b

[ "BSD-3-Clause" ]

450

2019-11-29T07:19:42.000Z

2022-03-27T08:38:18.000Z

tests/test_warp.py

Krande/ipygany

471355d043e3952ac68052613135fd5a5ee3a41b

[ "BSD-3-Clause" ]

47

2019-12-14T00:57:14.000Z

2022-01-27T16:14:40.000Z

tests/test_warp.py

Krande/ipygany

471355d043e3952ac68052613135fd5a5ee3a41b

[ "BSD-3-Clause" ]

51

2019-11-29T07:19:48.000Z

2022-03-25T13:07:19.000Z

import pytest from traitlets import TraitError from ipygany import PolyMesh, Warp from .utils import get_test_assets def test_default_input(): vertices, triangles, data_1d, data_3d = get_test_assets() poly = PolyMesh(vertices=vertices, triangle_indices=triangles, data=[data_1d, data_3d]) warped_mesh = Warp(poly) assert warped_mesh.input == (('1d', 'x'), 0, 0) poly = PolyMesh(vertices=vertices, triangle_indices=triangles, data=[data_3d]) warped_mesh = Warp(poly) assert warped_mesh.input == '3d' def test_input(): vertices, triangles, data_1d, data_3d = get_test_assets() poly = PolyMesh(vertices=vertices, triangle_indices=triangles, data=[data_1d, data_3d]) warped_mesh = Warp(poly) with pytest.raises(TraitError): warped_mesh.input = (('1d', 'x'), 0) warped_mesh.input = ('1d', 0, 0) assert warped_mesh.input == (('1d', 'x'), 0, 0) warped_mesh.input = ('1d', 0, 32) assert warped_mesh.input == (('1d', 'x'), 0, 32) warped_mesh.input = (0, 0, '1d') assert warped_mesh.input == (0, 0, ('1d', 'x')) with pytest.raises(TraitError): warped_mesh.input = ('3d', 0, 0) warped_mesh = Warp(poly, input=('1d', 0, 0)) assert warped_mesh.input == (('1d', 'x'), 0, 0) warped_mesh = Warp(poly, input=(0, 0, '1d')) assert warped_mesh.input == (0, 0, ('1d', 'x')) warped_mesh = Warp(warped_mesh, input=(0, '1d', 0)) assert warped_mesh.input == (0, ('1d', 'x'), 0)

26.571429

91

0.636425

212

1,488

4.273585

0.146226

0.220751

0.231788

0.18543

0.842163

0.794702

0.759382

0.610375

0.604857

0.543046

0

0.046823

0.196237

1,488

55

92

27.054545

0.710702

0

0.4375

0

0.026882

0

0.25

1

0.0625

false

0

0.125

0

0.1875

0

null

1

0

null

0

6

d06ac96e9708483c7fba688eecfd660ccb68f5f6

31

py

Python

Tasks_codes/task1_catdog.py

manasviaggarwal/tipr-second-assignment

45869a71c9de1a1d66c3581a06854f330f38e14d

[ "MIT" ]

null

Tasks_codes/task1_catdog.py

manasviaggarwal/tipr-second-assignment

45869a71c9de1a1d66c3581a06854f330f38e14d

[ "MIT" ]

null

Tasks_codes/task1_catdog.py

manasviaggarwal/tipr-second-assignment

45869a71c9de1a1d66c3581a06854f330f38e14d

[ "MIT" ]

null

ACCURACY :::62.86377259982597

15.5

30

0.774194

3

31

8

1

0

0.571429

0.096774

31

2

30

15.5

0.285714

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

d0703322f54aad95ad1c141cfb0733e4dbc48655

25

py

Python

src/masonite/events/__init__.py

cercos/masonite

f7f220efa7fae833683e9f07ce13c3795a87d3b8

[ "MIT" ]

1,816

2018-02-14T01:59:51.000Z

2022-03-31T17:09:20.000Z

src/masonite/events/__init__.py

cercos/masonite

f7f220efa7fae833683e9f07ce13c3795a87d3b8

[ "MIT" ]

340

2018-02-11T00:27:26.000Z

2022-03-21T12:00:24.000Z

src/masonite/events/__init__.py

cercos/masonite

f7f220efa7fae833683e9f07ce13c3795a87d3b8

[ "MIT" ]

144

2018-03-18T00:08:16.000Z

2022-02-26T01:51:58.000Z

from .Event import Event

12.5

24

0.8

4

25

5

0.75

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d0af98534b90208cb7e4f06f1ab2ae7e3d283c93

9,730

py

Python

server/plato/test/test_domains.py

zhlooking/plato

9daf0dfd8b376603453eadf2d981c71d3adb2632

[ "MIT" ]

null

server/plato/test/test_domains.py

zhlooking/plato

9daf0dfd8b376603453eadf2d981c71d3adb2632

[ "MIT" ]

null

server/plato/test/test_domains.py

zhlooking/plato

9daf0dfd8b376603453eadf2d981c71d3adb2632

[ "MIT" ]

null

import json from plato import db from plato.model.user import User from plato.test.base import BaseTestCase from plato.test.utils import add_user, add_domain class TestDomainService(BaseTestCase): def test_add_domain(self): '''Ensure a new domain can be added to database''' add_user('test', '[email protected]', 'test') with self.client: resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) response = self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', ip='http://111.13.100.91/', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 201) self.assertIn('www.baidu.com was added!', data['message']) self.assertIn('success', data['status']) def test_add_duplicate_domain(self): '''Ensure error is thrown if user's email already exists''' add_user('test', '[email protected]', 'test') with self.client: resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', ip='http://111.13.100.91/', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) response = self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', ip='http://111.13.100.91/', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 400) self.assertIn('Sorry, that domain already exists.', data['message']) self.assertIn('fail', data['status']) def test_add_domain_invalid_ip(self): add_user('test', '[email protected]', 'test') with self.client: resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) response = self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 400) self.assertIn('Invalid payload', data['message']) self.assertIn('fail', data['status']) def test_add_domain_invalid_payload(self): add_user('test', '[email protected]', 'test') with self.client: resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) response = self.client.post( '/domains', data=json.dumps(dict( ip='http://111.13.100.91/', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 400) self.assertIn('Invalid payload', data['message']) self.assertIn('fail', data['status']) def test_add_domain_invalid_master(self): add_user('test', '[email protected]', 'test') with self.client: resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) response = self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', ip='http://111.13.100.91/', )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 400) self.assertIn('Invalid payload', data['message']) self.assertIn('fail', data['status']) def test_single_domain(self): '''Ensure get single user behaves correctly''' domain = add_domain('www.baidu.com', 'http://10.0.0.122', 1) with self.client: response = self.client.get(f'/domain/{domain.id}') data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 200) self.assertIn('www.baidu.com', data['data']['domain']) self.assertIn('http://10.0.0.122', data['data']['ip']) self.assertIn('success', data['status']) def test_single_domain_no_id(self): '''Ensure error is thrown if an id is not provided''' with self.client: response = self.client.get('domain/test_id') data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 404) self.assertIn('Domain does not exist.', data['message']) self.assertIn('fail', data['status']) def test_single_domain_incorrect_id(self): '''Ensure error is thrown if the id is not correct''' with self.client: response = self.client.get('domain/666') data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 404) self.assertIn('Domain does not exist.', data['message']) self.assertIn('fail', data['status']) def test_add_user_not_admin(self): add_user('test', '[email protected]', 'test') user = User.query.filter_by(email='[email protected]').first() user.admin = True db.session.commit() with self.client: # user login resp_login = self.client.post( '/auth/login', data=json.dumps(dict( email='[email protected]', password='test' )), content_type='application/json' ) response = self.client.post( '/domains', data=json.dumps(dict( domain='www.baidu.com', ip='http://111.13.100.91/', master=1 )), content_type='application/json', headers=dict( Authorization='Bearer ' + json.loads( resp_login.data.decode() )['auth_token'] ) ) data = json.loads(response.data.decode()) self.assertTrue(data['status'] == 'error') self.assertTrue( data['message'] == 'You have no permission to do that.') self.assertEqual(response.status_code, 403) def test_all_domains(self): add_domain('www.baidu.com', 'http://111.13.100.91/', 1) add_domain('www.google.com', 'http://111.13.100.92/', 1) with self.client: response = self.client.get('/domains') data = json.loads(response.data.decode()) self.assertEqual(response.status_code, 200) self.assertEqual(len(data['data']['domains']), 2) self.assertIn('www.google.com', data['data']['domains'][0]['domain']) self.assertIn('www.baidu.com', data['data']['domains'][1]['domain']) self.assertIn('http://111.13.100.92/', data['data']['domains'][0]['ip']) self.assertIn('http://111.13.100.91/', data['data']['domains'][1]['ip'])

39.076305

84

0.481809

957

9,730

4.805643

0.125392

0.058708

0.031094

0.048054

0.821918

0.798434

0.769515

0.729506

0.689498

0

0.026941

0.385817

9,730

248

85

39.233871

0.742637

0.025385

0

0.734783

0

0.16538

0

0.147826

1

0.043478

false

0.026087

0.021739

0

0.069565

0

null

0

1

0

1

0

1

0

null

0

6

d0c3051d812d65b6baa90af1922f0a2918135e6d

128

py

Python

django_cenvars/tools/sanitize.py

martinphellwig/django-cenvars

2b7ae6e719fa6ae7ffb8f0cedad615114064dab1

[ "BSD-2-Clause" ]

null

django_cenvars/tools/sanitize.py

martinphellwig/django-cenvars

2b7ae6e719fa6ae7ffb8f0cedad615114064dab1

[ "BSD-2-Clause" ]

null

django_cenvars/tools/sanitize.py

martinphellwig/django-cenvars

2b7ae6e719fa6ae7ffb8f0cedad615114064dab1

[ "BSD-2-Clause" ]

null

""" Perform sanitization check prior of releasing the app as ready. """ def main(): "Perform sanitization checks" pass

16

63

0.695313

16

128

5.5625

0.875

0.426966

0

0.210938

128

7

64

18.285714

0.881188

0.710938

0

0.473684

0

1

0.333333

true

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

ef9bbe1541d0c953af96d087b8ca600f95dd7284

45

py

Python

way2sms/__init__.py

shubhamc183/way2sms

33d8c9e69ab9b053e50501baf887191c718d2d2a

[ "MIT" ]

38

2016-12-15T14:03:00.000Z

2022-03-22T01:28:29.000Z

way2sms/__init__.py

shubhamc183/way2sms

33d8c9e69ab9b053e50501baf887191c718d2d2a

[ "MIT" ]

10

2017-11-18T08:13:18.000Z

2020-09-06T11:18:32.000Z

way2sms/__init__.py

shubhamc183/way2sms

33d8c9e69ab9b053e50501baf887191c718d2d2a

[ "MIT" ]

41

2016-12-26T16:52:59.000Z

2022-03-22T01:31:40.000Z

""" Way2sms """ from way2sms.app import Sms

7.5

27

0.666667

6

45

5

0.833333

0

0.054054

0.177778

45

5

28

9

0.756757

0.155556

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

efd15e7fb718ba74481d809759853c9e66bc24c0

80

py

Python

bcap/__init__.py

keioku/bcap-python

5f1c912fcac515d8f26bda113f644d55a38e15d6

[ "MIT" ]

null

bcap/__init__.py

keioku/bcap-python

5f1c912fcac515d8f26bda113f644d55a38e15d6

[ "MIT" ]

null

bcap/__init__.py

keioku/bcap-python

5f1c912fcac515d8f26bda113f644d55a38e15d6

[ "MIT" ]

null

from .b_cap_client import BCapClient from .b_cap_exception import BCapException

26.666667

42

0.875

12

80

5.5

0.666667

0.151515

0.242424

0

0.1

80

2

43

40

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

ef3ec4855031980afb1650987b97c64ce63c1807

5,476

py

Python

origin_response_test.py

dnsinogeorgos/lambdas

4294089b311585c18e101e776aa2e8ca211413cd

[ "Apache-2.0" ]

null

origin_response_test.py

dnsinogeorgos/lambdas

4294089b311585c18e101e776aa2e8ca211413cd

[ "Apache-2.0" ]

null

origin_response_test.py

dnsinogeorgos/lambdas

4294089b311585c18e101e776aa2e8ca211413cd

[ "Apache-2.0" ]

null

# pylint: disable=C0114 import unittest from origin_response import lambda_handler event = { "Records": [ { "cf": { "config": {"requestId": "thisfakeidisthisfakeidisthisfakeidis"}, "request": {"uri": ""}, "response": {"headers": {}, "status": 0}, } } ] } class TestLambdaHandler(unittest.TestCase): # pylint: disable=C0115 def test_success(self): """ This sends a success response Should return identical result with injected security headers """ event["Records"][0]["cf"]["request"]["uri"] = "/v2/examplefolder/" response = event["Records"][0]["cf"]["response"] result = lambda_handler(event, None) headers = response["headers"] headers["strict-transport-security"] = [ {"value": "max-age=63072000; includeSubdomains; preload"} ] headers["content-security-policy"] = [{"value": "default-src 'self' https://*"}] headers["x-content-type-options"] = [{"value": "nosniff"}] headers["x-frame-options"] = [{"value": "DENY"}] headers["x-xss-protection"] = [{"value": "1; mode=block"}] headers["referrer-policy"] = [{"value": "strict-origin-when-cross-origin"}] self.assertEqual(result, response) def test_not_found_with_slash(self): """ This sends a not found response for a URI with trailing slash Should return identical result with injected security headers """ event["Records"][0]["cf"]["request"]["uri"] = "/v2/examplefolder/" response = event["Records"][0]["cf"]["response"] response["status"] = 404 result = lambda_handler(event, None) headers = response["headers"] headers["strict-transport-security"] = [ {"value": "max-age=63072000; includeSubdomains; preload"} ] headers["content-security-policy"] = [ { "value": "default-src 'self' https://*.everypay.gr:443; script-src 'self' 'unsafe-inline' 'unsafe-eval' https://*.everypay.gr:443 http://www.google-analytics.com https://www.google-analytics.com https://ssl.google-analytics.com http://html5shim.googlecode.com https://html5shim.googlecode.com https://ajax.googleapis.com https://netdna.bootstrapcdn.com https://code.jquery.com https://*.doubleclick.net https://www.googletagmanager.com https://connect.facebook.net; img-src 'self' data: https://*.everypay.gr:443 http://www.google-analytics.com https://www.google-analytics.com https://ssl.google-analytics.com https://*.doubleclick.net; style-src 'self' 'unsafe-inline' 'unsafe-eval' https://*.everypay.gr:443 https://fonts.googleapis.com; font-src 'self' https://*.everypay.gr:443 https://themes.googleusercontent.com https://fonts.gstatic.com; frame-src 'self' https://*; object-src 'self' https://*.everypay.gr:443" # pylint: disable=C0301 # noqa: E501 } ] headers["x-content-type-options"] = [{"value": "nosniff"}] headers["x-frame-options"] = [{"value": "DENY"}] headers["x-xss-protection"] = [{"value": "1; mode=block"}] headers["referrer-policy"] = [{"value": "strict-origin-when-cross-origin"}] self.assertEqual(result, response) def test_not_found_without_slash(self): """ This sends a not found response for a URI without trailing slash Should return 302 redirect result tp the original URI appended with "/" and injected security headers """ event["Records"][0]["cf"]["request"]["uri"] = "/v2/examplefolder" response = event["Records"][0]["cf"]["response"] response["status"] = 404 result = lambda_handler(event, None) headers = response["headers"] headers["strict-transport-security"] = [ {"value": "max-age=63072000; includeSubdomains; preload"} ] headers["content-security-policy"] = [ { "value": "default-src 'self' https://*.everypay.gr:443; script-src 'self' 'unsafe-inline' 'unsafe-eval' https://*.everypay.gr:443 http://www.google-analytics.com https://www.google-analytics.com https://ssl.google-analytics.com http://html5shim.googlecode.com https://html5shim.googlecode.com https://ajax.googleapis.com https://netdna.bootstrapcdn.com https://code.jquery.com https://*.doubleclick.net https://www.googletagmanager.com https://connect.facebook.net; img-src 'self' data: https://*.everypay.gr:443 http://www.google-analytics.com https://www.google-analytics.com https://ssl.google-analytics.com https://*.doubleclick.net; style-src 'self' 'unsafe-inline' 'unsafe-eval' https://*.everypay.gr:443 https://fonts.googleapis.com; font-src 'self' https://*.everypay.gr:443 https://themes.googleusercontent.com https://fonts.gstatic.com; frame-src 'self' https://*; object-src 'self' https://*.everypay.gr:443" # pylint: disable=C0301 # noqa: E501 } ] headers["x-content-type-options"] = [{"value": "nosniff"}] headers["x-frame-options"] = [{"value": "DENY"}] headers["x-xss-protection"] = [{"value": "1; mode=block"}] headers["referrer-policy"] = [{"value": "strict-origin-when-cross-origin"}] headers["location"] = [{"key": "Location", "value": "/v2/examplefolder/"}] response["status"] = 302 self.assertEqual(result, response) if __name__ == "__main__": unittest.main()

58.255319

974

0.626004

618

5,476

5.511327

0.205502

0.056371

0.052848

0.063418

0.857017

0

0.025316

0.192111

5,476

93

975

58.88172

0.744575

0.09076

0

0.542857

0

0.028571

0.599343

0.069581

0

0.042857

1

0.042857

false

0

0.028571

0

0.085714

0

null

0

1

0

null

0

6

32370b765a15f6662dcf75810cbf2bc84feab958

69

py

Python

tensorflow_toolkit/lpr/lpr/__init__.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

256

2020-09-09T03:27:57.000Z

2022-03-30T10:06:06.000Z

tensorflow_toolkit/lpr/lpr/__init__.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

604

2020-09-08T12:29:49.000Z

2022-03-31T21:51:08.000Z

tensorflow_toolkit/lpr/lpr/__init__.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

160

2020-09-09T14:06:07.000Z

2022-03-30T14:50:48.000Z

from tfutils.helpers import import_transformer import_transformer()

17.25

46

0.869565

8

69

7.25

0.625

0.586207

0

0.086957

69

3

47

23

0.920635

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

08613adf55222eb81cf9aea8d6ff94d2cf2ab660

105

py

Python

groups/views.py

AliAkberAakash/learn-in-groups

850601ddd5520c850ebec12003c8337670762948

[ "MIT" ]

null

groups/views.py

AliAkberAakash/learn-in-groups

850601ddd5520c850ebec12003c8337670762948

[ "MIT" ]

null

groups/views.py

AliAkberAakash/learn-in-groups

850601ddd5520c850ebec12003c8337670762948

[ "MIT" ]

null

from django.shortcuts import render def group_list(request): return render(request, 'group_list.html')

21

42

0.8

15

105

5.466667

0.733333

0.219512

0

0.104762

105

4

43

26.25

0.87234

0

0.142857

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

0874abf4b1ea7884b6edfbac6a754d49e5cc5678

154

py

Python

lichee/utils/__init__.py

Tencent/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

91

2021-10-30T02:25:05.000Z

2022-03-28T06:51:52.000Z

lichee/utils/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

1

2021-12-17T09:30:25.000Z

2022-03-05T12:30:13.000Z

lichee/utils/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

17

2021-11-04T07:50:23.000Z

2022-03-24T14:24:11.000Z

# -*- coding: utf-8 -*- """ 工具包 """ from . import convertor from . import model_loader from . import storage from . import parallel from . import logging

15.4

26

0.688312

20

154

5.25

0.6

0.47619

0

0.007937

0.181818

154

9

27

17.111111

0.825397

0.168831

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

088c4b3ec59271d9af6031e07d4cb3e300f061c4

62

py

Python

hermes1d/__init__.py

certik/hermes1d-llnl

8e3b76fd3022af90e5c4c3923337a422d79604d5

[ "BSD-3-Clause" ]

1

2016-08-18T23:21:55.000Z

hermes1d/__init__.py

certik/hermes1d-llnl

8e3b76fd3022af90e5c4c3923337a422d79604d5

[ "BSD-3-Clause" ]

null

hermes1d/__init__.py

certik/hermes1d-llnl

8e3b76fd3022af90e5c4c3923337a422d79604d5

[ "BSD-3-Clause" ]

null

from h1d_wrapper.h1d_wrapper import Element, Mesh, Linearizer

31

61

0.854839

9

62

5.666667

0.777778

0.392157

0

0.035714

0.096774

62

1

62

0.875

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

08e364b287cb5954101aa31b3cb5304b7c80b252

35

py

Python

excel4lib/macro/analysis/__init__.py

aaaddress1/boobsnail

c0c2067d7271ca76ee721998d28e8c3c81a48397

[ "MIT" ]

169

2021-05-26T13:35:16.000Z

2021-09-06T08:04:19.000Z

excel4lib/macro/analysis/__init__.py

H4xl0r/boobsnail

c0c2067d7271ca76ee721998d28e8c3c81a48397

[ "MIT" ]

2

2021-06-01T13:46:37.000Z

2021-07-12T19:06:37.000Z

excel4lib/macro/analysis/__init__.py

H4xl0r/boobsnail

c0c2067d7271ca76ee721998d28e8c3c81a48397

[ "MIT" ]

29

2021-05-27T17:28:29.000Z

2021-09-04T19:24:50.000Z

from .excel4_anti_analysis import *

35

0.857143

5

35

5.6

1

0

0.03125

0.085714

35

1

35

0.84375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3ed7524764506c9bf8a8c074a4bfe30279004c01

36

py

Python

example_package/model_eval.py

aaronengland/example_package

1e45fccc27af57b9d2bb58e16e58ef57d81b656e

[ "MIT" ]

null

example_package/model_eval.py

aaronengland/example_package

1e45fccc27af57b9d2bb58e16e58ef57d81b656e

[ "MIT" ]

null

example_package/model_eval.py

aaronengland/example_package

1e45fccc27af57b9d2bb58e16e58ef57d81b656e

[ "MIT" ]

null

# model eval def roc_curve(): pass

9

16

0.694444

6

36

4

1

0

0.194444

36

4

17

9

0.827586

0.277778

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

4104d25dc6796ebc87ccaf0dca4400cb888e648b

169

py

Python

AutotestWebD/all_models_for_mock/models/__init__.py

yangjourney/sosotest

2e88099a829749910ca325253c9b1a2e368d21a0

[ "MIT" ]

422

2019-08-18T05:04:20.000Z

2022-03-31T06:49:19.000Z

AutotestWebD/all_models_for_mock/models/__init__.py

LinSongJian1985/sosotest

091863dee531b5726650bb63efd6f169267cbeb4

[ "MIT" ]

10

2019-10-24T09:55:38.000Z

2021-09-29T17:28:43.000Z

AutotestWebD/all_models_for_mock/models/__init__.py

LinSongJian1985/sosotest

091863dee531b5726650bb63efd6f169267cbeb4

[ "MIT" ]

202

2019-08-18T05:04:27.000Z

2022-03-30T05:57:18.000Z

from all_models_for_mock.models.M0001_mock import * from all_models_for_mock.models.M0002_keywords import * from all_models_for_mock.models.M0003_statisic_task import *

42.25

60

0.87574

28

169

4.821429

0.428571

0.155556

0.288889

0.355556

0.666667

0.474074

0

0.076433

0.071006

169

3

61

56.333333

0.783439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

41252221870a25e5a2cfca108df770ea4c662895

2,955

py

Python

test/test_storage_v1beta1_api.py

Arvinhub/client-python

d67df30f635231d68dc4c20b9b7e234c616c1e6a

[ "Apache-2.0" ]

1

2021-06-16T02:57:18.000Z

test/test_storage_v1beta1_api.py

Arvinhub/client-python

d67df30f635231d68dc4c20b9b7e234c616c1e6a

[ "Apache-2.0" ]

null

test/test_storage_v1beta1_api.py

Arvinhub/client-python

d67df30f635231d68dc4c20b9b7e234c616c1e6a

[ "Apache-2.0" ]

null

# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: unversioned Generated by: https://github.com/swagger-api/swagger-codegen.git Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from __future__ import absolute_import import os import sys import unittest import k8sclient from k8sclient.rest import ApiException from k8sclient.apis.storage_v1beta1_api import StorageV1beta1Api class TestStorageV1beta1Api(unittest.TestCase): """ StorageV1beta1Api unit test stubs """ def setUp(self): self.api = k8sclient.apis.storage_v1beta1_api.StorageV1beta1Api() def tearDown(self): pass def test_create_storage_v1beta1_storage_class(self): """ Test case for create_storage_v1beta1_storage_class """ pass def test_delete_storage_v1beta1_collection_storage_class(self): """ Test case for delete_storage_v1beta1_collection_storage_class """ pass def test_delete_storage_v1beta1_storage_class(self): """ Test case for delete_storage_v1beta1_storage_class """ pass def test_get_storage_v1beta1_api_resources(self): """ Test case for get_storage_v1beta1_api_resources """ pass def test_list_storage_v1beta1_storage_class(self): """ Test case for list_storage_v1beta1_storage_class """ pass def test_patch_storage_v1beta1_storage_class(self): """ Test case for patch_storage_v1beta1_storage_class """ pass def test_read_storage_v1beta1_storage_class(self): """ Test case for read_storage_v1beta1_storage_class """ pass def test_replace_storage_v1beta1_storage_class(self): """ Test case for replace_storage_v1beta1_storage_class """ pass def test_watch_storage_v1beta1_storage_class(self): """ Test case for watch_storage_v1beta1_storage_class """ pass def test_watch_storage_v1beta1_storage_class_list(self): """ Test case for watch_storage_v1beta1_storage_class_list """ pass if __name__ == '__main__': unittest.main()

23.085938

105

0.666328

346

2,955

5.369942

0.33526

0.16577

0.18084

0.223897

0.543057

0.451561

0.436491

0.315931

0.159311

0.073197

0

0.028412

0.273435

2,955

127

106

23.267717

0.836982

0.441963

0

0.323529

1

0

0.006405

0

1

0.352941

false

0.323529

0.205882

0

0.588235

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

f5bc7050656c4c3afee2238a72f86661143054d5

598

py

Python

pysal/spreg/__init__.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

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

null

pysal/spreg/__init__.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

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

null

pysal/spreg/__init__.py

cubensys/pysal

8d50990f6e6603ba79ae1a887a20a1e3a0734e51

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

1

2021-07-19T01:46:17.000Z

from ols import * from diagnostics import * from diagnostics_sp import * from user_output import * from twosls import * from twosls_sp import * from error_sp import * from error_sp_het import * from error_sp_hom import * from ols_regimes import * from twosls_regimes import * from twosls_sp_regimes import * from error_sp_regimes import * from error_sp_het_regimes import * from error_sp_hom_regimes import * from probit import * from ml_lag import * from ml_lag_regimes import * from ml_error import * from ml_error_regimes import * from sur import * from sur_error import * from sur_lag import *

24.916667

34

0.807692

97

598

4.690722

0.164948

0.483516

0.298901

0.224176

0.305495

0.10989

0

0.153846

598

23

35

26

0.899209

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

f5bdaf65264833d8c298cbab96f3a7c910693f18

209

py

Python

tests/conftest.py

lambertsbennett/Encountertk

708aedb38cb1689da8d2f39c68bd8694c64a79da

[ "MIT" ]

null

tests/conftest.py

lambertsbennett/Encountertk

708aedb38cb1689da8d2f39c68bd8694c64a79da

[ "MIT" ]

null

tests/conftest.py

lambertsbennett/Encountertk

708aedb38cb1689da8d2f39c68bd8694c64a79da

[ "MIT" ]

null

from pytest import fixture from encountertk.e_model import EncounterModel, ps_encounter, mean_vol_encountered @fixture(scope='function') def EModel(): return EncounterModel(kernel=1,pop2c=[1],pop1c=[1])

26.125

82

0.789474

28

209

5.75

0.785714

0

0.026596

0.100478

209

7

83

29.857143

0.829787

0

0.038278

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

eb0939a06759c9dcb9a5c2eda6614c361061cde9

857

py

Python

pySINGLE/setup.py

piomonti/pySINGLE

2eae0b31334d8eae08fd7f96f591262c4abcf3d9

[ "MIT" ]

3

2015-12-21T15:14:08.000Z

2018-12-29T10:15:03.000Z

pySINGLE/setup.py

piomonti/pySINGLE

2eae0b31334d8eae08fd7f96f591262c4abcf3d9

[ "MIT" ]

null

pySINGLE/setup.py

piomonti/pySINGLE

2eae0b31334d8eae08fd7f96f591262c4abcf3d9

[ "MIT" ]

null

#from distutils.core import setup #from distutils.extension import Extension #from Cython.Distutils import build_ext #import numpy #setup( #cmdclass = {'build_ext': build_ext}, #ext_modules = [Extension("Z_shooting", ["Z_shooting.c"],)], #include_dirs=[numpy.get_include(),'.', ], #) from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext from Cython.Build import cythonize import numpy #extension = [Extension("Z_shooting", ["Z_shooting.c"],),] setup( cmdclass = {'build_ext': build_ext}, ext_modules = [Extension("FastFused_01", ["FastFused_01.pyx"], include_dirs=[numpy.get_include()])] ) #setup( #cmdclass = {'build_ext': build_ext}, #ext_modules = cythonize("FastFused_01.pyx"), #include_dirs=[numpy.get_include(),'.', ], #)

27.645161

104

0.687281

103

857

5.485437

0.213592

0.113274

0.095575

0.111504

0.856637

0.810619

0.727434

0.516814

0.346903

0

0.008345

0.161027

857

30

105

28.566667

0.777469

0.526254

0

0.102493

0

1

0

true

0

0.555556

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

6

de1e40b74da53919bbdc4c6c8dda38d5aba2c247

27

py

Python

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

from . import frame_manager

27

0.851852

4

27

5.5

1

0

0.111111

27

1

27

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

de53cfe343832488633720622d964252c48b5617

3,180

py

Python

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

import unittest from calculator import tokens, evaluator from calculator.parser import tokenize, infix_to_postfix class MyTestPostfixCase(unittest.TestCase): def test_simple_operator(self): expression = "2 + 1" computed_token_list = tokenize(expression) postfix_token_list = infix_to_postfix(computed_token_list) token_list = [ tokens.OperandToken(2), tokens.OperandToken(1), tokens.PlusOperatorToken(), ] self.assertListEqual(postfix_token_list, token_list) def test_multiple_operators(self): expression = "2 + 1 * 5" computed_token_list = tokenize(expression) postfix_token_list = infix_to_postfix(computed_token_list) token_list = [ tokens.OperandToken(2), tokens.OperandToken(1), tokens.OperandToken(5), tokens.ProductOperatorToken(), tokens.PlusOperatorToken(), ] self.assertListEqual(postfix_token_list, token_list) def test_multiple_operators_reversed(self): expression = "2 * 1 + 5" computed_token_list = tokenize(expression) postfix_token_list = infix_to_postfix(computed_token_list) token_list = [ tokens.OperandToken(2), tokens.OperandToken(1), tokens.ProductOperatorToken(), tokens.OperandToken(5), tokens.PlusOperatorToken(), ] self.assertListEqual(postfix_token_list, token_list) def test_parenthesis(self): expression = "2 * (1 + 5)" computed_token_list = tokenize(expression) postfix_token_list = infix_to_postfix(computed_token_list) token_list = [ tokens.OperandToken(2), tokens.OperandToken(1), tokens.OperandToken(5), tokens.PlusOperatorToken(), tokens.ProductOperatorToken() ] self.assertListEqual(postfix_token_list, token_list) def test_missing_left_parenthesis(self): expression = "2 * 2) + 1 + 5" computed_token_list = tokenize(expression) with self.assertRaises(RuntimeError): postfix_token_list = infix_to_postfix(computed_token_list) def test_missing_right_parenthesis(self): expression = "2 * (1 + 5" computed_token_list = tokenize(expression) with self.assertRaises(RuntimeError): postfix_token_list = infix_to_postfix(computed_token_list) def test_simple_function(self): expression = "sin 5" computed_token_list = tokenize(expression) postfix_token_list = infix_to_postfix(computed_token_list) token_list = [ tokens.OperandToken(5), tokens.SinFunctionToken(), ] self.assertListEqual(postfix_token_list, token_list) def test_equation_in_postfix_not_allowed(self): with self.assertRaises(RuntimeError): evaluator.evaluate('(5 + 2)', True) with self.assertRaises(RuntimeError): evaluator.evaluate('x + 1', True) with self.assertRaises(RuntimeError): evaluator.evaluate('x = 5', True)

31.485149

70

0.646226

321

3,180

6.093458

0.155763

0.165644

0.121677

0.092025

0.808793

0.783231

0.75818

0.702965

0.650818

0

0.015073

0.269811

3,180

100

71

31.8

0.827304

0

0.618421

0

0.025157

0

0.131579

1

0.105263

false

0

0.039474

0

0.157895

0

null

0

1

0

null

0

6

de69814605b1835959a1ffdafc1b9774d60d18ad

75

py

Python

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

from .progress_bar import ProgressBar from .read_config import read_config

25

37

0.866667

11

75

5.636364

0.636364

0.322581

0

0.106667

75

2

38

37.5

0.925373

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

de93263b9043812ffa8057bd744f43dfad03bbdf

27

py

Python

py2ifttt/__init__.py

moevis/py2ifttt

99dc2be647c53c9279f2f212528fef7190de7476

[ "MIT" ]

3

2018-05-04T12:50:04.000Z

2020-02-28T03:22:53.000Z

py2ifttt/__init__.py

moevis/py2ifttt

99dc2be647c53c9279f2f212528fef7190de7476

[ "MIT" ]

null

py2ifttt/__init__.py

moevis/py2ifttt

99dc2be647c53c9279f2f212528fef7190de7476

[ "MIT" ]

null

from .py2ifttt import IFTTT

27

0.851852

4

27

5.75

1

0

0.041667

0.111111

27

1

27

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

debc22c03ed999e303334d1da3320e421b5bfacc

119

py

Python

applications/jupyter-extension/nteract_on_jupyter/notebooks/utils/cb/python/__init__.py

jjhenkel/nteract

088222484b59af14b1da22de4d0990d8925adf95

[ "BSD-3-Clause" ]

null

applications/jupyter-extension/nteract_on_jupyter/notebooks/utils/cb/python/__init__.py

jjhenkel/nteract

088222484b59af14b1da22de4d0990d8925adf95

[ "BSD-3-Clause" ]

null

applications/jupyter-extension/nteract_on_jupyter/notebooks/utils/cb/python/__init__.py

jjhenkel/nteract

088222484b59af14b1da22de4d0990d8925adf95

[ "BSD-3-Clause" ]

null

from .surface import * from .modifiers import * from .evaluator import Evaluator from .lowlevel import display_results

23.8

37

0.815126

15

119

6.4

0.533333

0.208333

0

0.134454

119

4

38

29.75

0.932039

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

dec6337c650811d4d0bda0d9fb32eb5e333b7344

15,088

py

Python

Project-2/Ishan Pandey/job_compare.py

Mercury1508/IEEE-LEAD-2.0

91d24ccf2f24c62f92f0d23bcfcb3988e6d5acd8

[ "MIT" ]

1

2021-06-03T16:08:33.000Z

Project-2/Ishan Pandey/job_compare.py

Mercury1508/IEEE-LEAD-2.0

91d24ccf2f24c62f92f0d23bcfcb3988e6d5acd8

[ "MIT" ]

16

2021-04-27T12:58:03.000Z

2021-05-28T14:02:14.000Z

Project-2/Ishan Pandey/job_compare.py

Mercury1508/IEEE-LEAD-2.0

91d24ccf2f24c62f92f0d23bcfcb3988e6d5acd8

[ "MIT" ]

70

2021-04-26T13:48:35.000Z

2021-05-28T21:04:34.000Z

# from job_scrapper_gui import naukri_gui from tkinter import * from PIL import ImageTk import PIL.Image import naukri_scrapper import linkedin_scrapper import indeed import simply_hired_scrapper from selenium import webdriver root = Tk() root.title("Compare Jobs") root.geometry("1000x670") root.configure(background='white') # ----------------Header GUI---------------- # -------Creating All labels-------- logo = ImageTk.PhotoImage(PIL.Image.open("./Images\compare.png")) header_frame = LabelFrame(bg="#135EC2",borderwidth=0,highlightthickness=0) # logo logo_label = Label(header_frame,image=logo,bg='#135EC2') # job title container job_title_frame = LabelFrame(header_frame,bg='#135EC2',borderwidth=0,highlightthickness=0) job_label = Label(job_title_frame,text="JOB TITLE",bg='#135EC2',fg="white",font=('Bahnschrift Light', 13, 'normal')) job_profile_box = Entry(job_title_frame, width=30, font=('Bahnschrift Light', 13, 'normal')) # location container location_frame = LabelFrame(header_frame,bg='#135EC2',borderwidth=0,highlightthickness=0) location_label = Label(location_frame,text="LOCATION",bg='#135EC2',fg="white",font=('Bahnschrift Light', 13, 'normal')) location_box = Entry(location_frame, width=30, font=('Bahnschrift Light', 13, 'normal')) # compare button container compare_button_frame = LabelFrame(header_frame,padx=50,pady=10,bg='#135EC2',borderwidth=0,highlightthickness=0) # ------labels created------- # ------packing labels------- header_frame.pack(fill=X) logo_label.grid(row=0,column=0,pady=3,padx=10) job_title_frame.grid(row=0,column=1,pady=10,padx=20) job_profile_box.pack() job_label.pack(side=LEFT) location_frame.grid(row=0,column=2,pady=10,padx=20) location_box.pack() location_label.pack(side=LEFT) compare_button_frame.grid(row=1,column=1,columnspan=2) # ------------Header GUI ends-------------- # ------------Compare JOBS GUI-------------- card_container = LabelFrame(root,bg="white",pady=20,borderwidth=0,highlightthickness=0) card_container.pack(fill=X) def visit(website): if website=="Naukri.com": url = str(naukri_scrapper.card_link) driver = webdriver.Chrome("./chromedriver.exe") driver.get(url) if website=="indeed.com": url = str(indeed.card_link) driver = webdriver.Chrome("./chromedriver.exe") driver.get(url) if website=="Linkedin": url = str(linkedin_scrapper.card_link) driver = webdriver.Chrome("./chromedriver.exe") driver.get(url) if website=="SimplyHired": url = str(simply_hired_scrapper.card_link) driver = webdriver.Chrome("./chromedriver.exe") driver.get(url) # ----Naukri.com GUI---- def naukri_gui(): if len(naukri_scrapper.cards)==0: error_card_frame = LabelFrame(card_container,bg="white") site_name_label = Label(error_card_frame,text="Naukri.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) no_result_label = Label(error_card_frame,text="No result",bg="white",font=('Bahnschrift Light', 12, 'bold'),fg="#135EC2",highlightthickness=0) message_frame = LabelFrame(error_card_frame,bg="white",borderwidth=0,highlightthickness=0) message_lable = Label(message_frame,text="OOPS!!! check your keyword and try again",bg="white",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",highlightthickness=0) error_card_frame.pack() site_name_label.pack(fill=X,pady=1) no_result_label.pack() message_frame.pack() message_lable.pack(side=LEFT) else : card_frame = LabelFrame(card_container,bg="white") site_name_frame = LabelFrame(card_frame,bg="white",borderwidth=0,highlightthickness=0) site_name_lable = Label(site_name_frame,text="Naukri.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) visit_button = Button(site_name_frame,text="Visit",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",bg="white",command=lambda: visit("Naukri.com")) head_frame = LabelFrame(card_frame,borderwidth=0,highlightthickness=0,bg="#135EC2") title_lable = Label(head_frame,text=naukri_scrapper.title,wraplength=300,font=('Bahnschrift Light', 13, 'bold'),bg="#135EC2",fg="white") company_name_lable = Label(head_frame,text=naukri_scrapper.company_name,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") job_location_label = Label(head_frame,text=naukri_scrapper.job_location,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") rating_label = Label(head_frame,text=f"Rating: {naukri_scrapper.rating}",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") salary_label = Label(head_frame,text=f"Salary: {naukri_scrapper.salary}",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") skills_label = Label(head_frame,text=f"Skills:\n{naukri_scrapper.skills}",wraplength=300,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") card_frame.grid(row=0,column=0,padx=100) site_name_frame.pack(fill=X) visit_button.pack(side=RIGHT,pady=1,padx=1) site_name_lable.pack(pady=1) head_frame.pack(fill=X) title_lable.pack() company_name_lable.pack() job_location_label.pack() rating_label.pack() salary_label.pack() skills_label.pack() # ----Naukir.com GUI completed---- # --------indeed GUI-------------- def indeed_gui(): if len(indeed.cards)==0: error_card_frame = LabelFrame(card_container,bg="white") site_name_label = Label(error_card_frame,text="indeed.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) no_result_label = Label(error_card_frame,text="No result",bg="white",font=('Bahnschrift Light', 12, 'bold'),fg="#135EC2",highlightthickness=0) message_frame = LabelFrame(error_card_frame,bg="white",borderwidth=0,highlightthickness=0) message_lable = Label(message_frame,text="OOPS!!! check your keyword and try again",bg="white",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",highlightthickness=0) error_card_frame.grid(row=0,column=1) site_name_label.pack(fill=X,pady=1) no_result_label.pack() message_frame.pack() message_lable.pack(side=LEFT) else: card_frame = LabelFrame(card_container,bg="white") site_name_frame = LabelFrame(card_frame,bg="white",borderwidth=0,highlightthickness=0) site_name_lable = Label(site_name_frame,text="indeed.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) visit_button = Button(site_name_frame,text="Visit",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",bg="white",command=lambda: visit("indeed.com")) head_frame = LabelFrame(card_frame,borderwidth=0,highlightthickness=0,bg="#135EC2") title_lable = Label(head_frame,text=indeed.title,wraplength=300,font=('Bahnschrift Light', 13, 'bold'),bg="#135EC2",fg="white") company_name_lable = Label(head_frame,text=indeed.company_name,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") job_location_label = Label(head_frame,text=indeed.job_location,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") rating_label = Label(head_frame,text=f"Rating: {indeed.rating}",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") salary_label = Label(head_frame,text=f"Salary: {indeed.salary}",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") card_frame.grid(row=0,column=1,padx=50) site_name_frame.pack(fill=X) visit_button.pack(side=RIGHT,pady=1,padx=1) site_name_lable.pack(pady=1) head_frame.pack(fill=X) title_lable.pack(padx=50) company_name_lable.pack() job_location_label.pack() rating_label.pack() salary_label.pack() # ----indeed GUI completed---- # -------Linkedin GUI--------- def linkedin_gui(): if len(linkedin_scrapper.cards)==0: error_card_frame = LabelFrame(card_container,bg="white") site_name_label = Label(error_card_frame,text="Linkedin",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) no_result_label = Label(error_card_frame,text="No result",bg="white",font=('Bahnschrift Light', 12, 'bold'),fg="#135EC2",highlightthickness=0) message_frame = LabelFrame(error_card_frame,bg="white",borderwidth=0,highlightthickness=0) message_lable = Label(message_frame,text="OOPS!!! check your keyword and try again",bg="white",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",highlightthickness=0) error_card_frame.grid(row=1,column=0) site_name_label.pack(fill=X,pady=1) no_result_label.pack() message_frame.pack() message_lable.pack(side=LEFT) else: card_frame = LabelFrame(card_container,bg="white") site_name_frame = LabelFrame(card_frame,bg="white",borderwidth=0,highlightthickness=0) site_name_lable = Label(site_name_frame,text="indeed.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) visit_button = Button(site_name_frame,text="Visit",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",bg="white",command=lambda: visit("Linkedin")) head_frame = LabelFrame(card_frame,borderwidth=0,highlightthickness=0,bg="#135EC2") title_lable = Label(head_frame,text=linkedin_scrapper.title,wraplength=300,font=('Bahnschrift Light', 13, 'bold'),bg="#135EC2",fg="white") company_name_lable = Label(head_frame,text=linkedin_scrapper.company_name,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") job_location_label = Label(head_frame,text=linkedin_scrapper.job_location,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") rating_label = Label(head_frame,text="Rating: Not Available",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") salary_label = Label(head_frame,text="Salary: Not Available",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") card_frame.grid(row=1,column=0,padx=100,pady=5) site_name_frame.pack(fill=X) visit_button.pack(side=RIGHT,pady=1,padx=1) site_name_lable.pack(pady=1) head_frame.pack(fill=X) title_lable.pack(padx=50) company_name_lable.pack(pady=5) job_location_label.pack(pady=5) rating_label.pack() salary_label.pack() # --------Linkedin GUI completed------------ # ---------SimplyHired GUI------------------ def simply_hired_gui(): if len(simply_hired_scrapper.cards)==0: error_card_frame = LabelFrame(card_container,bg="white",borderwidth=0,highlightthickness=0) site_name_label = Label(error_card_frame,text="SimplyHired",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) no_result_label = Label(error_card_frame,text="No result",bg="white",font=('Bahnschrift Light', 12, 'bold'),fg="#135EC2",highlightthickness=0) message_frame = LabelFrame(error_card_frame,bg="white") message_lable = Label(message_frame,text="OOPS!!! check your keyword and try again",bg="white",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",highlightthickness=0) error_card_frame.grid(row=1,column=1) site_name_label.pack(fill=X,pady=1) no_result_label.pack() message_frame.pack() message_lable.pack(side=LEFT) else: card_frame = LabelFrame(card_container,bg="white") site_name_frame = LabelFrame(card_frame,bg="white",borderwidth=0,highlightthickness=0) site_name_lable = Label(site_name_frame,text="indeed.com",bg="white",font=('Bahnschrift Light', 11, 'bold'),fg="#135EC2",highlightthickness=0) visit_button = Button(site_name_frame,text="Visit",font=('Bahnschrift Light', 10, 'bold'),fg="#135EC2",bg="white",command=lambda: visit("SimplyHired")) head_frame = LabelFrame(card_frame,borderwidth=0,highlightthickness=0,bg="#135EC2") title_lable = Label(head_frame,text=simply_hired_scrapper.title,wraplength=300,font=('Bahnschrift Light', 13, 'bold'),bg="#135EC2",fg="white") company_name_lable = Label(head_frame,text=simply_hired_scrapper.company_name,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") job_location_label = Label(head_frame,text=simply_hired_scrapper.job_location,wraplength=250,font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") rating_label = Label(head_frame,text="Rating: Not Available",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") salary_label = Label(head_frame,text="Salary: Not Available",font=('Bahnschrift Light', 11, 'normal'),bg="#135EC2",fg="white") card_frame.grid(row=1,column=1,padx=50,pady=5) site_name_frame.pack(fill=X) visit_button.pack(side=RIGHT,pady=1,padx=1) site_name_lable.pack(pady=1) head_frame.pack(fill=X) title_lable.pack(pady=5,padx=50) company_name_lable.pack(pady=5) job_location_label.pack(pady=5) rating_label.pack() salary_label.pack() # ----------SimplyHired GUI------------- def close(): global card_container card_container.pack_forget() card_container = LabelFrame(root,bg="white",pady=20,borderwidth=0,highlightthickness=0) card_container.pack(fill=X) is_both_empty = False def compare(): global card_container card_container.pack_forget() card_container = LabelFrame(root,bg="white",pady=20,borderwidth=0,highlightthickness=0) card_container.pack(fill=X) if not str(job_profile_box.get()) and not str(location_box.get()) : global is_both_empty is_both_empty=True message_frame = LabelFrame(card_container,bg="white",padx=20,pady=30) message_label = Label(message_frame,text="Please enter Job Title and Location to compare",font=('Bahnschrift Light', 14, 'bold'),bg="white",fg="#135EC2") close_button = Button(card_container,text="Close",font=('Bahnschrift Light', 14, 'bold'),bg="white",fg="#135EC2",command=close) message_frame.pack(pady=20) message_label.pack() close_button.pack() else: job_profile = str(job_profile_box.get()) location = str(location_box.get()) naukri_scrapper.naukri_search(job_profile,location,0) indeed.indeed_search(job_profile,location,0) linkedin_scrapper.linkedin_search(job_profile,location,0) simply_hired_scrapper.simplyhired_search(job_profile,location,0) naukri_gui() indeed_gui() linkedin_gui() simply_hired_gui() compare_button = Button(compare_button_frame,text="Comapre",padx=15,pady=7,font=('Bahnschrift Light', 12, 'bold'),bg="white",fg="#135EC2",command=compare) compare_button.pack() root.mainloop()

57.808429

176

0.696911

2,036

15,088

4.97053

0.073183

0.071146

0.094862

0.054348

0.808498

0.787352

0.762451

0.758103

0.741403

0.724802

0

0.03989

0.136002

15,088

261

177

57.808429

0.736422

0.039767

0

0.486239

0

0.176723

0.005598

0

1

0.03211

false

0

0.036697

0

0.068807

0

null

0

1

0

null

0

6

decfc0841b9274cca97b69d13faf37aa2232005f

58

py

Python

tests/cases/print.py

wisn/py2many

e33871a3e54971407319e9df28dcadcdc3a49140

[ "MIT" ]

1

2021-05-14T00:35:04.000Z

tests/cases/print.py

wisn/py2many

e33871a3e54971407319e9df28dcadcdc3a49140

[ "MIT" ]

null

tests/cases/print.py

wisn/py2many

e33871a3e54971407319e9df28dcadcdc3a49140

[ "MIT" ]

null

def main(): print(2) print("b") print(2, "b")

11.6

17

0.448276

9

58

2.888889

0.555556

0.461538

0

0.05

0.310345

58

4

18

14.5

0.6

0

0.034483

0

1

0.25

true

0

0.25

0.75

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a0e7af4439dc68e76e3dc02f0c28bddc41d0fe5c

7,662

py

Python

robosuite/models/objects/xml_objects.py

ClaireLC/robosuite

b5c37f1110aefc02106ffd2aed0dfb106bc1bb33

[ "MIT" ]

1

2021-12-22T13:10:46.000Z

robosuite/models/objects/xml_objects.py

wangcongrobot/robosuite-jr

738be7a3a83447e78763f6a082faafc8b479c95d

[ "MIT" ]

null

robosuite/models/objects/xml_objects.py

wangcongrobot/robosuite-jr

738be7a3a83447e78763f6a082faafc8b479c95d

[ "MIT" ]

1

2020-12-29T01:38:01.000Z

from robosuite.models.objects import MujocoXMLObject from robosuite.utils.mjcf_utils import xml_path_completion, array_to_string, string_to_array class BottleObject(MujocoXMLObject): """ Bottle object """ def __init__(self): super().__init__(xml_path_completion("objects/bottle.xml")) class CanObject(MujocoXMLObject): """ Coke can object (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/can.xml")) class LemonObject(MujocoXMLObject): """ Lemon object """ def __init__(self): super().__init__(xml_path_completion("objects/lemon.xml")) class MilkObject(MujocoXMLObject): """ Milk carton object (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/milk.xml")) class BreadObject(MujocoXMLObject): """ Bread loaf object (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/bread.xml")) class CerealObject(MujocoXMLObject): """ Cereal box object (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/cereal.xml")) class SquareNutObject(MujocoXMLObject): """ Square nut object (used in SawyerNutAssembly) """ def __init__(self): super().__init__(xml_path_completion("objects/square-nut.xml")) class RoundNutObject(MujocoXMLObject): """ Round nut (used in SawyerNutAssembly) """ def __init__(self): super().__init__(xml_path_completion("objects/round-nut.xml")) class MilkVisualObject(MujocoXMLObject): """ Visual fiducial of milk carton (used in SawyerPickPlace). Fiducial objects are not involved in collision physics. They provide a point of reference to indicate a position. """ def __init__(self): super().__init__(xml_path_completion("objects/milk-visual.xml")) class BreadVisualObject(MujocoXMLObject): """ Visual fiducial of bread loaf (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/bread-visual.xml")) class CerealVisualObject(MujocoXMLObject): """ Visual fiducial of cereal box (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/cereal-visual.xml")) class CanVisualObject(MujocoXMLObject): """ Visual fiducial of coke can (used in SawyerPickPlace) """ def __init__(self): super().__init__(xml_path_completion("objects/can-visual.xml")) class PlateWithHoleObject(MujocoXMLObject): """ Square plate with a hole in the center (used in BaxterPegInHole) """ def __init__(self): super().__init__(xml_path_completion("objects/plate-with-hole.xml")) class DoorPullNoLatchObject(MujocoXMLObject): """ Door: pull with no latch """ def __init__(self): #super().__init__(xml_path_completion("objects/door_dapg.xml")) super().__init__(xml_path_completion("objects/door_pull_no_latch.xml")) def set_goal_xpos(self, x_delta, y_delta): """ Sets x,y position of goal site in door model with x and y offset from door center""" door_center_site = self.worldbody.find("./body/body/body/site[@name='door_center']") door_center_pos = string_to_array(door_center_site.get("pos")) goal_site = self.worldbody.find("./body/body/body/site[@name='goal']") goal_site.set("pos", array_to_string([door_center_pos[0] + x_delta, door_center_pos[1] + y_delta, -1.0])) @property def handle_contact_geoms(self): return[ "handle_base", "handle", ] @property def door_contact_geoms(self): return[ "door_box", "door_r_cyl", "door_l_cyl", "l_frame", "r_frame", ] class DoorPullWithLatchObject(MujocoXMLObject): """ Door: pull with latch """ def __init__(self): #super().__init__(xml_path_completion("objects/door_dapg.xml")) super().__init__(xml_path_completion("objects/door_pull_with_latch.xml")) def set_goal_xpos(self, x_delta, y_delta): """ Sets x,y position of goal site in door model with x and y offset from door center""" door_center_site = self.worldbody.find("./body/body/body/site[@name='door_center']") door_center_pos = string_to_array(door_center_site.get("pos")) goal_site = self.worldbody.find("./body/body/body/site[@name='goal']") goal_site.set("pos", array_to_string([door_center_pos[0] + x_delta, door_center_pos[1] + y_delta, -1.0])) @property def handle_contact_geoms(self): return[ "handle_base", "handle", ] @property def door_contact_geoms(self): return[ "door_box", "door_r_cyl", "door_l_cyl", "l_frame", "r_frame", ] class DoorPullNoLatchRoomObject(MujocoXMLObject): """ Door: pull with latch with walls """ def __init__(self): super().__init__(xml_path_completion("objects/door_pull_no_latch_room.xml")) def set_goal_xpos(self, x_delta, y_delta): """ Sets x,y position of goal site in door model with x and y offset from door center""" door_center_site = self.worldbody.find("./body/body/body/site[@name='door_center']") door_center_pos = string_to_array(door_center_site.get("pos")) goal_site = self.worldbody.find("./body/body/body/site[@name='goal']") goal_site.set("pos", array_to_string([door_center_pos[0] + x_delta, door_center_pos[1] + y_delta, -1.0])) @property def handle_contact_geoms(self): return[ "handle_base", "handle", ] @property def door_contact_geoms(self): return[ "door_box", "door_r_cyl", "door_l_cyl", "l_frame", "r_frame", ] @property def wall_contact_geoms(self): return[ "wall_g0", "wall_g1", "wall_g2", "wall_g3", ] class DoorPullNoLatchRoomWideObject(MujocoXMLObject): """ Door: pull with no latch with walls """ def __init__(self): super().__init__(xml_path_completion("objects/door_pull_no_latch_room_wide.xml")) def set_goal_xpos(self, x_delta, y_delta): """ Sets x,y position of goal site in door model with x and y offset from door center""" door_center_site = self.worldbody.find("./body/body/body/site[@name='door_center']") door_center_pos = string_to_array(door_center_site.get("pos")) goal_site = self.worldbody.find("./body/body/body/site[@name='goal']") goal_site.set("pos", array_to_string([door_center_pos[0] + x_delta, door_center_pos[1] + y_delta, -1.0])) @property def handle_contact_geoms(self): return[ "handle_base", "handle", ] @property def door_contact_geoms(self): return[ "door_box", "door_r_cyl", "door_l_cyl", "l_frame", "r_frame", ] @property def wall_contact_geoms(self): return[ "wall_g0", "wall_g1", "wall_g2", "wall_g3", ] class EmptyWithGoalObject(MujocoXMLObject): """ Empty arena with goal site """ def __init__(self): super().__init__(xml_path_completion("objects/empty_with_goal.xml")) def set_goal_xpos(self, x_delta, y_delta): """ Sets x,y position of goal site in door model with x and y offset from door center""" goal_site = self.worldbody.find("./body/body/site[@name='goal']") goal_site.set("pos", array_to_string([x_delta, y_delta, 0]))

26.512111

111

0.658053

970

7,662

4.806186

0.123711

0.062205

0.076577

0.06864

0.743887

0.732304

0.719863

0.7145

0.695624

0

0.004142

0.212216

7,662

288

112

26.604167

0.768224

0.178674

0

0.677419

0

0.18097

0.110982

0

1

0.212903

false

0

0.012903

0.064516

0.341935

0

null

0

1

0

null

0

1

0

6

9d192ebb1226024bcb7fe7faa5cd19ef549419f8

130

py

Python

illud/exceptions/quit_exception.py

AustinScola/illud

a6aca1de38bbe9d5a795aaa084bcbd6731767d18

[ "MIT" ]

1

2020-12-05T00:59:15.000Z

illud/exceptions/quit_exception.py

AustinScola/illud

a6aca1de38bbe9d5a795aaa084bcbd6731767d18

[ "MIT" ]

112

2021-01-15T21:42:27.000Z

2021-04-17T19:11:21.000Z

illud/exceptions/quit_exception.py

AustinScola/illud

a6aca1de38bbe9d5a795aaa084bcbd6731767d18

[ "MIT" ]

null

"""Raised to quit.""" from illud.exception import IlludException class QuitException(IlludException): """Raised to quit."""

18.571429

42

0.723077

14

130

6.714286

0.714286

0.170213

0.255319

0

0.138462

130

6

43

21.666667

0.839286

0.238462

0

1

0

true

0

0.5

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c2221b8872b6350f052296a7af3215fb075a5795

30

py

Python

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

34

2019-12-13T10:31:39.000Z

2022-03-09T15:59:07.000Z

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

49

2020-02-25T19:41:09.000Z

2022-02-27T12:05:25.000Z

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

23

2019-12-23T15:19:42.000Z

2022-03-09T16:00:15.000Z

from .rmq_item import RMQItem

15

29

0.833333

5

30

4.8

1

0

0.133333

30

1

30

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dfb68a5201db3b2abf55a2e729e1d1531d27950c

77

py

Python

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

11

2017-03-01T18:00:30.000Z

2021-12-10T05:11:02.000Z

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

397

2016-07-08T14:39:46.000Z

2022-03-30T12:45:09.000Z

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

14

2016-07-13T08:33:28.000Z

2020-04-22T21:42:21.000Z

import subprocess def execute(cmd): return subprocess.check_output(cmd)

15.4

39

0.779221

10

77

5.9

0.8

0

0.142857

77

4

40

19.25

0.893939

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

dfc53823fb3adccd40e9762c665f5bb3deecbf27

95

py

Python

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

2

2020-03-23T23:16:51.000Z

2020-04-26T21:15:11.000Z

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

null

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

null

NEWS_API_KEY= '138b22df68394ecbaa9c9af0d0377adb' SECRET_KEY= 'f9bf78b9a18ce6d46a0cd2b0b86df9da'

47.5

48

0.905263

7

95

11.857143

0.857143

0

0.347826

0.031579

95

2

49

47.5

0.554348

0

0.666667

0

1

0

false

0

1

0

1

null

0

1

0

1

0

1

null

0

6

dfe9372c929b790c9a52b80b77bdd70cddddba45

187

py

Python

problem1.py

bakwc/PyCodeMonkey

32ea3a8947133ee9f96bea269a5dfd7a5b264ac1

[ "MIT" ]

null

problem1.py

bakwc/PyCodeMonkey

32ea3a8947133ee9f96bea269a5dfd7a5b264ac1

[ "MIT" ]

null

problem1.py

bakwc/PyCodeMonkey

32ea3a8947133ee9f96bea269a5dfd7a5b264ac1

[ "MIT" ]

null

# find fibonacci number def myFib(n): pass def tests(): assert myFib(1) == 1 assert myFib(2) == 1 assert myFib(3) == 2 assert myFib(4) == 3 assert myFib(5) == 5

15.583333

24

0.561497

29

187

3.62069

0.482759

0.52381

0.228571

0

0.075758

0.294118

187

11

25

17

0.719697

0.112299

0

0.625

1

0.25

false

0.125

0

0.25

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

5f27a02869b20dee1a16b8900aaf7b0000709f7f

105

py

Python

discobot/utils.py

pythonology/disco

82cc0434e584a1053075b51f487a3ac9b03f6f7d

[ "MIT" ]

7

2016-04-13T23:03:36.000Z

2016-04-19T22:25:28.000Z

discobot/utils.py

pythonology/disco

82cc0434e584a1053075b51f487a3ac9b03f6f7d

[ "MIT" ]

2

2016-06-03T16:12:00.000Z

2021-03-25T21:40:27.000Z

discobot/utils.py

pythonology/disco

82cc0434e584a1053075b51f487a3ac9b03f6f7d

[ "MIT" ]

1

2016-04-13T18:43:19.000Z

def make_attachment_uri(discriminator, filename): return 'disco://%s/%s' % (discriminator, filename)

35

54

0.733333

12

105

6.25

0.75

0.56

0

0.114286

105

2

55

52.5

0.806452

0

0.12381

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a0595a142eaf248d183d94e735f0ba479dc117a7

48

py

Python

needlestack/indices/__init__.py

needlehaystack/needlestack

e00529a2a7c2d85059936a85f54dfb55e515b6ef

[ "Apache-2.0" ]

3

2019-10-03T22:15:21.000Z

2022-02-08T09:05:41.000Z

needlestack/indices/__init__.py

cungtv/needlestack

e00529a2a7c2d85059936a85f54dfb55e515b6ef

[ "Apache-2.0" ]

1

2021-04-30T21:08:47.000Z

needlestack/indices/__init__.py

cungtv/needlestack

e00529a2a7c2d85059936a85f54dfb55e515b6ef

[ "Apache-2.0" ]

2

2019-08-02T19:13:09.000Z

2019-10-25T01:47:17.000Z

from needlestack.indices.index import BaseIndex

24

47

0.875

6

48

7

1

0

0.083333

48

1

48

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a08b15176907fc58ba9177ee950b34ee8cb64ffe

691

py

Python

cd4ml/problems/houses/features/feature_functions/feature_functions.py

camila-contreras/CD4ML-Scenarios

806f812990c7cf33b5f78456f0065012b5b4cd35

[ "MIT" ]

113

2020-03-31T20:36:39.000Z

2022-01-11T15:06:58.000Z

cd4ml/problems/houses/features/feature_functions/feature_functions.py

camila-contreras/CD4ML-Scenarios

806f812990c7cf33b5f78456f0065012b5b4cd35

[ "MIT" ]

7

2020-06-10T05:11:35.000Z

2022-01-06T02:55:21.000Z

cd4ml/problems/houses/features/feature_functions/feature_functions.py

camila-contreras/CD4ML-Scenarios

806f812990c7cf33b5f78456f0065012b5b4cd35

[ "MIT" ]

260

2020-03-21T19:42:26.000Z

2022-01-25T22:08:36.000Z

def zipcode_to_feature(zipcode, lookup, feature): results = lookup.get(zipcode) if results is None: return None else: return results[feature] def zipcode_to_state(zipcode, lookup): return zipcode_to_feature(zipcode, lookup, 'state') def avg_price_by_zipcode(zipcode, lookup): return zipcode_to_feature(zipcode, lookup, 'avg_price_in_zip') def num_in_zipcode(zipcode, lookup): return zipcode_to_feature(zipcode, lookup, 'num_in_zip') def avg_price_by_state(zipcode, lookup): return zipcode_to_feature(zipcode, lookup, 'avg_price_in_state') def num_in_state(zipcode, lookup): return zipcode_to_feature(zipcode, lookup, 'num_in_state')

25.592593

68

0.751085

98

691

4.94898

0.193878

0.294845

0.197938

0.284536

0.676289

0.616495

0.457732

0

0.157742

691

26

69

26.576923

0.833333

0

0.088278

0

1

0.375

false

0

0.3125

0.8125

0

null

1

0

null

0

1

0

1

0

6

2602c88691bffc3721e77c257e3bfd7bb2de90f1

246

py

Python

skypy/galaxy/tests/test_import.py

ArthurTolley/skypy

5621877ada75c667b1af7e665b02a91026f7ef0f

[ "BSD-3-Clause" ]

1

2020-12-28T18:00:24.000Z

skypy/galaxy/tests/test_import.py

ArthurTolley/skypy

5621877ada75c667b1af7e665b02a91026f7ef0f

[ "BSD-3-Clause" ]

2

2020-12-28T20:14:40.000Z

2020-12-28T21:49:27.000Z

skypy/galaxy/tests/test_import.py

ArthurTolley/skypy

5621877ada75c667b1af7e665b02a91026f7ef0f

[ "BSD-3-Clause" ]

null

def test_import(): import skypy.galaxy import skypy.galaxy.ellipticity import skypy.galaxy.luminosity import skypy.galaxy.redshift import skypy.galaxy.size import skypy.galaxy.spectrum import skypy.galaxy.stellar_mass

27.333333

36

0.756098

31

246

5.935484

0.387097

0.418478

0.646739

0

0.178862

246

8

37

30.75

0.910891

0

1

0.125

true

0

1

0

1.125

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

260c11b37ca0f5a211fd4291ad4e8a2a93dbd3d4

83

py

Python

pybktreespellchecker/__init__.py

tomasrasymas/pybktree-spell-checker

e1f7547957a4257a9b6ce470ebc8bcc95767c5d4

[ "MIT" ]

3

2019-01-07T21:34:29.000Z

2020-07-20T23:43:01.000Z

pybktreespellchecker/__init__.py

tomasrasymas/pybktree-spell-checker

e1f7547957a4257a9b6ce470ebc8bcc95767c5d4

[ "MIT" ]

null

pybktreespellchecker/__init__.py

tomasrasymas/pybktree-spell-checker

e1f7547957a4257a9b6ce470ebc8bcc95767c5d4

[ "MIT" ]

null

from .levenshtein_distance import levenshtein_distance from .bk_tree import BKTree

27.666667

54

0.879518

11

83

6.363636

0.636364

0.542857

0

0.096386

83

2

55

41.5

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

cd26104c6eb130ab45214eda4f1934869ef8a4f2

3,524

py

Python

src/data_reader.py

jazzsewera/mops-projekt

75924546eb73c266ba81e8e22c68ad939dea19d6

[ "MIT" ]

null

src/data_reader.py

jazzsewera/mops-projekt

75924546eb73c266ba81e8e22c68ad939dea19d6

[ "MIT" ]

null

src/data_reader.py

jazzsewera/mops-projekt

75924546eb73c266ba81e8e22c68ad939dea19d6

[ "MIT" ]

null

from logger import Logger from numpy import average log = Logger(None) def show_queue_length_average(number_of_packets): timestamps = [] vals = [] if len(number_of_packets) == 0: log.info(f"Average number of packets: NO DATA") return 0 for k, v in number_of_packets.items(): timestamps.append(float(k)) vals.append(v) vals.pop() timedeltas = [] for i in range(len(timestamps) - 1): timedeltas.append(timestamps[i + 1] - timestamps[i]) av = average(vals, weights=timedeltas) log.info(f"Average number of packets: {av}") return av def show_average_queue_waiting_time_Q1(sent_packets): ts = [] if len(sent_packets) == 0: log.info(f"Average waiting time: NO DATA") return 0 for packet in sent_packets: ts.append(packet.out_of_queue_time - packet.in_queue_time) av = sum(ts) / len(ts) log.info(f"Average waiting time: {av}") return av def show_average_delay_Q1(sent_packets): ts = [] if len(sent_packets) == 0: log.info(f"Average delay time: NO DATA") return 0 for packet in sent_packets: ts.append(packet.in_second_queue_time - packet.in_queue_time) av = sum(ts) / len(ts) log.info(f"Average delay time: {av}") return av def show_average_server_load_Q1(sent_packets): if len(sent_packets) == 0: log.info(f"Average server load: NO DATA") return 0 av_service_time = ( sent_packets[0].in_second_queue_time - sent_packets[0].out_of_queue_time ) vals = [] for i in range(len(sent_packets) - 1): vals.append(sent_packets[i + 1].in_queue_time - sent_packets[i].in_queue_time) if sum(vals) == 0 or len(vals) == 0: log.info(f"Average server load: NO DATA") return 0 av_time_between_in_queue = sum(vals) / len(vals) influx = 1 / av_time_between_in_queue outflow = 1 / av_service_time av = influx / outflow log.info(f"Average server load: {av}") return av def show_average_queue_waiting_time_Q2(sent_packets): ts = [] if len(sent_packets) == 0: log.info(f"Average waiting time: NO DATA") return 0 for packet in sent_packets: ts.append(packet.out_of_second_queue - packet.in_second_queue_time) av = sum(ts) / len(ts) log.info(f"Average waiting time: {av}") return av def show_average_delay_Q2(sent_packets): ts = [] if len(sent_packets) == 0: log.info(f"Average delay time: NO DATA") return 0 for packet in sent_packets: ts.append(packet.out_of_system_time - packet.in_second_queue_time) av = sum(ts) / len(ts) log.info(f"Average delay time: {av}") return av def show_average_server_load_Q2(sent_packets): if len(sent_packets) == 0: log.info(f"Average server load: NO DATA") return 0 av_service_time = ( sent_packets[0].out_of_system_time - sent_packets[0].out_of_second_queue ) vals = [] for i in range(len(sent_packets) - 1): vals.append( sent_packets[i + 1].in_second_queue_time - sent_packets[i].in_second_queue_time ) if sum(vals) == 0 or len(vals) == 0: log.info(f"Average server load: NO DATA") return 0 av_time_between_in_queue = sum(vals) / len(vals) influx = 1 / av_time_between_in_queue outflow = 1 / av_service_time av = influx / outflow log.info(f"Average server load: {av}") return av

24.136986

86

0.638763

535

3,524

3.970093

0.106542

0.134652

0.060264

0.112994

0.842279

0.824388

0.767891

0.739642

0.706686

0

0.015203

0.253405

3,524

145

87

24.303448

0.792094

0

0.65

0

0.124574

0

1

0.07

false

0

0.02

0

0.25

0

null

0

1

0

null

0

6

cd6412162ab8b14b43aaaa41358897ddfe50fe48

136

py

Python

dlpt/__init__.py

damogranlabs/dlpt

e4cdbaf4b5496ed985eb255b17294aa7cf3d35e4

[ "MIT" ]

5

2021-08-09T19:39:06.000Z

2022-03-22T11:21:29.000Z

dlpt/__init__.py

damogranlabs/dlpt

e4cdbaf4b5496ed985eb255b17294aa7cf3d35e4

[ "MIT" ]

null

dlpt/__init__.py

damogranlabs/dlpt

e4cdbaf4b5496ed985eb255b17294aa7cf3d35e4

[ "MIT" ]

null

from . import utils from . import pth from . import proc from . import log from . import json from . import time from . import importer

17

22

0.742647

21

136

4.809524

0.428571

0.693069

0

0.205882

136

7

23

19.428571

0.935185

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

cd6f36cb0dc0dd674280cb84b43ef766b0e9c395

14,691

py

Python

Draft/08_compare_original_GBS_Exome_biallelic_variation_alle_count.py

peipeiwang6/Genomic_prediction_in_Switchgrass

1fba3508c0d81d16e0629e3cf94ff4d174a85b13

[ "MIT" ]

null

Draft/08_compare_original_GBS_Exome_biallelic_variation_alle_count.py

peipeiwang6/Genomic_prediction_in_Switchgrass

1fba3508c0d81d16e0629e3cf94ff4d174a85b13

[ "MIT" ]

null

Draft/08_compare_original_GBS_Exome_biallelic_variation_alle_count.py

peipeiwang6/Genomic_prediction_in_Switchgrass

1fba3508c0d81d16e0629e3cf94ff4d174a85b13

[ "MIT" ]

null

''' imput1: exome capture, biallelic indel matrix input2: exome capture, biallelic SNP matrix input3: GBS, biallelic indel matrix input4: GBS, biallelic SNP matrix input5: allele count file for exome homozygous or heterozygous genotype input6: allele count file for GBS homozygous or heterozygous genotype input7: tetraploid or octaploid ''' import sys,os import numpy as np exome_indel = open(sys.argv[1],'r').readlines() exome_snp = open(sys.argv[2],'r').readlines() gbs_indel = open(sys.argv[3],'r').readlines() gbs_snp = open(sys.argv[4],'r').readlines() EP = {} #EP[pos] = 1 for inl in exome_indel[1:]: tem = inl.split('\t') EP[tem[0] + '_' + tem[1]] = 1 for inl in exome_snp[1:]: tem = inl.split('\t') EP[tem[0] + '_' + tem[1]] = 1 S = {} #shared position, S[pos] = 1 for inl in gbs_indel[1:]: tem = inl.split('\t') if tem[0] + '_' + tem[1] in EP: S[tem[0] + '_' + tem[1]] = 1 for inl in gbs_snp[1:]: tem = inl.split('\t') if tem[0] + '_' + tem[1] in EP: S[tem[0] + '_' + tem[1]] = 1 E = {} # E[pos][ind] = A/T G = {} # G[pos][ind] = A/T EN = {} # EN[i] = ind GN = {} # GN[i] = ind IND = {} # IND[ind] = 1 tem = exome_indel[0].strip().split('\t') for i in range(4,len(tem)): EN[i] = tem[i] IND[tem[i]] = 1 tem = gbs_indel[0].strip().split('\t') for i in range(4,len(tem)): GN[i] = tem[i] for inl in exome_indel[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] E[pos] = {} E[pos]['ref'] = tem[2] E[pos]['alt'] = tem[3] for i in range(4,len(tem)): E[pos][EN[i]] = tem[i] for inl in exome_snp[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] E[pos] = {} E[pos]['ref'] = tem[2] E[pos]['alt'] = tem[3] for i in range(4,len(tem)): E[pos][EN[i]] = tem[i] for inl in gbs_indel[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] G[pos] = {} G[pos]['ref'] = tem[2] G[pos]['alt'] = tem[3] for i in range(4,len(tem)): G[pos][GN[i]] = tem[i] for inl in gbs_snp[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] G[pos] = {} G[pos]['ref'] = tem[2] G[pos]['alt'] = tem[3] for i in range(4,len(tem)): G[pos][GN[i]] = tem[i] out = open('Biallelic_variation_%s_Exome_VS_GBS.txt'%sys.argv[7],'w') Ind = sorted(IND.keys()) title = 'Chr\tPos\tRef\tAlt' for ind in Ind: title = title + '\t' + ind out.write(title + '\n') for pos in S: res = pos.split('_')[0] + '\t' + pos.split('_')[1] if E[pos]['ref'] == G[pos]['ref']: res = res + '\t' + E[pos]['ref'] else: res = res + '\t' + E[pos]['ref'] + '|' + G[pos]['ref'] if E[pos]['alt'] == G[pos]['alt']: res = res + '\t' + E[pos]['alt'] else: res = res + '\t' + E[pos]['alt'] + '|' + G[pos]['alt'] for ind in Ind: if E[pos][ind] == G[pos][ind] or (E[pos][ind].split('/')[0] == G[pos][ind].split('/')[1] and E[pos][ind].split('/')[1] == G[pos][ind].split('/')[0]): res = res + '\t' + E[pos][ind] else: res = res + '\t' + E[pos][ind] + '|' + G[pos][ind] out.write(res + '\n') out.close() ori_exome_indel = open(sys.argv[1],'r').readlines() ori_exome_snp = open(sys.argv[2],'r').readlines() ori_gbs_indel = open(sys.argv[3],'r').readlines() ori_gbs_snp = open(sys.argv[4],'r').readlines() ori_out = open('Shared_Biallelic_variation_%s_original_Exome_VS_GBS.txt'%sys.argv[7],'w') out = open('Distribution_of_discrepancy_Biallelic_variation_%s_between_exome_and_GBS.txt'%sys.argv[7],'w') ori_out.write(title + '\n') O_exome = {} O_gbs = {} EN = {} # EN[i] = ind GN = {} # GN[i] = ind tem = ori_exome_indel[0].strip().split('\t') for i in range(4,len(tem)): EN[i] = tem[i] IND[tem[i]] = 1 tem = ori_gbs_indel[0].strip().split('\t') for i in range(4,len(tem)): GN[i] = tem[i] for inl in ori_exome_indel[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] O_exome[pos] = {} O_exome[pos]['ref'] = tem[2] O_exome[pos]['alt'] = tem[3] for i in range(4,len(tem)): O_exome[pos][EN[i]] = tem[i] for inl in ori_exome_snp[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] O_exome[pos] = {} O_exome[pos]['ref'] = tem[2] O_exome[pos]['alt'] = tem[3] for i in range(4,len(tem)): O_exome[pos][EN[i]] = tem[i] for inl in ori_gbs_indel[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] O_gbs[pos] = {} O_gbs[pos]['ref'] = tem[2] O_gbs[pos]['alt'] = tem[3] for i in range(4,len(tem)): O_gbs[pos][GN[i]] = tem[i] for inl in ori_gbs_snp[1:]: tem = inl.strip().split('\t') if tem[0] + '_' + tem[1] in S: pos = tem[0] + '_' + tem[1] O_gbs[pos] = {} O_gbs[pos]['ref'] = tem[2] O_gbs[pos]['alt'] = tem[3] for i in range(4,len(tem)): O_gbs[pos][GN[i]] = tem[i] if sys.argv[7] == 'octaploid': N1 = 0 ### Exome has variation, GBS is ./. N2 = 0 ### have same variation N3 = 0 ### Exome has heteo(AATT), GBS has homo N3_02 = 0 ### Exome has heteo(ATTT or AAAT), GBS has homo N3_03 = 0 ### Exome has heteo(ATTT or AAAT), GBS has hetero(AATT) N4 = 0 ### Exome is ./., GBS has variation N5 = 0 ### Exome has homo, GBS has heteo(AATT) N5_02 = 0 ### Exome has homo, GBS has heteo(ATTT or AAAT) N5_03 = 0 ### Exome has hetero(AATT), GBS has heteo(ATTT or AAAT) N5_04 = 0 ### Exome has hetero(ATTT), GBS has heteo(TTTA) N6 = 0 ### both are ./. N7 = 0 ### both homo but different variation out.write('Chr\tpos\tID\tExome_SNP\tGBS_SNP\tType\n') for pos in S: res = pos.split('_')[0] + '\t' + pos.split('_')[1] if O_exome[pos]['ref'] == O_gbs[pos]['ref']: res = res + '\t' + O_exome[pos]['ref'] else: res = res + '\t' + O_exome[pos]['ref'] + '|' + O_gbs[pos]['ref'] print(pos) if O_exome[pos]['alt'] == O_gbs[pos]['alt']: res = res + '\t' + O_exome[pos]['alt'] else: res = res + '\t' + O_exome[pos]['alt'] + '|' + O_gbs[pos]['alt'] print(pos) for ind in Ind: if O_exome[pos][ind] == O_gbs[pos][ind] or sorted(O_exome[pos][ind].split('/')) == sorted(O_gbs[pos][ind].split('/')): res = res + '\t' + O_exome[pos][ind] else: res = res + '\t' + O_exome[pos][ind] + '|' + O_gbs[pos][ind] ### have same SNPs, AATT == TTAA, ATTT == TTTA if (O_exome[pos][ind] == O_gbs[pos][ind] or sorted(O_exome[pos][ind].split('/')) == sorted(O_gbs[pos][ind].split('/'))) and O_exome[pos][ind]!= './././.': N2 += 1 ### both are ./. elif O_exome[pos][ind] == O_gbs[pos][ind] and O_exome[pos][ind]== './././.': N6 += 1 ### Exome has SNPs, GBS is ./. elif O_exome[pos][ind] != './././.' and O_gbs[pos][ind] == './././.': N1 += 1 ### Exome is ./., GBS has SNPs elif O_exome[pos][ind] == './././.' and O_gbs[pos][ind] != './././.': N4 += 1 ### Exome has homo, GBS has hetero(AATT) elif len(np.unique(O_exome[pos][ind].split('/'))) == 1 and len(np.unique(O_gbs[pos][ind].split('/'))) == 2 and O_exome[pos][ind]!= './.' and O_gbs[pos][ind].split('/')[1] != O_gbs[pos][ind].split('/')[2]: N5 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_homo_GBS_hetero_AATT\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has homo, GBS has hetero(ATTT or AAAT) elif len(np.unique(O_exome[pos][ind].split('/'))) == 1 and len(np.unique(O_gbs[pos][ind].split('/'))) == 2 and O_exome[pos][ind]!= './.' and O_gbs[pos][ind].split('/')[1] == O_gbs[pos][ind].split('/')[2]: N5_02 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_homo_GBS_hetero_ATTT\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has AATT, GBS has hetero(ATTT or AAAT) elif len(np.unique(O_exome[pos][ind].split('/'))) == 2 and len(np.unique(O_gbs[pos][ind].split('/'))) == 2 and O_gbs[pos][ind].split('/')[1] == O_gbs[pos][ind].split('/')[2] and O_exome[pos][ind].split('/')[1] != O_exome[pos][ind].split('/')[2]: N5_03 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_AATT_GBS_hetero_ATTT\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has ATTT, GBS has heteroTTTA elif len(np.unique(O_exome[pos][ind].split('/'))) == 2 and len(np.unique(O_gbs[pos][ind].split('/'))) == 2 and O_gbs[pos][ind].split('/')[1] == O_gbs[pos][ind].split('/')[2] and O_exome[pos][ind].split('/')[1] == O_exome[pos][ind].split('/')[2] and sorted(O_exome[pos][ind].split('/')) != sorted(O_gbs[pos][ind].split('/')): N5_04 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_ATTT_GBS_hetero_AAAT\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has hetero(AATT), GBS has homo elif len(np.unique(O_exome[pos][ind].split('/'))) == 2 and len(np.unique(O_gbs[pos][ind].split('/'))) == 1 and O_exome[pos][ind].split('/')[1] != O_exome[pos][ind].split('/')[2] and O_gbs[pos][ind] != './.': N3 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_AATT_GBS_homo\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has hetero(ATTT or AAAT), GBS has homo elif len(np.unique(O_exome[pos][ind].split('/'))) == 2 and len(np.unique(O_gbs[pos][ind].split('/'))) == 1 and O_exome[pos][ind].split('/')[1] == O_exome[pos][ind].split('/')[2] and O_gbs[pos][ind] != './.': N3_02 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_ATTT_GBS_homo\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has hetero(ATTT or AAAT), GBS has hetero(AATT) elif len(np.unique(O_exome[pos][ind].split('/'))) == 2 and len(np.unique(O_gbs[pos][ind].split('/'))) == 2 and O_exome[pos][ind].split('/')[1] == O_exome[pos][ind].split('/')[2] and O_gbs[pos][ind].split('/')[1] != O_gbs[pos][ind].split('/')[2] : N3_03 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_ATTT_GBS_hetero_AATT\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### both homo, but diff elif len(np.unique(O_exome[pos][ind].split('/'))) == 1 and len(np.unique(O_gbs[pos][ind].split('/'))) == 1 and O_exome[pos][ind]!=O_gbs[pos][ind] and O_exome[pos][ind] != './././.' and O_gbs[pos][ind]!= './././.': N7 += 1 print([O_exome[pos][ind],O_gbs[pos][ind]]) out.write('%s\t%s\t%s\t%s\t%s\tBoth_homo_differ\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ori_out.write(res + '\n') ori_out.close() out.close() print([N1,N2,N3,N3_02,N3_03,N4,N5,N5_02,N5_03,N5_04,N6,N7]) if sys.argv[7] == 'tetraploid': N1 = 0 ### Exome has SNPs, GBS is ./. N2 = 0 ### have same SNPs N3 = 0 ### Exome has heteo, GBS has homo N4 = 0 ### Exome is ./., GBS has SNPs N5 = 0 ### Exome has homo, GBS has heteo N6 = 0 ### both are ./. N7 = 0 ### both homo but different SNPs out.write('Chr\tpos\tID\tExome_SNP\tGBS_SNP\tType\n') for pos in S: res = pos.split('_')[0] + '\t' + pos.split('_')[1] if O_exome[pos]['ref'] == O_gbs[pos]['ref']: res = res + '\t' + O_exome[pos]['ref'] else: res = res + '\t' + O_exome[pos]['ref'] + '|' + O_gbs[pos]['ref'] if O_exome[pos]['alt'] == O_gbs[pos]['alt']: res = res + '\t' + O_exome[pos]['alt'] else: res = res + '\t' + O_exome[pos]['alt'] + '|' + O_gbs[pos]['alt'] for ind in Ind: if O_exome[pos][ind] == O_gbs[pos][ind] or (O_exome[pos][ind].split('/')[0] == O_gbs[pos][ind].split('/')[1] and O_exome[pos][ind].split('/')[1] == O_gbs[pos][ind].split('/')[0]): res = res + '\t' + O_exome[pos][ind] else: res = res + '\t' + O_exome[pos][ind] + '|' + O_gbs[pos][ind] ### have same SNPs if (O_exome[pos][ind] == O_gbs[pos][ind] or (O_exome[pos][ind].split('/')[0] == O_gbs[pos][ind].split('/')[1] and O_exome[pos][ind].split('/')[1] == O_gbs[pos][ind].split('/')[0])) and O_exome[pos][ind]!= './.': N2 += 1 ### both are ./. elif O_exome[pos][ind] == O_gbs[pos][ind] and O_exome[pos][ind]== './.': N6 += 1 ### Exome has SNPs, GBS is ./. elif O_exome[pos][ind] != './.' and O_gbs[pos][ind] == './.': N1 += 1 ### Exome is ./., GBS has SNPs elif O_exome[pos][ind] == './.' and O_gbs[pos][ind] != './.': N4 += 1 ### Exome has homo, GBS has hetero elif O_exome[pos][ind].split('/')[0] == O_exome[pos][ind].split('/')[1] and O_exome[pos][ind]!= './.' and O_gbs[pos][ind].split('/')[0] != O_gbs[pos][ind].split('/')[1]: N5 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_homo_GBS_hetero\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ### Exome has hetero, GBS has homo elif O_exome[pos][ind].split('/')[0] != O_exome[pos][ind].split('/')[1] and O_gbs[pos][ind].split('/')[0] == O_gbs[pos][ind].split('/')[1] and O_gbs[pos][ind] != './.': N3 += 1 out.write('%s\t%s\t%s\t%s\t%s\tExome_hetero_GBS_homo\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) else: N7 += 1 print([O_exome[pos][ind],O_gbs[pos][ind]]) out.write('%s\t%s\t%s\t%s\t%s\tBoth_homo_differ\n'%(pos.split('_')[0],pos.split('_')[1],ind,O_exome[pos][ind],O_gbs[pos][ind])) ori_out.write(res + '\n') ori_out.close() out.close() print([N1,N2,N3,N4,N5,N6,N7]) inp = open('Distribution_of_discrepancy_Biallelic_variation_%s_between_exome_and_GBS.txt'%sys.argv[7],'r').readlines() out = open('Distribution_of_discrepancy_Biallelic_variation_%s_between_exome_and_GBS_alle_count.txt'%sys.argv[7],'w') P = {} for inl in inp[1:]: tem = inl.split('\t') chr = tem[0] pos = tem[1] ind = tem[2] if chr not in P: P[chr] = {} if pos not in P[chr]: P[chr][pos] = {} if ind not in P[chr][pos]: P[chr][pos][ind] = [0,0,0,0] Exome = open(sys.argv[5],'r') inl = Exome.readline() inl = Exome.readline() while inl: tem = inl.split('\t') chr = tem[0] pos = tem[1] ind = tem[2] if chr in P: if pos in P[chr]: if ind in P[chr][pos]: P[chr][pos][ind][0] = int(tem[6]) P[chr][pos][ind][1] = int(tem[7]) inl = Exome.readline() GBS = open(sys.argv[6],'r') inl = GBS.readline() inl = GBS.readline() while inl: tem = inl.split('\t') chr = tem[0] pos = tem[1] ind = tem[2] if chr in P: if pos in P[chr]: if ind in P[chr][pos]: P[chr][pos][ind][2] = int(tem[6]) P[chr][pos][ind][3] = int(tem[7]) inl = GBS.readline() out.write('Chr\tPos\tInd\tExome_SNP\tGBS_SNP\tType\tExome_alle_count\tExome_read_count\tGBS_alle_count\tGBS_read_count\n') for inl in inp[1:]: tem = inl.split('\t') chr = tem[0] pos = tem[1] ind = tem[2] if chr not in P: P[chr] = {} if pos not in P[chr]: P[chr][pos] = {} if ind not in P[chr][pos]: P[chr][pos][ind] = [0,0,0,0] out.write('%s\t%s\t%s\t%s\t%s\n'%(inl.strip(),P[chr][pos][ind][0],P[chr][pos][ind][1],P[chr][pos][ind][2],P[chr][pos][ind][3])) out.close()

39.176

327

0.571166

2,766

14,691

2.902386

0.04953

0.108371

0.095291

0.09716

0.857499

0.83844

0.808421

0.790857

0.734305

0.726707

0

0.029412

0.157579

14,691

374

328

39.280749

0.619263

0.11245

0

0.694006

0

0.037855

0.120663

0.080623

0

1

0

false

0

0.006309

0

0.006309

0.018927

0

null

0

1

0

null

0

6

cd83dd3751ba2089366bb8592c6a8484b3986736

1,167

py

Python

lib/utils/useragent.py

cckuailong/pocsploit

fe4a3154e59d2bebd55ccfdf62f4f7efb21b5a2a

[ "MIT" ]

106

2022-03-18T06:51:09.000Z

2022-03-31T19:11:41.000Z

lib/utils/useragent.py

cckuailong/pocsploit

fe4a3154e59d2bebd55ccfdf62f4f7efb21b5a2a

[ "MIT" ]

5

2022-03-27T07:37:32.000Z

2022-03-31T13:56:11.000Z

lib/utils/useragent.py

cckuailong/pocsploit

fe4a3154e59d2bebd55ccfdf62f4f7efb21b5a2a

[ "MIT" ]

30

2022-03-21T01:27:08.000Z

2022-03-31T12:28:01.000Z

import random from loguru import logger from lib.vars.vars import conf, th, paths from lib.vars.ua import UA_LIST def get_random_agent(): return random.sample(UA_LIST, 1)[0] def firefox(): return 'Mozilla/5.0 (Windows NT 5.1; rv:5.0) Gecko/20100101 Firefox/5.0' def ie(): return 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E)' def chrome(): return 'Mozilla/5.0 (Windows NT 5.2) AppleWebKit/534.30 (KHTML, like Gecko) Chrome/12.0.742.122 Safari/534.30' def opera(): return 'Opera/9.80 (Windows NT 5.1; U; zh-cn) Presto/2.9.168 Version/11.50' def iphone(): return 'Mozilla/5.0 (iPhone; U; CPU iPhone OS 3_0 like Mac OS X; en-us) AppleWebKit/528.18 (KHTML, like Gecko) Version/4.0 Mobile/7A341 Safari/528.16' def google_bot(): return 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)' def msn_bot(): return 'msnbot/1.1 (+http://search.msn.com/msnbot.htm)' def yahoo_bot(): return 'Mozilla/5.0 (compatible; Yahoo! Slurp; http://help.yahoo.com/help/us/ysearch/slurp)'

28.463415

184

0.685518

217

1,167

3.64977

0.43318

0.022727

0.106061

0.113636

0.165404

0.133838

0.063131

0

0.120324

0.152528

1,167

40

185

29.175

0.680485

0

0.318182

0.636675

0

1

0.409091

true

0

0.181818

0.409091

1

0

null

0

1

0

null

0

1

0

1

0

6

cd99a356df7305e9c0faf645726124d17a3abcde

41

py

Python

app.py

blogsley/blogsley-flask-site

40df6641cce8336d790549b7edac4f83d8b8fb8e

[ "MIT" ]

1

2020-12-18T03:52:25.000Z

app.py

blogsley/blogsley-flask-site

40df6641cce8336d790549b7edac4f83d8b8fb8e

[ "MIT" ]

3

2020-05-04T07:46:54.000Z

2022-02-10T19:39:19.000Z

app.py

blogsley/blogsley-flask-site

40df6641cce8336d790549b7edac4f83d8b8fb8e

[ "MIT" ]

null

from blogsley_site.app import create_app

20.5

40

0.878049

7

41

4.857143

0.857143

0

0.097561

41

1

41

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

269836dc4bbab3746b4c28419c5eabb4308cb54c

37

py

Python

parser/__init__.py

jbudis/dante

90177c33825d5f9ce3fba5463092fbcf20b72fe2

[ "Apache-2.0" ]

4

2018-09-28T14:50:47.000Z

2021-08-09T12:46:12.000Z

parser/__init__.py

jbudis/dante

90177c33825d5f9ce3fba5463092fbcf20b72fe2

[ "Apache-2.0" ]

6

2019-01-02T13:08:31.000Z

2021-03-25T21:45:40.000Z

parser/__init__.py

jbudis/dante

90177c33825d5f9ce3fba5463092fbcf20b72fe2

[ "Apache-2.0" ]

1

2017-12-12T10:38:26.000Z

from parser.readfile import ReadFile

18.5

36

0.864865

5

37

6.4

0.8

0

0.108108

37

1

37

0.969697

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

26c8a00c561378714f8ad7990f244b2a1e695121

671

py

Python

testsuite/tests/T618-047__Ada_2012/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

2

2017-10-22T18:04:26.000Z

2020-03-06T11:07:41.000Z

testsuite/tests/T618-047__Ada_2012/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

null

testsuite/tests/T618-047__Ada_2012/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

4

2018-05-22T12:08:54.000Z

2020-12-14T15:25:27.000Z

def test_pck_2012_adb(style_checker): """Style check test against pck_2012.adb.""" style_checker.set_year(2006) p = style_checker.run_style_checker('repo_name', 'pck_2012.ads') style_checker.assertEqual(p.status, 0, p.image) style_checker.assertRunOutputEmpty(p) def test_pck_2012_adb_with_alt_config_forcing_gnat2012(style_checker): """Style check test against pck_2012.adb with gnat12 config option.""" style_checker.set_year(2006) p = style_checker.run_style_checker( '--config', 'gnat2012_config.yaml', 'repo_name', 'pck_2012.ads') style_checker.assertEqual(p.status, 0, p.image) style_checker.assertRunOutputEmpty(p)

41.9375

74

0.754098

97

671

4.865979

0.309278

0.305085

0.084746

0.059322

0.84322

0.771186

0.588983

0

0.075472

0.131148

671

15

75

44.733333

0.734134

0.153502

0

0.545455

0

0.125673

0

0.363636

1

0.181818

false

0

0.181818

0

null

1

0

1

0

null

0

6

f80f8be872541cb1fed210e79dd3fff53a87f8a4

9,733

py

Python

tests/test_Dirichlet_NL_Poisson.py

bond-anton/BDPoisson1D

538cedc187ce83e90f340cc085738671d325d2e1

[ "Apache-2.0" ]

null

tests/test_Dirichlet_NL_Poisson.py

bond-anton/BDPoisson1D

538cedc187ce83e90f340cc085738671d325d2e1

[ "Apache-2.0" ]

2

2017-07-21T22:10:19.000Z

2018-07-14T21:39:07.000Z

tests/test_Dirichlet_NL_Poisson.py

bond-anton/BDPoisson1D

538cedc187ce83e90f340cc085738671d325d2e1

[ "Apache-2.0" ]

null

import math as m import numpy as np from BDMesh import Mesh1DUniform from BDFunction1D import Function from BDFunction1D.Functional import Functional from BDFunction1D.Interpolation import InterpolateFunction from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver_arrays from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver_mesh_arrays from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver_mesh from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver_recurrent_mesh from BDPoisson1D.DirichletNonLinear import dirichlet_non_linear_poisson_solver_amr import unittest class testPsi(Function): def evaluate_point(self, x): return m.exp(-x * 3.0) class testF(Functional): def __init__(self, Nd, kT, f): super(testF, self).__init__(f) self.Nd = Nd self.kT = kT def evaluate_point(self, x): return self.Nd * (1 - (m.exp(-self.f.evaluate_point(x) / self.kT))) class testdFdPsi(Functional): def __init__(self, Nd, kT, f): super(testdFdPsi, self).__init__(f) self.Nd = Nd self.kT = kT def evaluate_point(self, x): return self.Nd / self.kT * m.exp(-self.f.evaluate_point(x) / self.kT) class TestDirichletNL(unittest.TestCase): def setUp(self): self.Nd = 1.0 self.kT = 0.05 self.bc1 = 1.0 self.bc2 = 0.0 def test_dirichlet_poisson_solver_arrays(self): start = 0.0 stop = 4.0 nodes = np.linspace(start, stop, num=51, endpoint=True, dtype=np.float) Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) for i in range(100): result_1 = np.asarray(dirichlet_non_linear_poisson_solver_arrays(nodes, Psi.evaluate(nodes), f.evaluate(nodes), dfdPsi.evaluate(nodes), bc1=1, bc2=0, j=1, w=1)) Psi = InterpolateFunction(nodes, result_1[:, 0]) f.f = Psi dfdPsi.f = Psi nodes = np.linspace(start, stop, num=101, endpoint=True, dtype=np.float) Psi = testPsi() f.f = Psi dfdPsi.f = Psi for i in range(100): result_2 = np.asarray(dirichlet_non_linear_poisson_solver_arrays(nodes, Psi.evaluate(nodes), f.evaluate(nodes), dfdPsi.evaluate(nodes), bc1=1, bc2=0, j=1, w=1)) Psi = InterpolateFunction(nodes, result_2[:, 0]) f.f = Psi dfdPsi.f = Psi self.assertTrue(max(abs(result_2[:, 2])) < max(abs(result_1[:, 2]))) def test_dirichlet_poisson_solver(self): start = 0.0 stop = 4.0 nodes = np.linspace(start, stop, num=51, endpoint=True, dtype=np.float) Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) for i in range(100): Psi = dirichlet_non_linear_poisson_solver(nodes, Psi, f, dfdPsi, bc1=1, bc2=0, j=1, w=1) f.f = Psi dfdPsi.f = Psi error_1 = np.asarray(Psi.error(nodes)) nodes = np.linspace(start, stop, num=101, endpoint=True, dtype=np.float) Psi = testPsi() f.f = Psi dfdPsi.f = Psi for i in range(100): Psi = dirichlet_non_linear_poisson_solver(nodes, Psi, f, dfdPsi, bc1=1, bc2=0, j=1, w=1) f.f = Psi dfdPsi.f = Psi error_2 = np.asarray(Psi.error(nodes)) self.assertTrue(max(abs(error_2)) < max(abs(error_1))) def test_dirichlet_poisson_solver_mesh_arays(self): start = 0.0 stop = 4.0 step = 0.5 mesh_1 = Mesh1DUniform(start, stop, boundary_condition_1=1, boundary_condition_2=0, physical_step=step) Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) for i in range(100): dirichlet_non_linear_poisson_solver_mesh_arrays(mesh_1, Psi.evaluate(mesh_1.physical_nodes), f.evaluate(mesh_1.physical_nodes), dfdPsi.evaluate(mesh_1.physical_nodes), w=1) Psi = InterpolateFunction(mesh_1.physical_nodes, mesh_1.solution) f.f = Psi dfdPsi.f = Psi step = 0.1 mesh_2 = Mesh1DUniform(start, stop, boundary_condition_1=1, boundary_condition_2=0, physical_step=step) Psi = testPsi() f.f = Psi dfdPsi.f = Psi for i in range(100): dirichlet_non_linear_poisson_solver_mesh_arrays(mesh_2, Psi.evaluate(mesh_2.physical_nodes), f.evaluate(mesh_2.physical_nodes), dfdPsi.evaluate(mesh_2.physical_nodes), w=1) Psi = InterpolateFunction(mesh_2.physical_nodes, mesh_2.solution) f.f = Psi dfdPsi.f = Psi self.assertTrue(max(abs(np.asarray(mesh_2.residual))) < max(abs(np.asarray(mesh_1.residual)))) def test_dirichlet_poisson_solver_mesh(self): start = 0.0 stop = 4.0 step = 0.5 mesh_1 = Mesh1DUniform(start, stop, boundary_condition_1=1, boundary_condition_2=0, physical_step=step) Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) for i in range(100): dirichlet_non_linear_poisson_solver_mesh(mesh_1, Psi, f, dfdPsi, w=1) Psi = InterpolateFunction(mesh_1.physical_nodes, mesh_1.solution) f.f = Psi dfdPsi.f = Psi step = 0.1 mesh_2 = Mesh1DUniform(start, stop, boundary_condition_1=1, boundary_condition_2=0, physical_step=step) Psi = testPsi() f.f = Psi dfdPsi.f = Psi for i in range(100): dirichlet_non_linear_poisson_solver_mesh(mesh_2, Psi, f, dfdPsi, w=1) Psi = InterpolateFunction(mesh_2.physical_nodes, mesh_2.solution) f.f = Psi dfdPsi.f = Psi self.assertTrue(max(abs(np.asarray(mesh_2.residual))) < max(abs(np.asarray(mesh_1.residual)))) def test_dirichlet_poisson_solver_recurrent_mesh(self): start = 0.0 stop = 4.0 step = 0.5 threshold = 1e-6 max_iter = 1000 mesh_1 = Mesh1DUniform(start, stop, boundary_condition_1=1, boundary_condition_2=0, physical_step=step) Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) dirichlet_non_linear_poisson_solver_recurrent_mesh(mesh_1, Psi, f, dfdPsi, max_iter=max_iter, threshold=threshold) self.assertTrue(mesh_1.integrational_residual < threshold) def test_dirichlet_poisson_solver_mesh_amr(self): Psi = testPsi() f = testF(self.Nd, self.kT, Psi) dfdPsi = testdFdPsi(self.Nd, self.kT, Psi) start = 0.0 stop = 5 step = 0.433 bc1 = 1 bc2 = 0 residual_threshold = 1.5e-3 int_residual_threshold = 4e-4 mesh_refinement_threshold = 1e-7 max_iter = 1000 max_level = 20 # print('start') sol = dirichlet_non_linear_poisson_solver_amr(start, stop, step, Psi, f, dfdPsi, bc1, bc2, max_iter=max_iter, residual_threshold=residual_threshold, int_residual_threshold=int_residual_threshold, max_level=max_level, mesh_refinement_threshold=mesh_refinement_threshold) int_residual = np.trapz(sol.error(sol.x), sol.x) self.assertTrue(int_residual < int_residual_threshold) self.assertTrue(max(abs(np.asarray(sol.error(sol.x)))) < residual_threshold) residual_threshold = 1.5e-6 int_residual_threshold = 1.5e-4 mesh_refinement_threshold = 1e-5 max_iter = 1000 max_level = 20 sol = dirichlet_non_linear_poisson_solver_amr(start, stop, step, Psi, f, dfdPsi, bc1, bc2, max_iter=max_iter, residual_threshold=residual_threshold, int_residual_threshold=int_residual_threshold, max_level=max_level, mesh_refinement_threshold=mesh_refinement_threshold) int_residual = np.trapz(sol.error(sol.x), sol.x) self.assertTrue(int_residual < int_residual_threshold) self.assertTrue(max(abs(np.asarray(sol.error(sol.x)))) < residual_threshold)

44.646789

111

0.552348

1,152

9,733

4.4375

0.091146

0.018779

0.059859

0.083138

0.867567

0.809272

0.779147

0.75802

0.74198

0.728286

0

0.037245

0.360012

9,733

217

112

44.852535

0.783432

0.001438

0

0.661458

0

0.046875

1

0.0625

false

0

0.067708

0.015625

0.166667

0

null

0

1

0

null

0

6

f82d5b036daead0dff75c2761e785f8a14568edb

191

py

Python

src/Models/__init__.py

shulip/ShoppingMallSystem

01e5a04a8353ca319ed2dc002fc358f6e44c33dd

[ "MIT" ]

null

src/Models/__init__.py

shulip/ShoppingMallSystem

01e5a04a8353ca319ed2dc002fc358f6e44c33dd

[ "MIT" ]

null

src/Models/__init__.py

shulip/ShoppingMallSystem

01e5a04a8353ca319ed2dc002fc358f6e44c33dd

[ "MIT" ]

1

2021-04-22T15:14:21.000Z

#!/usr/bin/env python # -*- coding:utf-8 -*- from .Contract import * from .Receivable import * from .Receipt import * from .Shop import * from .Statement import * from .Application import *

21.222222

26

0.701571

25

191

5.36

0.6

0.373134

0

0.00625

0.162304

191

9

26

21.222222

0.83125

0.219895

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f83abdd41d8480514557524b539c95519e6c83ef

152

py

Python

__init__.py

cmt-qo/cm-flakes

c11f37b50b088cf5c876ef8a6161b7d8d775e99b

[ "MIT" ]

6

2019-11-04T07:04:24.000Z

2021-02-10T21:35:00.000Z

__init__.py

cmt-qo/cm-flakes

c11f37b50b088cf5c876ef8a6161b7d8d775e99b

[ "MIT" ]

null

__init__.py

cmt-qo/cm-flakes

c11f37b50b088cf5c876ef8a6161b7d8d775e99b

[ "MIT" ]

2

2020-08-07T09:29:41.000Z

2021-02-10T21:35:05.000Z

from .Camera import * from .GloveBox import * from .Microscope import * from .Stage import * from .UserInterface import * from .NeuralNetwork import *

25.333333

28

0.756579

18

152

6.388889

0.444444

0.434783

0

0.164474

152

6

29

25.333333

0.905512

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f8b46b6ff72b56497017d6f934899df81b96c51a

32

py

Python

badge/__init__.py

krisgesling/swag-badge-skill

7640264880d8ae14f9c49c3ba40c6e388e58dcaf

[ "Apache-2.0" ]

1

2021-01-24T01:42:15.000Z

badge/__init__.py

krisgesling/swag-badge-skill

7640264880d8ae14f9c49c3ba40c6e388e58dcaf

[ "Apache-2.0" ]

null

badge/__init__.py

krisgesling/swag-badge-skill

7640264880d8ae14f9c49c3ba40c6e388e58dcaf

[ "Apache-2.0" ]

null

from .client import MQTT_Client

16

31

0.84375

5

32

5.2

0.8

0

0.125

32

1

32

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3e09b3a92c71458b7e09905c1beec58ee515ed7a

106

py

Python

cython/wrap_c/test_cython_wrapper.py

tleonhardt/Python_Interface_Cpp

398eab0c6e7f5e0358edb6644c71b5fdc6b2606a

[ "MIT" ]

64

2017-03-10T09:32:22.000Z

2022-01-25T08:44:06.000Z

cython/wrap_c/test_cython_wrapper.py

tleonhardt/Python_Interface_Cpp

398eab0c6e7f5e0358edb6644c71b5fdc6b2606a

[ "MIT" ]

null

cython/wrap_c/test_cython_wrapper.py

tleonhardt/Python_Interface_Cpp

398eab0c6e7f5e0358edb6644c71b5fdc6b2606a

[ "MIT" ]

13

2017-03-13T23:28:56.000Z

2021-06-07T08:37:03.000Z

# coding=utf-8 import cyfib def test_valid(): assert (17711 == cyfib.compute_fibonacci_wrapper(20))

15.142857

57

0.726415

15

106

4.933333

0.933333

0

0.088889

0.150943

106

6

58

17.666667

0.733333

0.113208

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3e2dc345629e84a8ce9faa979c3f69774ad29ef7

132

py

Python

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

backend/actions.py

HiroshiFuu/django-rest-drf-yasg-boilerplate

93221b2dbca0635eb42a18096e805b00f36ff9c1

[ "Apache-2.0" ]

null

from django.http import HttpResponseRedirect from django.urls import reverse from django.utils.translation import gettext_lazy as _

33

54

0.863636

18

132

6.222222

0.666667

0.267857

0

0.106061

132

3

55

44

0.949153

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e42efd7b2e91e2b6ad55453d791a04774b95fe07

31

py

Python

swarm_tasks/utils/__init__.py

rmvanarse/swarm_tasks

3335297ba8fcdbff756ae519002bcce919d54a84

[ "MIT" ]

6

2021-03-13T12:54:18.000Z

2022-01-29T12:12:28.000Z

swarm_tasks/utils/__init__.py

rmvanarse/swarm_tasks

3335297ba8fcdbff756ae519002bcce919d54a84

[ "MIT" ]

null

swarm_tasks/utils/__init__.py

rmvanarse/swarm_tasks

3335297ba8fcdbff756ae519002bcce919d54a84

[ "MIT" ]

2

2021-08-06T15:02:15.000Z

2022-02-08T12:11:30.000Z

import swarm_tasks.utils.robot

15.5

30

0.870968

5

31

5.2

1

0

0.064516

31

1

31

0.896552

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e44339ec7d8d98173878c5ddc15f39e511c628ec

258

py

Python

tests/test_example.py

akoul1/mvlearn

177d391bb12c6e94335720d9af3608bd719d8be1

[ "Apache-2.0" ]

null

tests/test_example.py

akoul1/mvlearn

177d391bb12c6e94335720d9af3608bd719d8be1

[ "Apache-2.0" ]

null

tests/test_example.py

akoul1/mvlearn

177d391bb12c6e94335720d9af3608bd719d8be1

[ "Apache-2.0" ]

null

import pytest from mvlearn.example.example import example_function def test_example_function(): """ Test that example function returns correct value. """ assert example_function() == "param" assert example_function("hello") == "hello"

21.5

53

0.713178

29

258

6.172414

0.517241

0.418994

0.234637

0

0.186047

258

11

54

23.454545

0.852381

0.189922

0

0.07772

0

0.4

1

0.2

true

0

0.4

0

0.6

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

901a2b9dfc7f70764388119a93640679584d1a0d

112

py

Python

scikits/datasmooth/__init__.py

jjstickel/scikit-datasmooth

976ab86998d1648506684360ab9d65b8a3ccf078

[ "BSD-3-Clause" ]

7

2015-06-18T15:34:28.000Z

2021-09-14T13:04:08.000Z

scikits/datasmooth/__init__.py

jjstickel/scikit-datasmooth

976ab86998d1648506684360ab9d65b8a3ccf078

[ "BSD-3-Clause" ]

2

2018-03-10T03:46:39.000Z

2018-11-27T18:55:13.000Z

scikits/datasmooth/__init__.py

jjstickel/scikit-datasmooth

976ab86998d1648506684360ab9d65b8a3ccf078

[ "BSD-3-Clause" ]

6

2015-03-29T07:36:18.000Z

2020-09-15T16:25:06.000Z

__version__ = '0.7.1' try: from regularsmooth import * except ImportError: from .regularsmooth import *

18.666667

32

0.714286

13

112

5.846154

0.769231

0.447368

0.605263

0

0.033333

0.196429

112

5

33

22.4

0.811111

0

0.044643

0

1

0

false

0

0.6

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

903f6e6ec0a321ed686c231ab9ebc657c40c7407

1,500

py

Python

models/DenseNet.py

Apollo1840/DeepECG

5132b5fc8f6b40c4b2f175cd5e56c4aec128ab3e

[ "MIT" ]

2

2020-11-16T10:50:56.000Z

2020-11-23T12:31:30.000Z

models/DenseNet.py

Apollo1840/DeepECG

5132b5fc8f6b40c4b2f175cd5e56c4aec128ab3e

[ "MIT" ]

null

models/DenseNet.py

Apollo1840/DeepECG

5132b5fc8f6b40c4b2f175cd5e56c4aec128ab3e

[ "MIT" ]

1

2020-08-05T00:23:54.000Z

from keras.models import Sequential from keras.layers import Dense, Dropout def denseNet(input_dim, output_dim=4): model = Sequential() model.add(Dense(1024, input_shape=(input_dim,), kernel_initializer='normal', activation='relu')) model.add(Dense(1024, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1024, kernel_initializer='normal', activation='relu')) model.add(Dense(1024, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(512, kernel_initializer='normal', activation='relu')) model.add(Dense(512, kernel_initializer='normal', activation='relu')) model.add(Dense(512, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(256, kernel_initializer='normal', activation='relu')) model.add(Dense(256, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(128, kernel_initializer='normal', activation='relu')) model.add(Dense(128, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(64, kernel_initializer='normal', activation='relu')) model.add(Dense(64, kernel_initializer='normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(output_dim, kernel_initializer='normal', activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model

41.666667

100

0.719333

192

1,500

5.515625

0.203125

0.151086

0.17186

0.436261

0.78187

0.745042

0

0.040632

0.114

1,500

35

101

42.857143

0.756208

0

0.692308

0

0.119333

0.016

0

1

0.038462

false

0

0.076923

0

0.153846

0

null

0

1

0

1

0

null

0

6

5f3fad78b868dac1b90ecb78a5594353e0e31396

506

py

Python

dato-graphlab/src/config.py

warreee/apache-flink_vs_dato-graphlab

cd01cee208461479d3f27489ab45df439b8b9820

[ "Apache-2.0" ]

null

dato-graphlab/src/config.py

warreee/apache-flink_vs_dato-graphlab

cd01cee208461479d3f27489ab45df439b8b9820

[ "Apache-2.0" ]

null

dato-graphlab/src/config.py

warreee/apache-flink_vs_dato-graphlab

cd01cee208461479d3f27489ab45df439b8b9820

[ "Apache-2.0" ]

null

import os def getDataPath(): return os.getcwd().replace("dato-graphlab/src", "data/") def getSmall(): return getDataPath() + "sample-small.txt" def getMedium(): return getDataPath() + "sample-medium.txt" def getLarge(): return getDataPath() + "sample-large.txt" def getGoogle(): return getDataPath() + "web-Google.txt" def getStanford(): return getDataPath() + "web-Stanford.txt" def getOutputFolder(): return os.getcwd().replace("dato-graphlab/src", "results/")

16.322581

63

0.666008

57

506

5.912281

0.45614

0.252226

0.204748

0.124629

0.21365

0

0.167984

506

30

64

16.866667

0.800475

0

0.25

0

1

0.466667

true

0

0.066667

0.466667

1

0

null

1

0

null

0

1

0

1

0

6

5f4b11817e6c6f5664fb7eebcff8bd3df9ed5773

42

py

Python

varex/__init__.py

weiyi-bitw/varex

765e8876c0ced480a47c0e523736bd31b7897644

[ "MIT" ]

null

varex/__init__.py

weiyi-bitw/varex

765e8876c0ced480a47c0e523736bd31b7897644

[ "MIT" ]

null

varex/__init__.py

weiyi-bitw/varex

765e8876c0ced480a47c0e523736bd31b7897644

[ "MIT" ]

null

from .commons import VCFEntry, LabeledMat

21

41

0.833333

5

42

7

1

0

0.119048

42

1

42

0.945946

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5f99e058ef025684556e0579c4ec1d81fb084ff1

8,288

py

Python

analyzer/views.py

jonfang/CMPE295_DataAnalyzer

6d74f55fa7e38ff8d25aecc388a5ed87c95037ae

[ "MIT" ]

1

2020-10-12T18:17:05.000Z

analyzer/views.py

jonfang/CMPE295_DataAnalyzer

6d74f55fa7e38ff8d25aecc388a5ed87c95037ae

[ "MIT" ]

3

2019-11-19T20:41:50.000Z

2021-06-10T21:48:44.000Z

analyzer/views.py

jonfang/CMPE295_DataAnalyzer

6d74f55fa7e38ff8d25aecc388a5ed87c95037ae

[ "MIT" ]

2

2019-10-30T23:18:57.000Z

2019-11-23T00:23:17.000Z

from django.http import HttpResponse from pyspark.sql import SparkSession from django.shortcuts import render from datetime import datetime from core.chartfactory import createBarChart, createPieChart from core.dataprocessor import DataProcessor def sample(request): """ sample python report """ keys = ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp') values = [10,8,6,4,2,1] image_base64 = createBarChart(keys, values, 'Usage', 'Programming language usages') return render( request, 'analyzer/main.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, } ) def home(request): return render( request, 'analyzer/home.html', ) def submit(request): data = {} if request.method == 'POST': keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=7) image_base64 = createBarChart(keys, values, 'Company', 'Average Empoyee Rating') data = { "title": request.POST.get("title", "defaultTitle"), "description": request.POST.get("description", "defaultDescription"), "news": request.POST.get("news", "defaultNews"), "dataSet": request.POST.get("dataSet", "defaultDataset"), "bar": request.POST.get("bar", "defaultBar"), "pie": request.POST.get("pie", "defaultPie"), "report1":image_base64 } return render( request, 'analyzer/new.html', data ) def case1(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=1) image_base64 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category > 400 ') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=2) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category < 400', configs=config) return render( request, 'analyzer/case1.html', { 'report1':image_base64, 'report2':image_base64_1 } ) def case2(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=5) image_base64 = createPieChart(keys, values, 'India trade import 2010-2018') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=5) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'Total(millions $USD)', 'India trade import 2010-2018', configs=config) keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=6) image_base64_2 = createPieChart(keys, values, 'India trade export 2010-2018') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=6) config = {'rotation':90} image_base64_3 = createBarChart(keys, values, 'Total(millions $USD)', 'India trade export 2010-2018', configs=config) return render( request, 'analyzer/case2.html', { 'report5a':image_base64, 'report5b':image_base64_1, 'report6a':image_base64_2, 'report6b':image_base64_3, } ) def case3(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=4) image_base64 = createPieChart(keys, values, 'Oakland Crime Rate 2011-2016') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=4) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'Count', 'Oakland Crime Rate 2011-2016', configs=config) return render( request, 'analyzer/case3.html', { 'report4a':image_base64, 'report4b':image_base64_1, } ) #google play app report 1 def report1(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=1) image_base64 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category > 400') return render( request, 'analyzer/main.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, } ) #google play app report 2 def report2(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=2) config = {'rotation':90} image_base64 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category < 400', configs=config) return render( request, 'analyzer/main.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, } ) #google play app report 3 def report3(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=1) image_base64 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category > 400 ') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=2) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'App Count', 'Google Play App Store Count By Category < 400', configs=config) return render( request, 'analyzer/main1.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, 'image_base64_1':image_base64_1, } ) def report4(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=4) image_base64 = createPieChart(keys, values, 'Oakland Crime Rate 2011-2016') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=4) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'Count', 'Oakland Crime Rate 2011-2016', configs=config) return render( request, 'analyzer/main1.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, 'image_base64_1':image_base64_1, } ) def report5(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=5) image_base64 = createPieChart(keys, values, 'India trade import 2010-2018') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=5) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'Total(millions $USD)', 'India trade import 2010-2018', configs=config) return render( request, 'analyzer/main1.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, 'image_base64_1':image_base64_1, } ) def report6(request): keys = [] values = [] DataProcessor.getInstance().loadAndProcess(keys, values, report_type=6) image_base64 = createPieChart(keys, values, 'India trade export 2010-2018') keys.clear() values.clear() DataProcessor.getInstance().loadAndProcess(keys, values, report_type=6) config = {'rotation':90} image_base64_1 = createBarChart(keys, values, 'Total(millions $USD)', 'India trade export 2010-2018', configs=config) return render( request, 'analyzer/main1.html', { 'name': "Jon", 'date': datetime.now(), 'image_base64':image_base64, 'image_base64_1':image_base64_1, } )

35.418803

131

0.595077

836

8,288

5.789474

0.143541

0.115909

0.149174

0.164876

0.793182

0.78595

0.778512

0.764463

0

0.05204

0.28125

8,288

234

132

35.418803

0.76045

0.011221

0

0.611872

0

0.191245

0

1

0.054795

false

0

0.045662

0.004566

0.155251

0

null

0

1

0

1

0

null

0

6

398508cf7b96c7a53317b86338d3ac80d4ac69c4

106

py

Python

influxdb_client/client/__init__.py

rhajek/influxdb-client-python

852e6f1b1161df4d67eabc19cdb6b323a46b88e2

[ "MIT" ]

null

influxdb_client/client/__init__.py

rhajek/influxdb-client-python

852e6f1b1161df4d67eabc19cdb6b323a46b88e2

[ "MIT" ]

null

influxdb_client/client/__init__.py

rhajek/influxdb-client-python

852e6f1b1161df4d67eabc19cdb6b323a46b88e2

[ "MIT" ]

null

from __future__ import absolute_import from influxdb_client.client.influxdb_client import InfluxDBClient

26.5

65

0.896226

13

106

6.769231

0.538462

0.318182

0

0.084906

106

3

66

35.333333

0.907216

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

39d2a63c210e03ad35c58e5b3b5e1afaa5b2db56

36,251

py

Python

com/precisely/apis/model/validate_mailing_address_uscanapi_options.py

PreciselyData/PreciselyAPIsSDK-Python

28ffff0c96d81d3a53a5599c987d54d7b632b508

[ "Apache-2.0" ]

null

com/precisely/apis/model/validate_mailing_address_uscanapi_options.py

PreciselyData/PreciselyAPIsSDK-Python

28ffff0c96d81d3a53a5599c987d54d7b632b508

[ "Apache-2.0" ]

null

com/precisely/apis/model/validate_mailing_address_uscanapi_options.py

PreciselyData/PreciselyAPIsSDK-Python

28ffff0c96d81d3a53a5599c987d54d7b632b508

[ "Apache-2.0" ]

null

""" Precisely APIs Enhance & enrich your data, applications, business processes, and workflows with rich location, information, and identify APIs. # noqa: E501 The version of the OpenAPI document: 11.9.3 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from com.precisely.apis.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from com.precisely.apis.exceptions import ApiAttributeError class ValidateMailingAddressUSCANAPIOptions(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'output_address_blocks': (str,), # noqa: E501 'perform_us_processing': (str,), # noqa: E501 'perform_dpv': (str,), # noqa: E501 'output_formatted_on_fail': (str,), # noqa: E501 'output_postal_code_separator': (str,), # noqa: E501 'output_country_format': (str,), # noqa: E501 'keep_multimatch': (str,), # noqa: E501 'output_casing': (str,), # noqa: E501 'maximum_results': (str,), # noqa: E501 'output_record_type': (str,), # noqa: E501 'output_field_level_return_codes': (str,), # noqa: E501 'dpv_determine_no_stat': (str,), # noqa: E501 'street_matching_strictness': (str,), # noqa: E501 'can_french_apartment_label': (str,), # noqa: E501 'output_abbreviated_alias': (str,), # noqa: E501 'dpv_successful_status_condition': (str,), # noqa: E501 'standard_address_pmb_line': (str,), # noqa: E501 'firm_matching_strictness': (str,), # noqa: E501 'can_rural_route_format': (str,), # noqa: E501 'can_prefer_house_num': (str,), # noqa: E501 'output_preferred_alias': (str,), # noqa: E501 'directional_matching_strictness': (str,), # noqa: E501 'extract_firm': (str,), # noqa: E501 'fail_on_cmra_match': (str,), # noqa: E501 'can_non_civic_format': (str,), # noqa: E501 'can_sslvr_flg': (str,), # noqa: E501 'output_street_name_alias': (str,), # noqa: E501 'perform_ews': (str,), # noqa: E501 'can_output_city_format': (str,), # noqa: E501 'dual_address_logic': (str,), # noqa: E501 'perform_suite_link': (str,), # noqa: E501 'can_standard_address_format': (str,), # noqa: E501 'output_preferred_city': (str,), # noqa: E501 'output_multinational_characters': (str,), # noqa: E501 'can_delivery_office_format': (str,), # noqa: E501 'perform_lacs_link': (str,), # noqa: E501 'can_dual_address_logic': (str,), # noqa: E501 'extract_urb': (str,), # noqa: E501 'standard_address_format': (str,), # noqa: E501 'can_french_format': (str,), # noqa: E501 'dpv_determine_vacancy': (str,), # noqa: E501 'can_english_apartment_label': (str,), # noqa: E501 'suppress_zplus_phantom_carrier_r777': (str,), # noqa: E501 'can_output_city_alias': (str,), # noqa: E501 'output_short_city_name': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'output_address_blocks': 'OutputAddressBlocks', # noqa: E501 'perform_us_processing': 'PerformUSProcessing', # noqa: E501 'perform_dpv': 'PerformDPV', # noqa: E501 'output_formatted_on_fail': 'OutputFormattedOnFail', # noqa: E501 'output_postal_code_separator': 'OutputPostalCodeSeparator', # noqa: E501 'output_country_format': 'OutputCountryFormat', # noqa: E501 'keep_multimatch': 'KeepMultimatch', # noqa: E501 'output_casing': 'OutputCasing', # noqa: E501 'maximum_results': 'MaximumResults', # noqa: E501 'output_record_type': 'OutputRecordType', # noqa: E501 'output_field_level_return_codes': 'OutputFieldLevelReturnCodes', # noqa: E501 'dpv_determine_no_stat': 'DPVDetermineNoStat', # noqa: E501 'street_matching_strictness': 'StreetMatchingStrictness', # noqa: E501 'can_french_apartment_label': 'CanFrenchApartmentLabel', # noqa: E501 'output_abbreviated_alias': 'OutputAbbreviatedAlias', # noqa: E501 'dpv_successful_status_condition': 'DPVSuccessfulStatusCondition', # noqa: E501 'standard_address_pmb_line': 'StandardAddressPMBLine', # noqa: E501 'firm_matching_strictness': 'FirmMatchingStrictness', # noqa: E501 'can_rural_route_format': 'CanRuralRouteFormat', # noqa: E501 'can_prefer_house_num': 'CanPreferHouseNum', # noqa: E501 'output_preferred_alias': 'OutputPreferredAlias', # noqa: E501 'directional_matching_strictness': 'DirectionalMatchingStrictness', # noqa: E501 'extract_firm': 'ExtractFirm', # noqa: E501 'fail_on_cmra_match': 'FailOnCMRAMatch', # noqa: E501 'can_non_civic_format': 'CanNonCivicFormat', # noqa: E501 'can_sslvr_flg': 'CanSSLVRFlg', # noqa: E501 'output_street_name_alias': 'OutputStreetNameAlias', # noqa: E501 'perform_ews': 'PerformEWS', # noqa: E501 'can_output_city_format': 'CanOutputCityFormat', # noqa: E501 'dual_address_logic': 'DualAddressLogic', # noqa: E501 'perform_suite_link': 'PerformSuiteLink', # noqa: E501 'can_standard_address_format': 'CanStandardAddressFormat', # noqa: E501 'output_preferred_city': 'OutputPreferredCity', # noqa: E501 'output_multinational_characters': 'OutputMultinationalCharacters', # noqa: E501 'can_delivery_office_format': 'CanDeliveryOfficeFormat', # noqa: E501 'perform_lacs_link': 'PerformLACSLink', # noqa: E501 'can_dual_address_logic': 'CanDualAddressLogic', # noqa: E501 'extract_urb': 'ExtractUrb', # noqa: E501 'standard_address_format': 'StandardAddressFormat', # noqa: E501 'can_french_format': 'CanFrenchFormat', # noqa: E501 'dpv_determine_vacancy': 'DPVDetermineVacancy', # noqa: E501 'can_english_apartment_label': 'CanEnglishApartmentLabel', # noqa: E501 'suppress_zplus_phantom_carrier_r777': 'SuppressZplusPhantomCarrierR777', # noqa: E501 'can_output_city_alias': 'CanOutputCityAlias', # noqa: E501 'output_short_city_name': 'OutputShortCityName', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """ValidateMailingAddressUSCANAPIOptions - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) output_address_blocks (str): Specifies whether to return a formatted version of the address.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_us_processing (str): Specifies whether or not to process U.S. addresses.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_dpv (str): Delivery Point Validation (DPV®) validates that a specific address exists. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_formatted_on_fail (str): Specifies whether to return a formatted address when an address cannot be validated.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_postal_code_separator (str): Specifies whether to use separators (spaces or hyphens) in ZIP™ Codes or Canadian postal codes.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 output_country_format (str): Specifies the format to use for the country name returned in the Country output field.. [optional] if omitted the server will use the default value of "E" # noqa: E501 keep_multimatch (str): Indicates whether to return multiple address for input addresses that have more than one possible matches.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_casing (str): Specifies the casing of the output address. M for mixed case and U for upper case.. [optional] if omitted the server will use the default value of "M" # noqa: E501 maximum_results (str): Specifies a number between 1 and 10 that indicates the maximum number of addresses to be returned.. [optional] if omitted the server will use the default value of "10" # noqa: E501 output_record_type (str): Specifies the type of the output record.. [optional] if omitted the server will use the default value of "A" # noqa: E501 output_field_level_return_codes (str): Identifies which output addresses are candidate addresses as value if Y for OutputFieldLevelReturnCodes.. [optional] if omitted the server will use the default value of "N" # noqa: E501 dpv_determine_no_stat (str): Determines the no stat status of an address which means it exists but cannot receive mails.. [optional] if omitted the server will use the default value of "N" # noqa: E501 street_matching_strictness (str): Specifies the algorithm to determe if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 can_french_apartment_label (str): Specifies the default apartment label for the output if there is no apartment label in the input address. This is specific to French address. . [optional] if omitted the server will use the default value of "Appartement" # noqa: E501 output_abbreviated_alias (str): Specifies whether to use a street's abbreviated alias in the output if the output address line is longer than 31 characters.. [optional] if omitted the server will use the default value of "N" # noqa: E501 dpv_successful_status_condition (str): Selecting the match condition where a DPV result does NOT cause a record to fail.. [optional] if omitted the server will use the default value of "A" # noqa: E501 standard_address_pmb_line (str): Specifies where Private Mailbox (PMB) information is placed.. [optional] if omitted the server will use the default value of "N" # noqa: E501 firm_matching_strictness (str): Specifies the algorithm to determining if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 can_rural_route_format (str): Specifies where to place rural route delivery information.. [optional] if omitted the server will use the default value of "A" # noqa: E501 can_prefer_house_num (str): Specifies whether to select a house number of postal code in case of conflict.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_preferred_alias (str): Specifies whether to use a street's preferred alias in the output.. [optional] if omitted the server will use the default value of "N" # noqa: E501 directional_matching_strictness (str): Specifies the algorithm to determine if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 extract_firm (str): Specifies whether to extract the firm name from AddressLine1 through AddressLine4 and place it in the FirmName output field.. [optional] if omitted the server will use the default value of "N" # noqa: E501 fail_on_cmra_match (str): Specifies whether to consider Treat Commercial Mail Receiving Agency (CMRA) matches as failures?. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_non_civic_format (str): Specifies whether or not non-civic keywords are abbreviated in the output. . [optional] if omitted the server will use the default value of "A" # noqa: E501 can_sslvr_flg (str): Changes the civic and/or suite information to match the LVR or single-single record.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_street_name_alias (str): Specifies how to handle street name aliases used in the input. This is specific to US.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_ews (str): Specifies the Early Warning System (EWS) that uses the USPS EWS File to validate addresses that are not in the ZIP + 4 database.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_output_city_format (str): Specifies whether to use the long, medium, or short version of the city if the city has a long name.. [optional] if omitted the server will use the default value of "D" # noqa: E501 dual_address_logic (str): Specifies how to return a match if multiple non-blank address lines are present or multiple address types are on the same address line. (U.S. addresses only.). [optional] if omitted the server will use the default value of "N" # noqa: E501 perform_suite_link (str): Specifies whether to perform SuiteLink processing.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_standard_address_format (str): Specifies where to place secondary address information in the output address.. [optional] if omitted the server will use the default value of "D" # noqa: E501 output_preferred_city (str): Specifies whether the preferred last line city name should be stored.. [optional] if omitted the server will use the default value of "Z" # noqa: E501 output_multinational_characters (str): Specifies whether to return multinational characters, including diacritical marks such as umlauts or accents.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_delivery_office_format (str): Specifies where to place station information.. [optional] if omitted the server will use the default value of "I" # noqa: E501 perform_lacs_link (str): Facilitates the conversion of rural route address converting into street-style address using the LACS.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 can_dual_address_logic (str): Specifies whether ValidateMailingAddressUSCAN should return a street match or a PO Box/non-civic match when the address contains both civic and non-civic information.. [optional] if omitted the server will use the default value of "D" # noqa: E501 extract_urb (str): Specifies whether to extract the urbanization name from AddressLine1 through AddressLine4 and place it in the USUrbanName output field. . [optional] if omitted the server will use the default value of "N" # noqa: E501 standard_address_format (str): Specifies where to place secondary address information for U.S. addresses.. [optional] if omitted the server will use the default value of "C" # noqa: E501 can_french_format (str): Specifies how to determine the language (English or French) to use to format the address and directional.. [optional] if omitted the server will use the default value of "C" # noqa: E501 dpv_determine_vacancy (str): Determines if the location has been unoccupied for at least 90 days.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_english_apartment_label (str): Specifies the default apartment label to use in the output if there is no apartment label in the input address. rhis is specific to English addresses.. [optional] if omitted the server will use the default value of "Apt" # noqa: E501 suppress_zplus_phantom_carrier_r777 (str): Specifies whether to supress addresses with Carrier Route R777.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_output_city_alias (str): Specifies whether or not to return the city alias when the alias is in the input address.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_short_city_name (str): Specifies how to format city names that have short city name or non-mailing city name alternatives.. [optional] if omitted the server will use the default value of "N" # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """ValidateMailingAddressUSCANAPIOptions - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) output_address_blocks (str): Specifies whether to return a formatted version of the address.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_us_processing (str): Specifies whether or not to process U.S. addresses.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_dpv (str): Delivery Point Validation (DPV®) validates that a specific address exists. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_formatted_on_fail (str): Specifies whether to return a formatted address when an address cannot be validated.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_postal_code_separator (str): Specifies whether to use separators (spaces or hyphens) in ZIP™ Codes or Canadian postal codes.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 output_country_format (str): Specifies the format to use for the country name returned in the Country output field.. [optional] if omitted the server will use the default value of "E" # noqa: E501 keep_multimatch (str): Indicates whether to return multiple address for input addresses that have more than one possible matches.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_casing (str): Specifies the casing of the output address. M for mixed case and U for upper case.. [optional] if omitted the server will use the default value of "M" # noqa: E501 maximum_results (str): Specifies a number between 1 and 10 that indicates the maximum number of addresses to be returned.. [optional] if omitted the server will use the default value of "10" # noqa: E501 output_record_type (str): Specifies the type of the output record.. [optional] if omitted the server will use the default value of "A" # noqa: E501 output_field_level_return_codes (str): Identifies which output addresses are candidate addresses as value if Y for OutputFieldLevelReturnCodes.. [optional] if omitted the server will use the default value of "N" # noqa: E501 dpv_determine_no_stat (str): Determines the no stat status of an address which means it exists but cannot receive mails.. [optional] if omitted the server will use the default value of "N" # noqa: E501 street_matching_strictness (str): Specifies the algorithm to determe if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 can_french_apartment_label (str): Specifies the default apartment label for the output if there is no apartment label in the input address. This is specific to French address. . [optional] if omitted the server will use the default value of "Appartement" # noqa: E501 output_abbreviated_alias (str): Specifies whether to use a street's abbreviated alias in the output if the output address line is longer than 31 characters.. [optional] if omitted the server will use the default value of "N" # noqa: E501 dpv_successful_status_condition (str): Selecting the match condition where a DPV result does NOT cause a record to fail.. [optional] if omitted the server will use the default value of "A" # noqa: E501 standard_address_pmb_line (str): Specifies where Private Mailbox (PMB) information is placed.. [optional] if omitted the server will use the default value of "N" # noqa: E501 firm_matching_strictness (str): Specifies the algorithm to determining if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 can_rural_route_format (str): Specifies where to place rural route delivery information.. [optional] if omitted the server will use the default value of "A" # noqa: E501 can_prefer_house_num (str): Specifies whether to select a house number of postal code in case of conflict.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_preferred_alias (str): Specifies whether to use a street's preferred alias in the output.. [optional] if omitted the server will use the default value of "N" # noqa: E501 directional_matching_strictness (str): Specifies the algorithm to determine if an input address matches in the postal database.. [optional] if omitted the server will use the default value of "M" # noqa: E501 extract_firm (str): Specifies whether to extract the firm name from AddressLine1 through AddressLine4 and place it in the FirmName output field.. [optional] if omitted the server will use the default value of "N" # noqa: E501 fail_on_cmra_match (str): Specifies whether to consider Treat Commercial Mail Receiving Agency (CMRA) matches as failures?. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_non_civic_format (str): Specifies whether or not non-civic keywords are abbreviated in the output. . [optional] if omitted the server will use the default value of "A" # noqa: E501 can_sslvr_flg (str): Changes the civic and/or suite information to match the LVR or single-single record.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_street_name_alias (str): Specifies how to handle street name aliases used in the input. This is specific to US.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 perform_ews (str): Specifies the Early Warning System (EWS) that uses the USPS EWS File to validate addresses that are not in the ZIP + 4 database.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_output_city_format (str): Specifies whether to use the long, medium, or short version of the city if the city has a long name.. [optional] if omitted the server will use the default value of "D" # noqa: E501 dual_address_logic (str): Specifies how to return a match if multiple non-blank address lines are present or multiple address types are on the same address line. (U.S. addresses only.). [optional] if omitted the server will use the default value of "N" # noqa: E501 perform_suite_link (str): Specifies whether to perform SuiteLink processing.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_standard_address_format (str): Specifies where to place secondary address information in the output address.. [optional] if omitted the server will use the default value of "D" # noqa: E501 output_preferred_city (str): Specifies whether the preferred last line city name should be stored.. [optional] if omitted the server will use the default value of "Z" # noqa: E501 output_multinational_characters (str): Specifies whether to return multinational characters, including diacritical marks such as umlauts or accents.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_delivery_office_format (str): Specifies where to place station information.. [optional] if omitted the server will use the default value of "I" # noqa: E501 perform_lacs_link (str): Facilitates the conversion of rural route address converting into street-style address using the LACS.. [optional] if omitted the server will use the default value of "Y" # noqa: E501 can_dual_address_logic (str): Specifies whether ValidateMailingAddressUSCAN should return a street match or a PO Box/non-civic match when the address contains both civic and non-civic information.. [optional] if omitted the server will use the default value of "D" # noqa: E501 extract_urb (str): Specifies whether to extract the urbanization name from AddressLine1 through AddressLine4 and place it in the USUrbanName output field. . [optional] if omitted the server will use the default value of "N" # noqa: E501 standard_address_format (str): Specifies where to place secondary address information for U.S. addresses.. [optional] if omitted the server will use the default value of "C" # noqa: E501 can_french_format (str): Specifies how to determine the language (English or French) to use to format the address and directional.. [optional] if omitted the server will use the default value of "C" # noqa: E501 dpv_determine_vacancy (str): Determines if the location has been unoccupied for at least 90 days.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_english_apartment_label (str): Specifies the default apartment label to use in the output if there is no apartment label in the input address. rhis is specific to English addresses.. [optional] if omitted the server will use the default value of "Apt" # noqa: E501 suppress_zplus_phantom_carrier_r777 (str): Specifies whether to supress addresses with Carrier Route R777.. [optional] if omitted the server will use the default value of "N" # noqa: E501 can_output_city_alias (str): Specifies whether or not to return the city alias when the alias is in the input address.. [optional] if omitted the server will use the default value of "N" # noqa: E501 output_short_city_name (str): Specifies how to format city names that have short city name or non-mailing city name alternatives.. [optional] if omitted the server will use the default value of "N" # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

83.914352

290

0.670354

4,755

36,251

4.961304

0.103891

0.062397

0.064855

0.0763

0.883727

0.857658

0.812259

0.782587

0.779195

0

0.023009

0.263882

36,251

431

291

84.109049

0.860821

0.703815

0

0.283582

0

0.363429

0.216645

0

1

0.024876

false

0.00995

0.019901

0.004975

0.104478

0

null

0

1

0

null

0

6

39daa2204b3c5436de83103da0b269b9aadad179

1,540

py

Python

tests/test_movies.py

dipakgupta12/taste_dive

37df3f67e6efdf961cca230a4b2c8cfe23a38984

[ "MIT" ]

null

tests/test_movies.py

dipakgupta12/taste_dive

37df3f67e6efdf961cca230a4b2c8cfe23a38984

[ "MIT" ]

null

tests/test_movies.py

dipakgupta12/taste_dive

37df3f67e6efdf961cca230a4b2c8cfe23a38984

[ "MIT" ]

null

import mock def test_index_template(app, client, db, captured_templates): res = client.get('/') assert res.status_code == 200 template, context = captured_templates[0] assert template.name == "index.html" @mock.patch("taste_dive.main.routes.get_movies", mock.MagicMock(return_value=[{"Title": "Fast & Furious 6"}])) def test_search_template(app, client, db, captured_templates): res = client.get('/search', data={"name": "Fast & Furious 6"}) assert res.status_code == 200 template, context = captured_templates[0] assert template.name == "search.html" @mock.patch("taste_dive.main.routes.get_movies", mock.MagicMock(return_value=[{"Title": "Fast & Furious 6"}])) def test_search(app, client, db, captured_templates): res = client.get('/search', data={"name": "Fast & Furious 6"}) assert res.status_code == 200 @mock.patch("taste_dive.main.routes.get_movie_detail", mock.MagicMock(return_value={"Title": "Fast & Furious 6"})) def test_detail_template(app, client, db, movie, captured_templates): res = client.get('/detail/{{movie.imdbID}}', data={"name": "Fast & Furious 6"}) assert res.status_code == 200 template, context = captured_templates[0] assert template.name == "detail.html" @mock.patch("taste_dive.main.routes.get_movie_detail", mock.MagicMock(return_value={"Title": "Fast & Furious 6"})) def test_detail(app, client, movie, captured_templates): res = client.get('/detail/{{movie.imdbID}}', data={"name": "Fast & Furious 6"}) assert res.status_code == 200

42.777778

114

0.701948

210

1,540

4.980952

0.185714

0.130019

0.091778

0.124283

0.935946

0.932122

0.878585

0

0.019461

0.132468

1,540

35

115

44

0.763473

0

0.615385

0

0.261688

0.124675

0

0.307692

1

0.192308

false

0

0.038462

0

0.230769

0

null

0

1

0

null

0

6

f2d339d173f754cc9a0dd3025640fbb292c58b5b

36

py

Python

CADRE/power_dymos/__init__.py

johnjasa/CADRE

a4ffd61582b8474953fc309aa540838a14f29dcf

[ "Apache-2.0" ]

null

CADRE/power_dymos/__init__.py

johnjasa/CADRE

a4ffd61582b8474953fc309aa540838a14f29dcf

[ "Apache-2.0" ]

null

CADRE/power_dymos/__init__.py

johnjasa/CADRE

a4ffd61582b8474953fc309aa540838a14f29dcf

[ "Apache-2.0" ]

null

from .power_group import PowerGroup

18

35

0.861111

5

36

6

1

0

0.111111

36

1

36

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

081ea6e893da219c4f8a085a91c4473a4ef03166

191

py

Python

tasking/utils/models.py

cmanallen/tasking

0a613357148afdb4b55078c58429ec45bb60a436

[ "MIT" ]

null

tasking/utils/models.py

cmanallen/tasking

0a613357148afdb4b55078c58429ec45bb60a436

[ "MIT" ]

null

tasking/utils/models.py

cmanallen/tasking

0a613357148afdb4b55078c58429ec45bb60a436

[ "MIT" ]

null

from django.db import models class TimeStamp(models.Model): created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) class Meta: abstract = True

21.222222

50

0.78534

26

191

5.653846

0.653846

0.258503

0.312925

0.353742

0

0.125654

191

9

51

21.222222

0.88024

0

1

0

false

0

0.166667

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

6

f25714dd2e5fb95e7b87e1b330afecfe7458cf18

49

py

Python

libs/pytvmaze/__init__.py

Sparklingx/nzbhydra

e2433e1155255ba37341cc79750b104e7dd8889a

[ "Apache-2.0" ]

674

2015-11-06T04:22:47.000Z

2022-02-26T17:31:43.000Z

libs/pytvmaze/__init__.py

Sparklingx/nzbhydra

e2433e1155255ba37341cc79750b104e7dd8889a

[ "Apache-2.0" ]

713

2015-11-06T10:48:58.000Z

2018-11-27T16:32:18.000Z

libs/pytvmaze/__init__.py

Sparklingx/nzbhydra

e2433e1155255ba37341cc79750b104e7dd8889a

[ "Apache-2.0" ]

106

2015-12-07T11:21:06.000Z

2022-03-11T10:58:41.000Z

#!/usr/bin/python from pytvmaze.tvmaze import *

12.25

29

0.734694

7

49

5.142857

1

0

0.122449

49

3

30

16.333333

0.837209

0.326531

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f26f70f686db6ff49ef92baf12b70818b5613277

209

py

Python

ddtrace/contrib/sqlite3/connection.py

sharov/dd-trace-py

d0995b49cf7147ab463d0a67a38779fad3f539b4

[ "BSD-3-Clause" ]

1

2019-11-24T23:09:29.000Z

ddtrace/contrib/sqlite3/connection.py

sharov/dd-trace-py

d0995b49cf7147ab463d0a67a38779fad3f539b4

[ "BSD-3-Clause" ]

null

ddtrace/contrib/sqlite3/connection.py

sharov/dd-trace-py

d0995b49cf7147ab463d0a67a38779fad3f539b4

[ "BSD-3-Clause" ]

2

2017-05-27T05:58:36.000Z

2019-02-07T13:38:53.000Z

from sqlite3 import Connection from ddtrace.util import deprecated @deprecated(message='Use patching instead (see the docs).', version='0.6.0') def connection_factory(*args, **kwargs): return Connection

26.125

76

0.76555

28

209

5.678571

0.785714

0

0.021858

0.124402

209

7

77

29.857143

0.846995

0

0.196172

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

f2a2b6ab09a985aa72dfef0d5e15e51b49c536f0

607,382

py

Python

submission/custom_reinforcement_learning_geesenet.py

peterbonnesoeur/HandyRL

bb180677cb2d8268317b95c35c98d4536dd906f1

[ "MIT" ]

null

submission/custom_reinforcement_learning_geesenet.py

peterbonnesoeur/HandyRL

bb180677cb2d8268317b95c35c98d4536dd906f1

[ "MIT" ]

null

submission/custom_reinforcement_learning_geesenet.py

peterbonnesoeur/HandyRL

bb180677cb2d8268317b95c35c98d4536dd906f1

[ "MIT" ]

null

# This is a lightweight ML agent trained by self-play. # After sharing this notebook, # we will add Hungry Geese environment in our HandyRL library. # https://github.com/DeNA/HandyRL # We hope you enjoy reinforcement learning! import pickle import bz2 import base64 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F # Neural Network for Hungry Geese class TorusConv2d(nn.Module): def __init__(self, input_dim, output_dim, kernel_size, bn): super().__init__() self.edge_size = (kernel_size[0] // 2, kernel_size[1] // 2) self.conv = nn.Conv2d(input_dim, output_dim, kernel_size=kernel_size) self.bn = nn.BatchNorm2d(output_dim) if bn else None def forward(self, x): h = torch.cat([x[:,:,:,-self.edge_size[1]:], x, x[:,:,:,:self.edge_size[1]]], dim=3) h = torch.cat([h[:,:,-self.edge_size[0]:], h, h[:,:,:self.edge_size[0]]], dim=2) h = self.conv(h) h = self.bn(h) if self.bn is not None else h return h class GeeseNet(nn.Module): def __init__(self): super().__init__() layers, filters = 12, 32 self.conv0 = TorusConv2d(17, filters, (3, 3), True) self.blocks = nn.ModuleList([TorusConv2d(filters, filters, (3, 3), True) for _ in range(layers)]) self.head_p = nn.Linear(filters, 4, bias=False) self.head_v = nn.Linear(filters * 2, 1, bias=False) def forward(self, x): h = F.relu_(self.conv0(x)) for block in self.blocks: h = F.relu_(h + block(h)) h_head = (h * x[:,:1]).view(h.size(0), h.size(1), -1).sum(-1) h_avg = h.view(h.size(0), h.size(1), -1).mean(-1) p = self.head_p(h_head) v = torch.tanh(self.head_v(torch.cat([h_head, h_avg], 1))) return {'policy': p, 'value': v} # Input for Neural Network def make_input(obses): b = np.zeros((17, 7 * 11), dtype=np.float32) obs = obses[-1] for p, pos_list in enumerate(obs['geese']): # head position for pos in pos_list[:1]: b[0 + (p - obs['index']) % 4, pos] = 1 # tip position for pos in pos_list[-1:]: b[4 + (p - obs['index']) % 4, pos] = 1 # whole position for pos in pos_list: b[8 + (p - obs['index']) % 4, pos] = 1 # previous head position if len(obses) > 1: obs_prev = obses[-2] for p, pos_list in enumerate(obs_prev['geese']): for pos in pos_list[:1]: b[12 + (p - obs['index']) % 4, pos] = 1 # food for pos in obs['food']: b[16, pos] = 1 return b.reshape(-1, 7, 11) # Load PyTorch Model PARAM = b'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' state_dict = pickle.loads(bz2.decompress(base64.b64decode(PARAM))) model = GeeseNet() model.load_state_dict(state_dict) model.eval() #model = GeeseNet() #model.load_state_dict(torch.load('./latest.pth')) #model.eval() # Main Function of Agent obses = [] def agent(obs, _): obses.append(obs) x = make_input(obses) with torch.no_grad(): xt = torch.from_numpy(x).unsqueeze(0) o = model(xt) p = o['policy'].squeeze(0).detach().numpy() actions = ['NORTH', 'SOUTH', 'WEST', 'EAST'] return actions[np.argmax(p)]

5,281.582609

604,151

0.966882

18,764

607,382

31.294234

0.942656

0.000072

0.000102

0.000075

0.000804

0.000589

0.000257

0.000143

0

0.15684

0.001355

607,382

114

604,152

5,327.912281

0.811252

0.000764

0

0.086957

0

0.014493

0.995568

0.995454

0

1

0

1

0.086957

false

0

0.101449

0

0.275362

0

1

null

0

1

0

1

null

1

0

6