xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

1e5268950470311876f7f86a137de61245cf0a46

12,443

py

Python

venv/Lib/site-packages/pymessenger/bot.py

shivamsahni/Bot-to-live

7b02e02012965bae873ae042aafe0ba76e12e7f5

[ "Apache-2.0" ]

null

venv/Lib/site-packages/pymessenger/bot.py

shivamsahni/Bot-to-live

7b02e02012965bae873ae042aafe0ba76e12e7f5

[ "Apache-2.0" ]

null

venv/Lib/site-packages/pymessenger/bot.py

shivamsahni/Bot-to-live

7b02e02012965bae873ae042aafe0ba76e12e7f5

[ "Apache-2.0" ]

null

import json import requests from requests_toolbelt import MultipartEncoder from pymessenger.graph_api import FacebookGraphApi import pymessenger.utils as utils class Bot(FacebookGraphApi): def __init__(self, *args, **kwargs): super(Bot, self).__init__(*args, **kwargs) def send_text_message(self, recipient_id, message): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/text-message Input: recipient_id: recipient id to send to message: message to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { 'text': message } } return self.send_raw(payload) def send_message(self, recipient_id, message): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/text-message Input: recipient_id: recipient id to send to message: raw message to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': message } return self.send_raw(payload) def send_generic_message(self, recipient_id, elements): '''Send generic messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/generic-template Input: recipient_id: recipient id to send to elements: generic message elements to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { "attachment": { "type": "template", "payload": { "template_type": "generic", "elements": elements } } } } return self.send_raw(payload) def send_button_message(self, recipient_id, text, buttons): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/button-template Input: recipient_id: recipient id to send to text: text of message to send buttons: buttons to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { "attachment": { "type": "template", "payload": { "template_type": "button", "text": text, "buttons": buttons } } } } return self.send_raw(payload) def send_image(self, recipient_id, image_path): '''Send an image to the specified recipient. Image must be PNG or JPEG or GIF (more might be supported). https://developers.facebook.com/docs/messenger-platform/send-api-reference/image-attachment Input: recipient_id: recipient id to send to image_path: path to image to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'image', 'payload': {} } } ), 'filedata': (image_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_image_url(self, recipient_id, image_url): '''Send an image to specified recipient using URL. Image must be PNG or JPEG or GIF (more might be supported). https://developers.facebook.com/docs/messenger-platform/send-api-reference/image-attachment Input: recipient_id: recipient id to send to image_url: url of image to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'image', 'payload': { 'url': image_url } } } ) } return self.send_raw(payload) def send_action(self, recipient_id, action): '''Send typing indicators or send read receipts to the specified recipient. Image must be PNG or JPEG. https://developers.facebook.com/docs/messenger-platform/send-api-reference/sender-actions Input: recipient_id: recipient id to send to action: action type (mark_seen, typing_on, typing_off) Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'sender_action': action } return self.send_raw(payload) def _send_payload(self, payload): ''' Deprecated, use send_raw instead ''' return self.send_raw(payload) def send_raw(self, payload): request_endpoint = '{0}/me/messages'.format(self.graph_url) response = requests.post( request_endpoint, params=self.auth_args, json=payload ) result = response.json() return result def send_audio(self, recipient_id, audio_path): '''Send audio to the specified recipient. Audio must be MP3 or WAV https://developers.facebook.com/docs/messenger-platform/send-api-reference/audio-attachment Input: recipient_id: recipient id to send to audio_path: path to audio to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': {} } } ), 'filedata': (audio_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_audio_url(self, recipient_id, audio_url): '''Send audio to specified recipient using URL. Audio must be MP3 or WAV https://developers.facebook.com/docs/messenger-platform/send-api-reference/audio-attachment Input: recipient_id: recipient id to send to audio_url: url of audio to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': { 'url': audio_url } } } ) } return self.send_raw(payload) def send_video(self, recipient_id, video_path): '''Send video to the specified recipient. Video should be MP4 or MOV, but supports more (https://www.facebook.com/help/218673814818907). https://developers.facebook.com/docs/messenger-platform/send-api-reference/video-attachment Input: recipient_id: recipient id to send to video_path: path to video to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': {} } } ), 'filedata': (video_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_video_url(self, recipient_id, video_url): '''Send video to specified recipient using URL. Video should be MP4 or MOV, but supports more (https://www.facebook.com/help/218673814818907). https://developers.facebook.com/docs/messenger-platform/send-api-reference/video-attachment Input: recipient_id: recipient id to send to video_url: url of video to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': { 'url': video_url } } } ) } return self.send_raw(payload) def send_file(self, recipient_id, file_path): '''Send file to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/file-attachment Input: recipient_id: recipient id to send to file_path: path to file to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'file', 'payload': {} } } ), 'filedata': (file_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_file_url(self, recipient_id, file_url): '''Send file to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/file-attachment Input: recipient_id: recipient id to send to file_url: url of file to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'file', 'payload': { 'url': file_url } } } ) } return self.send_raw(payload)

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py

Python

Python_Network_Automation_II/chapter15_codes/print_hello_friend.py

yasser296/Python-Projects

eae3598e2d4faf08d9def92c8b417c2e7946c5f4

[ "MIT" ]

null

Python_Network_Automation_II/chapter15_codes/print_hello_friend.py

yasser296/Python-Projects

eae3598e2d4faf08d9def92c8b417c2e7946c5f4

[ "MIT" ]

null

Python_Network_Automation_II/chapter15_codes/print_hello_friend.py

yasser296/Python-Projects

eae3598e2d4faf08d9def92c8b417c2e7946c5f4

[ "MIT" ]

null

#print_hello_friend.py from datetime import datetime print(datetime.now()) print("G'day Mate!")

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1e789078e52f00f46c0cd236933246f1a80c7cb5

183

py

Python

Beginner/SolutionByJagmeet_CheckEvenOdd.py

man21/IOSD-UIETKUK-HacktoberFest-Meetup-2019

8ca1a8bf95ee98d303d3a909c448288fa5992210

[ "Apache-2.0" ]

22

2019-10-02T16:48:10.000Z

2020-11-14T23:28:41.000Z

Beginner/SolutionByJagmeet_CheckEvenOdd.py

man21/IOSD-UIETKUK-HacktoberFest-Meetup-2019

8ca1a8bf95ee98d303d3a909c448288fa5992210

[ "Apache-2.0" ]

46

2019-10-01T03:53:30.000Z

2020-10-20T16:34:37.000Z

Beginner/SolutionByJagmeet_CheckEvenOdd.py

man21/IOSD-UIETKUK-HacktoberFest-Meetup-2019

8ca1a8bf95ee98d303d3a909c448288fa5992210

[ "Apache-2.0" ]

415

2019-10-01T03:48:22.000Z

2021-02-27T04:57:28.000Z

def checkEvenOdd(num): if(num%2 == 0): print("Number ",num," is even ") elif(num %2 ==1): print("Number ",num," is odd ") checkEvenOdd(113)

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1e998b03ed3e633be6c3bad25c5af0ce7f26be6b

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py

Python

bikeshed/h/__init__.py

deniak/bikeshed

adf8655b3507377825e3aa77cc43a867687daf7b

[ "CC0-1.0" ]

1

2020-12-10T20:26:43.000Z

bikeshed/h/__init__.py

deniak/bikeshed

adf8655b3507377825e3aa77cc43a867687daf7b

[ "CC0-1.0" ]

null

bikeshed/h/__init__.py

deniak/bikeshed

adf8655b3507377825e3aa77cc43a867687daf7b

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- from .serializer import Serializer from .dom import addClass from .dom import addOldIDs from .dom import appendChild from .dom import appendContents from .dom import approximateLineNumber from .dom import childElements from .dom import childNodes from .dom import circledDigits from .dom import clearContents from .dom import closestAncestor from .dom import closestAttr from .dom import createElement from .dom import dedupIDs from .dom import E from .dom import emptyText from .dom import escapeAttr from .dom import escapeCSSIdent from .dom import escapeHTML from .dom import escapeUrlFrag from .dom import filterAncestors from .dom import find from .dom import findAll from .dom import fixSurroundingTypography from .dom import fixTypography from .dom import fixupIDs from .dom import foldWhitespace from .dom import hasAncestor from .dom import hasAttr from .dom import hasAttrs from .dom import hasChildElements from .dom import hasClass from .dom import hashContents from .dom import hasOnlyChild from .dom import headingLevelOfElement from .dom import innerHTML from .dom import insertAfter from .dom import insertBefore from .dom import isElement from .dom import isEmpty from .dom import isNormative from .dom import isOddNode from .dom import moveContents from .dom import nodeIter from .dom import outerHTML from .dom import parentElement from .dom import parseDocument from .dom import parseHTML from .dom import prependChild from .dom import previousElements from .dom import relevantHeadings from .dom import removeAttr from .dom import removeClass from .dom import removeNode from .dom import replaceAwkwardCSSShorthands from .dom import replaceContents from .dom import replaceMacros from .dom import replaceNode from .dom import safeID from .dom import scopingElements from .dom import sectionName from .dom import serializeTag from .dom import textContent from .dom import textContentIgnoringDecorative from .dom import treeAttr from .dom import unescape from .dom import unfixTypography from .dom import wrapContents

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py

Python

utils/__init__.py

SiriusKY/pytorch-ocr

c739a13116c6833e8dca3be4bc7b66fc757328b4

[ "MIT" ]

2

2021-07-05T15:57:54.000Z

2021-07-05T15:58:51.000Z

utils/__init__.py

SiriusKY/pytorch-ocr

c739a13116c6833e8dca3be4bc7b66fc757328b4

[ "MIT" ]

null

utils/__init__.py

SiriusKY/pytorch-ocr

c739a13116c6833e8dca3be4bc7b66fc757328b4

[ "MIT" ]

null

from .util import * from .prefix_beam_search import decode

19.666667

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94e9bd57fb88963fb602f07741a2e2fafa9948ec

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py

Python

tests/pyflakes_bears/pep8_naming_test_files/E04/invalid.py

MacBox7/coala-pyflakes

637f8a2e77973384be79d30b0dae1f43072e60c8

[ "MIT" ]

null

tests/pyflakes_bears/pep8_naming_test_files/E04/invalid.py

MacBox7/coala-pyflakes

637f8a2e77973384be79d30b0dae1f43072e60c8

[ "MIT" ]

12

2018-05-21T06:12:59.000Z

2018-07-30T10:37:16.000Z

tests/pyflakes_bears/pep8_naming_test_files/E04/invalid.py

MacBox7/coala-pyflakes

637f8a2e77973384be79d30b0dae1f43072e60c8

[ "MIT" ]

1

2018-06-10T16:16:47.000Z

def foo(): ''' >>> from mod import CamelCase as CONST ''' pass

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py

Python

tests/conftest.py

andredias/rst2html5

1f2938c843b4912a2c05df5d3a6708fb4e67eb16

[ "MIT" ]

1

2021-04-27T20:25:44.000Z

tests/conftest.py

andredias/rst2html5

1f2938c843b4912a2c05df5d3a6708fb4e67eb16

[ "MIT" ]

8

2020-03-14T23:34:23.000Z

2021-04-20T14:06:07.000Z

tests/conftest.py

andredias/rst2html5

1f2938c843b4912a2c05df5d3a6708fb4e67eb16

[ "MIT" ]

null

import os from pathlib import Path from pytest import fixture @fixture(autouse=True, scope='session') def chdir() -> None: os.chdir(Path(__file__).parent)

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bf73ab2efa36aa2f2e67822fb2e61d93d45566d1

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py

Python

src/kgmk/io/writing/__init__.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

src/kgmk/io/writing/__init__.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

src/kgmk/io/writing/__init__.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

from .df_writer import ( DFWriter, )

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bf7d512629d958e60aaa663791c9575360983509

131

py

Python

mathics/builtin/files_io/__init__.py

skirpichev/Mathics

318e06dea8f1c70758a50cb2f95c9900150e3a68

[ "Apache-2.0" ]

1,920

2015-01-06T17:56:26.000Z

2022-03-24T14:33:29.000Z

mathics/builtin/files_io/__init__.py

skirpichev/Mathics

318e06dea8f1c70758a50cb2f95c9900150e3a68

[ "Apache-2.0" ]

868

2015-01-04T06:19:40.000Z

2022-03-14T13:39:38.000Z

mathics/builtin/files_io/__init__.py

skirpichev/Mathics

318e06dea8f1c70758a50cb2f95c9900150e3a68

[ "Apache-2.0" ]

240

2015-01-16T13:31:26.000Z

2022-03-12T12:52:46.000Z

""" Input/Output, Files, and Filesystem """ from mathics.version import __version__ # noqa used in loading to check consistency.

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bfa8dff4a33110979569972331ac56feda87a711

181

py

Python

example/try1.py

sano-jin/go-in-ocaml

b5e5fca33e194776477a0db389f6e52bdc0a66fe

[ "MIT" ]

1

2021-09-24T10:25:40.000Z

example/try1.py

sano-jin/go-in-ocaml

b5e5fca33e194776477a0db389f6e52bdc0a66fe

[ "MIT" ]

null

example/try1.py

sano-jin/go-in-ocaml

b5e5fca33e194776477a0db389f6e52bdc0a66fe

[ "MIT" ]

null

try: print('enter try statement') raise Exception() print('exit try statement') except Exception as inst: print(inst.__class__.__name__)

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bfb05f1d5382e3ff980e7ec3344781d6b3584404

3,393

py

Python

cashup/migrations/0002_auto_20170110_2035.py

remarkablerocket/cashup

86ee12f20feab2edb383224126aaf824764be656

[ "BSD-3-Clause" ]

null

cashup/migrations/0002_auto_20170110_2035.py

remarkablerocket/cashup

86ee12f20feab2edb383224126aaf824764be656

[ "BSD-3-Clause" ]

null

cashup/migrations/0002_auto_20170110_2035.py

remarkablerocket/cashup

86ee12f20feab2edb383224126aaf824764be656

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.4 on 2017-01-10 20:35 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import django.utils.timezone def populate_new_fields(apps, schema_editor): TillClosure = apps.get_model('cashup', 'TillClosure') for tc in TillClosure.objects.all(): tc.identity = tc.pk tc.object_created_time = tc.close_time tc.updated_by = tc.closed_by tc.version_created_time = tc.close_time tc.save() class Migration(migrations.Migration): dependencies = [ ('cashup', '0001_initial'), ] operations = [ migrations.AddField( model_name='tillclosure', name='identity', field=models.PositiveIntegerField(default=0, editable=False, blank=True), preserve_default=False, ), migrations.AddField( model_name='tillclosure', name='object_created_time', field=models.DateTimeField(default=django.utils.timezone.now, editable=False, blank=True), preserve_default=False, ), migrations.AddField( model_name='tillclosure', name='updated_by', field=models.ForeignKey(editable=False, blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='updated_tillclosures', to='cashup.Personnel'), ), migrations.AddField( model_name='tillclosure', name='version_created_time', field=models.DateTimeField(default=django.utils.timezone.now, editable=False, blank=True), preserve_default=False, ), migrations.AddField( model_name='tillclosure', name='version_number', field=models.PositiveIntegerField(default=1, editable=False, blank=True), preserve_default=False, ), migrations.AddField( model_name='tillclosure', name='version_superseded_time', field=models.DateTimeField(editable=False, blank=True, null=True), ), migrations.RunPython(populate_new_fields, migrations.RunPython.noop), migrations.AlterField( model_name='tillclosure', name='identity', field=models.PositiveIntegerField(editable=False, blank=True), preserve_default=True, ), migrations.AlterField( model_name='tillclosure', name='object_created_time', field=models.DateTimeField(editable=False, blank=True), preserve_default=True, ), migrations.AlterField( model_name='tillclosure', name='updated_by', field=models.ForeignKey(editable=False, blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='updated_tillclosures', to='cashup.Personnel'), ), migrations.AlterField( model_name='tillclosure', name='version_created_time', field=models.DateTimeField(editable=False, blank=True), preserve_default=True, ), migrations.AlterField( model_name='tillclosure', name='version_number', field=models.PositiveIntegerField(editable=False, blank=True), preserve_default=True, ), ]

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0.625995

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0.127845

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1

0

null

0

5

bfc3a943b8a6d0ab083b6d9dede6a66561e87d82

97

py

Python

imagenet/__init__.py

TrustworthyDL/LeBA

3289c1330585f438dc5b931951cbb682c5513053

[ "Apache-2.0" ]

19

2020-10-20T10:17:50.000Z

2022-03-27T10:56:08.000Z

imagenet/__init__.py

TrustworthyDL/LeBA

3289c1330585f438dc5b931951cbb682c5513053

[ "Apache-2.0" ]

3

2020-11-03T03:08:54.000Z

2021-01-08T02:38:09.000Z

imagenet/__init__.py

TrustworthyDL/LeBA

3289c1330585f438dc5b931951cbb682c5513053

[ "Apache-2.0" ]

4

2020-12-14T06:52:00.000Z

2022-01-25T07:58:22.000Z

#from .load_data import load_data, preprocess, load_new_images from .get_model import get_model

32.333333

62

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97

4.6875

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null

0

1

0

1

0

5

bfc3b1df590bb8ed76e80c0448b22508ed37ca10

45

py

Python

aiomatrix/dispatcher/storage/room_events/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

2

2021-10-29T18:07:08.000Z

2021-11-19T00:25:43.000Z

aiomatrix/dispatcher/storage/room_events/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

1

2022-03-06T11:17:43.000Z

aiomatrix/dispatcher/storage/room_events/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

null

from .sqlite import SqliteEventStorageEngine

22.5

44

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45

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1

45

0.97561

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null

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1

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null

0

1

0

1

0

5

bfc41979bb9c66231daf1053424eff7793160759

18

py

Python

unko.py

kentaroy47/deep_running

405d57b21320a127e36665b5bbb939581f4dee85

[ "MIT" ]

null

unko.py

kentaroy47/deep_running

405d57b21320a127e36665b5bbb939581f4dee85

[ "MIT" ]

null

unko.py

kentaroy47/deep_running

405d57b21320a127e36665b5bbb939581f4dee85

[ "MIT" ]

null

print("puripuri")

9

17

0.722222

2

18

6.5

1

0

0.055556

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1

18

0.764706

0

0.444444

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true

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null

0

1

0

null

0

1

0

1

0

5

bfc70e7bf8036ca3ce90a8747b5de67300cedc53

113

py

Python

Python-For-Everyone-Horstmann/Chapter1-Introduction/P1.1.py

islayy/Books-solutions

5fe05deb4e9f65875284d8af43bd383bf9ae145b

[ "MIT" ]

null

Python-For-Everyone-Horstmann/Chapter1-Introduction/P1.1.py

islayy/Books-solutions

5fe05deb4e9f65875284d8af43bd383bf9ae145b

[ "MIT" ]

null

Python-For-Everyone-Horstmann/Chapter1-Introduction/P1.1.py

islayy/Books-solutions

5fe05deb4e9f65875284d8af43bd383bf9ae145b

[ "MIT" ]

1

2019-09-22T06:27:49.000Z

# Write a program that prints a greeting of your choice, perhaps in a language other #than English print("Hey!")

28.25

84

0.761062

19

113

4.526316

0.894737

0

0.168142

113

3

85

37.666667

0.914894

0.831858

0

0.25

0

1

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true

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1

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null

0

1

0

1

0

null

0

1

0

1

0

5

44e0e614da875d17076dcce581257e2800b24952

151

py

Python

run_es_repo_mgr.py

wjimenez5271/curator

68c2311327dc276394aa8cc4be30296da13fe23f

[ "Apache-2.0" ]

null

run_es_repo_mgr.py

wjimenez5271/curator

68c2311327dc276394aa8cc4be30296da13fe23f

[ "Apache-2.0" ]

null

run_es_repo_mgr.py

wjimenez5271/curator

68c2311327dc276394aa8cc4be30296da13fe23f

[ "Apache-2.0" ]

null

#!/usr/bin/env python """Wrapper for running es_repo_mgr from source.""" from curator.es_repo_mgr import main if __name__ == '__main__': main()

16.777778

50

0.708609

23

151

4.130435

0.73913

0.126316

0.189474

0

0.15894

151

8

51

18.875

0.748032

0.430464

0

0.1

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7807baf056670dd578e2ed8eb9ec36d9c71dac2c

43

py

Python

Contest/ABC068/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC068/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC068/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 print("ABC"+input())

21.5

22

0.674419

7

43

4.142857

1

0

0.02439

0.046512

43

2

23

21.5

0.682927

0.488372

0

0.136364

0

1

0

true

0

1

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null

0

1

0

null

0

1

0

1

0

5

781c4e91ca71190fae5347e85451c2040e6e049e

290

py

Python

SpringSemester2021/01_Jupyter-Python/Ex01_01_Sol.py

KretschiGL/DataScienceLecture

e6bbb3efd531b08aa4757fb6e89d12e959678a44

[ "MIT" ]

1

2021-05-09T11:02:35.000Z

SpringSemester2021/01_Jupyter-Python/Ex01_01_Sol.py

KretschiGL/DataScienceLecture

e6bbb3efd531b08aa4757fb6e89d12e959678a44

[ "MIT" ]

null

SpringSemester2021/01_Jupyter-Python/Ex01_01_Sol.py

KretschiGL/DataScienceLecture

e6bbb3efd531b08aa4757fb6e89d12e959678a44

[ "MIT" ]

1

2020-05-26T15:35:40.000Z

# Numbers 0 to 10 print(list(range(0,11))) print() # Even numbers 0 to 10 print(list(range(0,11,2))) print() # Odd numbers 0 to 10 print(list(range(1,11,2))) print() # Numbers 20 to 10 print(list(range(20,9,-1))) print() # Numbers 45 to 75 divisable by 5 print(list(range(45, 76, 5)))

14.5

33

0.658621

58

290

3.293103

0.344828

0.235602

0.366492

0.272251

0.534031

0.439791

0.303665

0

0.155738

0.158621

290

19

34

15.263158

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0.362069

0

0.444444

0

1

0

true

0

1

0

null

1

0

null

0

1

0

1

0

5

7827a2aba2b29a5cee49d89ade1d4b7fbe15953d

7,098

py

Python

python/kmeans.py

exTerEX/kmeans

1a9be9241d1ccad5755c9a92cdd2c9d4726baa3c

[ "MIT" ]

null

python/kmeans.py

exTerEX/kmeans

1a9be9241d1ccad5755c9a92cdd2c9d4726baa3c

[ "MIT" ]

2

2022-01-01T07:08:39.000Z

2022-03-01T07:03:31.000Z

python/kmeans.py

exTerEX/kmeans

1a9be9241d1ccad5755c9a92cdd2c9d4726baa3c

[ "MIT" ]

null

from ctypes import * from typing import * from pathlib import Path from numpy import array, cos, ndarray, pi, random, sin, zeros, tan try: lib = cdll.LoadLibrary(str(Path(__file__).with_name("libkmeans.so"))) except Exception as E: print(f"Cannot load DLL") print(E) class observation_2d(Structure): _fields_ = [("x", c_double), ("y", c_double), ("group", c_size_t)] class observation_3d(Structure): _fields_ = [("x", c_double), ("y", c_double), ("z", c_double), ("group", c_size_t)] class cluster_2d(Structure): _fields_ = [("x", c_double), ("y", c_double), ("count", c_size_t)] class cluster_3d(Structure): _fields_ = [("x", c_double), ("y", c_double), ("z", c_double), ("count", c_size_t)] lib.kmeans_2d.restype = POINTER(cluster_2d) def kmeans_2d(observations: ndarray, k: Optional[int] = 5) -> Tuple[ndarray, ndarray]: """Partition observations into k clusters. Parameters ---------- observations : ndarray, `shape (N, 2)` An array of observations (x, y) to be clustered. Data should be provided as: `[(x, y), (x, y), (x, y), ...]` k : int, optional Amount of clusters to partition observations into, by default 5 Returns ------- center : ndarray, `shape (k, 2)` An array of positions to center of each cluster. count : ndarray, `shape (k, )` Array of counts of datapoints closest to the center of its cluster. Examples ------- >>> observations = [[6, 1], [-4, -4], [1, -7], [9, -2], [6, -6]] >>> center, count = kmeans_2d(observations, k=2) >>> center [[-4, -4 5, -3]] >>> count [1, 4] """ if not isinstance(observations, ndarray): raise TypeError("Observations must be a ndarray.") # Fix orientation on data if observations.shape[-1] == 2: observations = observations.T else: raise ValueError("Provided array should contain ((x, y), ) observations.") # Find observation_2d length n = observations.shape[-1] # Create a Python list of observations py_observations_list = map(observation_2d, *observations) # Convert the Python list into a c-array c_observations_array = (observation_2d * n)(*py_observations_list) # Get c-array of cluster_2d c_clusters_array = lib.kmeans_2d( c_observations_array, c_size_t(n), c_size_t(k)) # Convert c-array of clusters into a python list py_clusters_list = [c_clusters_array[index] for index in range(k)] # Split clusters center = zeros([k, 2], dtype=observations.dtype) count = zeros(k, dtype=int) for index, cluster_object in enumerate(py_clusters_list): center[index][0] = cluster_object.x center[index][1] = cluster_object.y count[index] = cluster_object.count # Pack into DataFrame and return return (center, count) lib.kmeans_3d.restype = POINTER(cluster_3d) def kmeans_3d(observations: ndarray, k: Optional[int] = 5) -> Tuple[ndarray, ndarray]: """Partition observations into k clusters. Parameters ---------- observations : ndarray, `shape (N, 3)` An array of observations (x, y) to be clustered. Data should be provided as: `[(x, y, z), (x, y, z), (x, y, z), ...]` k : int, optional Amount of clusters to partition observations into, by default 5 Returns ------- center : ndarray, `shape (k, 3)` An array of positions to center of each cluster. count : ndarray, `shape (k, )` Array of counts of datapoints closest to the center of its cluster. Examples ------- >>> observations = [[6, 1, 3], [-4, -4, -4], [1, -7, 7], [9, -2, 1], [6, -6, 6]] >>> center, count = kmeans_3d(observations, k=2) >>> center [[ -0.35830777 -7.41219447 201.90265473] [ 1.83808572 -5.86460671 -28.00696338] [ -0.81562641 -1.20418037 1.60364838]] >>> count [2, 3] """ if not isinstance(observations, ndarray): raise TypeError("Observations must be a ndarray.") # Fix orientation on data if observations.shape[-1] == 3: observations = observations.T else: raise ValueError("Provided array should contain ((x, y, z), ) observations.") # Find observation_3d length n = observations.shape[-1] # Create a Python list of observations py_observations_list = map(observation_3d, *observations) # Convert the Python list into a c-array c_observations_array = (observation_3d * n)(*py_observations_list) # Get c-array of cluster_2d c_clusters_array = lib.kmeans_3d(c_observations_array, c_size_t(n), c_size_t(k)) # Convert c-array of clusters into a python list py_clusters_list = [c_clusters_array[index] for index in range(k)] # Split clusters center = zeros([k, 3], dtype=observations.dtype) count = zeros(k, dtype=int) for index, cluster_object in enumerate(py_clusters_list): center[index][0] = cluster_object.x center[index][1] = cluster_object.y center[index][2] = cluster_object.z count[index] = cluster_object.count # Pack into DataFrame and return return (center, count) def kmeans(observations: ndarray, k: Optional[int] = 5) -> Tuple[ndarray, ndarray]: """Partition observations into k clusters. Parameters ---------- observations : ndarray, `shape (N, 2)` or `shape (N, 3)` An array of observations (x, y) to be clustered. Data should be provided as: `[(x, y), (x, y), (x, y), ...]` or `[(x, y, z), (x, y, z), (x, y, z), ...]` k : int, optional Amount of clusters to partition observations into, by default 5 Returns ------- center : ndarray, `shape (k, 2)` or `shape (k, 3)` An array of positions to center of each cluster. count : ndarray, `shape (k, )` Array of counts of datapoints closest to the center of its cluster. Examples ------- >>> observations = [[6, 1], [-4, -4], [1, -7], [9, -2], [6, -6]] >>> center, count = kmeans_2d(observations, k=2) >>> center [[-4, -4 5, -3]] >>> count [1, 4] """ if not isinstance(observations, ndarray): raise TypeError("Observations must be a ndarray.") if observations.shape[-1] == 3: return kmeans_3d(observations, k) elif observations.shape[-1] == 2: return kmeans_2d(observations, k) else: pass if __name__ == "__main__": random.seed(1234) rand_list = random.random(100) x = 10 * rand_list * cos(2 * pi * rand_list) y = 10 * rand_list * sin(2 * pi * rand_list) z = 10 * rand_list * tan(2 * pi * rand_list) df = array([x, y, z]).T print(f"Observations:\n{df[0:5]}\n...\n\nshape {len(df), len(df[0])}\n") centers, count = kmeans(df, 3) print(f"Centers:\n{centers}\n") print(f"Count:\n{count}") observations = [[6, 1], [-4, -4], [1, -7], [9, -2], [6, -6]] center, count = kmeans_2d(array(observations), k=2) print(f"Centers:\n{centers}\n") print(f"Count:\n{count}")

29.090164

87

0.609749

979

7,098

4.297242

0.156282

0.008557

0.01141

0.016164

0.797243

0.782981

0.777751

0.770145

0.754457

0

0.041582

0.241054

7,098

243

88

29.209877

0.739373

0.394196

0

0.39759

0

0.012048

0.101409

0.020131

0

1

0.036145

false

0.012048

0.048193

0

0.228916

0.084337

0

null

0

1

0

null

0

5

7841b220284cba0896edfacdffd8303e8fbaac07

9,690

py

Python

tests/test_cdigraphlayout.py

idekerlab/cdigraphlayout

59684fd009e9e43db2b951e130308f014a46e379

[ "BSD-3-Clause" ]

null

tests/test_cdigraphlayout.py

idekerlab/cdigraphlayout

59684fd009e9e43db2b951e130308f014a46e379

[ "BSD-3-Clause" ]

null

tests/test_cdigraphlayout.py

idekerlab/cdigraphlayout

59684fd009e9e43db2b951e130308f014a46e379

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ test_cdigraphlayout ---------------------------------- Tests for `cdigraphlayout` module. """ import os import sys import unittest import io import tempfile import shutil import json import ndex2 from cdigraphlayout import cdigraphlayoutcmd class TestCdIgraphLayout(unittest.TestCase): TEST_DIR = os.path.dirname(__file__) HUNDRED_NODE_DIR = os.path.join(TEST_DIR, 'data', '100node_example') def setUp(self): pass def tearDown(self): pass def test_parse_args_all_defaults(self): myargs = ['inputarg'] res = cdigraphlayoutcmd._parse_arguments('desc', myargs) self.assertEqual('inputarg', res.input) self.assertEqual('auto', res.layout) self.assertEqual(None, res.scale) self.assertEqual(None, res.fit_into) def test_parse_args_scale_and_layoutset(self): myargs = ['inputarg', '--layout', 'circle', '--scale', '5.0'] res = cdigraphlayoutcmd._parse_arguments('desc', myargs) self.assertEqual('inputarg', res.input) self.assertEqual('circle', res.layout) self.assertEqual(5.0, res.scale) self.assertEqual(None, res.fit_into) def test_parse_args_fit_into_set(self): myargs = ['inputarg', '--fit_into', '1,2,3,4'] res = cdigraphlayoutcmd._parse_arguments('desc', myargs) self.assertEqual('inputarg', res.input) self.assertEqual('auto', res.layout) self.assertEqual(None, res.scale) self.assertEqual('1,2,3,4', res.fit_into) def test_get_node_size_from_cyvisual_properties_with_none(self): try: cdigraphlayoutcmd._get_node_size_from_cyvisual_properties() self.fail('Expected ValueError') except ValueError as ve: self.assertEqual('Network passed in cannot be None', str(ve)) def test_get_node_size_from_cyvisual_properties_network_missing_aspect(self): net = ndex2.nice_cx_network.NiceCXNetwork() res = cdigraphlayoutcmd._get_node_size_from_cyvisual_properties(net_cx=net) self.assertIsNone(res) def test_get_node_size_from_cyvisual_properties_on_real_network(self): five_node = os.path.join(os.path.dirname(__file__), 'data', '5node.cx') net = ndex2.create_nice_cx_from_file(five_node) res = cdigraphlayoutcmd._get_node_size_from_cyvisual_properties(net_cx=net) self.assertEqual(85.0, res) def test_get_bounding_box_based_on_node_size_with_none(self): try: cdigraphlayoutcmd._get_bounding_box_based_on_node_size() self.fail('Expected ValueError') except ValueError as ve: self.assertEqual('Network passed in cannot be None', str(ve)) def test_get_bounding_box_based_on_node_size_with_5node(self): five_node = os.path.join(os.path.dirname(__file__), 'data', '5node.cx') net = ndex2.create_nice_cx_from_file(five_node) res = cdigraphlayoutcmd._get_bounding_box_based_on_node_size(net_cx=net) self.assertEqual((0.0, 0.0, 550.0, 550.0), res.coords) def test_get_bounding_box_from_user_str(self): self.assertIsNone(cdigraphlayoutcmd. _get_bounding_box_from_user_str(None)) # test empty str try: cdigraphlayoutcmd._get_bounding_box_from_user_str('') self.fail('Expected ValueError') except ValueError as ve: self.assertEqual('Could not parse bounding box coordinates from ' 'input string: ', str(ve)) # test str with only 1 comma try: cdigraphlayoutcmd._get_bounding_box_from_user_str('1,2') self.fail('Expected ValueError') except ValueError as ve: self.assertEqual('Could not parse bounding box coordinates from ' 'input string: 1,2', str(ve)) # test str with non numeric values try: cdigraphlayoutcmd._get_bounding_box_from_user_str('1,b,c,d') self.fail('Expected ValueError') except ValueError as ve: self.assertTrue('invalid coordinate' in str(ve)) # test valid res = cdigraphlayoutcmd._get_bounding_box_from_user_str('0.0,1.0,2,3') self.assertEqual((0.0, 1.0, 2.0, 3.0), res.coords) def test_runlayout_input_is_not_a_file(self): temp_dir = tempfile.mkdtemp() try: args = cdigraphlayoutcmd._parse_arguments('desc', [os.path.join(temp_dir, 'input')]) o_stream = io.StringIO() e_stream = io.StringIO() res = cdigraphlayoutcmd.run_layout(args, out_stream=o_stream, err_stream=e_stream) self.assertEqual(3, res) finally: shutil.rmtree(temp_dir) def test_runlayout_input_is_not_an_empty_file(self): temp_dir = tempfile.mkdtemp() try: input_file = os.path.join(temp_dir, 'input') open(input_file, 'a').close() args = cdigraphlayoutcmd._parse_arguments('desc', [input_file]) o_stream = io.StringIO() e_stream = io.StringIO() res = cdigraphlayoutcmd.run_layout(args, out_stream=o_stream, err_stream=e_stream) self.assertEqual(4, res) finally: shutil.rmtree(temp_dir) def test_runlayout_on_5node(self): temp_dir = tempfile.mkdtemp() try: five_node = os.path.join(os.path.dirname(__file__), 'data', '5node.cx') args = cdigraphlayoutcmd._parse_arguments('desc', [five_node]) o_stream = io.StringIO() e_stream = io.StringIO() res = cdigraphlayoutcmd.run_layout(args, out_stream=o_stream, err_stream=e_stream) self.assertEqual('', e_stream.getvalue()) self.assertEqual(0, res) cart_layout = json.loads(o_stream.getvalue()) self.assertTrue(isinstance(cart_layout, list)) self.assertEqual(5, len(cart_layout)) for entry in cart_layout: self.assertTrue('node' in entry) self.assertTrue('x' in entry) self.assertTrue('y' in entry) self.assertTrue(entry['node'] in [175, 180, 185, 190, 195]) finally: shutil.rmtree(temp_dir) def test_runlayout_on_5node_scale_set(self): temp_dir = tempfile.mkdtemp() try: five_node = os.path.join(os.path.dirname(__file__), 'data', '5node.cx') args = cdigraphlayoutcmd._parse_arguments('desc', [five_node, '--scale', '10.0']) o_stream = io.StringIO() e_stream = io.StringIO() res = cdigraphlayoutcmd.run_layout(args, out_stream=o_stream, err_stream=e_stream) self.assertEqual('', e_stream.getvalue()) self.assertEqual(0, res) cart_layout = json.loads(o_stream.getvalue()) self.assertTrue(isinstance(cart_layout, list)) self.assertEqual(5, len(cart_layout)) for entry in cart_layout: self.assertTrue('node' in entry) self.assertTrue('x' in entry) self.assertTrue('y' in entry) self.assertTrue(entry['node'] in [175, 180, 185, 190, 195]) finally: shutil.rmtree(temp_dir) def test_runlayout_on_5node_fit_into_set(self): temp_dir = tempfile.mkdtemp() try: five_node = os.path.join(os.path.dirname(__file__), 'data', '5node.cx') args = cdigraphlayoutcmd._parse_arguments('desc', [five_node, '--fit_into', '0.0,0.0,1.0,1.0']) o_stream = io.StringIO() e_stream = io.StringIO() res = cdigraphlayoutcmd.run_layout(args, out_stream=o_stream, err_stream=e_stream) self.assertEqual('', e_stream.getvalue()) self.assertEqual(0, res) cart_layout = json.loads(o_stream.getvalue()) self.assertTrue(isinstance(cart_layout, list)) self.assertEqual(5, len(cart_layout)) print(cart_layout) for entry in cart_layout: self.assertTrue('node' in entry) self.assertTrue('x' in entry) self.assertTrue('y' in entry) self.assertTrue(0.0 <= entry['x'] <= 1.1) self.assertTrue(0.0 <= entry['y'] <= 1.1) self.assertTrue(entry['node'] in [175, 180, 185, 190, 195]) finally: shutil.rmtree(temp_dir) if __name__ == '__main__': sys.exit(unittest.main())

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null

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1

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1

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null

0

5

785345682b65e2102bad10ac7194e53a101fa0e3

37

py

Python

backend/admin/views/__init__.py

gsw945/flask-bigger

cc5ba476c20129a009ad8a8366daf4dc060bd4ac

[ "MIT" ]

29

2018-11-13T09:03:29.000Z

2021-11-07T20:20:38.000Z

backend/admin/views/__init__.py

gsw945/flask-bigger

cc5ba476c20129a009ad8a8366daf4dc060bd4ac

[ "MIT" ]

null

backend/admin/views/__init__.py

gsw945/flask-bigger

cc5ba476c20129a009ad8a8366daf4dc060bd4ac

[ "MIT" ]

21

2018-11-14T01:11:24.000Z

2021-12-08T09:20:30.000Z

# -*- coding: utf-8 -*- '''admin视图'''

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1

0

1

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null

0

1

0

5

788b41630b9b5f35dd37ecd1a3387835bcbf60e0

8,963

py

Python

bluebottle/assignments/tests/test_tasks.py

jayvdb/bluebottle

305fea238e6aa831598a8b227223a1a2f34c4fcc

[ "BSD-3-Clause" ]

null

bluebottle/assignments/tests/test_tasks.py

jayvdb/bluebottle

305fea238e6aa831598a8b227223a1a2f34c4fcc

[ "BSD-3-Clause" ]

null

bluebottle/assignments/tests/test_tasks.py

jayvdb/bluebottle

305fea238e6aa831598a8b227223a1a2f34c4fcc

[ "BSD-3-Clause" ]

null

import mock from datetime import timedelta from django.core import mail from django.db import connection from django.utils import timezone from django.utils.timezone import now from bluebottle.assignments.models import Applicant from bluebottle.assignments.tasks import assignment_tasks from bluebottle.assignments.tests.factories import AssignmentFactory, ApplicantFactory from bluebottle.clients.utils import LocalTenant from bluebottle.initiatives.tests.factories import ( InitiativePlatformSettingsFactory, InitiativeFactory ) from bluebottle.test.factory_models.accounts import BlueBottleUserFactory from bluebottle.test.utils import BluebottleTestCase, JSONAPITestClient class AssignmentTasksTestCase(BluebottleTestCase): def setUp(self): super(AssignmentTasksTestCase, self).setUp() self.settings = InitiativePlatformSettingsFactory.create( activity_types=['assignment'] ) self.client = JSONAPITestClient() self.initiative = InitiativeFactory.create(status='approved') self.initiative.save() def test_assignment_reminder_task_deadline(self): user = BlueBottleUserFactory.create(first_name='Nono') end = now() + timedelta(days=4) assignment = AssignmentFactory.create( owner=user, status='open', end_date_type='deadline', initiative=self.initiative, date=end ) ApplicantFactory.create_batch(2, activity=assignment, status='new') ApplicantFactory.create(activity=assignment, status='accepted') withdrawn = ApplicantFactory.create(activity=assignment, status='new') withdrawn.states.withdraw(save=True) mail.outbox = [] tenant = connection.tenant assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() recipients = [message.to[0] for message in mail.outbox] for applicant in assignment.contributions.instance_of(Applicant).all(): if applicant.status in ['new', 'accepted']: self.assertTrue(applicant.user.email in recipients) else: self.assertFalse(applicant.user.email in recipients) self.assertEqual( mail.outbox[0].subject, 'The deadline for your task "{}" is getting close'.format(assignment.title) ) def test_assignment_reminder_task_on_date(self): user = BlueBottleUserFactory.create(first_name='Nono') end = now() + timedelta(days=4) assignment = AssignmentFactory.create( owner=user, status='open', end_date_type='on_date', initiative=self.initiative, date=end ) ApplicantFactory.create_batch(2, activity=assignment, status='new') ApplicantFactory.create(activity=assignment, status='accepted') withdrawn = ApplicantFactory.create(activity=assignment, status='new') withdrawn.states.withdraw(save=True) mail.outbox = [] tenant = connection.tenant assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() recipients = [message.to[0] for message in mail.outbox] for applicant in assignment.contributions.instance_of(Applicant).all(): if applicant.status in ['new', 'accepted']: self.assertTrue(applicant.user.email in recipients) else: self.assertFalse(applicant.user.email in recipients) self.assertEqual( mail.outbox[0].subject, '"{}" will take place in 5 days!'.format(assignment.title) ) def test_assignment_reminder_task_twice(self): user = BlueBottleUserFactory.create(first_name='Nono') end = now() + timedelta(days=4) assignment = AssignmentFactory.create( owner=user, status='open', initiative=self.initiative, date=end, ) ApplicantFactory.create_batch(3, activity=assignment, status='new') withdrawn = ApplicantFactory.create(activity=assignment, status='new') withdrawn.states.withdraw(save=True) assignment_tasks() mail.outbox = [] assignment_tasks() self.assertEqual(len(mail.outbox), 0) def test_assignment_check_registration_deadline(self): user = BlueBottleUserFactory.create(first_name='Nono') deadline = now() - timedelta(days=1) end = now() + timedelta(days=4) assignment = AssignmentFactory.create( owner=user, status='open', capacity=3, end_date_type='on_date', registration_deadline=deadline.date(), initiative=self.initiative, duration=4, date=end, ) applicants = ApplicantFactory.create_batch(3, activity=assignment, status='new') for applicant in applicants: applicant.states.accept(save=True) tenant = connection.tenant assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() self.assertEqual(assignment.status, 'full') def test_assignment_check_start_date(self): user = BlueBottleUserFactory.create(first_name='Nono') registration_deadline = now() - timedelta(days=1) date = now() + timedelta(hours=6) assignment = AssignmentFactory.create( owner=user, status='open', capacity=3, registration_deadline=registration_deadline.date(), initiative=self.initiative, end_date_type='on_date', duration=10, date=date ) applicants = ApplicantFactory.create_batch(3, activity=assignment, status='new') for applicant in applicants: applicant.states.accept(save=True) tenant = connection.tenant with mock.patch.object(timezone, 'now', return_value=(timezone.now() + timedelta(hours=7))): assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() self.assertEqual(assignment.status, 'running') def test_assignment_check_start_date_no_applicants(self): user = BlueBottleUserFactory.create(first_name='Nono') deadline = now() - timedelta(days=1) date = now() - timedelta(hours=2) assignment = AssignmentFactory.create( owner=user, status='open', capacity=3, registration_deadline=deadline.date(), initiative=self.initiative, duration=10, date=date ) tenant = connection.tenant assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() self.assertEqual(assignment.status, 'cancelled') def test_assignment_check_end_date(self): user = BlueBottleUserFactory.create(first_name='Nono') deadline = now() - timedelta(days=4) date = now() - timedelta(days=2) assignment = AssignmentFactory.create( owner=user, status='open', capacity=3, registration_deadline=deadline.date(), initiative=self.initiative, date=date, ) ApplicantFactory.create_batch(3, activity=assignment) tenant = connection.tenant assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() self.assertEqual(assignment.status, 'succeeded') for applicant in assignment.applicants: self.assertEqual(applicant.status, 'succeeded') def test_assignment_check_end_date_future(self): user = BlueBottleUserFactory.create(first_name='Nono') deadline = now() - timedelta(days=4) date = now() + timedelta(days=2) assignment = AssignmentFactory.create( owner=user, status='open', capacity=3, registration_deadline=deadline.date(), initiative=self.initiative, date=date, ) applicants = ApplicantFactory.create_batch(3, activity=assignment) for applicant in applicants: applicant.states.accept(save=True) ApplicantFactory.create_batch(3, activity=assignment) tenant = connection.tenant future = timezone.now() + timedelta(days=3) with mock.patch.object(timezone, 'now', return_value=future): assignment_tasks() with LocalTenant(tenant, clear_tenant=True): assignment.refresh_from_db() self.assertEqual(assignment.status, 'succeeded') for applicant in assignment.applicants: self.assertEqual(applicant.status, 'succeeded')

34.473077

100

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872

8,963

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0.149083

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0.033988

0.049566

0.790051

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8,963

259

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0.848971

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null

0

1

0

1

0

1

0

null

0

5

789b22f887aa45e3116e9e4194f85c5bbddb673f

289

py

Python

candemachine/exceptions.py

Ricyteach/candemachine

0d0baa26aeee358ea6c2fb8148bbe112e36a7fda

[ "MIT" ]

null

candemachine/exceptions.py

Ricyteach/candemachine

0d0baa26aeee358ea6c2fb8148bbe112e36a7fda

[ "MIT" ]

null

candemachine/exceptions.py

Ricyteach/candemachine

0d0baa26aeee358ea6c2fb8148bbe112e36a7fda

[ "MIT" ]

null

class CandeError(Exception): pass class CandeSerializationError(CandeError): pass class CandeDeserializationError(CandeError): pass class CandeReadError(CandeError): pass class CandePartError(CandeError): pass class CandeFormatError(CandePartError): pass

12.565217

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0.757785

24

289

9.125

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0.205479

0.347032

0

0.179931

289

22

45

13.136364

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0

0.5

0

1

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true

0.5

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0.5

0

1

0

null

1

0

null

0

1

0

5

78a656398a16cf92b1840550a99d0c8c24bbfa30

79

py

Python

wetterdienst/dwd/__init__.py

kmuehlbauer/wetterdienst

85e72ccdbd00f0e8285e1ba24800dfafb81ccd63

[ "MIT" ]

1

2021-01-23T22:52:52.000Z

wetterdienst/dwd/__init__.py

kmuehlbauer/wetterdienst

85e72ccdbd00f0e8285e1ba24800dfafb81ccd63

[ "MIT" ]

null

wetterdienst/dwd/__init__.py

kmuehlbauer/wetterdienst

85e72ccdbd00f0e8285e1ba24800dfafb81ccd63

[ "MIT" ]

null

# Load Pandas DataFrame extension. import wetterdienst.dwd.pandas # noqa:F401

26.333333

43

0.797468

10

79

6.3

0.9

0

0.043478

0.126582

79

2

44

39.5

0.869565

0.531646

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

78c8a373f4941b1a89cd464353930c266a29e398

96

py

Python

efocus/__init__.py

shimonuri/cmdb

cc2640cdc6e6f0bb6efe0e74dab5b75bbc5b6fb6

[ "MIT" ]

null

efocus/__init__.py

shimonuri/cmdb

cc2640cdc6e6f0bb6efe0e74dab5b75bbc5b6fb6

[ "MIT" ]

null

efocus/__init__.py

shimonuri/cmdb

cc2640cdc6e6f0bb6efe0e74dab5b75bbc5b6fb6

[ "MIT" ]

null

from . import wrapper from . import logging_filter from . import preload __version__ = "0.1.4"

16

28

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96

4.785714

0.714286

0.447761

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0.166667

96

5

29

19.2

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false

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0.75

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0.75

0

1

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null

1

0

1

0

null

0

1

0

1

0

5

155085da3a095fac77bdf23ba97f9817c0906c08

200

py

Python

__init__.py

traits/dynamic_plot

666f4dd78de590b6f126429fa217ce401c45f0a3

[ "MIT" ]

null

__init__.py

traits/dynamic_plot

666f4dd78de590b6f126429fa217ce401c45f0a3

[ "MIT" ]

null

__init__.py

traits/dynamic_plot

666f4dd78de590b6f126429fa217ce401c45f0a3

[ "MIT" ]

null

import sys if sys.version_info[0] >= 3 and sys.version_info[1] >= 6: pass else: raise Exception("dynamic_plot: Python 3.6 or a more recent version is required.") from .dynamic_plot import *

22.222222

85

0.71

34

200

4.058824

0.705882

0.144928

0.202899

0

0.03681

0.185

200

8

86

25

0.809816

0

0.31

0

1

0

true

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0.333333

0

0.333333

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null

0

1

0

1

0

null

0

1

0

5

1551d427c2e1fdc853a1df3c8ff47f46979e93c7

182

py

Python

unobase/support/admin.py

unomena/unobase

175e768afa1608f9f34d1e5a053763ad27db0f7e

[ "BSD-3-Clause" ]

null

unobase/support/admin.py

unomena/unobase

175e768afa1608f9f34d1e5a053763ad27db0f7e

[ "BSD-3-Clause" ]

null

unobase/support/admin.py

unomena/unobase

175e768afa1608f9f34d1e5a053763ad27db0f7e

[ "BSD-3-Clause" ]

null

from unobase.support import models __author__ = 'michael' from django.contrib import admin admin.site.register(models.Case) admin.site.register(models.FrequentlyAskedQuestion)

22.75

51

0.813187

22

182

6.545455

0.636364

0.125

0.236111

0.319444

0

0.104396

182

8

51

22.75

0.883436

0

0.038251

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

5

156974bee2f1222ae03c71fb0762fd715537c66c

216

py

Python

src/pyrin/cli/core/template/__init__.py

wilsonGmn/pyrin

25dbe3ce17e80a43eee7cfc7140b4c268a6948e0

[ "BSD-3-Clause" ]

null

src/pyrin/cli/core/template/__init__.py

wilsonGmn/pyrin

25dbe3ce17e80a43eee7cfc7140b4c268a6948e0

[ "BSD-3-Clause" ]

null

src/pyrin/cli/core/template/__init__.py

wilsonGmn/pyrin

25dbe3ce17e80a43eee7cfc7140b4c268a6948e0

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ cli core template package. """ from pyrin.packaging.base import Package class CLICoreTemplatePackage(Package): """ cli core template package class. """ NAME = __name__

14.4

40

0.648148

23

216

5.913043

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0.102941

0.220588

0.323529

0

0.005917

0.217593

216

14

41

15.428571

0.798817

0.37963

0

1

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false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ecb9df22ecc546090ba5729a9cfb8d1724772548

255

py

Python

db/scripts/script_select/select_efetividades.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

db/scripts/script_select/select_efetividades.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

db/scripts/script_select/select_efetividades.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

# Requer atributo e atributo_comp = {"atributo": int (id_atributo), "atributo_comp" : int (id_atributo)} select_efetividades = lambda : """ Select fator FROM efetividades WHERE atributo = :atributo AND atributo_comp = :atributo_comp """

36.428571

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null

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bf0eacddd2853377451624b292038d303779566f

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py

Python

src/rimuc/__init__.py

srackham/rimu-py

3da67cb362b6d34fd363e9f4ce5e0afb019baa4c

[ "MIT" ]

null

src/rimuc/__init__.py

srackham/rimu-py

3da67cb362b6d34fd363e9f4ce5e0afb019baa4c

[ "MIT" ]

4

2020-03-24T17:59:43.000Z

2021-06-02T00:48:53.000Z

src/rimuc/__init__.py

srackham/rimu-py

3da67cb362b6d34fd363e9f4ce5e0afb019baa4c

[ "MIT" ]

null

from rimuc.rimuc import main, readResource from rimuc.resources import resources

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1

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5

170bb4eaeeac53dd43d40e3d448c3de33bba6519

219

py

Python

self_finance/front_end/routes/reference.py

MaksimDan/self-finance

788fe067e6814eacde65ec2b9c7122826a61a89b

[ "CNRI-Python", "Condor-1.1", "Naumen", "Xnet", "X11", "MS-PL" ]

null

self_finance/front_end/routes/reference.py

MaksimDan/self-finance

788fe067e6814eacde65ec2b9c7122826a61a89b

[ "CNRI-Python", "Condor-1.1", "Naumen", "Xnet", "X11", "MS-PL" ]

null

self_finance/front_end/routes/reference.py

MaksimDan/self-finance

788fe067e6814eacde65ec2b9c7122826a61a89b

[ "CNRI-Python", "Condor-1.1", "Naumen", "Xnet", "X11", "MS-PL" ]

null

from flask import render_template from self_finance.front_end import app def _standard_render(): return render_template("reference.html") @app.route('/reference') def reference(): return _standard_render()

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null

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0

1

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5

170e4ce72e70f258bdf691e476a876b779e8fc45

121

py

Python

cfn_review_bot/error.py

biochimia/cfn-review-bot

1c8a84b51f7c398c21725cb888a9ab694ddfbb56

[ "Apache-2.0" ]

1

2019-04-04T12:09:16.000Z

cfn_review_bot/error.py

biochimia/cfn-review-bot

1c8a84b51f7c398c21725cb888a9ab694ddfbb56

[ "Apache-2.0" ]

null

cfn_review_bot/error.py

biochimia/cfn-review-bot

1c8a84b51f7c398c21725cb888a9ab694ddfbb56

[ "Apache-2.0" ]

null

''' Defines `Error`, the base class for all exceptions generated in this package. ''' class Error(Exception): pass

15.125

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121

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121

7

78

17.285714

0.867347

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1

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1

0

true

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null

0

1

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1

0

null

0

1

0

5

17274e554735111f7b40dfbebb44ca1f4cb0d34b

120

py

Python

endpoints/routers/__init__.py

tszan/project_skeleton

df794e123727c45aab42af2d0cd3e597abd3bd9f

[ "Unlicense" ]

2

2022-02-22T20:41:26.000Z

2022-02-25T16:26:03.000Z

endpoints/routers/__init__.py

tszan/project_skeleton

df794e123727c45aab42af2d0cd3e597abd3bd9f

[ "Unlicense" ]

1

2022-01-27T16:05:31.000Z

endpoints/routers/__init__.py

tszan/project_skeleton

df794e123727c45aab42af2d0cd3e597abd3bd9f

[ "Unlicense" ]

null

from flask_restful import Resource class Home(Resource): def get(self): return "Hello, why are you here?!"

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42

0.683333

17

120

4.764706

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120

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1

0.25

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0

1

0

null

0

1

0

null

0

1

0

1

0

5

17572c21670b463b41462346a8fee753be1b236c

100

py

Python

enthought/mayavi/filters/cell_derivatives.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/mayavi/filters/cell_derivatives.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/mayavi/filters/cell_derivatives.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from mayavi.filters.cell_derivatives import *

25

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0.85

13

100

6.076923

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0.11

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null

0

1

0

1

0

1

0

5

17923cc669b3d6e37a0820fad8af5f2adffae4ca

61

py

Python

dataent/config/tools.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

null

dataent/config/tools.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

6

2020-03-24T17:15:56.000Z

2022-02-10T18:41:31.000Z

dataent/config/tools.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

null

from __future__ import unicode_literals from dataent import _

30.5

39

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61

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61

2

40

30.5

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1

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1

0

null

0

1

0

1

0

1

0

5

bd5ad960a3192280c44dddfe3d9acdbbbbcded14

113

py

Python

realtime-analysis-with-simple-model/app/__init__.py

natanascimento/realtime-image-analysis

1df09874e02c59991f5e9cd39d3ad5d09a167fef

[ "MIT" ]

null

realtime-analysis-with-simple-model/app/__init__.py

natanascimento/realtime-image-analysis

1df09874e02c59991f5e9cd39d3ad5d09a167fef

[ "MIT" ]

null

realtime-analysis-with-simple-model/app/__init__.py

natanascimento/realtime-image-analysis

1df09874e02c59991f5e9cd39d3ad5d09a167fef

[ "MIT" ]

null

from app.infrastructure.repositories.camera.capture import CameraCapture def main(): CameraCapture().run()

18.833333

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113

7.333333

0.916667

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113

5

73

22.6

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true

0

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null

0

1

0

null

0

1

0

1

0

5

bd91fe8e99e0df0490b864f1ab29fd3c2696704a

74

py

Python

Software/qlocktoo/timewords/__init__.py

tfeldmann/QlockToo

778a955ac84348342db036b1989931c6d9be9242

[ "MIT" ]

1

2019-10-12T16:26:25.000Z

Software/qlocktoo/timewords/__init__.py

tfeldmann/QlockToo

778a955ac84348342db036b1989931c6d9be9242

[ "MIT" ]

null

Software/qlocktoo/timewords/__init__.py

tfeldmann/QlockToo

778a955ac84348342db036b1989931c6d9be9242

[ "MIT" ]

1

2020-02-02T23:42:55.000Z

from qlocktoo.assets import assets_rc from .timewords import TimeWordsApp

24.666667

37

0.864865

10

74

6.3

0.7

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74

2

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true

0

1

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1

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null

0

1

0

1

0

1

0

5

bdab06659f6e1e61c708d46b2c0ccb475f99aa05

32

py

Python

ledger/__main__.py

Funk66/ledger

b06f39281b81cebb75a6c5f92fa3b8e47b65800c

[ "MIT" ]

null

ledger/__main__.py

Funk66/ledger

b06f39281b81cebb75a6c5f92fa3b8e47b65800c

[ "MIT" ]

3

2021-11-16T06:38:48.000Z

2021-11-16T06:43:18.000Z

ledger/__main__.py

Funk66/ledger

b06f39281b81cebb75a6c5f92fa3b8e47b65800c

[ "MIT" ]

null

from .client import run run()

6.4

23

0.6875

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32

4

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1

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null

0

1

0

1

0

5

bdd95548fe506437e03c5f8630769e55e0fd2be5

12,556

py

Python

data_preprocessing/cvat_annotation_converter.py

Hoclor/CoSADUV-Contextual-Saliency-for-Detecting-Anomalies-in-UAV-Video

674b72af15ba8833317b8daa9d1e614ea63151c1

[ "MIT" ]

4

2019-07-01T14:55:33.000Z

2021-01-18T02:34:38.000Z

data_preprocessing/cvat_annotation_converter.py

Hoclor/CoSADUV-Contextual-Saliency-for-Detecting-Anomalies-in-UAV-Video

674b72af15ba8833317b8daa9d1e614ea63151c1

[ "MIT" ]

null

data_preprocessing/cvat_annotation_converter.py

Hoclor/CoSADUV-Contextual-Saliency-for-Detecting-Anomalies-in-UAV-Video

674b72af15ba8833317b8daa9d1e614ea63151c1

[ "MIT" ]

null

"""A tool to convert annotation files created with CVAT into ground-truth style images for machine learning. The initial code was copied from: https://gist.github.com/cheind/9850e35bb08cfe12500942fb8b55531f originally written for a similar purpose for the tool BeaverDam (which produces json), and was then adapted for use with CVAT (which produces xml). """ import cv2 import xml.etree.ElementTree as ET import numpy as np from tqdm import tqdm # Create a list of BGR colours stored as 3-tuples of uint_8s colours = [ [255, 0, 0], # Blue [0, 255, 0], # Green [0, 0, 255], # Red [0, 255, 255], # Yellow [255, 255, 0], # Cyan [255, 0, 255], # Magenta [192, 192, 192], # Silver [0, 0, 128], # Maroon [0, 128, 128], # Olive [0, 165, 255], # Orange ] def draw_annotations(video, annotations, display=False): tree = ET.parse(args.ann) root = tree.getroot() # Create a list of 'track' nodes that are children of the root tracks = [child for child in root if child.tag == "track"] # Read the video in as a video object cap = cv2.VideoCapture(args.video) # Get a rough count of the number of frames in the video rough_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Find the name/path of the video, without file type name_index = -1 while args.video[name_index] != ".": name_index -= 1 # Define the codec and create VideoWriter object fourcc = cv2.VideoWriter_fourcc(*"DIVX") framerate = cap.get(cv2.CAP_PROP_FPS) (width, height) = ( int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), ) out = cv2.VideoWriter( args.video[:name_index] + "_annotated.avi", fourcc, framerate, (width, height) ) for frame_count in tqdm(range(rough_frame_count)): # Read the next frame of the video ret, frame = cap.read() if not ret: # Video is done, so break out of the loop break # Loop over the track objects. For all that have an annotation for this frame, # draw a corresponding rectangle with a colour from the colours list for track in tracks: # Check that this track has any box nodes left if len(track) > 0: # Since the nodes are sorted by frame number, we only have to check the first one box = track[0] if int(box.attrib["frame"]) == frame_count: # Draw the rectangle described by this 'box' node on this frame # Cast the coordinates to floats, then to ints, # as the cv2.rectangle function cannot handle float pixel values # And int(str) cannot handle float strings x_tl = int(float(box.attrib["xtl"])) y_tl = int(float(box.attrib["ytl"])) x_br = int(float(box.attrib["xbr"])) y_br = int(float(box.attrib["ybr"])) cv2.rectangle( frame, (x_tl, y_tl), (x_br, y_br), colours[int(track.attrib["id"]) % len(colours)], 2, -1, ) # delete this box from the track,so we can keep # only checking the first box in the future track.remove(box) # Write the frame with boxes out.write(frame) # Display the resulting frame if display: cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break frame_count += 1 # Keep going, as the frame count is not necessarily accurate so we might not be done while True: # Read the next frame of the video ret, frame = cap.read() if not ret: # Video is done, so break out of the loop break # Loop over the track objects. For all that have an annotation for this frame, # draw a corresponding rectangle with a colour from the colours list for track in tracks: # Check that this track has any box nodes left if len(track) > 0: # Since the nodes are sorted by frame number, # we only have to check the first one box = track[0] if int(box.attrib["frame"]) == frame_count: # Draw the rectangle described by this 'box' node on this frame # Cast the coordinates to floats, then to ints, # as the cv2.rectangle function cannot handle float pixel values # And int(str) cannot handle float strings x_tl = int(float(box.attrib["xtl"])) y_tl = int(float(box.attrib["ytl"])) x_br = int(float(box.attrib["xbr"])) y_br = int(float(box.attrib["ybr"])) cv2.rectangle( frame, (x_tl, y_tl), (x_br, y_br), colours[int(track.attrib["id"]) % len(colours)], 2, -1, ) # delete this box from the track,so we can keep # only checking the first box in the future track.remove(box) # Write the frame with boxes out.write(frame) # Display the resulting frame if display: cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break frame_count += 1 # Release everything cap.release() out.release() cv2.destroyAllWindows() def draw_groundtruth(video, annotations, display=False): tree = ET.parse(args.ann) root = tree.getroot() # Create a list of 'track' nodes that are children of the root tracks = [child for child in root if child.tag == "track"] # Read the video in as a video object cap = cv2.VideoCapture(args.video) # Get a rough count of the number of frames in the video rough_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Find the name/path of the video, without file type name_index = -1 while args.video[name_index] != ".": name_index -= 1 # Define the codec and create VideoWriter object fourcc = cv2.VideoWriter_fourcc(*"DIVX") framerate = cap.get(cv2.CAP_PROP_FPS) (width, height) = ( int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), ) out = cv2.VideoWriter( args.video[:name_index] + "_groundtruth.avi", fourcc, framerate, (width, height), 0, ) blank_frame = np.zeros((height, width), dtype=np.uint8) for frame_count in tqdm(range(rough_frame_count)): # Copy a new blank frame frame = np.copy(blank_frame) # Read the next frame but discard it, to check if the video is done yet ret, _ = cap.read() if not ret: # Video is done, so break out of the loop break # Loop over the track objects. For all that have an annotation for this frame, # draw a corresponding rectangle with a colour from the colours list for track in tracks: # Check that this track has any box nodes left if len(track) > 0: # Since the nodes are sorted by frame number, # we only have to check the first one box = track[0] if int(box.attrib["frame"]) == frame_count: # Draw the rectangle described by this 'box' node on this frame # Cast the coordinates to floats, then to ints, # as the cv2.rectangle function cannot handle float pixel values # And int(str) cannot handle float strings x_tl = int(float(box.attrib["xtl"])) y_tl = int(float(box.attrib["ytl"])) x_br = int(float(box.attrib["xbr"])) y_br = int(float(box.attrib["ybr"])) cv2.rectangle(frame, (x_tl, y_tl), (x_br, y_br), 255, cv2.FILLED) # delete this box from the track, so we can keep # only checking the first box in the future track.remove(box) # Write the frame with boxes # Convert to BGR so video can be properly saved # frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) out.write(frame) # Display the resulting frame if display: cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break frame_count += 1 # Keep going, as the frame count is not necessarily accurate so we might not be done while True: # Copy a new blank frame frame = np.copy(blank_frame) # Read the next frame but discard it, to check if the video is done yet ret, og_frame = cap.read() if not ret: # Video is done, so break out of the loop break # Loop over the track objects. For all that have an annotation for this frame, # draw a corresponding rectangle with a colour from the colours list for track in tracks: # Check that this track has any box nodes left if len(track) > 0: # Since the nodes are sorted by frame number, we only have to check the first one box = track[0] if int(box.attrib["frame"]) == frame_count: # Draw the rectangle described by this 'box' node on this frame # Cast the coordinates to floats, then to ints, # as the cv2.rectangle function cannot handle float pixel values # And int(str) cannot handle float strings x_tl = int(float(box.attrib["xtl"])) y_tl = int(float(box.attrib["ytl"])) x_br = int(float(box.attrib["xbr"])) y_br = int(float(box.attrib["ybr"])) cv2.rectangle(frame, (x_tl, y_tl), (x_br, y_br), 255, cv2.FILLED) # delete this box from the track, so we can keep # only checking the first box in the future track.remove(box) # Write the frame with boxes out.write(frame) # Display the resulting frame if display: cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break frame_count += 1 # Release everything cap.release() out.release() cv2.destroyAllWindows() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser( description="Draw annotations, either on original videos or as ground-truth saliency maps" ) parser.add_argument( "--folder", "-f", dest="folder", help="Folder containing input files. Video and annotation file names must match exactly, with videos as .mp4 or .avi, and annotations as .xml", required=False, ) parser.add_argument( "--video", "-vid", dest="video", help="Input video file", required=False ) parser.add_argument( "--annotation", "-ann", dest="ann", help="Dense annotation file", required=False ) parser.add_argument( "--bounding_boxes", "-bb", dest="drawing_function", action="store_const", const=draw_annotations, default=draw_groundtruth, ) parser.add_argument("--verbose", "-v", dest="verbose", action="store_true") args = parser.parse_args() # Check that either folder was given, or if not then both video and ann was given if args.folder == None and (args.video == None or args.ann == None): print( "Error: invalid inputs given. Either -folder, or both -video and -ann must be specified." ) if args.folder != None: # Read all files in the folder and call the appropriate function on each # video/annotation pair found # TODO: implement pass else: # Draw bounding boxes on the original video, or ground-truth saliency maps, # depending on if -bb was specified args.drawing_function(args.video, args.ann, args.verbose)

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null

0

1

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null

0

5

bdde56a5f3d286d1bafe98ef4b315a28e95bec3c

126

py

Python

Tensorflow/Test Project/basic classification.py

iggy12345/Neural-Network-Vault

01e6826ea674599c847fd8f2128d326fdea2fe44

[ "MIT" ]

null

Tensorflow/Test Project/basic classification.py

iggy12345/Neural-Network-Vault

01e6826ea674599c847fd8f2128d326fdea2fe44

[ "MIT" ]

null

Tensorflow/Test Project/basic classification.py

iggy12345/Neural-Network-Vault

01e6826ea674599c847fd8f2128d326fdea2fe44

[ "MIT" ]

null

import tensorflow as tf from tensorflow import keras import numpy as np import matplotlib.pyplot as plt print(tf.__version__)

21

31

0.833333

20

126

5.05

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126

6

32

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null

0

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0

1

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1

0

5

da503e27ceb13d56ec6f0447a90c2a91b5d5d016

91

py

Python

test cases/common/95 dep fallback/gensrc.py

NNemec/meson

d72a5c14f83253bafaf6b2531442d981ea1df2ed

[ "Apache-2.0" ]

null

test cases/common/95 dep fallback/gensrc.py

NNemec/meson

d72a5c14f83253bafaf6b2531442d981ea1df2ed

[ "Apache-2.0" ]

null

test cases/common/95 dep fallback/gensrc.py

NNemec/meson

d72a5c14f83253bafaf6b2531442d981ea1df2ed

[ "Apache-2.0" ]

null

#!/usr/bin/env python import sys import shutil shutil.copyfile(sys.argv[1], sys.argv[2])

13

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true

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da731d554915258285d3db92dc37e110a862893f

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py

Python

gitconsensusservice/www.py

tedivm/GitConsensusService

c4c920eaf39aefdca2fa3d82a92c89e27da46506

[ "MIT" ]

16

2018-03-15T10:23:16.000Z

2022-01-30T11:21:56.000Z

gitconsensusservice/www.py

tedivm/GitConsensusService

c4c920eaf39aefdca2fa3d82a92c89e27da46506

[ "MIT" ]

null

gitconsensusservice/www.py

tedivm/GitConsensusService

c4c920eaf39aefdca2fa3d82a92c89e27da46506

[ "MIT" ]

1

2018-07-07T07:45:39.000Z

from flask import Flask, session, redirect, url_for, escape, request, render_template, flash, send_from_directory from gitconsensusservice import app import gitconsensusservice.routes.webhooks @app.route('/') def index(): return redirect('https://www.gitconsensus.com/')

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da758948e9f436b2b6e80031138a444785186a7f

73

py

Python

pushkin/util/__init__.py

Nordeus/pushkin

39f7057d3eb82c811c5c6b795d8bc7df9352a217

[ "MIT" ]

281

2016-03-29T16:36:22.000Z

2022-03-13T10:28:10.000Z

pushkin/util/__init__.py

Nordeus/pushkin

39f7057d3eb82c811c5c6b795d8bc7df9352a217

[ "MIT" ]

34

2016-04-11T08:48:51.000Z

2019-08-17T15:36:15.000Z

pushkin/util/__init__.py

Nordeus/pushkin

39f7057d3eb82c811c5c6b795d8bc7df9352a217

[ "MIT" ]

66

2016-04-07T14:29:26.000Z

2022-03-30T13:17:38.000Z

from . import pool from . import multiprocesslogging from . import tools

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5

da75b4c5d61b6c1b99ae6b83a45f075920d7ce33

100

py

Python

utils/__init__.py

zhanglz95/RS-semantic-segmentation-pytorch-past

17f714024fa61da14f0b74f076feae27d7a782d6

[ "Apache-2.0" ]

1

2021-03-17T10:22:56.000Z

utils/__init__.py

zhanglz95/RS-semantic-segmentation-pytorch-past

17f714024fa61da14f0b74f076feae27d7a782d6

[ "Apache-2.0" ]

null

utils/__init__.py

zhanglz95/RS-semantic-segmentation-pytorch-past

17f714024fa61da14f0b74f076feae27d7a782d6

[ "Apache-2.0" ]

1

2021-04-22T02:18:41.000Z

from .augmentation import * from .optim import * from .metrics import * from .transfunction import *

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py

Python

release/stubs.min/Tekla/Structures/ModelInternal.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/Tekla/Structures/ModelInternal.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/Tekla/Structures/ModelInternal.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

# encoding: utf-8 # module Tekla.Structures.ModelInternal calls itself ModelInternal # from Tekla.Structures.Model,Version=2017.0.0.0,Culture=neutral,PublicKeyToken=2f04dbe497b71114 # by generator 1.145 # no doc # no imports # no functions # classes from ModelInternal_parts.AreWeUnitTesting import AreWeUnitTesting from ModelInternal_parts.BasePoint import BasePoint from ModelInternal_parts.BentPlateTestingTool import BentPlateTestingTool from ModelInternal_parts.BentPlateTools import BentPlateTools from ModelInternal_parts.CDelegateSetter import CDelegateSetter from ModelInternal_parts.CDelegateWrapper import CDelegateWrapper from ModelInternal_parts.ChangeManager import ChangeManager from ModelInternal_parts.ConversionLink import ConversionLink from ModelInternal_parts.DelegateFake import DelegateFake from ModelInternal_parts.dotAreaPolygons_t import dotAreaPolygons_t from ModelInternal_parts.dotAssembly_t import dotAssembly_t from ModelInternal_parts.dotBaseComponent_t import dotBaseComponent_t from ModelInternal_parts.dotBasePointData_t import dotBasePointData_t from ModelInternal_parts.dotBoltGroup_t import dotBoltGroup_t from ModelInternal_parts.dotBoltPolygon_t import dotBoltPolygon_t from ModelInternal_parts.dotBoltShapeData_t import dotBoltShapeData_t from ModelInternal_parts.dotBooleanPart_t import dotBooleanPart_t from ModelInternal_parts.dotBoolean_t import dotBoolean_t from ModelInternal_parts.dotCamera_t import dotCamera_t from ModelInternal_parts.dotChamfer_t import dotChamfer_t from ModelInternal_parts.dotClash_t import dotClash_t from ModelInternal_parts.dotClientId_t import dotClientId_t from ModelInternal_parts.dotClipPlane_t import dotClipPlane_t from ModelInternal_parts.dotColor_t import dotColor_t from ModelInternal_parts.dotComponentAttribute_t import dotComponentAttribute_t from ModelInternal_parts.dotComponentInputObject_t import dotComponentInputObject_t from ModelInternal_parts.dotContourPoint_t import dotContourPoint_t from ModelInternal_parts.dotContour_t import dotContour_t from ModelInternal_parts.dotControlObject_t import dotControlObject_t from ModelInternal_parts.dotConversionLink_t import dotConversionLink_t from ModelInternal_parts.dotCreateIFCFromModel_t import dotCreateIFCFromModel_t from ModelInternal_parts.dotCreateNCFromModel_t import dotCreateNCFromModel_t from ModelInternal_parts.dotCreateReportFromModel_t import dotCreateReportFromModel_t from ModelInternal_parts.dotDeformingData_t import dotDeformingData_t from ModelInternal_parts.dotDrawPolygonSurface_t import dotDrawPolygonSurface_t from ModelInternal_parts.dotDrawPolyLine_t import dotDrawPolyLine_t from ModelInternal_parts.dotDrawText_t import dotDrawText_t from ModelInternal_parts.dotEdgeChamfer_t import dotEdgeChamfer_t from ModelInternal_parts.dotEdges_t import dotEdges_t from ModelInternal_parts.dotEnumerator_t import dotEnumerator_t from ModelInternal_parts.dotFittingOrCutPlane_t import dotFittingOrCutPlane_t from ModelInternal_parts.dotFormingStates_t import dotFormingStates_t from ModelInternal_parts.dotGetClipPlanes_t import dotGetClipPlanes_t from ModelInternal_parts.dotGetProperties_t import dotGetProperties_t from ModelInternal_parts.dotGraphicPolyLine_t import dotGraphicPolyLine_t from ModelInternal_parts.dotGridPlane_t import dotGridPlane_t from ModelInternal_parts.dotGrid_t import dotGrid_t from ModelInternal_parts.dotGuideline_t import dotGuideline_t from ModelInternal_parts.dotHierarchicDefinition_t import dotHierarchicDefinition_t from ModelInternal_parts.dotHierarchicList_t import dotHierarchicList_t from ModelInternal_parts.dotHierarchicObject_t import dotHierarchicObject_t from ModelInternal_parts.dotIFC2X3_Application_t import dotIFC2X3_Application_t from ModelInternal_parts.dotIFC2X3_Organization_t import dotIFC2X3_Organization_t from ModelInternal_parts.dotIFC2X3_OwnerHistoryChangeAction_t import ( dotIFC2X3_OwnerHistoryChangeAction_t, ) from ModelInternal_parts.dotIFC2X3_OwnerHistoryState_t import ( dotIFC2X3_OwnerHistoryState_t, ) from ModelInternal_parts.dotIFC2X3_OwnerHistory_t import dotIFC2X3_OwnerHistory_t from ModelInternal_parts.dotIFC2X3_ParametricObject_ShapeProfile_t import ( dotIFC2X3_ParametricObject_ShapeProfile_t, ) from ModelInternal_parts.dotIFC2X3_PersonAndOrganization_t import ( dotIFC2X3_PersonAndOrganization_t, ) from ModelInternal_parts.dotIFC2X3_Person_t import dotIFC2X3_Person_t from ModelInternal_parts.dotIFC2X3_Product_t import dotIFC2X3_Product_t from ModelInternal_parts.dotIntersectionPoints_t import dotIntersectionPoints_t from ModelInternal_parts.dotIntersectionSolid_t import dotIntersectionSolid_t from ModelInternal_parts.dotLegFace_t import dotLegFace_t from ModelInternal_parts.dotLoadClassAttributes_t import dotLoadClassAttributes_t from ModelInternal_parts.dotLoadCommonAttributes_t import dotLoadCommonAttributes_t from ModelInternal_parts.dotLoadGroup_t import dotLoadGroup_t from ModelInternal_parts.dotManipulateObject_t import dotManipulateObject_t from ModelInternal_parts.dotMaterial_t import dotMaterial_t from ModelInternal_parts.dotModelCommit_t import dotModelCommit_t from ModelInternal_parts.dotModelInfoModeEnum import dotModelInfoModeEnum from ModelInternal_parts.dotModelInfo_t import dotModelInfo_t from ModelInternal_parts.dotModelObjectType_t import dotModelObjectType_t from ModelInternal_parts.dotModelObject_t import dotModelObject_t from ModelInternal_parts.dotModificationStamp_t import dotModificationStamp_t from ModelInternal_parts.dotModStampCompare_t import dotModStampCompare_t from ModelInternal_parts.dotModStamp_t import dotModStamp_t from ModelInternal_parts.dotnetDoubleList_t import dotnetDoubleList_t from ModelInternal_parts.dotnetEdgeList_t import dotnetEdgeList_t from ModelInternal_parts.dotnetIntList_t import dotnetIntList_t from ModelInternal_parts.DotNetModelProxy import DotNetModelProxy from ModelInternal_parts.dotnetPointList_t import dotnetPointList_t from ModelInternal_parts.dotnetStringList_t import dotnetStringList_t from ModelInternal_parts.dotNumberingQuery_t import dotNumberingQuery_t from ModelInternal_parts.dotNumberingSeries_t import dotNumberingSeries_t from ModelInternal_parts.dotObjectOperationsEnum import dotObjectOperationsEnum from ModelInternal_parts.dotObject_t import dotObject_t from ModelInternal_parts.dotOffset_t import dotOffset_t from ModelInternal_parts.dotPartLine_t import dotPartLine_t from ModelInternal_parts.dotPartMark_t import dotPartMark_t from ModelInternal_parts.dotPart_t import dotPart_t from ModelInternal_parts.dotPhaseNumbers_t import dotPhaseNumbers_t from ModelInternal_parts.dotPhase_t import dotPhase_t from ModelInternal_parts.dotPlane_t import dotPlane_t from ModelInternal_parts.dotPolygon_t import dotPolygon_t from ModelInternal_parts.dotPolymeshObject_t import dotPolymeshObject_t from ModelInternal_parts.dotPolymeshValidateInvalidInfo_t import ( dotPolymeshValidateInvalidInfo_t, ) from ModelInternal_parts.dotPolymesh_t import dotPolymesh_t from ModelInternal_parts.dotPosition_t import dotPosition_t from ModelInternal_parts.dotPourObject_t import dotPourObject_t from ModelInternal_parts.dotProfile_t import dotProfile_t from ModelInternal_parts.dotProgressBar_t import dotProgressBar_t from ModelInternal_parts.dotProjectInfo_t import dotProjectInfo_t from ModelInternal_parts.dotRebarEndDetailStrip_t import dotRebarEndDetailStrip_t from ModelInternal_parts.dotRebarGroup_t import dotRebarGroup_t from ModelInternal_parts.dotRebarHookData_t import dotRebarHookData_t from ModelInternal_parts.dotRebarMesh_t import dotRebarMesh_t from ModelInternal_parts.dotRebarProperties_t import dotRebarProperties_t from ModelInternal_parts.dotRebarPropertyStrip_t import dotRebarPropertyStrip_t from ModelInternal_parts.dotRebarSetAddition_t import dotRebarSetAddition_t from ModelInternal_parts.dotRebarSet_t import dotRebarSet_t from ModelInternal_parts.dotRebarSpacing_t import dotRebarSpacing_t from ModelInternal_parts.dotRebarSplice_t import dotRebarSplice_t from ModelInternal_parts.dotRebarSplitter_t import dotRebarSplitter_t from ModelInternal_parts.dotRebarStrand_t import dotRebarStrand_t from ModelInternal_parts.dotRebarStrip_t import dotRebarStrip_t from ModelInternal_parts.dotRebarThreading_t import dotRebarThreading_t from ModelInternal_parts.dotReferenceModelObjectAttributeEnumerator_t import ( dotReferenceModelObjectAttributeEnumerator_t, ) from ModelInternal_parts.dotReferenceModelObjectAttribute_t import ( dotReferenceModelObjectAttribute_t, ) from ModelInternal_parts.dotReferenceModelObject_t import dotReferenceModelObject_t from ModelInternal_parts.dotReferenceModelRevision_t import dotReferenceModelRevision_t from ModelInternal_parts.dotReferenceModel_t import dotReferenceModel_t from ModelInternal_parts.dotReinforcement_t import dotReinforcement_t from ModelInternal_parts.dotSaveAsWebModel_t import dotSaveAsWebModel_t from ModelInternal_parts.dotSaveOperation_t import dotSaveOperation_t from ModelInternal_parts.dotSetGetProperty_t import dotSetGetProperty_t from ModelInternal_parts.dotSetProperty_t import dotSetProperty_t from ModelInternal_parts.dotSetTemporaryColors_t import dotSetTemporaryColors_t from ModelInternal_parts.dotSetTemporaryStates_t import dotSetTemporaryStates_t from ModelInternal_parts.dotSharingOperation_t import dotSharingOperation_t from ModelInternal_parts.dotSingleRebar_t import dotSingleRebar_t from ModelInternal_parts.dotSolid_t import dotSolid_t from ModelInternal_parts.dotSpacingZone_t import dotSpacingZone_t from ModelInternal_parts.dotStringProperty_t import dotStringProperty_t from ModelInternal_parts.dotSurfaceObject_t import dotSurfaceObject_t from ModelInternal_parts.dotSurfaceTreatment_t import dotSurfaceTreatment_t from ModelInternal_parts.dotTaskDependency_t import dotTaskDependency_t from ModelInternal_parts.dotTaskObjectAttacher_t import dotTaskObjectAttacher_t from ModelInternal_parts.dotTaskWorktype_t import dotTaskWorktype_t from ModelInternal_parts.dotTask_t import dotTask_t from ModelInternal_parts.dotTemporaryState import dotTemporaryState from ModelInternal_parts.dotTemporaryStatesEnum import dotTemporaryStatesEnum from ModelInternal_parts.dotTemporaryTransparenciesEnum import ( dotTemporaryTransparenciesEnum, ) from ModelInternal_parts.dotTransformationPlane_t import dotTransformationPlane_t from ModelInternal_parts.dotUIModelObjectSelector_t import dotUIModelObjectSelector_t from ModelInternal_parts.dotUIPicker_t import dotUIPicker_t from ModelInternal_parts.dotUndoOperation_t import dotUndoOperation_t from ModelInternal_parts.dotViewSelector_t import dotViewSelector_t from ModelInternal_parts.dotViewVisibilitySettings_t import dotViewVisibilitySettings_t from ModelInternal_parts.dotView_t import dotView_t from ModelInternal_parts.dotWeldGeometry_t import dotWeldGeometry_t from ModelInternal_parts.dotWeld_t import dotWeld_t from ModelInternal_parts.dotWire_t import dotWire_t from ModelInternal_parts.DoubleList import DoubleList from ModelInternal_parts.EventHandlerWrapper import EventHandlerWrapper from ModelInternal_parts.GeometryImporter import GeometryImporter from ModelInternal_parts.GeometryTree import GeometryTree from ModelInternal_parts.ICDelegate import ICDelegate from ModelInternal_parts.IEventHandler import IEventHandler from ModelInternal_parts.IntList import IntList from ModelInternal_parts.ModelExtensions import ModelExtensions from ModelInternal_parts.ModelObjectFactory import ModelObjectFactory from ModelInternal_parts.NumberingQueryModeEnum import NumberingQueryModeEnum from ModelInternal_parts.Operation import Operation from ModelInternal_parts.PointList import PointList from ModelInternal_parts.PolygonExtensions import PolygonExtensions from ModelInternal_parts.RebarSetAction import RebarSetAction from ModelInternal_parts.Remoter import Remoter from ModelInternal_parts.ScopedCDelegateSetter import ScopedCDelegateSetter from ModelInternal_parts.Serializer import Serializer from ModelInternal_parts.StringList import StringList from ModelInternal_parts.SurfaceObjectCreator import SurfaceObjectCreator from ModelInternal_parts.SyncHandler import SyncHandler from ModelInternal_parts.UDAChanges import UDAChanges

61.562814

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null

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1

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5

da847575ffbfbf01189591df5dfaeb376a28f185

404

py

Python

django_world/admin.py

iamabhishekchakraborty/djangoProject

a71be90da2899c5cf17e94404b2056438b52fe35

[ "MIT" ]

null

django_world/admin.py

iamabhishekchakraborty/djangoProject

a71be90da2899c5cf17e94404b2056438b52fe35

[ "MIT" ]

null

django_world/admin.py

iamabhishekchakraborty/djangoProject

a71be90da2899c5cf17e94404b2056438b52fe35

[ "MIT" ]

null

from django.contrib import admin from .models import Succession,Succession_Seasons,Succession_Casts,Succession_Season_Episodes # Register your models here. # admin.site.register(Succession) # The model Succession is abstract so it can't be registered with admin admin.site.register(Succession_Seasons) admin.site.register(Succession_Casts) admin.site.register(Succession_Season_Episodes)

44.888889

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404

6.09434

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0.111455

0.210526

0.334365

0

0.116337

404

8

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null

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1

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5

e540f8c6d5fba988d4c42de6ea9c1fbacb4b4f7a

302

py

Python

unipipeline/brokers/uni_broker_message_manager.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

null

unipipeline/brokers/uni_broker_message_manager.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

1

2021-09-14T13:08:13.000Z

unipipeline/brokers/uni_broker_message_manager.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

null

class UniBrokerMessageManager: def reject(self) -> None: raise NotImplementedError(f'method reject must be specified for class "{type(self).__name__}"') def ack(self) -> None: raise NotImplementedError(f'method acknowledge must be specified for class "{type(self).__name__}"')

43.142857

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0.711538

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302

6

109

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null

0

1

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1

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5

e571dfc8f064be1cb41ad77510b528b26dec92c5

1,994

py

Python

nets/vgg.py

bubbliiiing/faster-rcnn-tf2

c3982016c7c209a3fa71a30facbe53396e6340ee

[ "MIT" ]

133

2020-11-23T05:43:08.000Z

2022-03-30T08:10:50.000Z

nets/vgg.py

Whale1024/faster-rcnn-tf2

6343860e5d44e8441fa736620762e4c256b27083

[ "MIT" ]

15

2020-12-01T12:07:14.000Z

2021-09-30T02:27:37.000Z

nets/vgg.py

Whale1024/faster-rcnn-tf2

6343860e5d44e8441fa736620762e4c256b27083

[ "MIT" ]

40

2020-11-24T13:01:41.000Z

2022-03-30T08:10:53.000Z

from tensorflow.keras.layers import (Conv2D, Dense, Flatten, MaxPooling2D, TimeDistributed) def VGG16(inputs): x = Conv2D(64,(3,3),activation = 'relu',padding = 'same',name = 'block1_conv1')(inputs) x = Conv2D(64,(3,3),activation = 'relu',padding = 'same', name = 'block1_conv2')(x) x = MaxPooling2D((2,2), strides = (2,2), name = 'block1_pool')(x) x = Conv2D(128,(3,3),activation = 'relu',padding = 'same',name = 'block2_conv1')(x) x = Conv2D(128,(3,3),activation = 'relu',padding = 'same',name = 'block2_conv2')(x) x = MaxPooling2D((2,2),strides = (2,2), name = 'block2_pool')(x) x = Conv2D(256,(3,3),activation = 'relu',padding = 'same',name = 'block3_conv1')(x) x = Conv2D(256,(3,3),activation = 'relu',padding = 'same',name = 'block3_conv2')(x) x = Conv2D(256,(3,3),activation = 'relu',padding = 'same',name = 'block3_conv3')(x) x = MaxPooling2D((2,2),strides = (2,2), name = 'block3_pool')(x) # 第四个卷积部分 # 14,14,512 x = Conv2D(512,(3,3),activation = 'relu',padding = 'same', name = 'block4_conv1')(x) x = Conv2D(512,(3,3),activation = 'relu',padding = 'same', name = 'block4_conv2')(x) x = Conv2D(512,(3,3),activation = 'relu',padding = 'same', name = 'block4_conv3')(x) x = MaxPooling2D((2,2),strides = (2,2), name = 'block4_pool')(x) # 第五个卷积部分 # 7,7,512 x = Conv2D(512,(3,3),activation = 'relu', padding = 'same', name = 'block5_conv1')(x) x = Conv2D(512,(3,3),activation = 'relu', padding = 'same', name = 'block5_conv2')(x) x = Conv2D(512,(3,3),activation = 'relu', padding = 'same', name = 'block5_conv3')(x) return x def vgg_classifier_layers(x): # num_rois, 14, 14, 1024 -> num_rois, 7, 7, 2048 x = TimeDistributed(Flatten(name='flatten'))(x) x = TimeDistributed(Dense(4096, activation='relu'), name='fc1')(x) x = TimeDistributed(Dense(4096, activation='relu'), name='fc2')(x) return x

49.85

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1

0

null

0

5

e57aa2bef618d1871eb73c2b27d8c100f4c7c1b4

117

py

Python

genie/cbs.py

karawoo/Genie

d39451655ec3632df6002c1d73b17dacba2a8720

[ "MIT" ]

10

2017-08-31T21:32:18.000Z

2022-03-07T21:37:17.000Z

genie/cbs.py

karawoo/Genie

d39451655ec3632df6002c1d73b17dacba2a8720

[ "MIT" ]

216

2016-10-24T21:30:12.000Z

2022-03-31T15:04:37.000Z

genie/cbs.py

karawoo/Genie

d39451655ec3632df6002c1d73b17dacba2a8720

[ "MIT" ]

12

2016-10-21T13:48:06.000Z

2020-06-04T19:21:23.000Z

from .seg import seg class cbs(seg): ''' cbs file type extends from seg files ''' _fileType = "cbs"

14.625

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117

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117

7

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null

0

1

0

1

0

5

e59a96f9cbef90916ed0e04cd8d6ed7564a75b9b

83

py

Python

core/dr_utils/dib_renderer_x/__init__.py

weiqi-luo/Self6D-Diff-Renderer

1e1caad49f0f8de90a332995814de29261598982

[ "Apache-2.0" ]

90

2020-08-15T16:14:45.000Z

2022-01-22T10:24:13.000Z

core/dr_utils/dib_renderer_x/__init__.py

weiqi-luo/Self6D-Diff-Renderer

1e1caad49f0f8de90a332995814de29261598982

[ "Apache-2.0" ]

11

2020-09-07T17:31:18.000Z

2021-11-25T12:07:30.000Z

core/dr_utils/dib_renderer_x/__init__.py

weiqi-luo/Self6D-Diff-Renderer

1e1caad49f0f8de90a332995814de29261598982

[ "Apache-2.0" ]

13

2020-09-03T04:25:50.000Z

2021-12-23T08:23:33.000Z

# NOTE: override the kaolin one from .renderer.base import Renderer as DIBRenderer

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1

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e5ad6ee6714dfcdaecbb3585508060507bc3d9b4

260

py

Python

app/main/errors.py

ZxShane/slam_hospital

302704b3a188cea07dddfb23595dd75f8d3cd636

[ "Apache-2.0" ]

2

2022-03-21T20:43:02.000Z

2022-03-21T21:59:16.000Z

app/main/errors.py

liliangbin/webBot

b538dbbf1d05217e52c3713af2aff26bf8692a53

[ "Apache-2.0" ]

1

2020-11-17T16:47:19.000Z

2021-01-26T10:16:33.000Z

app/main/errors.py

ZxShane/slam_hospital

302704b3a188cea07dddfb23595dd75f8d3cd636

[ "Apache-2.0" ]

1

2022-03-21T20:43:04.000Z

from app.main import main from flask import render_template @main.app_errorhandler(404) def page_not_found(): return render_template('404.html'), 404 @main.app_errorhandler(500) def internal_server_error(e): return render_template('500.html'), 500

20

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5

e5af907f4c075a0a78dd76e98c96640ee83b5d8a

46

py

Python

destiny_timelost/exceptions.py

dmfigol/destiny-timelost

ba61bfbfea8b0d4136f64c815b4f6bf37c3b4a5e

[ "MIT" ]

null

destiny_timelost/exceptions.py

dmfigol/destiny-timelost

ba61bfbfea8b0d4136f64c815b4f6bf37c3b4a5e

[ "MIT" ]

null

destiny_timelost/exceptions.py

dmfigol/destiny-timelost

ba61bfbfea8b0d4136f64c815b4f6bf37c3b4a5e

[ "MIT" ]

null

class IncorrectSideError(Exception): pass

15.333333

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null

0

1

0

null

0

1

0

5

e5b37ed1248ab6d9033193e9bfde652e9f214f58

77

py

Python

ap/tests/test_request_certs.py

oscar-king/A-Decentralised-Digital-Identity-Architecture

99d80529d00f135d5de642fc98f817cb289d8b3c

[ "MIT" ]

4

2020-10-13T15:47:19.000Z

2021-01-10T14:00:57.000Z

ap/tests/test_request_certs.py

oscar-king/A-Decentralised-Digital-Identity-Architecture

99d80529d00f135d5de642fc98f817cb289d8b3c

[ "MIT" ]

1

2021-06-02T00:22:25.000Z

ap/tests/test_request_certs.py

oscar-king/A-Decentralised-Digital-Identity-Architecture

99d80529d00f135d5de642fc98f817cb289d8b3c

[ "MIT" ]

1

2020-01-27T14:16:11.000Z

class TestRequest_certs(): def test_request_certs(self): return

15.4

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0

5

e5c5696b8bef2a55f03c57b8d36284a7a1f944c2

154

py

Python

gspread_pandas/__init__.py

andmatt/gspread-pandas

a862f995f50217be4e45c1db858e3b901f2b68df

[ "BSD-3-Clause" ]

null

gspread_pandas/__init__.py

andmatt/gspread-pandas

a862f995f50217be4e45c1db858e3b901f2b68df

[ "BSD-3-Clause" ]

null

gspread_pandas/__init__.py

andmatt/gspread-pandas

a862f995f50217be4e45c1db858e3b901f2b68df

[ "BSD-3-Clause" ]

null

from .client import Spread, Client from ._version import __version__, __version_info__ __all__ = ["Spread", "Client", "__version__", "__version_info__"]

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e5c871f3f8f4cec7f1ec598959c70b9c56324233

173

py

Python

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test25.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

450

2015-09-05T09:12:51.000Z

2018-08-30T01:45:36.000Z

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test25.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

1,274

2015-09-22T20:06:16.000Z

2018-08-31T22:14:00.000Z

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test25.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

278

2015-09-21T19:15:06.000Z

2018-08-31T00:36:51.000Z

'doc' import sys class A: 'doc' z = 1 def x(self): pass def xxx(): print A.x() print A.z print A.a print A.y() print sys.lkjsdflksjasdlf

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5

e5ccafc8059c0671229da4af053e6aac65820444

410

py

Python

networks/decoders/__init__.py

Yaoyi-Li/HOP-Matting

4ac22d92b5432734ffe416cf2c0a99fb730d0c04

[ "MIT" ]

56

2020-04-26T16:19:50.000Z

2021-12-30T07:20:40.000Z

networks/decoders/__init__.py

Yaoyi-Li/HOP-Matting

4ac22d92b5432734ffe416cf2c0a99fb730d0c04

[ "MIT" ]

5

2020-04-27T19:17:17.000Z

2021-07-17T13:55:35.000Z

networks/decoders/__init__.py

Yaoyi-Li/HOP-Matting

4ac22d92b5432734ffe416cf2c0a99fb730d0c04

[ "MIT" ]

11

2020-04-29T10:01:35.000Z

2022-03-31T03:34:50.000Z

from .resnet_dec import ResNet_D_Dec, BasicBlock from .res_localHOP_posEmb_dec import ResLocalHOP_PosEmb_Dec __all__ = ['res_localHOP_posEmb_decoder_22'] def _res_localHOP_posEmb_dec(block, layers, **kwargs): model = ResLocalHOP_PosEmb_Dec(block, layers, **kwargs) return model def res_localHOP_posEmb_decoder_22(**kwargs): return _res_localHOP_posEmb_dec(BasicBlock, [2, 3, 3, 2], **kwargs)

27.333333

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0.291096

0.205479

0.356164

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0.022222

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410

14

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null

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1

0

1

0

5

f920a341583ba0552d2d24741d13184d533dc057

173

py

Python

app/main.py

PythonBiellaGroup/ModernDataEngineering

369fcb89d119ccd1d73882e492cf7c5331087d20

[ "MIT" ]

12

2021-12-12T22:19:52.000Z

2022-03-18T11:45:17.000Z

app/main.py

PythonBiellaGroup/ModernDataEngineering

369fcb89d119ccd1d73882e492cf7c5331087d20

[ "MIT" ]

1

2021-02-02T09:21:23.000Z

app/main.py

PythonBiellaGroup/ModernDataEngineering

369fcb89d119ccd1d73882e492cf7c5331087d20

[ "MIT" ]

7

2021-02-01T22:09:14.000Z

2021-06-22T08:30:16.000Z

# import streamlit.cli from app.src.launch import app if __name__ == "__main__": # streamlit.cli._main_run_clExplicit("./src/launch.py", "streamlit run") app.run()

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0

5

f92538fc2b7784c83c2fb8a696ed36d394f74a8b

94

py

Python

test/conftest.py

atztogo/aiida-donothing

1bd4c8c07213147e82fa32e38e9508c925d87507

[ "MIT" ]

null

test/conftest.py

atztogo/aiida-donothing

1bd4c8c07213147e82fa32e38e9508c925d87507

[ "MIT" ]

null

test/conftest.py

atztogo/aiida-donothing

1bd4c8c07213147e82fa32e38e9508c925d87507

[ "MIT" ]

null

"""pytest fixtures.""" import pytest pytest_plugins = ["aiida.manage.tests.pytest_fixtures"]

18.8

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94

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1

null

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1

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5

008b87796c61d9c7cad8a8c451eb6e712bd0b528

242

py

Python

django_twilio/settings.py

km-pg/django-twilio

d50ead89ca0996f6fe7e1b9359d877637627594e

[ "Unlicense" ]

null

django_twilio/settings.py

km-pg/django-twilio

d50ead89ca0996f6fe7e1b9359d877637627594e

[ "Unlicense" ]

10

2019-12-26T17:31:31.000Z

2022-03-21T22:17:33.000Z

django_twilio/settings.py

km-pg/django-twilio

d50ead89ca0996f6fe7e1b9359d877637627594e

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals, absolute_import """ django_twilio specific settings. """ from .utils import discover_twilio_credentials TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN = discover_twilio_credentials()

22

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null

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1

0

1

0

1

0

5

00a4d8f0286421d9f4cf38faa6eb4989443c341b

25

py

Python

web/web/domain/models/__init__.py

michelangelo-prog/wishlist

0a17194274c4339425768b9ea08986fcf735efc9

[ "MIT" ]

null

web/web/domain/models/__init__.py

michelangelo-prog/wishlist

0a17194274c4339425768b9ea08986fcf735efc9

[ "MIT" ]

null

web/web/domain/models/__init__.py

michelangelo-prog/wishlist

0a17194274c4339425768b9ea08986fcf735efc9

[ "MIT" ]

null

# web/models/__init__.py

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1

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1

0

null

0

1

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5

970ed693f23636178c5bb2e32a7f866dc966fbe4

93

py

Python

project/admin.py

abrusebas1997/Contractor1.2

b8f76e3e48282002b3500353970163e572454dc9

[ "MIT" ]

null

project/admin.py

abrusebas1997/Contractor1.2

b8f76e3e48282002b3500353970163e572454dc9

[ "MIT" ]

7

2019-12-20T04:52:30.000Z

2022-02-10T14:08:29.000Z

project/admin.py

abrusebas1997/Contractor1.2

b8f76e3e48282002b3500353970163e572454dc9

[ "MIT" ]

null

from django.contrib import admin from project.models import Code admin.site.register(Code)

15.5

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93

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1

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1

0

5

9742325fc6596f6a1c089f4cbeb1c756bd91db93

232

py

Python

utils/common.py

OptimusPrimus/dcase2019_task1b

321e10affd47ccecc9dd4d2d327e466481f457c9

[ "MIT" ]

8

2019-06-30T10:05:33.000Z

2022-01-09T20:40:05.000Z

utils/common.py

OptimusPrimus/dcase2019_task1b

321e10affd47ccecc9dd4d2d327e466481f457c9

[ "MIT" ]

null

utils/common.py

OptimusPrimus/dcase2019_task1b

321e10affd47ccecc9dd4d2d327e466481f457c9

[ "MIT" ]

3

2019-11-06T19:06:53.000Z

2020-06-09T16:01:50.000Z

import importlib def load_class(cls, *args, **kwargs): if cls is None: return None module_name, class_name = cls.rsplit(".", 1) return getattr(importlib.import_module(module_name), class_name)(*args, **kwargs)

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5

9742e9d63f5329ee657a039a5fd1bd1e6a2f3b7f

27,643

py

Python

tf3d/losses/box_prediction_losses.py

muell-monster/google-research

04d2024f4723bc4be3d639a668c19fb1f6a31478

[ "Apache-2.0" ]

2

2021-01-06T04:28:23.000Z

2021-02-24T13:46:04.000Z

tf3d/losses/box_prediction_losses.py

Alfaxad/google-research

2c0043ecd507e75e2df9973a3015daf9253e1467

[ "Apache-2.0" ]

7

2021-11-10T19:44:38.000Z

2022-02-10T06:48:39.000Z

tf3d/losses/box_prediction_losses.py

Alfaxad/google-research

2c0043ecd507e75e2df9973a3015daf9253e1467

[ "Apache-2.0" ]

4

2021-02-08T10:25:45.000Z

2021-04-17T14:46:26.000Z

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # 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. """Object detection box prediction losses.""" import gin import gin.tf import tensorflow as tf from tf3d import standard_fields from tf3d.losses import utils as loss_utils from tf3d.utils import batch_utils from tf3d.utils import box_utils from tf3d.utils import mask_utils def _box_rotation_regression_loss(loss_type, is_balanced, input_boxes_rotation_matrix, input_boxes_instance_id, output_boxes_rotation_matrix, delta): """Computes regression loss on object rotations.""" def fn(): """Loss function for when number of input and output boxes is positive.""" if is_balanced: weights = loss_utils.get_balanced_loss_weights_multiclass( labels=input_boxes_instance_id) else: weights = tf.ones([tf.shape(input_boxes_instance_id)[0], 1], dtype=tf.float32) gt_rotation_matrix = tf.reshape(input_boxes_rotation_matrix, [-1, 9]) predicted_rotation_matrix = tf.reshape(output_boxes_rotation_matrix, [-1, 9]) if loss_type == 'huber': loss_fn = tf.keras.losses.Huber( delta=delta, reduction=tf.keras.losses.Reduction.NONE) elif loss_type == 'absolute_difference': loss_fn = tf.keras.losses.MeanAbsoluteError( reduction=tf.keras.losses.Reduction.NONE) else: raise ValueError(('Unknown loss type %s.' % loss_type)) rotation_losses = loss_fn( y_true=gt_rotation_matrix, y_pred=predicted_rotation_matrix) return tf.reduce_mean(rotation_losses * tf.reshape(weights, [-1])) cond_input = tf.greater(tf.shape(input_boxes_rotation_matrix)[0], 0) cond_output = tf.greater(tf.shape(output_boxes_rotation_matrix)[0], 0) cond = tf.logical_and(cond_input, cond_output) return tf.cond(cond, fn, lambda: tf.constant(0.0, dtype=tf.float32)) def _box_size_regression_loss(loss_type, is_balanced, input_boxes_length, input_boxes_height, input_boxes_width, input_boxes_instance_id, output_boxes_length, output_boxes_height, output_boxes_width, delta): """Computes regression loss on object sizes.""" def fn(): """Loss function for when number of input and output boxes is positive.""" if is_balanced: weights = loss_utils.get_balanced_loss_weights_multiclass( labels=input_boxes_instance_id) else: weights = tf.ones([tf.shape(input_boxes_instance_id)[0], 1], dtype=tf.float32) gt_length = tf.reshape(input_boxes_length, [-1, 1]) gt_height = tf.reshape(input_boxes_height, [-1, 1]) gt_width = tf.reshape(input_boxes_width, [-1, 1]) predicted_length = tf.reshape(output_boxes_length, [-1, 1]) predicted_height = tf.reshape(output_boxes_height, [-1, 1]) predicted_width = tf.reshape(output_boxes_width, [-1, 1]) predicted_length /= gt_length predicted_height /= gt_height predicted_width /= gt_width predicted_size = tf.concat( [predicted_length, predicted_height, predicted_width], axis=1) gt_size = tf.ones_like(predicted_size) if loss_type == 'huber': loss_fn = tf.keras.losses.Huber( delta=delta, reduction=tf.keras.losses.Reduction.NONE) elif loss_type == 'absolute_difference': loss_fn = tf.keras.losses.MeanAbsoluteError( reduction=tf.keras.losses.Reduction.NONE) else: raise ValueError(('Unknown loss type %s.' % loss_type)) size_losses = loss_fn(y_true=gt_size, y_pred=predicted_size) return tf.reduce_mean(size_losses * tf.reshape(weights, [-1])) cond_input = tf.greater(tf.shape(input_boxes_length)[0], 0) cond_output = tf.greater(tf.shape(output_boxes_length)[0], 0) cond = tf.logical_and(cond_input, cond_output) return tf.cond(cond, fn, lambda: tf.constant(0.0, dtype=tf.float32)) def _box_center_distance_loss(loss_type, is_balanced, input_boxes_center, input_boxes_instance_id, output_boxes_center, delta): """Computes regression loss on object center locations.""" def fn(): """Loss function for when number of input and output boxes is positive.""" if is_balanced: weights = loss_utils.get_balanced_loss_weights_multiclass( labels=input_boxes_instance_id) else: weights = tf.ones([tf.shape(input_boxes_instance_id)[0], 1], dtype=tf.float32) gt_center = tf.reshape(input_boxes_center, [-1, 3]) predicted_center = tf.reshape(output_boxes_center, [-1, 3]) if loss_type == 'huber': loss_fn = tf.keras.losses.Huber( delta=delta, reduction=tf.keras.losses.Reduction.NONE) elif loss_type == 'absolute_difference': loss_fn = tf.keras.losses.MeanAbsoluteError( reduction=tf.keras.losses.Reduction.NONE) else: raise ValueError(('Unknown loss type %s.' % loss_type)) center_losses = loss_fn(y_true=gt_center, y_pred=predicted_center) return tf.reduce_mean(center_losses * tf.reshape(weights, [-1])) cond_input = tf.greater(tf.shape(input_boxes_center)[0], 0) cond_output = tf.greater(tf.shape(output_boxes_center)[0], 0) cond = tf.logical_and(cond_input, cond_output) return tf.cond(cond, fn, lambda: tf.constant(0.0, dtype=tf.float32)) def _box_corner_distance_loss( loss_type, is_balanced, input_boxes_length, input_boxes_height, input_boxes_width, input_boxes_center, input_boxes_rotation_matrix, input_boxes_instance_id, output_boxes_length, output_boxes_height, output_boxes_width, output_boxes_center, output_boxes_rotation_matrix, delta): """Computes regression loss on object corner locations.""" def fn(): """Loss function for when number of input and output boxes is positive.""" if is_balanced: weights = loss_utils.get_balanced_loss_weights_multiclass( labels=input_boxes_instance_id) else: weights = tf.ones([tf.shape(input_boxes_instance_id)[0], 1], dtype=tf.float32) normalized_box_size = 5.0 predicted_boxes_length = output_boxes_length predicted_boxes_height = output_boxes_height predicted_boxes_width = output_boxes_width predicted_boxes_center = output_boxes_center predicted_boxes_rotation_matrix = output_boxes_rotation_matrix gt_boxes_length = input_boxes_length gt_boxes_height = input_boxes_height gt_boxes_width = input_boxes_width gt_boxes_center = input_boxes_center gt_boxes_rotation_matrix = input_boxes_rotation_matrix if loss_type in ['normalized_huber', 'normalized_euclidean']: predicted_boxes_length /= (gt_boxes_length / normalized_box_size) predicted_boxes_height /= (gt_boxes_height / normalized_box_size) predicted_boxes_width /= (gt_boxes_width / normalized_box_size) gt_boxes_length = tf.ones_like( gt_boxes_length, dtype=tf.float32) * normalized_box_size gt_boxes_height = tf.ones_like( gt_boxes_height, dtype=tf.float32) * normalized_box_size gt_boxes_width = tf.ones_like( gt_boxes_width, dtype=tf.float32) * normalized_box_size gt_box_corners = box_utils.get_box_corners_3d( boxes_length=gt_boxes_length, boxes_height=gt_boxes_height, boxes_width=gt_boxes_width, boxes_rotation_matrix=gt_boxes_rotation_matrix, boxes_center=gt_boxes_center) predicted_box_corners = box_utils.get_box_corners_3d( boxes_length=predicted_boxes_length, boxes_height=predicted_boxes_height, boxes_width=predicted_boxes_width, boxes_rotation_matrix=predicted_boxes_rotation_matrix, boxes_center=predicted_boxes_center) corner_weights = tf.tile(weights, [1, 8]) if loss_type in ['huber', 'normalized_huber']: loss_fn = tf.keras.losses.Huber( delta=delta, reduction=tf.keras.losses.Reduction.NONE) elif loss_type in ['normalized_absolute_difference', 'absolute_difference']: loss_fn = tf.keras.losses.MeanAbsoluteError( reduction=tf.keras.losses.Reduction.NONE) else: raise ValueError(('Unknown loss type %s.' % loss_type)) box_corner_losses = loss_fn( y_true=tf.reshape(gt_box_corners, [-1, 3]), y_pred=tf.reshape(predicted_box_corners, [-1, 3])) return tf.reduce_mean(box_corner_losses * tf.reshape(corner_weights, [-1])) cond_input = tf.greater(tf.shape(input_boxes_length)[0], 0) cond_output = tf.greater(tf.shape(output_boxes_length)[0], 0) cond = tf.logical_and(cond_input, cond_output) return tf.cond(cond, fn, lambda: tf.constant(0.0, dtype=tf.float32)) def _get_voxels_valid_mask(inputs_1): """Returns the mask that removes voxels that are outside objects.""" num_voxels_mask = mask_utils.num_voxels_mask(inputs=inputs_1) within_objects_mask = mask_utils.voxels_within_objects_mask(inputs=inputs_1) return tf.logical_and(within_objects_mask, num_voxels_mask) def _get_voxels_valid_inputs_outputs(inputs_1, outputs_1): """Applies the valid mask to input and output voxel tensors.""" valid_mask = _get_voxels_valid_mask(inputs_1=inputs_1) inputs_1 = mask_utils.apply_mask_to_input_voxel_tensors( inputs=inputs_1, valid_mask=valid_mask) mask_utils.apply_mask_to_output_voxel_tensors( outputs=outputs_1, valid_mask=valid_mask) return inputs_1, outputs_1, valid_mask def _box_rotation_regression_loss_on_voxel_tensors_unbatched( inputs_1, outputs_1, loss_type, delta, is_balanced, is_intermediate): """Computes regression loss on predicted object rotation for each voxel.""" inputs_1, outputs_1, valid_mask = _get_voxels_valid_inputs_outputs( inputs_1=inputs_1, outputs_1=outputs_1) def loss_fn_unbatched(): """Loss function.""" if is_intermediate: output_boxes_rotation_matrix = outputs_1[ standard_fields.DetectionResultFields .intermediate_object_rotation_matrix_voxels] else: output_boxes_rotation_matrix = outputs_1[ standard_fields.DetectionResultFields.object_rotation_matrix_voxels] return _box_rotation_regression_loss( loss_type=loss_type, is_balanced=is_balanced, input_boxes_rotation_matrix=inputs_1[ standard_fields.InputDataFields.object_rotation_matrix_voxels], input_boxes_instance_id=inputs_1[ standard_fields.InputDataFields.object_instance_id_voxels], output_boxes_rotation_matrix=output_boxes_rotation_matrix, delta=delta) return tf.cond( tf.reduce_any(valid_mask), loss_fn_unbatched, lambda: tf.constant(0.0, dtype=tf.float32)) @gin.configurable( 'box_rotation_regression_loss_on_voxel_tensors', blacklist=['inputs', 'outputs']) def box_rotation_regression_loss_on_voxel_tensors(inputs, outputs, loss_type, delta=0.5, is_balanced=False, is_intermediate=False): """Computes regression loss on object size. Args: inputs: A dictionary of tf.Tensors with our input data. outputs: A dictionary of tf.Tensors with the network output. loss_type: Loss type. delta: float, the voxel where the huber loss function changes from a quadratic to linear. is_balanced: If True, the per-voxel losses are re-weighted to have equal total weight for each object instance. is_intermediate: If True, intermediate tensors are used for computing the loss. Returns: localization_loss: A tf.float32 scalar corresponding to localization loss. """ standard_fields.check_input_voxel_fields(inputs=inputs) standard_fields.check_output_voxel_fields(outputs=outputs) def fn(inputs_1, outputs_1): return _box_rotation_regression_loss_on_voxel_tensors_unbatched( inputs_1=inputs_1, outputs_1=outputs_1, loss_type=loss_type, delta=delta, is_balanced=is_balanced, is_intermediate=is_intermediate) return loss_utils.apply_unbatched_loss_on_voxel_tensors( inputs=inputs, outputs=outputs, unbatched_loss_fn=fn) def _box_size_regression_loss_on_voxel_tensors_unbatched( inputs_1, outputs_1, loss_type, delta, is_balanced, is_intermediate): """Computes regression loss on predicted object size for each voxel.""" inputs_1, outputs_1, valid_mask = _get_voxels_valid_inputs_outputs( inputs_1=inputs_1, outputs_1=outputs_1) def loss_fn_unbatched(): """Loss function.""" if is_intermediate: output_boxes_length = outputs_1[standard_fields.DetectionResultFields .intermediate_object_length_voxels] output_boxes_height = outputs_1[standard_fields.DetectionResultFields .intermediate_object_height_voxels] output_boxes_width = outputs_1[standard_fields.DetectionResultFields .intermediate_object_width_voxels] else: output_boxes_length = outputs_1[ standard_fields.DetectionResultFields.object_length_voxels] output_boxes_height = outputs_1[ standard_fields.DetectionResultFields.object_height_voxels] output_boxes_width = outputs_1[ standard_fields.DetectionResultFields.object_width_voxels] return _box_size_regression_loss( loss_type=loss_type, is_balanced=is_balanced, input_boxes_length=inputs_1[ standard_fields.InputDataFields.object_length_voxels], input_boxes_height=inputs_1[ standard_fields.InputDataFields.object_height_voxels], input_boxes_width=inputs_1[ standard_fields.InputDataFields.object_width_voxels], input_boxes_instance_id=inputs_1[ standard_fields.InputDataFields.object_instance_id_voxels], output_boxes_length=output_boxes_length, output_boxes_height=output_boxes_height, output_boxes_width=output_boxes_width, delta=delta) return tf.cond( tf.reduce_any(valid_mask), loss_fn_unbatched, lambda: tf.constant(0.0, dtype=tf.float32)) @gin.configurable( 'box_size_regression_loss_on_voxel_tensors', blacklist=['inputs', 'outputs']) def box_size_regression_loss_on_voxel_tensors(inputs, outputs, loss_type, delta=0.5, is_balanced=False, is_intermediate=False): """Computes regression loss on object size. Args: inputs: A dictionary of tf.Tensors with our input data. outputs: A dictionary of tf.Tensors with the network output. loss_type: Loss type. delta: float, the voxel where the huber loss function changes from a quadratic to linear. is_balanced: If True, the per-voxel losses are re-weighted to have equal total weight for each object instance. is_intermediate: If True, intermediate tensors are used for computing the loss. Returns: localization_loss: A tf.float32 scalar corresponding to localization loss. """ standard_fields.check_input_voxel_fields(inputs=inputs) standard_fields.check_output_voxel_fields(outputs=outputs) def fn(inputs_1, outputs_1): return _box_size_regression_loss_on_voxel_tensors_unbatched( inputs_1=inputs_1, outputs_1=outputs_1, loss_type=loss_type, delta=delta, is_balanced=is_balanced, is_intermediate=is_intermediate) return loss_utils.apply_unbatched_loss_on_voxel_tensors( inputs=inputs, outputs=outputs, unbatched_loss_fn=fn) def _box_center_distance_loss_on_voxel_tensors_unbatched( inputs_1, outputs_1, loss_type, delta, is_balanced, is_intermediate): """Computes huber loss on predicted object centers for each voxel.""" inputs_1, outputs_1, valid_mask = _get_voxels_valid_inputs_outputs( inputs_1=inputs_1, outputs_1=outputs_1) def loss_fn_unbatched(): """Loss function.""" if is_intermediate: output_boxes_center = outputs_1[standard_fields.DetectionResultFields .intermediate_object_center_voxels] else: output_boxes_center = outputs_1[ standard_fields.DetectionResultFields.object_center_voxels] return _box_center_distance_loss( loss_type=loss_type, is_balanced=is_balanced, input_boxes_center=inputs_1[ standard_fields.InputDataFields.object_center_voxels], input_boxes_instance_id=inputs_1[ standard_fields.InputDataFields.object_instance_id_voxels], output_boxes_center=output_boxes_center, delta=delta) return tf.cond( tf.reduce_any(valid_mask), loss_fn_unbatched, lambda: tf.constant(0.0, dtype=tf.float32)) @gin.configurable( 'box_center_distance_loss_on_voxel_tensors', blacklist=['inputs', 'outputs']) def box_center_distance_loss_on_voxel_tensors(inputs, outputs, loss_type, delta=1.0, is_balanced=False, is_intermediate=False): """Computes huber loss on object center locations. Args: inputs: A dictionary of tf.Tensors with our input data. outputs: A dictionary of tf.Tensors with the network output. loss_type: Loss type. delta: float, the voxel where the huber loss function changes from a quadratic to linear. is_balanced: If True, the per-voxel losses are re-weighted to have equal total weight for each object instance. is_intermediate: If True, intermediate tensors are used for computing the loss. Returns: localization_loss: A tf.float32 scalar corresponding to localization loss. """ standard_fields.check_input_voxel_fields(inputs=inputs) standard_fields.check_output_voxel_fields(outputs=outputs) def fn(inputs_1, outputs_1): return _box_center_distance_loss_on_voxel_tensors_unbatched( inputs_1=inputs_1, outputs_1=outputs_1, loss_type=loss_type, delta=delta, is_balanced=is_balanced, is_intermediate=is_intermediate) return loss_utils.apply_unbatched_loss_on_voxel_tensors( inputs=inputs, outputs=outputs, unbatched_loss_fn=fn) def _box_corner_distance_loss_on_voxel_tensors_unbatched( inputs_1, outputs_1, loss_type, delta, is_balanced, is_intermediate): """Computes huber loss on predicted objects for each voxel.""" inputs_1, outputs_1, valid_mask = _get_voxels_valid_inputs_outputs( inputs_1=inputs_1, outputs_1=outputs_1) def loss_fn_unbatched(): """Loss function.""" if is_intermediate: output_boxes_length = outputs_1[standard_fields.DetectionResultFields .intermediate_object_length_voxels] output_boxes_height = outputs_1[standard_fields.DetectionResultFields .intermediate_object_height_voxels] output_boxes_width = outputs_1[standard_fields.DetectionResultFields .intermediate_object_width_voxels] output_boxes_center = outputs_1[standard_fields.DetectionResultFields .intermediate_object_center_voxels] output_boxes_rotation_matrix = outputs_1[ standard_fields.DetectionResultFields .intermediate_object_rotation_matrix_voxels] else: output_boxes_length = outputs_1[ standard_fields.DetectionResultFields.object_length_voxels] output_boxes_height = outputs_1[ standard_fields.DetectionResultFields.object_height_voxels] output_boxes_width = outputs_1[ standard_fields.DetectionResultFields.object_width_voxels] output_boxes_center = outputs_1[ standard_fields.DetectionResultFields.object_center_voxels] output_boxes_rotation_matrix = outputs_1[ standard_fields.DetectionResultFields.object_rotation_matrix_voxels] return _box_corner_distance_loss( loss_type=loss_type, is_balanced=is_balanced, input_boxes_length=inputs_1[ standard_fields.InputDataFields.object_length_voxels], input_boxes_height=inputs_1[ standard_fields.InputDataFields.object_height_voxels], input_boxes_width=inputs_1[ standard_fields.InputDataFields.object_width_voxels], input_boxes_center=inputs_1[ standard_fields.InputDataFields.object_center_voxels], input_boxes_rotation_matrix=inputs_1[ standard_fields.InputDataFields.object_rotation_matrix_voxels], input_boxes_instance_id=inputs_1[ standard_fields.InputDataFields.object_instance_id_voxels], output_boxes_length=output_boxes_length, output_boxes_height=output_boxes_height, output_boxes_width=output_boxes_width, output_boxes_center=output_boxes_center, output_boxes_rotation_matrix=output_boxes_rotation_matrix, delta=delta) return tf.cond( tf.reduce_any(valid_mask), loss_fn_unbatched, lambda: tf.constant(0.0, dtype=tf.float32)) @gin.configurable( 'box_corner_distance_loss_on_voxel_tensors', blacklist=['inputs', 'outputs']) def box_corner_distance_loss_on_voxel_tensors( inputs, outputs, loss_type, delta=1.0, is_balanced=False, is_intermediate=False): """Computes regression loss on object corner locations using object tensors. Args: inputs: A dictionary of tf.Tensors with our input data. outputs: A dictionary of tf.Tensors with the network output. loss_type: Loss type. delta: float, the voxel where the huber loss function changes from a quadratic to linear. is_balanced: If True, the per-voxel losses are re-weighted to have equal total weight for each object instance. is_intermediate: If True, intermediate tensors are used for computing the loss. Returns: localization_loss: A tf.float32 scalar corresponding to localization loss. """ standard_fields.check_input_voxel_fields(inputs=inputs) standard_fields.check_output_voxel_fields(outputs=outputs) def fn(inputs_1, outputs_1): return _box_corner_distance_loss_on_voxel_tensors_unbatched( inputs_1=inputs_1, outputs_1=outputs_1, loss_type=loss_type, delta=delta, is_balanced=is_balanced, is_intermediate=is_intermediate) return loss_utils.apply_unbatched_loss_on_voxel_tensors( inputs=inputs, outputs=outputs, unbatched_loss_fn=fn) def _box_corner_distance_loss_on_object_tensors( inputs, outputs, loss_type, delta, is_balanced): """Computes huber loss on object corner locations.""" valid_mask_class = tf.greater( tf.reshape(inputs[standard_fields.InputDataFields.objects_class], [-1]), 0) valid_mask_instance = tf.greater( tf.reshape(inputs[standard_fields.InputDataFields.objects_instance_id], [-1]), 0) valid_mask = tf.logical_and(valid_mask_class, valid_mask_instance) def fn(): for field in standard_fields.get_input_object_fields(): if field in inputs: inputs[field] = tf.boolean_mask(inputs[field], valid_mask) for field in standard_fields.get_output_object_fields(): if field in outputs: outputs[field] = tf.boolean_mask(outputs[field], valid_mask) return _box_corner_distance_loss( loss_type=loss_type, is_balanced=is_balanced, input_boxes_length=inputs[ standard_fields.InputDataFields.objects_length], input_boxes_height=inputs[ standard_fields.InputDataFields.objects_height], input_boxes_width=inputs[standard_fields.InputDataFields.objects_width], input_boxes_center=inputs[ standard_fields.InputDataFields.objects_center], input_boxes_rotation_matrix=inputs[ standard_fields.InputDataFields.objects_rotation_matrix], input_boxes_instance_id=inputs[ standard_fields.InputDataFields.objects_instance_id], output_boxes_length=outputs[ standard_fields.DetectionResultFields.objects_length], output_boxes_height=outputs[ standard_fields.DetectionResultFields.objects_height], output_boxes_width=outputs[ standard_fields.DetectionResultFields.objects_width], output_boxes_center=outputs[ standard_fields.DetectionResultFields.objects_center], output_boxes_rotation_matrix=outputs[ standard_fields.DetectionResultFields.objects_rotation_matrix], delta=delta) return tf.cond( tf.reduce_any(valid_mask), fn, lambda: tf.constant(0.0, dtype=tf.float32)) @gin.configurable( 'box_corner_distance_loss_on_object_tensors', blacklist=['inputs', 'outputs']) def box_corner_distance_loss_on_object_tensors( inputs, outputs, loss_type, delta=1.0, is_balanced=False): """Computes regression loss on object corner locations using object tensors. Args: inputs: A dictionary of tf.Tensors with our input data. outputs: A dictionary of tf.Tensors with the network output. loss_type: Loss type. delta: float, the voxel where the huber loss function changes from a quadratic to linear. is_balanced: If True, the per-voxel losses are re-weighted to have equal total weight for each object instance. Returns: localization_loss: A tf.float32 scalar corresponding to localization loss. """ def fn(inputs_1, outputs_1): return _box_corner_distance_loss_on_object_tensors( inputs=inputs_1, outputs=outputs_1, loss_type=loss_type, delta=delta, is_balanced=is_balanced) batch_size = len(inputs[standard_fields.InputDataFields.objects_length]) losses = [] for b in range(batch_size): inputs_1 = batch_utils.get_batch_size_1_input_objects(inputs=inputs, b=b) outputs_1 = batch_utils.get_batch_size_1_output_objects( outputs=outputs, b=b) cond_input = tf.greater( tf.shape(inputs_1[standard_fields.InputDataFields.objects_length])[0], 0) cond_output = tf.greater( tf.shape( outputs_1[standard_fields.DetectionResultFields.objects_length])[0], 0) cond = tf.logical_and(cond_input, cond_output) # pylint: disable=cell-var-from-loop loss = tf.cond(cond, lambda: fn(inputs_1=inputs_1, outputs_1=outputs_1), lambda: tf.constant(0.0, dtype=tf.float32)) # pylint: enable=cell-var-from-loop losses.append(loss) return tf.reduce_mean(tf.stack(losses))

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py

Python

medium_multiply/__init__.py

yahyatamim/pyidw

014344518f40b13b622b780af55975da8bb755ae

[ "MIT" ]

2

2022-02-28T17:30:57.000Z

2022-03-22T17:29:27.000Z

medium_multiply/__init__.py

yahyatamim/pyidw

014344518f40b13b622b780af55975da8bb755ae

[ "MIT" ]

1

2021-08-19T15:33:44.000Z

2021-08-22T08:18:39.000Z

medium_multiply/__init__.py

yahyatamim/pyidw

014344518f40b13b622b780af55975da8bb755ae

[ "MIT" ]

null

# This line of code will allow shorter imports from medium_multiply.multiplication import Multiplication

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py

Python

testsuite/array-reg/run.py

LongerVision/OpenShadingLanguage

30d2a4a089c5c9d521b27519329c205763dfe483

[ "BSD-3-Clause" ]

1,105

2015-01-02T20:47:19.000Z

2021-01-25T13:20:56.000Z

testsuite/array-reg/run.py

LongerVision/OpenShadingLanguage

30d2a4a089c5c9d521b27519329c205763dfe483

[ "BSD-3-Clause" ]

696

2015-01-07T23:42:08.000Z

2021-01-25T03:55:08.000Z

testsuite/array-reg/run.py

LongerVision/OpenShadingLanguage

30d2a4a089c5c9d521b27519329c205763dfe483

[ "BSD-3-Clause" ]

248

2015-01-05T13:41:28.000Z

2021-01-24T23:29:55.000Z

#!/usr/bin/env python # Copyright Contributors to the Open Shading Language project. # SPDX-License-Identifier: BSD-3-Clause # https://github.com/AcademySoftwareFoundation/OpenShadingLanguage command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_float.tif test_varying_index_float") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_int.tif test_varying_index_int") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_string.tif test_varying_index_string") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_matrix.tif test_varying_index_matrix") outputs.append ("out_varying_index_float.tif") outputs.append ("out_varying_index_int.tif") outputs.append ("out_varying_index_string.tif") outputs.append ("out_varying_index_matrix.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_color.tif test_varying_index_color") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_point.tif test_varying_index_point") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_vector.tif test_varying_index_vector") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_normal.tif test_varying_index_normal") outputs.append ("out_varying_index_color.tif") outputs.append ("out_varying_index_point.tif") outputs.append ("out_varying_index_vector.tif") outputs.append ("out_varying_index_normal.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_out_of_bounds_index_int.tif test_varying_out_of_bounds_index_int") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_out_of_bounds_index_float.tif test_varying_out_of_bounds_index_float") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_out_of_bounds_index_string.tif test_varying_out_of_bounds_index_string") outputs.append ("out_varying_out_of_bounds_index_int.tif") outputs.append ("out_varying_out_of_bounds_index_float.tif") outputs.append ("out_varying_out_of_bounds_index_string.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_ray.tif test_varying_index_ray") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_cube.tif test_varying_index_cube") outputs.append ("out_varying_index_ray.tif") outputs.append ("out_varying_index_cube.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_float.tif test_varying_index_varying_float") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_int.tif test_varying_index_varying_int") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_point.tif test_varying_index_varying_point") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_normal.tif test_varying_index_varying_normal") outputs.append ("out_varying_index_varying_float.tif") outputs.append ("out_varying_index_varying_int.tif") outputs.append ("out_varying_index_varying_point.tif") outputs.append ("out_varying_index_varying_normal.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_vector.tif test_varying_index_varying_vector") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_color.tif test_varying_index_varying_color") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_string.tif test_varying_index_varying_string") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_matrix.tif test_varying_index_varying_matrix") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_varying_index_varying_ray.tif test_varying_index_varying_ray") outputs.append ("out_varying_index_varying_vector.tif") outputs.append ("out_varying_index_varying_color.tif") outputs.append ("out_varying_index_varying_string.tif") outputs.append ("out_varying_index_varying_matrix.tif") outputs.append ("out_varying_index_varying_ray.tif") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_float.tif test_uniform_index_varying_float") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_int.tif test_uniform_index_varying_int") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_point.tif test_uniform_index_varying_point") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_normal.tif test_uniform_index_varying_normal") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_vector.tif test_uniform_index_varying_vector") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_color.tif test_uniform_index_varying_color") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_string.tif test_uniform_index_varying_string") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_matrix.tif test_uniform_index_varying_matrix") command += testshade("-t 1 -g 256 256 -od uint8 -o Cout out_uniform_index_varying_ray.tif test_uniform_index_varying_ray") outputs.append ("out_uniform_index_varying_float.tif") outputs.append ("out_uniform_index_varying_int.tif") outputs.append ("out_uniform_index_varying_point.tif") outputs.append ("out_uniform_index_varying_normal.tif") outputs.append ("out_uniform_index_varying_vector.tif") outputs.append ("out_uniform_index_varying_color.tif") outputs.append ("out_uniform_index_varying_string.tif") outputs.append ("out_uniform_index_varying_matrix.tif") outputs.append ("out_uniform_index_varying_ray.tif") # expect a few LSB failures failthresh = 0.008 failpercent = 3

65.034091

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null

0

5

c1554a35bab60b73f5aa060ae8181704c33ebdbb

289

py

Python

automated_survey/admin.py

gaganmac/d4surveyHeroku

7331a18cb56422d1f20fb9f1c6ffa178897f9b5f

[ "MIT" ]

55

2015-10-01T15:39:05.000Z

2021-11-15T20:08:01.000Z

automated_survey/admin.py

gaganmac/d4surveyHeroku

7331a18cb56422d1f20fb9f1c6ffa178897f9b5f

[ "MIT" ]

151

2015-08-26T18:21:49.000Z

2022-03-23T22:07:42.000Z

automated_survey/admin.py

gaganmac/d4surveyHeroku

7331a18cb56422d1f20fb9f1c6ffa178897f9b5f

[ "MIT" ]

34

2015-10-26T05:42:23.000Z

2021-04-20T08:00:45.000Z

from django.contrib import admin from .models import Question, QuestionResponse, Survey # Register for models for use in admin interface admin.site.register(Question, admin.ModelAdmin) admin.site.register(QuestionResponse, admin.ModelAdmin) admin.site.register(Survey, admin.ModelAdmin)

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5

c17cb0a339ab514cc3f39408d3eba9ae3c247b47

168

py

Python

puft/constants/hints.py

ryzhovalex/omen

c3a36741b40ff28a61c1e62f3e549d39f9847feb

[ "MIT" ]

null

puft/constants/hints.py

ryzhovalex/omen

c3a36741b40ff28a61c1e62f3e549d39f9847feb

[ "MIT" ]

null

puft/constants/hints.py

ryzhovalex/omen

c3a36741b40ff28a61c1e62f3e549d39f9847feb

[ "MIT" ]

null

from .enums import ( CLIDatabaseEnum, CLIRunEnum, CLIHelperEnum, HTTPMethodEnum, TurboActionEnum ) CLIModeEnumUnion = CLIDatabaseEnum | CLIRunEnum | CLIHelperEnum

28

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1

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null

0

5

a9b4d86041eed870edb12f00cf0408b19c04986b

7,514

py

Python

tests/test_q.py

gityoav/pyg-mongo

f414b8923562e273f95c51e4cfb47e2c27cd3948

[ "MIT" ]

null

tests/test_q.py

gityoav/pyg-mongo

f414b8923562e273f95c51e4cfb47e2c27cd3948

[ "MIT" ]

null

tests/test_q.py

gityoav/pyg-mongo

f414b8923562e273f95c51e4cfb47e2c27cd3948

[ "MIT" ]

null

from pyg_mongo import Q, q, mongo_table import re import pytest regex = re.compile def D(value): if isinstance(value, dict): return {x : D(y) for x, y in value.items()} ### this converts mdict to normal dict elif isinstance(value, list): return [D(y) for y in value] else: return value def test_q(): assert D(q.a == 1) == {'a': {'$eq': 1}} assert D(q.a != 1) == {'a': {'$ne': 1}} assert D(q.a != [1,2,3]) == {"a": {"$nin": [1, 2, 3]}} assert D(q.a <= 1) == {'a': {'$lte': 1}} assert D(q.a >= 1) == {'a': {'$gte': 1}} assert D(q.a < 1) == {'a': {'$lt': 1}} assert D(q.a > 1) == {'a': {'$gt': 1}} assert D((q.a == 1) + (q.b == 2)) == {'$and': [{'a': {'$eq': 1}}, {'b': {'$eq': 2}}]} assert D(-(q.b == 2)) == {"$not": {"b": {"$eq": 2}}} assert D(q.a % 2 == 1) == {'a': {'$mod': [2, 1]}} assert D(-(q.a % 2 == 1)) == {'$not': {'a': {'$mod': [2, 1]}}} assert D((q.a ==1) + (q.b == 1)) == {'$and': [{'a': {'$eq': 1}}, {'b': {'$eq': 1}}]} assert D((q.a ==1) & (q.b == 1)) == {'$and': [{'a': {'$eq': 1}}, {'b': {'$eq': 1}}]} assert D((q.a ==1) - (q.b == 1)) == {'$and': [{'$not': {'b': {'$eq': 1}}}, {'a': {'$eq': 1}}]} assert D((q.a % 2 == 1)|(q.b == 1)) == {'$or': [{'a': {'$mod': [2, 1]}}, {'b': {'$eq': 1}}]} assert D((q.a % 2 == 1) & (q.b == 1)) == {'$and': [{'a': {'$mod': [2, 1]}}, {'b': {'$eq': 1}}]} assert D((q.a % 2 == 1) + (q.b == 1)) == {'$and': [{'a': {'$mod': [2, 1]}}, {'b': {'$eq': 1}}]} assert D((q.a % 2 == 1) - (q.b == 1)) == {'$and': [{'$not': {'b': {'$eq': 1}}}, {'a': {'$mod': [2, 1]}}]} assert D(q.a.isinstance(float)) == {"a": {"$type": [1, 19]}} assert D(-(q.a % 2 == 1)) == {'$not': {'a': {'$mod': [2, 1]}}} assert D(+(q.a % 2 == 1)) == {'a': {'$mod': [2, 1]}} assert D(q.a % 2 + q.b == 1) == {'$and': [{'a': {'$not': {'$mod': [2, 0]}}}, {'b': {'$eq': 1}}]} assert D(q.a % 2 + (q.b == 1)) == {'$and': [{'a': {'$not': {'$mod': [2, 0]}}}, {'b': {'$eq': 1}}]} assert D(q.a % 2 & (q.b == 1)) == {'$and': [{'a': {'$not': {'$mod': [2, 0]}}}, {'b': {'$eq': 1}}]} assert D(q.a % 2 - (q.b == 1)) == {'$and': [{'$not': {'b': {'$eq': 1}}}, {'a': {'$not': {'$mod': [2, 0]}}}]} assert D(~(q.a % 2)) == {'a': {'$mod': [2, 0]}} assert D(+(q.a % 2)) == {'a': {'$not': {'$mod': [2, 0]}}} assert str(q.a % 3) == 'mod(a, 3)' assert D((q.a == 1) | (q.b == 2)) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$eq': 2}}]} assert D((q.a % 3 == 1) | (q.b == 2)) == {"$or": [{"a": {"$mod": [3, 1]}}, {"b": {"$eq": 2}}]} assert D(q['some text'] == 1) == {'some text': {'$eq': 1}} assert D(q.a == re.compile('^test')) == {'a': {'$regex': '^test'}} assert D(q.a == re.compile('^test', re.IGNORECASE)) == {"a": {"$regex": "^test", "$options": "i"}} assert D(q.a != re.compile('^test')) == {'a': {'$not': {'$regex': '^test'}}} assert D(q.a != [1]) == {'a': {'$ne': [1]}} assert D(q.a == [1]) == {'a': {'$eq': 1}} assert D(q.a == [[1]]) == {'a': {'$eq': [1]}} assert D(q.a['b'] == 1) == {'a.b': {'$eq': 1}} assert D(q.a.b == 1) == {'a.b': {'$eq': 1}} assert D(+q.a) == {'a': {'$exists': True}} assert D(q.a.exists) == {'a': {'$exists': True}} assert D(-q.a) == {'a': {'$exists': False}} assert D(q.a.not_exists) == {'a': {'$exists': False}} assert D(q.a == True) == {'a': {'$in': [True, 1]}} assert D(q.a == False) == {'a': {'$in': [False, 0]}} assert D(q.a == dict(b=1, c=2)) == {'$and': [{'a.b': {'$eq': 1}}, {'a.c': {'$eq': 2}}]} assert D(q.a.exists & q.b == 2) == {'$and': [{'a': {'$exists': True}}, {'b': {'$eq': 2}}]} assert D(q.a.exists | q.b == 2) == {'$or': [{'a': {'$exists': True}}, {'b': {'$eq': 2}}]} assert D(q.a & q.b == 1) == {'$and': [{'a': {'$exists': True}}, {'b': {'$eq': 1}}]} assert D(q.a + q.b == 1) == {'$and': [{'a': {'$exists': True}}, {'b': {'$eq': 1}}]} assert D(q.a | (q.b == 1)) == {'$or': [{'a': {'$exists': True}}, {'b': {'$eq': 1}}]} assert D(q.a - q.b) == {'$and': [{'a': {'$exists': True}}, {'b': {'$exists': False}}]} assert D(q.a + q.b) == {'$and': [{'a': {'$exists': True}}, {'b': {'$exists': True}}]} assert D(q.a - (q.b == 1)) == {'$and': [{'$not': {'b': {'$eq': 1}}}, {'a': {'$exists': True}}]} assert D(q.a[5:10]) == {'$and': [{'a': {'$gte': 5}}, {'a': {'$lt': 10}}]} assert D(q.a[5:]) == {'a': {'$gte': 5}} assert D(q.a[:10]) == {'a': {'$lt': 10}} assert D(q.a[::] == 1) == {'a': {'$eq': 1}} assert D(q.a[2]) == {'a': {'$eq': 2}} assert D(+q.a.b) == {'a.b': {'$exists': True}} assert D((q.a == 1) | q.b > 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$gt': 1}}]} assert D((q.a == 1) | q.b >= 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$gte': 1}}]} assert D((q.a == 1) | q.b != 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$ne': 1}}]} assert D((q.a == 1) | q.b == 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$eq': 1}}]} assert D((q.a == 1) | q.b <= 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$lte': 1}}]} assert D((q.a == 1) | q.b < 1) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$lt': 1}}]} def test_q_fails(): with pytest.raises(ValueError): (q.a == 1) > 2 with pytest.raises(ValueError): (q.a == 1) >= 2 with pytest.raises(ValueError): (q.a == 1) < 2 with pytest.raises(ValueError): (q.a == 1) <= 2 assert D((q.a > 1) & q.b == 2) == {"$and": [{"a": {"$gt": 1}}, {"b": {"$eq": 2}}]} assert D((q.a > 1) | q.b == 2) == {"$or": [{"a": {"$gt": 1}}, {"b": {"$eq": 2}}]} def test_Q_with_proxies(): assert D(Q({'hello world': 1}).hello_world == 1) == {"hello world": {"$eq": 1}} assert D(Q(['hello world']).hello_world == 1) == {"hello world": {"$eq": 1}} def test_Q_with_keys(): x = Q(['Adam Aaron', 'Beth Brown', 'James Joyce']) assert D((x.adam_aaron == 1) & (x.JAMES_JOYCE == 2)) == {'$and': [{'Adam Aaron': {'$eq': 1}}, {'James Joyce': {'$eq': 2}}]} assert sorted(dir(x)) == sorted(['ADAM_AARON', 'Adam Aaron', 'Adam_Aaron', 'BETH_BROWN', 'Beth Brown', 'Beth_Brown', 'JAMES_JOYCE', 'James Joyce', 'James_Joyce', 'adam_aaron', 'beth_brown', 'james_joyce']) x = Q(['@@Adam%+Aaron', '++Beth---Brown++', '---James%%% Joyce%']) assert D((x.adam_aaron == 1) & (x.JAMES_JOYCE == 2)) == {'$and': [{'---James%%% Joyce%': {'$eq': 2}}, {'@@Adam%+Aaron': {'$eq': 1}}]} def test_q_callable(): assert D(q(a = 1, b = 2)) == {'$and': [{'a': {'$eq': 1}}, {'b': {'$eq': 2}}]} assert D(q(a = 1, b = 2)) == {'$and': [{'a': {'$eq': 1}}, {'b': {'$eq': 2}}]} assert D(q([q.a == 1, q.b == 2])) == {'$or': [{'a': {'$eq': 1}}, {'b': {'$eq': 2}}]} def test_q_regex(): t = mongo_table('test', 'test') t.drop() t.insert_one(dict(item = 'Test1', value = 1)) t.insert_one(dict(item = 'test2', value = 2)) t.insert_one(dict(item = 'TEST2', value = 3)) assert len(t.inc(item = re.compile('^test'))) == 1 assert len(t.inc(item = re.compile('^test', re.IGNORECASE))) == 3 t.drop()

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0.215408

0.225505

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0.47083

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null

0

1

0

5

e702da51c55eafae57b2b359c2184ae1c870e5ff

68

py

Python

python/sandbox/__init__.py

geometer/sandbox

373ec96e69df76744a19b51f7caa865cbc6b58cd

[ "Apache-2.0" ]

6

2020-04-19T11:26:18.000Z

2021-06-21T18:42:51.000Z

python/sandbox/__init__.py

geometer/sandbox

373ec96e69df76744a19b51f7caa865cbc6b58cd

[ "Apache-2.0" ]

31

2020-04-21T17:24:39.000Z

2020-08-27T15:59:12.000Z

python/sandbox/__init__.py

geometer/sandbox

373ec96e69df76744a19b51f7caa865cbc6b58cd

[ "Apache-2.0" ]

null

from .scene import Scene from .placement import iterative_placement

22.666667

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null

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1

0

1

0

1

0

5

e71de36e7fe0ebd966ccc163ef6dfc7f79bb4fb2

1,276

py

Python

system/functions/date.py

u-n-i-c-o-rn/jimi

bbd647fa9cd4326305a33a99122d8d8a2614967d

[ "Apache-2.0" ]

1

2021-03-14T21:27:51.000Z

system/functions/date.py

u-n-i-c-o-rn/jimi

bbd647fa9cd4326305a33a99122d8d8a2614967d

[ "Apache-2.0" ]

5

2021-04-29T11:16:18.000Z

2021-05-02T16:40:01.000Z

system/functions/date.py

u-n-i-c-o-rn/jimi

bbd647fa9cd4326305a33a99122d8d8a2614967d

[ "Apache-2.0" ]

null

import time import datetime def now(milliseconds=False): if milliseconds: return time.time() * 1000 return time.time() def day(): return datetime.datetime.now().strftime('%A') def year(): return int(datetime.datetime.now().strftime('%Y')) def month(): return int(datetime.datetime.now().strftime('%m')) def dt(format="%d-%m-%Y"): return datetime.datetime.now().strftime(format) def dateBetween(startDateStr, endDateStr, dateStr=None): if dateStr == None: dateStr = datetime.datetime.now().strftime('%H:%M %d-%m-%Y') startDate = datetime.datetime.strptime(startDateStr, '%H:%M %d-%m-%Y') endDate = datetime.datetime.strptime(endDateStr, '%H:%M %d-%m-%Y') date = datetime.datetime.strptime(dateStr, '%H:%M %d-%m-%Y') return startDate < date < endDate def timeBetween(startTimeStr, endTimeStr, timeStr=None): if timeStr == None: timeStr = datetime.datetime.now().strftime('%H:%M') startTime = datetime.datetime.strptime(startTimeStr, '%H:%M') endTime = datetime.datetime.strptime(endTimeStr, '%H:%M') time = datetime.datetime.strptime(timeStr, '%H:%M') if startTime > endTime: return time >= startTime or time <= endTime else: return startTime <= time <= endTime

31.9

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1

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1

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null

0

1

0

1

0

5

99bc3505ff55486cbfded79b4475d68071dee9b9

181

py

Python

tests/test_conversions.py

lehvitus/eln

b78362af20cacffe076bf3dbfd27dcc090e43e39

[ "BSD-3-Clause" ]

2

2020-02-05T04:00:32.000Z

2020-03-18T02:12:33.000Z

tests/test_conversions.py

oleoneto/eln

b78362af20cacffe076bf3dbfd27dcc090e43e39

[ "BSD-3-Clause" ]

1

2020-03-18T02:36:04.000Z

tests/test_conversions.py

oleoneto/eln

b78362af20cacffe076bf3dbfd27dcc090e43e39

[ "BSD-3-Clause" ]

null

from click.testing import CliRunner # from eln.commands.conversions.main import convert # def test_conversions(): # runner = CliRunner() # result = runner.invoke(convert)

22.625

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1

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5

99c56f50835a677720b53b77e5038bf11849511f

86

py

Python

noodles/draw_workflow/__init__.py

BvB93/noodles

7547471baa18a11b8020c017388a7e208d194ef4

[ "Apache-2.0" ]

22

2016-07-26T20:42:08.000Z

2022-02-08T16:04:25.000Z

noodles/draw_workflow/__init__.py

BvB93/noodles

7547471baa18a11b8020c017388a7e208d194ef4

[ "Apache-2.0" ]

71

2015-12-24T18:44:32.000Z

2022-02-11T11:31:08.000Z

noodles/draw_workflow/__init__.py

BvB93/noodles

7547471baa18a11b8020c017388a7e208d194ef4

[ "Apache-2.0" ]

8

2016-12-22T10:14:28.000Z

2020-06-05T21:01:51.000Z

from .draw_workflow import draw_workflow, graph __all__ = ['draw_workflow', 'graph']

21.5

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0.767442

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0.610169

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null

0

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0

5

82218be1186fae9dba583c3db3d51aa6acba58d1

38

py

Python

src/dl_plus/__main__.py

un-def/dl-plus

1f5198043bd0885e666c2880a8486e8075e4a0c2

[ "MIT" ]

30

2020-10-24T16:35:48.000Z

2021-11-11T11:04:12.000Z

src/dl_plus/__main__.py

un-def/dl-plus

1f5198043bd0885e666c2880a8486e8075e4a0c2

[ "MIT" ]

null

src/dl_plus/__main__.py

un-def/dl-plus

1f5198043bd0885e666c2880a8486e8075e4a0c2

[ "MIT" ]

3

2020-11-30T07:11:44.000Z

2021-01-26T08:05:13.000Z

from dl_plus.cli import main main()

7.6

28

0.736842

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38

3.857143

0.857143

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0.184211

38

4

29

9.5

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1

0

1

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5

8232e2ba7c239fb84fa8ac9f936ac3905c320bde

151

py

Python

adv_cache_tag/tests/__init__.py

pgcd/django-adv-cache-tag

59671cc94160ba311833affc0619590275e49829

[ "MIT" ]

29

2015-01-27T13:04:26.000Z

2022-03-30T18:11:20.000Z

adv_cache_tag/tests/__init__.py

pgcd/django-adv-cache-tag

59671cc94160ba311833affc0619590275e49829

[ "MIT" ]

8

2015-09-10T10:29:52.000Z

2022-03-30T17:33:05.000Z

adv_cache_tag/tests/__init__.py

pgcd/django-adv-cache-tag

59671cc94160ba311833affc0619590275e49829

[ "MIT" ]

6

2015-03-10T19:46:19.000Z

2021-02-12T07:02:24.000Z

from django import VERSION if VERSION < (1, 6): # Before django 1.6, Django was not able to find tests in tests/tests.py from .tests import *

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0.688742

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151

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0.034483

0.231788

151

5

77

30.2

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0.463576

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null

0

1

0

1

0

1

0

5

823f981dae40e6a5d3229238d634439ab34bee76

72

py

Python

brief/__init__.py

madre/PersonalWeb

27d88a3c6c4f86028887b0455b60eceeeb663e25

[ "Apache-2.0" ]

null

brief/__init__.py

madre/PersonalWeb

27d88a3c6c4f86028887b0455b60eceeeb663e25

[ "Apache-2.0" ]

null

brief/__init__.py

madre/PersonalWeb

27d88a3c6c4f86028887b0455b60eceeeb663e25

[ "Apache-2.0" ]

null

#coding=utf-8 """ __create_time__ = '13-10-29' __author__ = 'Madre' """

12

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72

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14.4

0.47619

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0

null

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null

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null

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true

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1

0

null

0

1

0

1

0

null

0

1

0

5

823f9b13c3264d0462236aea283c12aa66c45463

74

py

Python

dtreeviz/__init__.py

alitrack/dtreeviz

f5d3bcc25ebe87f00cdcb2defb203947283cd08d

[ "MIT" ]

1,905

2018-09-29T20:41:19.000Z

2022-03-28T07:45:07.000Z

dtreeviz/__init__.py

alitrack/dtreeviz

f5d3bcc25ebe87f00cdcb2defb203947283cd08d

[ "MIT" ]

163

2018-09-29T15:57:07.000Z

2022-03-30T16:43:05.000Z

dtreeviz/__init__.py

alitrack/dtreeviz

f5d3bcc25ebe87f00cdcb2defb203947283cd08d

[ "MIT" ]

257

2018-09-30T21:49:45.000Z

2022-03-23T11:41:56.000Z

from .version import __version__ from dtreeviz.classifiers import clfviz

18.5

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0.666667

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74

3

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24.666667

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null

0

1

0

1

0

1

0

5

41355a13039174c7bfbe3a8ccc6eb4f22b921f59

5,872

py

Python

benchmarks/call_benchmark.py

usalko/nogil

7a480f849f1f12a159a10bb0aa0f3431f24ce5f6

[ "0BSD" ]

1

2021-11-19T02:20:24.000Z

benchmarks/call_benchmark.py

usalko/nogil

7a480f849f1f12a159a10bb0aa0f3431f24ce5f6

[ "0BSD" ]

null

benchmarks/call_benchmark.py

usalko/nogil

7a480f849f1f12a159a10bb0aa0f3431f24ce5f6

[ "0BSD" ]

null

import time # N = 1000 N = 1000000 UNROLL = 10 loop_delta = 2.0 * N * UNROLL / 1e9 def call0(): pass def call2(a, b): pass def call4(a, b, c, d): pass def call4_dflt(a, b, c=3, d=4): pass def call_vararg(*args, **kwargs): pass def benchmark_loop(): # global loop_delta start = time.perf_counter() for _ in range(N): pass loop_delta2 = (time.perf_counter() - start - loop_delta) * 1e9 print(f'loop overhead (unaccounted for) {loop_delta2 / (N * UNROLL):.1f} ns') def benchmark_call0(): start = time.perf_counter() f = call0 for _ in range(N): f() f() f() f() f() f() f() f() f() f() delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call0 {delta / (N * UNROLL):.1f} ns') def benchmark_call2(): start = time.perf_counter() a, b = 0, 1 f = call2 for _ in range(N): f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call2 {delta / (N * UNROLL):.1f} ns') def benchmark_call4(): start = time.perf_counter() f = call4 a, b, c, d = 0, 1, 2, 3 for _ in range(N): f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) f(a, b, c, d) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call4 {delta / (N * UNROLL):.1f} ns') def benchmark_call4_dflt(): start = time.perf_counter() f = call4_dflt a, b = 0, 1 for _ in range(N): f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) f(a, b) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call4_dflt {delta / (N * UNROLL):.1f} ns') def benchmark_call4_kwd(): start = time.perf_counter() f = call4 a, b, c, d = 0, 1, 2, 3 for _ in range(N): f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call4_kwd {delta / (N * UNROLL):.1f} ns') def benchmark_call4_kwd_mismatch(): start = time.perf_counter() f = call4 a, b, c, d = 0, 1, 2, 3 for _ in range(N): f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) f(a, b, d=d, c=c) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call4_kwd_mismatch {delta / (N * UNROLL):.1f} ns') def benchmark_call4_vararg_stararg(): start = time.perf_counter() f = call4 args = (1, 2) kwargs = {"c": 3, "d": 4} for _ in range(N): f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call4_vararg_stararg {delta / (N * UNROLL):.1f} ns') def benchmark_call_vararg_stararg(): start = time.perf_counter() f = call_vararg args = () kwargs = {} for _ in range(N): f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call_vararg_stararg {delta / (N * UNROLL):.1f} ns') def benchmark_call_vararg4_stararg(): start = time.perf_counter() f = call_vararg args = (1, 2) kwargs = {"c": 3, "d": 4} for _ in range(N): f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) f(*args, **kwargs) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call_vararg4_stararg {delta / (N * UNROLL):.1f} ns') def benchmark_call_vararg4_kwd(): start = time.perf_counter() f = call_vararg a, b, c, d = 0, 1, 2, 3 for _ in range(N): f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) f(a, b, c=c, d=d) delta = (time.perf_counter() - start - loop_delta) * 1e9 print(f'call_vararg4_kwd {delta / (N * UNROLL):.1f} ns') def benchmark(): benchmark_loop() for _ in range(3): benchmark_call0() for _ in range(3): benchmark_call2() for _ in range(3): benchmark_call4() for _ in range(3): benchmark_call4_dflt() for _ in range(3): benchmark_call4_kwd() for _ in range(3): benchmark_call4_kwd_mismatch() for _ in range(3): benchmark_call4_vararg_stararg() for _ in range(3): benchmark_call_vararg_stararg() for _ in range(3): benchmark_call_vararg4_stararg() for _ in range(3): benchmark_call_vararg4_kwd() benchmark()

24.881356

81

0.484673

955

5,872

2.852356

0.050262

0.050661

0.066079

0.044053

0.858297

0.828561

0.780103

0.723201

0.631791

0.55837

0

0.033488

0.338896

5,872

236

82

24.881356

0.668212

0.004428

0

0.767442

0

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0

1

0.07907

false

0.027907

0.004651

0

0.083721

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0

null

0

1

0

1

0

null

0

5

41a5b6b38108b4168c957b2862040c55b1835d9d

360

py

Python

plugins/avro-java/register.py

arron-green/pants-avro

fda2605cace481e79a8a5027219776af6d896d33

[ "Apache-2.0" ]

null

plugins/avro-java/register.py

arron-green/pants-avro

fda2605cace481e79a8a5027219776af6d896d33

[ "Apache-2.0" ]

null

plugins/avro-java/register.py

arron-green/pants-avro

fda2605cace481e79a8a5027219776af6d896d33

[ "Apache-2.0" ]

null

from pants.build_graph.build_file_aliases import BuildFileAliases from .targets import AvroJava from .gen import AvroJavaGen from pants.goal.task_registrar import TaskRegistrar as task def build_file_aliases(): return BuildFileAliases(targets={'avro_java': AvroJava}) def register_goals(): task(name='avro-java', action=AvroJavaGen).install('gen')

27.692308

65

0.8

47

360

5.957447

0.553191

0.064286

0.114286

0

0.108333

360

12

66

30

0.872274

0

0.058333

0

1

0.25

true

0

0.5

0.125

0.875

0

null

0

null

0

1

0

1

0

5

41b065470fffdf9eae5d6c19c821f5e6d4ce2c57

3,733

py

Python

lingvo/jax/layers/__init__.py

drpngx/lingvo

adb50163814fd5dd2f07ac186adaedfe57ba4b2a

[ "Apache-2.0" ]

null

lingvo/jax/layers/__init__.py

drpngx/lingvo

adb50163814fd5dd2f07ac186adaedfe57ba4b2a

[ "Apache-2.0" ]

null

lingvo/jax/layers/__init__.py

drpngx/lingvo

adb50163814fd5dd2f07ac186adaedfe57ba4b2a

[ "Apache-2.0" ]

null

# Lint as: python3 # Copyright 2021 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Exposes the public layer functionalities.""" from lingvo.jax.layers.activations import Activation from lingvo.jax.layers.attentions import AttentionProjection from lingvo.jax.layers.attentions import causal_mask from lingvo.jax.layers.attentions import causal_segment_mask from lingvo.jax.layers.attentions import convert_paddings_to_mask from lingvo.jax.layers.attentions import DotProductAttention from lingvo.jax.layers.attentions import PerDimScale from lingvo.jax.layers.attentions import segment_mask from lingvo.jax.layers.augmentations import MaskedLmDataAugmenter from lingvo.jax.layers.conformers import Conformer from lingvo.jax.layers.convolutions import Conv2D from lingvo.jax.layers.convolutions import ConvBNAct from lingvo.jax.layers.convolutions import DepthwiseConv1D from lingvo.jax.layers.convolutions import LightConv1D from lingvo.jax.layers.embedding_softmax import PositionalEmbedding from lingvo.jax.layers.embedding_softmax import SingleShardEmbedding from lingvo.jax.layers.embedding_softmax import SingleShardFullSoftmax from lingvo.jax.layers.embedding_softmax import SingleShardSharedEmbeddingSoftmax from lingvo.jax.layers.flax_wrapper import FlaxModule from lingvo.jax.layers.linears import Bias from lingvo.jax.layers.linears import FeedForward from lingvo.jax.layers.linears import Linear from lingvo.jax.layers.linears import project_last_dim from lingvo.jax.layers.ngrammer import get_bigram_ids from lingvo.jax.layers.ngrammer import Ngrammer from lingvo.jax.layers.ngrammer import VectorQuantization from lingvo.jax.layers.ngrammer import VQNgrammer from lingvo.jax.layers.normalizations import BatchNorm from lingvo.jax.layers.normalizations import compute_moments from lingvo.jax.layers.normalizations import LayerNorm from lingvo.jax.layers.pipeline import LayerwiseShardablePipelined from lingvo.jax.layers.poolings import GlobalPooling from lingvo.jax.layers.poolings import Pooling from lingvo.jax.layers.recurrent import AutodiffCheckpointType from lingvo.jax.layers.recurrent import recurrent_func from lingvo.jax.layers.recurrent import recurrent_static from lingvo.jax.layers.recurrent import scan from lingvo.jax.layers.repeats import Repeat from lingvo.jax.layers.resnets import ResNet from lingvo.jax.layers.resnets import ResNetBlock from lingvo.jax.layers.stochastics import Dropout from lingvo.jax.layers.stochastics import StochasticResidual from lingvo.jax.layers.transformers import compute_attention_masks_for_extend_step from lingvo.jax.layers.transformers import compute_attention_masks_for_fprop from lingvo.jax.layers.transformers import StackedTransformer from lingvo.jax.layers.transformers import StackedTransformerRepeated from lingvo.jax.layers.transformers import Transformer from lingvo.jax.layers.transformers import TransformerEncoderDecoder from lingvo.jax.layers.transformers import TransformerFeedForward from lingvo.jax.layers.transformers import TransformerFeedForwardMoe from lingvo.jax.layers.transformers import TransformerLm

43.917647

82

0.835253

487

3,733

6.344969

0.318275

0.165049

0.214563

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0.560518

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0.039482

0

0.003531

0.08974

3,733

84

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44.440476

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0.192874

0

1

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true

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null

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1

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null

0

1

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1

0

1

0

5

41c45ed05c4758d4e2d6a787be613269792fe8b5

354

py

Python

spikey/snn/readout/__init__.py

SpikeyCNS/spikey

03a49073491974eff01bc017fd8eadb822e13f0d

[ "MIT" ]

4

2021-02-25T20:53:41.000Z

2022-01-18T15:27:07.000Z

spikey/snn/readout/__init__.py

SpikeyCNS/spikey

03a49073491974eff01bc017fd8eadb822e13f0d

[ "MIT" ]

5

2021-03-06T05:35:10.000Z

2021-03-31T09:27:57.000Z

spikey/snn/readout/__init__.py

SpikeyCNS/spikey

03a49073491974eff01bc017fd8eadb822e13f0d

[ "MIT" ]

null

""" Readout __init__. """ try: from spikey.snn.readout.threshold import Threshold from spikey.snn.readout.neuron_rates import NeuronRates from spikey.snn.readout.population_vector import PopulationVector from spikey.snn.readout.topaction import TopAction except ImportError as e: raise ImportError(f"readout/__init__.py failed: {e}")

32.181818

69

0.776836

45

354

5.888889

0.511111

0.150943

0.196226

0.301887

0

0.138418

354

10

70

35.4

0.868852

0.048023

0

0.094225

0

1

0

true

0

0.857143

0

0.857143

0

null

0

1

0

null

0

1

0

1

0

1

0

5

41ce532a4040ebb98f597bfd44d50c48154e9a75

171

py

Python

avx512-cnnopt/TileLoopGenerator/solver/main.py

Mastli/ASPLOS_artifact

1a592f7cfb0592f0a786e156a249087b56a4e647

[ "BSD-4-Clause" ]

7

2021-05-21T02:01:50.000Z

2021-11-22T05:51:22.000Z

avx512-cnnopt/TileLoopGenerator/solver/main.py

Mastli/ASPLOS_artifact

1a592f7cfb0592f0a786e156a249087b56a4e647

[ "BSD-4-Clause" ]

null

avx512-cnnopt/TileLoopGenerator/solver/main.py

Mastli/ASPLOS_artifact

1a592f7cfb0592f0a786e156a249087b56a4e647

[ "BSD-4-Clause" ]

3

2021-03-27T13:11:21.000Z

2021-11-01T15:40:35.000Z

from SymbolPool import * from Tensor import * from LoopStacker import * from Test import * def main(): test_modgen() if __name__ == "__main__": main()

13.153846

26

0.649123

20

171

5.1

0.55

0.294118

0

0.25731

171

13

27

13.153846

0.80315

0

0.046512

0

1

0.125

true

0

0.5

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0.625

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1

0

null

0

1

0

1

0

1

0

5

68c3f71371da194345e2c06a2e4bb2bc19f623aa

126

py

Python

skyportal/facility_apis/__init__.py

steveschulze/skyportal

47e334d71e34e82ff41bd0e32326e4107741e8e6

[ "BSD-3-Clause" ]

null

skyportal/facility_apis/__init__.py

steveschulze/skyportal

47e334d71e34e82ff41bd0e32326e4107741e8e6

[ "BSD-3-Clause" ]

null

skyportal/facility_apis/__init__.py

steveschulze/skyportal

47e334d71e34e82ff41bd0e32326e4107741e8e6

[ "BSD-3-Clause" ]

null

from .interface import FollowUpAPI, Listener from .sedm import SEDMAPI, SEDMListener from .lt import IOOAPI, IOIAPI, SPRATAPI

31.5

44

0.81746

16

126

6.4375

0.75

0

0.126984

126

3

45

42

0.936364

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

68d00b8abf63bb6256ef3652a434e5b648392eca

132

py

Python

hello-world/hello_world/__init__.py

pyodide/pyodide-examples

248f0a1de14ffccf3b1ff541b5965eedc2483051

[ "MIT" ]

1

2021-12-27T13:50:25.000Z

hello-world/hello_world/__init__.py

pyodide/pyodide-examples

248f0a1de14ffccf3b1ff541b5965eedc2483051

[ "MIT" ]

null

hello-world/hello_world/__init__.py

pyodide/pyodide-examples

248f0a1de14ffccf3b1ff541b5965eedc2483051

[ "MIT" ]

1

2022-02-17T11:10:15.000Z

print("Initializing hello world module") from .some_funcs import say_hello, repeat_string __all__ = ["say_hello", "repeat_string"]

26.4

48

0.787879

18

132

5.277778

0.722222

0.168421

0.294737

0.421053

0

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132

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null

0

1

0

5

68f82cc3a0add1f60132b1b00586e3e5f7e1c07e

149

py

Python

lab2/CmdView.py

YevhenKhomenko/crossplatform_labs

15bc2268ec699c380c1c78a0bdca7c1bf1b85aa6

[ "MIT" ]

null

lab2/CmdView.py

YevhenKhomenko/crossplatform_labs

15bc2268ec699c380c1c78a0bdca7c1bf1b85aa6

[ "MIT" ]

null

lab2/CmdView.py

YevhenKhomenko/crossplatform_labs

15bc2268ec699c380c1c78a0bdca7c1bf1b85aa6

[ "MIT" ]

null

class View: @staticmethod def show_message(message): print(message) @staticmethod def get_input(): return input()

13.545455

30

0.604027

15

149

5.866667

0.666667

0.340909

0

0.308725

149

10

31

14.9

0.854369

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0.285714

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false

0

0.142857

0.571429

0.142857

1

0

null

1

0

null

0

1

0

1

0

5

ec041b84d5ff3b3b634262773ef8feaafa547730

173

py

Python

msp/datasets/__init__.py

bilalsp/msp

a336e9dfc3aa19352c21de5d3ce90d2b5c6f38c6

[ "MIT" ]

2

2021-12-26T02:40:19.000Z

2022-01-14T05:44:48.000Z

msp/datasets/__init__.py

bilalsp/msp

a336e9dfc3aa19352c21de5d3ce90d2b5c6f38c6

[ "MIT" ]

null

msp/datasets/__init__.py

bilalsp/msp

a336e9dfc3aa19352c21de5d3ce90d2b5c6f38c6

[ "MIT" ]

null

""" The :mod:`mps.datasets` module includes utility to generate sample data. """ from msp.datasets._samples_generator import make_sparse_data __all__ = ['make_sparse_data']

28.833333

72

0.786127

24

173

5.25

0.791667

0.15873

0.222222

0

0.104046

173

6

73

28.833333

0.812903

0.416185

0

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0

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0.5

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1

0

null

0

1

0

5

ec26ce25392b66bace5b04d3ab23d087cf1ac662

6,640

py

Python

nntoolbox/sequence/models/encoder.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

16

2019-07-11T15:57:41.000Z

2020-09-08T13:52:45.000Z

nntoolbox/sequence/models/encoder.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

1

2022-01-18T22:21:57.000Z

nntoolbox/sequence/models/encoder.py

nhatsmrt/nn-toolbox

689b9924d3c88a433f8f350b89c13a878ac7d7c3

[ "Apache-2.0" ]

1

2019-08-07T10:07:09.000Z

import torch from torch import nn, Tensor from typing import Tuple from ..components import ResidualRNN __all__ = ['Encoder', 'RNNEncoder', 'GRUEncoder'] class Encoder(nn.Module): def __init__(self, input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token=0, drop_rate=0.1): super(Encoder, self).__init__() self._hidden_size = hidden_size self._input_size = input_size self._embedding_dim = embedding_dim self._num_layers = num_layers self._bidirectional = bidirectional self._device = device self._embedding = nn.Embedding(input_size, self._embedding_dim, padding_idx=pad_token) self._dropout = nn.Dropout(drop_rate) def forward(self, input: Tensor, states: Tuple[Tensor, ...]) -> Tuple[Tensor, Tuple[Tensor, ...]]: """ :param input: (seq_len, batch_size, input_dim) :param states: internal states of the RNN, each having dimension (num_layers * num_directions, batch_size, hidden_size) :return: output: (seq_len, batch, num_directions * hidden_size) states: states at final time step, each having dimension (num_layers * num_directions, batch_size, hidden_size) """ raise NotImplementedError def init_hidden(self, batch_size: int) -> Tuple[Tensor, ...]: """ Initialize the first zero hidden state :param batch_size: :return: Initial internal states, each of dim (num_layers * num_directions, batch_size, hidden_size) """ raise NotImplementedError class RNNEncoder(Encoder): def __init__( self, rnn, input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token=0, drop_rate=0.1 ): super(RNNEncoder, self).__init__( input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token, drop_rate ) self.rnn = rnn def forward(self, input: Tensor, states: Tuple[Tensor, ...]) -> Tuple[Tensor, Tuple[Tensor, ...]]: embedded = self._dropout(self._embedding(input)) output, hidden = self.rnn(embedded, states) return output, hidden class GRUEncoder(RNNEncoder): def __init__( self, input_size, hidden_size, embedding_dim, device, bias=False, num_layers=1, dropout=0, bidirectional=False, pad_token=0, drop_rate=0.1 ): super(GRUEncoder, self).__init__( nn.GRU( embedding_dim, hidden_size, bias=bias, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional ), input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token, drop_rate ) def init_hidden(self, batch_size: int) -> Tuple[Tensor, ...]: """ Initialize the first zero hidden state :param batch_size: :return: Initial hidden state, of dimensision (num_layers * num_directions, batch_size, hidden_size) """ first_dim = self._num_layers if self._bidirectional: first_dim *= 2 return (torch.zeros(first_dim, batch_size, self._hidden_size, device=self._device),) class LSTMEncoder(RNNEncoder): def __init__( self, input_size, hidden_size, embedding_dim, device, bias=False, num_layers=1, dropout=0, bidirectional=False, pad_token=0, drop_rate=0.1 ): super(LSTMEncoder, self).__init__( nn.LSTM( embedding_dim, hidden_size, bias=bias, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional ), input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token, drop_rate ) def init_hidden(self, batch_size: int) -> Tuple[Tensor, ...]: """ Initialize the first zero hidden state :param batch_size: :return: Initial hidden state and cell state, each of dim (num_layers * num_directions, batch_size, hidden_size) """ first_dim = self._num_layers if self._bidirectional: first_dim *= 2 return ( torch.zeros(first_dim, batch_size, self._hidden_size, device=self._device), torch.zeros(first_dim, batch_size, self._hidden_size, device=self._device) ) class ResidualRNNEncoder(RNNEncoder): def __init__( self, base_rnn, input_size, hidden_size, embedding_dim, device, bias=False, num_layers=1, dropout=0, bidirectional=False, pad_token=0, drop_rate=0.1 ): super(ResidualRNNEncoder, self).__init__( ResidualRNN( base_rnn=base_rnn, input_size=embedding_dim, bias=bias, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional ), input_size, hidden_size, embedding_dim, num_layers, bidirectional, device, pad_token, drop_rate ) # class ResidualGRUEncoder(RNNEncoder, GRUEncoder): # def __init__( # self, input_size, hidden_size, embedding_dim, device, bias=False, # num_layers=1, dropout=0, bidirectional=False, pad_token=0, drop_rate=0.1 # ): # super(ResidualGRUEncoder, self).__init__( # nn.GRU, input_size, hidden_size, embedding_dim, num_layers, bidirectional, # device, bias, num_layers, dropout, bidirectional, pad_token, drop_rate # ) # # class GRUEncoder(Encoder): # def __init__( # self, input_size, hidden_size, embedding_dim, device, bias=False, # num_layers=1, dropout=0, bidirectional=False, pad_token=0, drop_rate=0.1): # super(GRUEncoder, self).__init__( # input_size, hidden_size, embedding_dim, # num_layers, bidirectional, device, pad_token, drop_rate) # self._gru = nn.GRU( # embedding_dim, hidden_size, # bias=bias, num_layers=num_layers, # dropout=dropout, # bidirectional=bidirectional # ) # # self._gru = ResidualRNN( # # nn.GRU, input_size=hidden_size, # # bias=bias, num_layers=num_layers, # # dropout=dropout, # # ) # self.to(device) # # def forward(self, input: Tensor, hidden: Tensor) -> Tuple[Tensor, Tensor]: # embedded = self._dropout(self._embedding(input)) # output, hidden = self._gru(embedded, hidden) # return output, hidden #

38.16092

126

0.626054

752

6,640

5.194149

0.107713

0.076037

0.071685

0.0681

0.751408

0.72555

0.720686

0.71915

0.716334

0

0.006864

0.275904

6,640

173

127

38.381503

0.805532

0.345934

0

0.5

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0.006561

0

1

0.113636

false

0

0.045455

0

0.25

0

null

0

1

0

null

0

5

ec318f2a5d510ddb8d5b7037fff0c57d1ec7fd4a

25

py

Python

GalleryMan/assets/__init__.py

0xsapphir3/GalleryMan

b04f4ccdbb9004ed9e915d1cd0ddf36ccffe386c

[ "MIT" ]

9

2021-08-21T01:05:13.000Z

2022-01-26T15:06:32.000Z

GalleryMan/assets/__init__.py

AsianCat54x/galleryman

b04f4ccdbb9004ed9e915d1cd0ddf36ccffe386c

[ "MIT" ]

1

2021-08-19T09:42:06.000Z

GalleryMan/assets/__init__.py

AsianCat54x/galleryman

b04f4ccdbb9004ed9e915d1cd0ddf36ccffe386c

[ "MIT" ]

2

2021-08-19T08:13:00.000Z

2021-08-24T18:27:10.000Z

# Nothing To See Here ._.

25

0.68

4

25

4

1

0

0.2

25

1

25

0.8

0.92

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ec3bb2c057a7335bc7b7c259d5b3bdf7afe090da

36

py

Python

python/user/bot/__init__.py

star123good/DAML-Chat-Projectdabl

977cf3ae6ac4d283e903e22430962bb03f8a863f

[ "Apache-2.0" ]

1

2021-01-07T02:20:58.000Z

python/user/bot/__init__.py

star123good/DAML-Chat-Projectdabl

977cf3ae6ac4d283e903e22430962bb03f8a863f

[ "Apache-2.0" ]

11

2020-03-30T17:54:23.000Z

2022-02-26T22:54:04.000Z

python/user/bot/__init__.py

star123good/DAML-Chat-Projectdabl

977cf3ae6ac4d283e903e22430962bb03f8a863f

[ "Apache-2.0" ]

3

2020-04-30T19:56:09.000Z

2021-04-14T10:15:40.000Z

from .user_bot import main main()

7.2

26

0.722222

6

36

4.166667

0.833333

0

0.194444

36

4

27

9

0.862069

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ec43fe0d488358049728fe978556c36abaf5d9bd

170

py

Python

vkbottle/api/token_generator/__init__.py

homus32/vkbottle

8247665ef74835abe0c2c5e5981826540d0ecdb5

[ "MIT" ]

698

2019-08-09T17:32:52.000Z

2021-07-22T08:30:32.000Z

vkbottle/api/token_generator/__init__.py

homus32/vkbottle

8247665ef74835abe0c2c5e5981826540d0ecdb5

[ "MIT" ]

216

2019-08-18T19:22:50.000Z

2021-07-30T12:15:17.000Z

vkbottle/api/token_generator/__init__.py

homus32/vkbottle

8247665ef74835abe0c2c5e5981826540d0ecdb5

[ "MIT" ]

268

2019-08-10T14:52:04.000Z

2021-07-28T07:06:42.000Z

from .abc import ABCTokenGenerator, Token from .consistent import ConsistentTokenGenerator from .single import SingleTokenGenerator from .util import get_token_generator

34

48

0.870588

19

170

7.684211

0.631579

0

0.1

170

4

49

42.5

0.954248

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6b899987252b758a6dd30249000acacfb5d5426c

32,485

py

Python

flask_app/main.py

SamuelHoward/open-source-platform

eda201117a66e101c11727242e8bff470cea0385

[ "MIT" ]

null

flask_app/main.py

SamuelHoward/open-source-platform

eda201117a66e101c11727242e8bff470cea0385

[ "MIT" ]

null

flask_app/main.py

SamuelHoward/open-source-platform

eda201117a66e101c11727242e8bff470cea0385

[ "MIT" ]

null

# Import necessary modules from flask import render_template, request, redirect, url_for, flash, Blueprint from flask_app import app, db from flask_app.decorators import check_confirmed from flask_app.models import * from flask_login import login_required, current_user from sqlalchemy import or_, and_, func import random import requests # This file includes all main routes (no login related routes) main = Blueprint('main', __name__) # Define global variables orgs_search_results_data = Organizations.query.filter( Organizations.name.contains("")) projects_search_results_data = Projects.query.filter( Projects.description.contains("")) proj_search_term = None org_search_term = None projectsPerPage = 10 orgsPerPage = 10 # Route for home page @app.route('/') @app.route('/index') def index(): # Calculate amount of projects projsLength = Projects.query.count() # Calculate amount of organizations orgsLength = Organizations.query.count() # Return the static homepage return render_template( 'index.html', title='Open Source Platform', numProjs=projsLength, numOrgs=orgsLength) # Route for projects page, includes searching and favoriting projects @app.route('/projects', methods=['GET', 'POST']) def projects(): # Pull in the necessary global variables global projects_search_results_data global proj_search_term global projectsPerPage # Get the page number from request arguments page = request.args.get('page', 1, type=int) # Logic for processing project searches if request.method == 'POST' and 'search_term' in request.form: # Pull in search term from search form proj_search_term=request.form['search_term'] # Basic search logic: Look in project parameters for search term projects_search_results_data = Projects.query.filter( or_(Projects.description.contains(proj_search_term), Projects.name.contains(proj_search_term), Projects.language.contains(proj_search_term), Projects.owner.contains(proj_search_term), Projects.source.contains(proj_search_term))) # Pull in the item per page count, if necessary if 'per_page' in request.form: projectsPerPage = int(request.form['per_page']) # Logic for sorting items by name if 'sort_by' in request.form and request.form['sort_by'] == 'name': # Logic for reverse alphabetic search if 'reverse' in request.form: projects_search_results_data = projects_search_results_data \ .order_by(Projects.name.desc()) # Logic for standard alphabetic search else: projects_search_results_data = projects_search_results_data \ .order_by(Projects.name) # Logic for sorting items by creation date elif 'sort_by' in request.form and request.form['sort_by'] == 'created': # Logic for standard recent search if 'reverse' in request.form: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by(func.date( Projects.created_time)) # Logic for reverse recent search else: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by(func.date( Projects \ .created_time).desc()) # Logic for sorting items by number of forks elif 'sort_by' in request.form and request.form['sort_by'] == 'forks': # Logic for sorting items by fork count, least first if 'reverse' in request.form: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by(Projects.forks) # Logic for sorting items by fork count, greatest first else: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by(Projects.forks.desc()) # Logic for sorting items by number of watchers elif 'sort_by' in request.form \ and request.form['sort_by'] == 'watchers': # Logic for sorting items by watcher count, least first if 'reverse' in request.form: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by(Projects.watchers) # Logic for sorting items by watcher count, greatest first else: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by( Projects.watchers.desc()) # Logic for sorting items by number of issues elif 'sort_by' in request.form \ and request.form['sort_by'] == 'issues': # Logic for sorting items by issues count, least first if 'reverse' in request.form: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by( Projects.open_issues) # Logic for sorting items by issues count, greatest first else: projects_search_results_data = projects_search_results_data \ .filter(Projects.source == 'github') \ .order_by( Projects \ .open_issues.desc()) # Reset the page after search page = 1 # Refresh the page after searching try: # Render the page for logged-in users return render_template( 'projects_search.html', search_results=projects_search_results_data.paginate( page=page, per_page=projectsPerPage), search_term=proj_search_term, title='OSP | Projects', favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')) \ .with_entities( Favorites.fav_name)) # If the previous rendering fails, default to anonymous rendering except: # Render the page for anonymous users return render_template( 'projects_search.html', search_results=projects_search_results_data.paginate( page=page, per_page=projectsPerPage), search_term=proj_search_term, title='OSP | Projects', favorites=[]) # Logic for favoriting a project elif request.method == 'POST' and 'fav_name' in request.form: # Look for whether this project is already favorited favorite = Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['fav_name'], Favorites.fav_type=='project')).first() # If the favorite does not already exist, continue adding the favorite if favorite is None: # Form the Favorites object new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=request.form['fav_name'], fav_type='project') # Add and commit the Favorites object db.session.add(new_fav) db.session.commit() # Flash add message flash('Added ' + request.form['fav_name'] + ' to favorites') # Refresh the page after favoriting return redirect(request.path) # Logic for unfavoriting a project elif request.method == 'POST' and 'unfav_name' in request.form: # Look for the Favorite going to be deleted fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['unfav_name'], Favorites.fav_type=='project')).first() # Remove the Favorites record and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Render the page for logged-in users and anonymous users else: try: # Render the page for logged-in users return render_template( 'projects_search.html', search_results=projects_search_results_data.paginate( page=page, per_page=projectsPerPage), search_term=proj_search_term, title='OSP | Projects', favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')) \ .with_entities( Favorites.fav_name)) # If the previous rendering fails, default to anonymous rendering except: # Render the page for anonymous users return render_template( 'projects_search.html', search_results=projects_search_results_data.paginate( page=page, per_page=projectsPerPage), search_term=proj_search_term, title='OSP | Projects', favorites=[]) # Route for the organizations page @app.route('/organizations', methods=['GET', 'POST']) def organizations(): # Bring in te necessary global variables global orgs_search_results_data global org_search_term global orgsPerPage # Bring in the page number from the arguments page = request.args.get('page', 1, type=int) # Logic for searching orgs if request.method == 'POST' and 'search_term' in request.form: # Bring in the search term from the form org_search_term=request.form['search_term'] # Perform search by looking for search term in org names orgs_search_results_data = Organizations.query.filter( Organizations.name.contains(org_search_term)) # Pull in the item count per page if necessary if 'per_page' in request.form: orgsPerPage = int(request.form['per_page']) # Logic for search by name if 'name' in request.form: # Logic for reverse alphabetic search if 'reverse' in request.form: orgs_search_results_data = orgs_search_results_data.order_by( Organizations.name.desc()) # Logic for standard alphabetic search else: orgs_search_results_data = orgs_search_results_data.order_by( Organizations.name) # Reset the page after search page = 1 # Calculate search results and org names search_results=orgs_search_results_data.paginate( page=page, per_page=orgsPerPage) orgNames = [item.name for item in search_results.items] # Find the projects whose owners are in the search results projects=Projects.query.filter(Projects.owner.in_(orgNames)) # Refresh the page after search try: # Render page for logged-in users return render_template( 'organizations.html', search_results=search_results, search_term=org_search_term, projects=projects, title='OSP | Organizations', favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='org')) \ .with_entities(Favorites.fav_name)) # If the above rendering fails, render the anonymous page except: #Render page for anonymous users return render_template( 'organizations.html', search_results=search_results, search_term=org_search_term, projects=projects, title='OSP | Organizations', favorites=[]) # Logic for favoriting an org if request.method == 'POST' and 'fav_name' in request.form: # Check if the favorites object already exists favorite=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['fav_name'], Favorites.fav_type=='org')).first() # If the favorite does not already exist, form the Favorites record if favorite is None: # Form the favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=request.form['fav_name'], fav_type='org') # Add the favorite and commit it db.session.add(new_fav) db.session.commit() # Flash add message flash('Added ' + request.form['fav_name'] + ' to favorites') # Refresh the page after searching return redirect(request.path) # Logic for unfavoriting an org elif request.method == 'POST' and 'unfav_name' in request.form: # Look for the existing favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['unfav_name'], Favorites.fav_type=='org')).first() # delete the favorite and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Render the page for logged-in users and anonymous users else: # Calculate search results and org names search_results=orgs_search_results_data.paginate( page=page, per_page=orgsPerPage) orgNames = [item.name for item in search_results.items] # Find the projects whose owners are in the search results projects=Projects.query.filter(Projects.owner.in_(orgNames)) try: # Render page for logged-in users return render_template( 'organizations.html', search_results=search_results, search_term=org_search_term, projects=projects, title='OSP | Organizations', favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='org')) \ .with_entities(Favorites.fav_name)) # If the above rendering fails, render the anonymous page except: #Render page for anonymous users return render_template( 'organizations.html', search_results=search_results, search_term=org_search_term, projects=projects, title='OSP | Organizations', favorites=[]) # Route for individual project pages @app.route('/project/<projectName>', methods=['GET', 'POST']) def project(projectName): # Logic for favoriting the project if request.method == 'POST' and 'fav_name' in request.form: # Look for whether the favorite record already exists favorite=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['fav_name'], Favorites.fav_type=='project')).first() # If the Favorite record does not yet exist, create it if favorite is None: # Create the Favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=request.form['fav_name'], fav_type='project') # Flash add message flash('Added ' + request.form['fav_name'] + ' to favorites') # Add and commit the Favorites record db.session.add(new_fav) db.session.commit() # Refresh the page after favoriting return redirect(request.path) # Logic for unfavoriting the project elif request.method == 'POST' and 'unfav_name' in request.form: # Look for the existing Favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['unfav_name'], Favorites.fav_type=='project')).first() # Delete the Favorite and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Try to render the page for this project try: # Retrieve the data for the project page proj = Projects.query.filter(Projects.name==projectName).one() orgName = Organizations.query.filter( Organizations.name==proj.owner).one() projs = Projects.query.filter(Projects.owner==orgName.name) count = projs.count() # Render the page for logged-in users try: return render_template( 'project.html', project=proj, projects=projs, count=count, title='OSP | ' + projectName, favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')) \ .with_entities(Favorites.fav_name), favCount=Favorites.query.filter( Favorites.fav_name==projectName).count()) # Render the page for anonymous users except: return render_template( 'project.html', project=proj, projects=projs, count=count, title='OSP | ' + projectName, favorites=[], favCount=Favorites.query.filter( Favorites.fav_name==projectName).count()) # If the project does not exist, render the 404 page except: return render_template('404.html', title='OSP | 404'), 404 # Route for individual organization pages @app.route('/org/<orgName>', methods=['GET', 'POST']) def organization(orgName): # Logic for favoriting an org if request.method == 'POST' and 'fav_name' in request.form: # Look for whether the org exists favorite=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['fav_name'], Favorites.fav_type=='org')).first() # If the favorite does not exist, create it if favorite is None: # Create the Favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=request.form['fav_name'], fav_type='org') # Add the favorite and commit it db.session.add(new_fav) db.session.commit() # Flash add message flash('Added ' + request.form['fav_name'] + ' to favorites') # Refresh the page after favoriting return redirect(request.path) # Logic for unfavoriting the org elif request.method == 'POST' and 'unfav_name' in request.form: # Look for the existing Favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['unfav_name'], Favorites.fav_type=='org')).first() # Delete the favorites record and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Logic for favoriting project on org page elif request.method == 'POST' and 'proj_fav_name' in request.form: # Look for whether the proj exists favorite=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['proj_fav_name'], Favorites.fav_type=='project')).first() # If the favorite does not exist, create it if favorite is None: # Create the Favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=request.form['proj_fav_name'], fav_type='project') # Add the favorite and commit it db.session.add(new_fav) db.session.commit() # Flash add message flash('Added ' + request.form['proj_fav_name'] + ' to favorites') # Refresh the page after favoriting return redirect(request.path) # Logic for unfavoriting proj on org page elif request.method == 'POST' and 'proj_unfav_name' in request.form: # Look for the existing Favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['proj_unfav_name'], Favorites.fav_type=='project')).first() # Delete the favorites record and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['proj_unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Try to render the page for the org try: # Retrieve the necessary data org = Organizations.query.filter(Organizations.name==orgName).one() projs = Projects.query.filter(Projects.owner==orgName) # Render the page for logged-in users try: return render_template( 'organization.html', organization=org, projects=projs, title='OSP | ' + orgName, favorites=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='org')) \ .with_entities(Favorites.fav_name), proj_favs=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')) \ .with_entities(Favorites.fav_name), favCount=Favorites.query.filter( Favorites.fav_name==orgName).count()) # Render the page for anonymous users except: return render_template( 'organization.html', organization=org, projects=projs, title='OSP | ' + orgName, favorites=[], proj_favs=[], favCount=Favorites.query.filter( Favorites.fav_name==orgName).count()) # If page rendering fails, render the 404 page except: return render_template('404.html', title='OSP | 404'), 404 # Route for profile page @main.route('/profile', methods=['GET', 'POST']) @login_required @check_confirmed def profile(): # Logic for unfavoriting the org if request.method == 'POST' and 'unfav_name' in request.form: # Look for the existing Favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['unfav_name'], Favorites.fav_type=='org')).first() # Delete the favorites record and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Logic for unfavoriting proj on org page elif request.method == 'POST' and 'proj_unfav_name' in request.form: # Look for the existing Favorites record fav = db.session.query(Favorites).filter( and_(Favorites.user_id==current_user.id, Favorites.fav_name==request.form['proj_unfav_name'], Favorites.fav_type=='project')).first() # Delete the favorites record and commit db.session.delete(fav) db.session.commit() # Flash delete message flash('Removed ' + request.form['proj_unfav_name'] + ' from favorites') # Refresh the page after unfavoriting return redirect(request.path) # Find project favorites for current user favProjs=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')) \ .with_entities( Favorites.fav_name) # Find favorite projects projects=db.session.query(Projects).filter(Projects.name.in_(favProjs)) # Find organization favorites for current user favOrgs=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='org')) \ .with_entities( Favorites.fav_name) # Find favorite organizations organizations=db.session.query( Organizations).filter(Organizations.name.in_(favOrgs)) # Render the profile for a logged in user return render_template('profile.html', name=current_user.name, title='OSP | Profile', projects=projects, organizations=organizations, favProjsCount=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='project')).count(), favOrgsCount=Favorites.query.filter( and_(Favorites.user_id==current_user.id, Favorites.fav_type=='org')).count(), favorites=Favorites.query.filter( Favorites.user_id==current_user.id), subProjects=Projects.query.all()) # Route for submitting projects @main.route('/project-submit', methods=['GET', 'POST']) @login_required @check_confirmed def project_submit(): # Logic for submitting github project if request.method == 'POST': # Pull in submit term from form submit_term = request.form['submit_term'] # Use github api to access user's project item = requests.get( "https://api.github.com/repos/" + submit_term).json() # Check if project was found if "name" not in item: flash('Project not found on github') return redirect(url_for('main.project_submit')) # Check if project is already in database q = db.session.query(Projects.name).filter(Projects.name==item["name"]) # If the project exists, do not add it again if db.session.query(q.exists()).scalar(): flash('Project already exists in database') return redirect(url_for('project', projectName=item["name"])) # Create the new Project record new_proj = Projects( name = item["name"], url = item["html_url"], description = item["description"], source = "github", owner = item["owner"]["login"], owner_avatar = item["owner"]["avatar_url"], language = item["language"], created_time = item["created_at"], last_updated = item["updated_at"], forks = item["forks"], watchers = item["watchers"], open_issues = item["open_issues"], owner_type = item["owner"]["type"]) # Add the project and commit db.session.add(new_proj) db.session.commit() # Logic for adding favorite project if 'favProj' in request.form: # Create the Favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=item["name"], fav_type='project') # Add the favorite and commit it db.session.add(new_fav) db.session.commit() # Check if org is already in database q = db.session.query(Organizations.name).filter( Organizations.name==item["owner"]["login"]) # If the organization exists, do not add it again if not db.session.query(q.exists()).scalar(): # Create the new Project record new_org = Organizations( name = item["owner"]["login"], avatar = item["owner"]["avatar_url"], owner_type = item["owner"]["type"], url = "https://github.com/" + item["owner"]["login"]) # Add the org and commit db.session.add(new_org) db.session.commit() # flash project and org added message flash('Project and Organization Added!') else: # flash project added message flash('Project Added!') # Logic for adding favorite org if 'favOrg' in request.form: # Create the Favorites record new_fav = Favorites( id=random.randint(-2147483648, 2147483647), user_id=current_user.id, fav_name=item["owner"]["login"], fav_type='org') # Add the favorite and commit it db.session.add(new_fav) db.session.commit() # Load the manage page return redirect(url_for('main.project_submit')) # Render project submit page return render_template('project-submit.html', title='OSP | Project Submit') # Route for 404 page @app.errorhandler(404) def page_not_found(e): # Render the 404 page return render_template('404.html', title='OSP | 404'), 404

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null

0

1

0

1

0

1

0

null

0

5

6ba4ad801a9aa7d778472b993d995c3148627cc0

149

py

Python

telethon/default.py

yeung1704/Telethon

4562ba9ccfb1aef33facd5ca1a2675705d390e1c

[ "MIT" ]

1

2019-07-20T08:28:10.000Z

telethon/default.py

yeung1704/Telethon

4562ba9ccfb1aef33facd5ca1a2675705d390e1c

[ "MIT" ]

null

telethon/default.py

yeung1704/Telethon

4562ba9ccfb1aef33facd5ca1a2675705d390e1c

[ "MIT" ]

null

""" Sentinel module to signify that a parameter should use its default value. Useful when the default value or ``None`` are both valid options. """

24.833333

73

0.744966

23

149

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0.174497

149

5

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0.939597

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0

1

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1

0

5

6bff350f518f439e181a5a3b43b8b57339abb3a9

96

py

Python

bitmovin_api_sdk/encoding/filters/watermark/customdata/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/encoding/filters/watermark/customdata/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/encoding/filters/watermark/customdata/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.encoding.filters.watermark.customdata.customdata_api import CustomdataApi

48

95

0.90625

12

96

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0.833333

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0.913043

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1

0

1

0

1

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5

d41235cfd426754bc747b9f2f6052745a07393cf

157

py

Python

main.py

James-Leslie/github-actions

adb4206f365c879ab5297e52e1153fb93191e190

[ "Unlicense" ]

null

main.py

James-Leslie/github-actions

adb4206f365c879ab5297e52e1153fb93191e190

[ "Unlicense" ]

null

main.py

James-Leslie/github-actions

adb4206f365c879ab5297e52e1153fb93191e190

[ "Unlicense" ]

null

def add_two(a, b): '''Adds two numbers together''' return a + b def multiply_two(a, b): '''Multiplies two numbers together''' return a * b

17.444444

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0.605096

24

157

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157

8

42

19.625

0.794872

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1

0.5

false

0

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null

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1

0

1

0

null

0

1

0

1

0

5

d4249f69da262cb3a39e11f79c9b161bcd9b7173

48

py

Python

tests/components/luftdaten/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/luftdaten/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/luftdaten/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Define tests for the Luftdaten component."""

24

47

0.729167

6

48

5.833333

1

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48

1

48

0.833333

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0

null

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1

0

null

0

1

0

5

d43249d872053b3e214cf2a595c7a9acd99c13ea

134

py

Python

vulture/pipelines.py

aksiksi/vulture

5b5a198b64e3a4ce2822e71ba148c50e0ea29b9a

[ "MIT" ]

null

vulture/pipelines.py

aksiksi/vulture

5b5a198b64e3a4ce2822e71ba148c50e0ea29b9a

[ "MIT" ]

null

vulture/pipelines.py

aksiksi/vulture

5b5a198b64e3a4ce2822e71ba148c50e0ea29b9a

[ "MIT" ]

null

from scrapy.exceptions import DropItem class DubizzlePipeline(object): def process_item(self, item, spider): return item

22.333333

41

0.746269

16

134

6.1875

0.875

0

0.186567

134

5

42

26.8

0.908257

0

1

0.25

false

0

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d468b4040e163972a066288b8759fdddb7630bcc

138

py

Python

nailgun-client/py/__init__.py

h4l/nailgun

84f3b051e99f7be17260b1cae8da0217be0bb0a4

[ "Apache-2.0" ]

362

2017-10-16T18:51:28.000Z

2022-03-29T00:44:30.000Z

nailgun-client/py/__init__.py

h4l/nailgun

84f3b051e99f7be17260b1cae8da0217be0bb0a4

[ "Apache-2.0" ]

88

2017-10-30T22:52:49.000Z

2022-03-04T21:56:39.000Z

nailgun-client/py/__init__.py

h4l/nailgun

84f3b051e99f7be17260b1cae8da0217be0bb0a4

[ "Apache-2.0" ]

89

2017-10-16T18:51:29.000Z

2022-03-18T00:52:28.000Z

27.6

50

0.804348

16

138

6.9375

0.875

0

0.099174

0.123188

138

4

51

34.5

0.818182

0.586957

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2e17d42191b40ddd586a3685d06dd1dc28ec5c8e

262

py

Python

Statistics/Randomgen_1.py

Sakthi0722/StatsCalculator

f18b8ddb300ccaba796665829ddf20777ff15ed9

[ "MIT" ]

1

2021-09-20T18:39:53.000Z

Statistics/Randomgen_1.py

Sakthi0722/StatsCalculator

f18b8ddb300ccaba796665829ddf20777ff15ed9

[ "MIT" ]

null

Statistics/Randomgen_1.py

Sakthi0722/StatsCalculator

f18b8ddb300ccaba796665829ddf20777ff15ed9

[ "MIT" ]

2

2021-07-12T18:58:23.000Z

2021-07-14T17:47:40.000Z

import random r1 = random.randint(1, 40) print("Generate a random number without a seed between a range of two numbers - Integer:", r1) r2 = random.uniform(1, 40) print("Generate a random number without a seed between a range of two numbers - Decimal:", r2)

26.2

94

0.732824

44

262

4.363636

0.477273

0.03125

0.083333

0.166667

0.6875

0

0.046296

0.175573

262

9

95

29.111111

0.842593

0

1

0

0.618321

0

1

0

false

0

0.2

0

0.2

0.4

0

null

0

1

0

1

0

1

0

1

0

1

0

null

0

5

5cfa15c4217764a7243acf381d48de88ecf54b14

90

py

Python

app/album/__init__.py

chaosbus/MemorySeepage

d72fc9fc1826e9e54cfbe899f65e508ec8ea090f

[ "Apache-2.0" ]

null

app/album/__init__.py

chaosbus/MemorySeepage

d72fc9fc1826e9e54cfbe899f65e508ec8ea090f

[ "Apache-2.0" ]

null

app/album/__init__.py

chaosbus/MemorySeepage

d72fc9fc1826e9e54cfbe899f65e508ec8ea090f

[ "Apache-2.0" ]

null

from flask import Blueprint bp_album = Blueprint('album', __name__) from . import views

15

39

0.766667

12

90

5.333333

0.666667

0

0.155556

90

5

40

18

0.842105

0

0.055556

0

1

0

false

0

0.666667

0

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

5

5cfb5f7fde73fb94febdbb0039f0e0103831ec8f

123

py

Python

BeerChallenge_Scordino_Marco.py

Py101/py101-assignments-marcosco

f70889899f97a96e1821bbd79c9bcc323e8aa6b0

[ "MIT" ]

null

BeerChallenge_Scordino_Marco.py

Py101/py101-assignments-marcosco

f70889899f97a96e1821bbd79c9bcc323e8aa6b0

[ "MIT" ]

null

BeerChallenge_Scordino_Marco.py

Py101/py101-assignments-marcosco

f70889899f97a96e1821bbd79c9bcc323e8aa6b0

[ "MIT" ]

null

from functools import reduce def fact(n): fact_lamba = lambda x, y: x * y return reduce(fact_lamba, range(1,n+1))

20.5

43

0.674797

22

123

3.681818

0.636364

0.222222

0

0.020619

0.211382

123

5

44

24.6

0.814433

0

1

0.25

false

0

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cf2b77b413257fb41d5d5fa3c9259818e5490edc

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py

Python

s_core/admin.py

jrbenriquez/sarimuson2

cdaedb130bd7b3ef065e4503228cc1610ae43fda

[ "MIT" ]

null

s_core/admin.py

jrbenriquez/sarimuson2

cdaedb130bd7b3ef065e4503228cc1610ae43fda

[ "MIT" ]

null

s_core/admin.py

jrbenriquez/sarimuson2

cdaedb130bd7b3ef065e4503228cc1610ae43fda

[ "MIT" ]

null

from django.contrib import admin from .models.customer import Customer from .models.purchase import Purchase from .models.purchase import PurchaseItem @admin.register(Customer) class CustomerAdmin(admin.ModelAdmin): pass @admin.register(Purchase) class PurchaseAdmin(admin.ModelAdmin): pass @admin.register(PurchaseItem) class PurchaseItemAdmin(admin.ModelAdmin): pass

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0.798969

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388

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0.363636

0.096774

0.183871

0.154839

0.206452

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0.121134

388

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