ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

7cb23f9d984ca01ba8f682afe13184f98d4f5e92

389

py

Python

qtask/utils/testing.py

LinkTsang/qtask-legacy-python

9b264b8e33313e4d3615472d59a2a39948eeeaa1

[ "MIT" ]

null

qtask/utils/testing.py

LinkTsang/qtask-legacy-python

9b264b8e33313e4d3615472d59a2a39948eeeaa1

[ "MIT" ]

null

qtask/utils/testing.py

LinkTsang/qtask-legacy-python

9b264b8e33313e4d3615472d59a2a39948eeeaa1

[ "MIT" ]

null

import asyncio import traceback import unittest

22.882353

63

0.62982

7cb2d3d2cb22c43c3c911d744e22c33bc37cdf49

1,661

py

Python

landing/views.py

theflatladder/kyrsovaya

d6d661854cd955e544a199e201f325decc360cc1

[ "MIT" ]

null

landing/views.py

theflatladder/kyrsovaya

d6d661854cd955e544a199e201f325decc360cc1

[ "MIT" ]

null

landing/views.py

theflatladder/kyrsovaya

d6d661854cd955e544a199e201f325decc360cc1

[ "MIT" ]

null

from django.shortcuts import render, render_to_response, redirect from django.contrib import auth from django.contrib.auth.forms import UserCreationForm from django.template.context_processors import csrf from django.http import HttpResponseRedirect

31.339623

140

0.668874

7cb376c84e1d9faf8d7802d4ab0284c278818e8c

441

py

Python

FPRun11.py

yecfly/DEPRESSIONEST

21b72906aac9f310e264f7a5eea348480a647197

[ "Unlicense" ]

null

FPRun11.py

yecfly/DEPRESSIONEST

21b72906aac9f310e264f7a5eea348480a647197

[ "Unlicense" ]

null

FPRun11.py

yecfly/DEPRESSIONEST

21b72906aac9f310e264f7a5eea348480a647197

[ "Unlicense" ]

null

from Facepatchindependenttrain import runPatch import sys if len(sys.argv)==6: runPatch(GPU_Device_ID=1, FacePatchID=int(sys.argv[1]), trainpklID=int(sys.argv[2]), testpklID=int(sys.argv[3]), NetworkType=int(sys.argv[4]), runs=int(sys.argv[5])) else: print("argument errors, try\npython runfile.py <FacePatchID> <trainpklID> <testpklID> <NetworkType> <runs>")

40.090909

113

0.621315

7cb439e7ed9a5e950d6cf894c40e5a62043d06e9

5,183

py

Python

vendor/packages/translate-toolkit/translate/convert/test_po2tmx.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

2

2019-08-19T17:08:47.000Z

2019-10-05T11:37:02.000Z

vendor/packages/translate-toolkit/translate/convert/test_po2tmx.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

null

vendor/packages/translate-toolkit/translate/convert/test_po2tmx.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from translate.convert import po2tmx from translate.convert import test_convert from translate.misc import wStringIO from translate.storage import tmx from translate.storage import lisa

33.43871

128

0.641134

7cb54fa0b7a5c349c3088529c91a97ac9de21c8e

2,684

py

Python

plugin.video.yatp/libs/client/commands.py

mesabib/kodi.yatp

d874df43047b5b58f84cb3760fc891d9a133a69f

[ "CNRI-Python" ]

54

2015-08-01T20:31:36.000Z

2022-02-06T11:06:01.000Z

plugin.video.yatp/libs/client/commands.py

mesabib/kodi.yatp

d874df43047b5b58f84cb3760fc891d9a133a69f

[ "CNRI-Python" ]

57

2015-08-31T09:54:49.000Z

2018-08-30T20:39:12.000Z

plugin.video.yatp/libs/client/commands.py

mesabib/kodi.yatp

d874df43047b5b58f84cb3760fc891d9a133a69f

[ "CNRI-Python" ]

16

2016-01-17T11:44:41.000Z

2021-12-12T00:41:29.000Z

# coding: utf-8 # Module: commands # Created on: 28.07.2015 # Author: Roman Miroshnychenko aka Roman V.M. ([email protected]) # Licence: GPL v.3: http://www.gnu.org/copyleft/gpl.html """ Context menu commands """ import sys import xbmc import xbmcgui import json_requests as jsonrq from simpleplugin import Addon addon = Addon('plugin.video.yatp') _ = addon.initialize_gettext() def show_torrent_info(info_hash): """ Display current torrent info :param info_hash: :return: """ torr_info = jsonrq.get_torrent_info(info_hash) info_dialog = xbmcgui.DialogProgress() info_dialog.create(torr_info['name']) while not info_dialog.iscanceled(): info_dialog.update(torr_info['progress'], _('state: {0}; seeds: {1}; peers: {2}').format( torr_info['state'], torr_info['num_seeds'], torr_info['num_peers'] ), _('size: {0}MB; DL speed: {1}KB/s; UL speed: {2}KB/s').format( torr_info['size'], torr_info['dl_speed'], torr_info['ul_speed'] ), _('total DL: {0}MB; total UL: {1}MB').format( torr_info['total_download'], torr_info['total_upload']) ) xbmc.sleep(1000) torr_info = jsonrq.get_torrent_info(info_hash) if __name__ == '__main__': if sys.argv[1] == 'pause': jsonrq.pause_torrent(sys.argv[2]) elif sys.argv[1] == 'resume': jsonrq.resume_torrent(sys.argv[2]) elif sys.argv[1] == 'delete' and xbmcgui.Dialog().yesno( _('Confirm delete'), _('Do you really want to delete the torrent?')): jsonrq.remove_torrent(sys.argv[2], False) elif sys.argv[1] == 'delete_with_files'and xbmcgui.Dialog().yesno( _('Confirm delete'), _('Do you really want to delete the torrent with files?'), _('Warning: The files will be deleted permanently!')): jsonrq.remove_torrent(sys.argv[2], True) elif sys.argv[1] == 'pause_all': jsonrq.pause_all() elif sys.argv[1] == 'resume_all': jsonrq.resume_all() elif sys.argv[1] == 'show_info': show_torrent_info(sys.argv[2]) elif sys.argv[1] == 'restore_finished': jsonrq.restore_finished(sys.argv[2]) else: addon.log_debug('Command cancelled or invalid command: {0}'.format(sys.argv[1])) xbmc.executebuiltin('Container.Refresh')

35.786667

89

0.554396

7cb5817de3a17f08a3afdfbe15a3bbd0fbe2d1d8

346

py

Python

setup.py

GeorgeDittmar/MarkovTextGenerator

df6a56e23051e1f263ba22889dc3b5d0dc03e370

[ "Apache-2.0" ]

1

2021-11-26T15:49:31.000Z

setup.py

GeorgeDittmar/Mimic

df6a56e23051e1f263ba22889dc3b5d0dc03e370

[ "Apache-2.0" ]

1

2019-06-24T17:30:41.000Z

2019-06-26T04:53:00.000Z

setup.py

GeorgeDittmar/MarkovTextGenerator

df6a56e23051e1f263ba22889dc3b5d0dc03e370

[ "Apache-2.0" ]

2

2020-05-04T07:57:17.000Z

2021-02-23T05:10:11.000Z

#!/usr/bin/env python from distutils.core import setup setup(name='Mimik', version='1.0', description='Python framework for markov models', author='George Dittmar', author_email='[email protected]', url='https://www.python.org/sigs/distutils-sig/', packages=['distutils', 'distutils.command'], )

26.615385

55

0.65896

7cb5d1b6022bb826ecb887e64d632c52c31ffdb9

5,563

py

Python

pipeline/scripts/package.py

deplatformr/open-images

3726c9802bda1d7ecbbbd9920d5566daaecc9faa

[ "MIT" ]

2

2020-10-12T02:37:54.000Z

2020-10-14T15:16:49.000Z

pipeline/scripts/package.py

deplatformr/open-images

3726c9802bda1d7ecbbbd9920d5566daaecc9faa

[ "MIT" ]

null

pipeline/scripts/package.py

deplatformr/open-images

3726c9802bda1d7ecbbbd9920d5566daaecc9faa

[ "MIT" ]

null

import os import shutil import sqlite3 import tarfile from datetime import datetime import bagit

47.144068

735

0.585655

7cb6009fc34f03127073ead641d466f1b2a5c978

2,313

py

Python

app/search/hot_eval/hl_reportable.py

don4apaev/anfisa

2e4bdd83c584c0000f037413ccc1f9067c07fa70

[ "Apache-2.0" ]

null

app/search/hot_eval/hl_reportable.py

don4apaev/anfisa

2e4bdd83c584c0000f037413ccc1f9067c07fa70

[ "Apache-2.0" ]

null

app/search/hot_eval/hl_reportable.py

don4apaev/anfisa

2e4bdd83c584c0000f037413ccc1f9067c07fa70

[ "Apache-2.0" ]

null

def evalRec(env, rec): """hl_reportable""" return (len(set(rec.Genes) & { 'ABHD12', 'ACTG1', 'ADGRV1', 'AIFM1', 'ATP6V1B1', 'BCS1L', 'BSND', 'CABP2', 'CACNA1D', 'CDC14A', 'CDH23', 'CEACAM16', 'CEP78', 'CHD7', 'CIB2', 'CISD2', 'CLDN14', 'CLIC5', 'CLPP', 'CLRN1', 'COCH', 'COL11A2', 'DIAPH1', 'DIAPH3', 'DMXL2', 'DNMT1', 'DSPP', 'EDN3', 'EDNRB', 'EPS8', 'EPS8L2', 'ESPN', 'ESRRB', 'EYA1', 'EYA4', 'GIPC3', 'GJB2', 'GJB6', 'GPSM2', 'GRHL2', 'GRXCR1', 'GSDME', 'HGF', 'HSD17B4', 'ILDR1', 'KCNE1', 'KCNQ1', 'KCNQ4', 'LARS2', 'LHFPL5', 'LOXHD1', 'LRTOMT', 'MARVELD2', 'MIR96', 'MITF', 'MSRB3', 'MT-RNR1', 'MT-TS1', 'MYH14', 'MYH9', 'MYO15A', 'MYO3A', 'MYO6', 'MYO7A', 'OSBPL2', 'OTOA', 'OTOF', 'OTOG', 'OTOGL', 'P2RX2', 'PAX3', 'PDZD7', 'PJVK', 'POU3F4', 'POU4F3', 'PRPS1', 'PTPRQ', 'RDX', 'RIPOR2', 'S1PR2', 'SERPINB6', 'SIX1', 'SLC17A8', 'SLC26A4', 'SLC52A2', 'SLITRK6', 'SMPX', 'SOX10', 'STRC', 'SYNE4', 'TBC1D24', 'TECTA', 'TIMM8A', 'TMC1', 'TMIE', 'TMPRSS3', 'TPRN', 'TRIOBP', 'TUBB4B', 'USH1C', 'USH1G', 'USH2A', 'WFS1', 'WHRN', } ) > 0)

21.027273

32

0.253783

7cb6f4beed1a08b09244a31819b47421774b7914

6,486

py

Python

eval/util/metrics.py

fau-is/grm

78b1559ea0dda1b817283adecd58da50ca232223

[ "MIT" ]

5

2020-09-15T18:57:01.000Z

2021-12-13T14:14:08.000Z

eval/util/metrics.py

fau-is/grm

78b1559ea0dda1b817283adecd58da50ca232223

[ "MIT" ]

null

eval/util/metrics.py

fau-is/grm

78b1559ea0dda1b817283adecd58da50ca232223

[ "MIT" ]

1

2020-09-10T17:45:22.000Z

import sklearn import pandas import seaborn as sns import matplotlib.pyplot as pyplot from functools import reduce # import numpy as np

36.234637

118

0.646007

7cb7c886108da63565062eb8d192b4df3da78f64

3,566

py

Python

dpgs_sandbox/tests/test_bug_migrations_in_base_models.py

gabrielpiassetta/django-pgschemas

1e76db4cef31c7534bf4ba109961e835a1dd3c96

[ "MIT" ]

null

dpgs_sandbox/tests/test_bug_migrations_in_base_models.py

gabrielpiassetta/django-pgschemas

1e76db4cef31c7534bf4ba109961e835a1dd3c96

[ "MIT" ]

null

dpgs_sandbox/tests/test_bug_migrations_in_base_models.py

gabrielpiassetta/django-pgschemas

1e76db4cef31c7534bf4ba109961e835a1dd3c96

[ "MIT" ]

null

import warnings from unittest.mock import patch from django.apps import apps from django.core import management from django.core.management.base import CommandError from django.db import models from django.db.utils import ProgrammingError from django.test import TransactionTestCase, tag from django_pgschemas.checks import check_schema_names from django_pgschemas.models import TenantMixin from django_pgschemas.utils import get_tenant_model TenantModel = get_tenant_model()

37.536842

118

0.714526

7cb900078da95ed33cbe2fdf9bd9a465b5e9a56e

6,330

py

Python

tfx/components/transform/component.py

pingsutw/tfx

bf0d1d74e3f6ea429989fc7b80b82bea08077857

[ "Apache-2.0" ]

null

tfx/components/transform/component.py

pingsutw/tfx

bf0d1d74e3f6ea429989fc7b80b82bea08077857

[ "Apache-2.0" ]

null

tfx/components/transform/component.py

pingsutw/tfx

bf0d1d74e3f6ea429989fc7b80b82bea08077857

[ "Apache-2.0" ]

null

# Lint as: python2, python3 # Copyright 2019 Google LLC. 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. """TFX Transform component definition.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from typing import Optional, Text, Union import absl from tfx import types from tfx.components.base import base_component from tfx.components.base import executor_spec from tfx.components.transform import executor from tfx.orchestration import data_types from tfx.types import artifact from tfx.types import artifact_utils from tfx.types import standard_artifacts from tfx.types.standard_component_specs import TransformSpec

43.356164

105

0.733017

7cb9aea67a579bf1b09555b59098bc7f2315e25f

959

py

Python

objects/GitIndexEntry.py

anderslatif/alg

d5902a05a4cb249e554f65a7e8016d7d050b6da9

[ "MIT" ]

null

objects/GitIndexEntry.py

anderslatif/alg

d5902a05a4cb249e554f65a7e8016d7d050b6da9

[ "MIT" ]

null

objects/GitIndexEntry.py

anderslatif/alg

d5902a05a4cb249e554f65a7e8016d7d050b6da9

[ "MIT" ]

null

# https://github.com/git/git/blob/master/Documentation/technical/index-format.txt

22.302326

85

0.657977

7cba9b9fb8b398f82ae1a8d924fec2ad7e1b9ddf

2,437

py

Python

matdgl/layers/partitionpaddinglayer.py

huzongxiang/CrystalNetwork

a434f76fa4347d42b3c905852ce265cd0bcefca3

[ "BSD-2-Clause" ]

6

2022-03-30T13:47:03.000Z

2022-03-31T09:27:46.000Z

matdgl/layers/partitionpaddinglayer.py

huzongxiang/CrystalNetwork

a434f76fa4347d42b3c905852ce265cd0bcefca3

[ "BSD-2-Clause" ]

null

matdgl/layers/partitionpaddinglayer.py

huzongxiang/CrystalNetwork

a434f76fa4347d42b3c905852ce265cd0bcefca3

[ "BSD-2-Clause" ]

2

2022-03-30T20:53:11.000Z

2022-03-31T22:20:05.000Z

# -*- coding: utf-8 -*- """ Created on Wed Oct 13 14:47:13 2021 @author: huzongxiang """ import tensorflow as tf from tensorflow.keras import layers

27.077778

81

0.585556

7cbab3e957076a86de0198f2fb2ae52e8d52e634

173

py

Python

lino_book/projects/min9/settings/memory.py

khchine5/book

b6272d33d49d12335d25cf0a2660f7996680b1d1

[ "BSD-2-Clause" ]

1

2018-01-12T14:09:58.000Z

lino_book/projects/min9/settings/memory.py

khchine5/book

b6272d33d49d12335d25cf0a2660f7996680b1d1

[ "BSD-2-Clause" ]

4

2018-02-06T19:53:10.000Z

2019-08-01T21:47:44.000Z

lino_book/projects/min9/settings/memory.py

khchine5/book

b6272d33d49d12335d25cf0a2660f7996680b1d1

[ "BSD-2-Clause" ]

null

from .demo import * SITE.verbose_name = SITE.verbose_name + " (:memory:)" # SITE = Site(globals(), title=Site.title+" (:memory:)") DATABASES['default']['NAME'] = ':memory:'

34.6

56

0.653179

7cbb1dd15de4d3e88890e2caf26d07b7deb3f4b0

152

py

Python

reservation/urls.py

aryamanak10/diner-restaurant-website

6d2d9de89a73c5535ebf782c4d8bbfc6ca9489fc

[ "MIT" ]

1

2020-05-07T17:18:36.000Z

reservation/urls.py

aryamanak10/Restaurant-Site-using-Django

6d2d9de89a73c5535ebf782c4d8bbfc6ca9489fc

[ "MIT" ]

null

reservation/urls.py

aryamanak10/Restaurant-Site-using-Django

6d2d9de89a73c5535ebf782c4d8bbfc6ca9489fc

[ "MIT" ]

null

from django.urls import path from . import views app_name = 'reservation' urlpatterns = [ path('', views.reserve_table, name = 'reserve_table'), ]

19

58

0.710526

7cbb90e215684507ec88ead7205a67d14728eaf9

809

py

Python

chainer/_version.py

yumetov/chainer

522e017a18008ee00e39f4ae4b30f4f9db3824b2

[ "MIT" ]

3,705

2017-06-01T07:36:12.000Z

2022-03-30T10:46:15.000Z

chainer/_version.py

yumetov/chainer

522e017a18008ee00e39f4ae4b30f4f9db3824b2

[ "MIT" ]

5,998

2017-06-01T06:40:17.000Z

2022-03-08T01:42:44.000Z

chainer/_version.py

yumetov/chainer

522e017a18008ee00e39f4ae4b30f4f9db3824b2

[ "MIT" ]

1,150

2017-06-02T03:39:46.000Z

2022-03-29T02:29:32.000Z

__version__ = '7.8.0' _optional_dependencies = [ { 'name': 'CuPy', 'packages': [ 'cupy-cuda120', 'cupy-cuda114', 'cupy-cuda113', 'cupy-cuda112', 'cupy-cuda111', 'cupy-cuda110', 'cupy-cuda102', 'cupy-cuda101', 'cupy-cuda100', 'cupy-cuda92', 'cupy-cuda91', 'cupy-cuda90', 'cupy-cuda80', 'cupy', ], 'specifier': '>=7.7.0,<8.0.0', 'help': 'https://docs.cupy.dev/en/latest/install.html', }, { 'name': 'iDeep', 'packages': [ 'ideep4py', ], 'specifier': '>=2.0.0.post3, <2.1', 'help': 'https://docs.chainer.org/en/latest/tips.html', }, ]

23.114286

63

0.410383

7cbc5cd567a3861d37ece4294dbac699b11bc6a2

10,435

py

Python

image_aug.py

qwerasdf887/image_augmentation

7d465eba4d6af5d9a4cd79bf1981c8ef206ffe42

[ "MIT" ]

null

image_aug.py

qwerasdf887/image_augmentation

7d465eba4d6af5d9a4cd79bf1981c8ef206ffe42

[ "MIT" ]

null

image_aug.py

qwerasdf887/image_augmentation

7d465eba4d6af5d9a4cd79bf1981c8ef206ffe42

[ "MIT" ]

null

# coding=UTF-8 # This Python file uses the following encoding: utf-8 import cv2 import numpy as np import xml.etree.cElementTree as ET from random import sample #default args: default_args = {'noise_prob': 0.1, 'gasuss_mean': 0, 'gasuss_var': 0.001, 'rand_hug': 30, 'rand_saturation':30, 'rand_light': 30, 'rot_angle': 15, 'bordervalue': (127, 127, 127), 'zoom_out_value': 0.7, 'output_shape': (416, 416), 'take_value' : 5 } #noise #noise #(-N~N) #(-N~N) #(-N~N) # def horizontal_flip(image, box_loc=None, **kwargs): ''' Args: box_loc: bounding box location(x_min, y_min, x_max, y_max) ''' if box_loc is None: return cv2.flip(image, 1) else: w = image.shape[1] for i in box_loc: if i[2] == 0: break else: x_min, x_max = i[0], i[2] i[0] = w - x_max i[2] = w - x_min return cv2.flip(image, 1), box_loc # def vertical_flip(image, box_loc=None, **kwargs): ''' Args: box_loc: bounding box location(num box,(x_min, y_min, x_max, y_max, label)) ''' if box_loc is None: return cv2.flip(image, 0) else: h = image.shape[0] for i in box_loc: if i[3] == 0: break else: y_min, y_max = i[1], i[3] i[1] = h - y_max i[3] = h - y_min return cv2.flip(image, 0), box_loc #-n~n def rot_image(image, box_loc=None, **kwargs): ''' Args: box_loc: bounding box location(num box,(x_min, y_min, x_max, y_max, label)) rot: bordervalue: ''' h, w, _ = image.shape center = ( w // 2, h // 2) angle = np.random.randint(-kwargs['rot_angle'], kwargs['rot_angle']) M = cv2.getRotationMatrix2D(center, angle, 1) out_img = cv2.warpAffine(image, M, (w, h), borderValue = kwargs['bordervalue']) if box_loc is None: return out_img else: loc = box_loc[:,0:4].copy() loc = np.append(loc, loc[:, 0:1], axis=-1) loc = np.append(loc, loc[:, 3:4], axis=-1) loc = np.append(loc, loc[:, 2:3], axis=-1) loc = np.append(loc, loc[:, 1:2], axis=-1) loc = loc.reshape(-1, 4, 2) loc = loc - np.array(center) rot_loc = loc.dot(np.transpose(M[:,0:2])) rot_loc = rot_loc + np.array(center) rot_box = np.hstack([np.min(rot_loc, axis=-2), np.max(rot_loc, axis=-2), box_loc[:, 4:5]]) rot_box = np.floor(rot_box) rot_box[...,0:4] = np.clip(rot_box[...,0:4], [0,0,0,0], [w-1, h-1, w-1, h-1]) return out_img, rot_box # # # value~1 #load csv data #draw rectangle #0~N image augmentation if __name__ == "__main__": img = cv2.imread('./00002.jpg') bbox = load_csv('./00002.xml') #noise #aug_img = sp_noise(img, **default_args) #aug_img, bbox = sp_noise(img, bbox, **default_args) #gasuss_noise #aug_img = gasuss_noise(img, **default_args) #aug_img, bbox = gasuss_noise(img, bbox, **default_args) #Hue #aug_img = mod_hue(img, **default_args) #aug_img, bbox = mod_hue(img, bbox, **default_args) #saturation #aug_img = mod_saturation(img, **default_args) #aug_img, bbox = mod_saturation(img, bbox, **default_args) #light #aug_img = mod_light(img, **default_args) #aug_img, bbox = mod_light(img, bbox, **default_args) # #aug_img = horizontal_flip(img, **default_args) #aug_img, bbox = horizontal_flip(img, bbox, **default_args) # #aug_img = vertical_flip(img, **default_args) #aug_img, bbox = vertical_flip(img, bbox, **default_args) # #aug_img = rot_image(img, **default_args) #aug_img, bbox = rot_image(img, bbox, **default_args) #resize #aug_img = resize_img(img, **default_args) #aug_img, bbox = resize_img(img, bbox, **default_args) # #aug_img = padding_img(aug_img, **default_args) #aug_img, bbox = padding_img(aug_img, bbox, **default_args) # N~1 #aug_img = random_zoom_out(img, **default_args) #aug_img, bbox = random_zoom_out(img, bbox, **default_args) #augmentation aug_img = rand_aug_image(img, **default_args) #aug_img, bbox = rand_aug_image(img, bbox, **default_args) print(bbox) draw_rect(aug_img, bbox) cv2.imshow('img', img) cv2.imshow('aug img', aug_img) cv2.waitKey(0) cv2.destroyAllWindows()

31.430723

117

0.58965

7cbd6ca4479663e9722341b796b7cdd0073b6b18

1,507

py

Python

03_picnic/picnic.py

intimanipuchi/tiny_python_projects

5e419620ae07b0bcf8df073ba3f6c6c3d7d1a93c

[ "MIT" ]

null

03_picnic/picnic.py

intimanipuchi/tiny_python_projects

5e419620ae07b0bcf8df073ba3f6c6c3d7d1a93c

[ "MIT" ]

null

03_picnic/picnic.py

intimanipuchi/tiny_python_projects

5e419620ae07b0bcf8df073ba3f6c6c3d7d1a93c

[ "MIT" ]

null

#!/usr/bin/env python3 """ Author : Roman Koziy <[email protected]> Date : 2021-12-15 Purpose: Working with lists """ import argparse # -------------------------------------------------- def get_args(): """Get command-line arguments""" parser = argparse.ArgumentParser( description="Working with lists", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument("items", type=str, nargs="+", metavar="str", help="item(s) to bring") parser.add_argument("-s", "--sorted", help="a boolean flag", action="store_true") return parser.parse_args() # -------------------------------------------------- def main(): """The main function: formatting and printing the output""" args = get_args() sort_flag = args.sorted items = args.items if sort_flag: items = sorted(items) if len(items) == 1: print(f"You are bringing {items[0]}.") elif len(items) < 3: items.insert(-1, "and") print(f"You are bringing {' '.join(items)}.") else: # print(items) last = items[-1] and_last = "and " + last items[-1] = and_last # print(items) print(f"You are bringing {', '.join(items)}.") # -------------------------------------------------- if __name__ == "__main__": main()

25.116667

63

0.472462

7cbd753ba4fba3e640a8395f823b9af2ba40868f

400

py

Python

triangle.py

montyshyama/python-basics

d71156d70fdadc722a192b984e9bff66401ab894

[ "MIT" ]

null

triangle.py

montyshyama/python-basics

d71156d70fdadc722a192b984e9bff66401ab894

[ "MIT" ]

null

triangle.py

montyshyama/python-basics

d71156d70fdadc722a192b984e9bff66401ab894

[ "MIT" ]

1

2020-04-05T20:06:08.000Z

side_a=int(input("Enter the first side(a):")) side_b=int(input("Enter the second side(b):")) side_c=int(input("Enter the third side(c):")) if side_a==side_b and side_a==side_c: print("The triangle is an equilateral triangle.") elif side_a==side_b or side_a==side_c or side_b==side_c: print("The triangle is an isosceles triangle.") else: print("The triangle is scalene triangle.")

44.444444

57

0.7075

7cbd766d520e1888b731cf3cea3bb5f44d830c1f

520

py

Python

david/modules/artist/view.py

ktmud/david

4b8d6f804b73cdfa1a8ddf784077fa9a39f1e36f

[ "MIT" ]

2

2016-04-07T08:21:32.000Z

2020-11-26T11:49:20.000Z

david/modules/artist/view.py

ktmud/david

4b8d6f804b73cdfa1a8ddf784077fa9a39f1e36f

[ "MIT" ]

null

david/modules/artist/view.py

ktmud/david

4b8d6f804b73cdfa1a8ddf784077fa9a39f1e36f

[ "MIT" ]

null

# -*- coding: utf-8 -*- from flask import Blueprint, request from david.lib.template import st from .model import Artist bp = Blueprint('artist', __name__)

23.636364

57

0.688462

7cbdd46842ad893e844a14b8fc15ffc18db30ecc

2,832

py

Python

Volume Estimation/volume.py

JessieRamaux/Food-Volume-Estimation

260b0e78a3b6a7b8bbe9daf98956502beea92552

[ "MIT" ]

10

2021-02-19T09:31:43.000Z

2022-02-09T08:29:02.000Z

Volume Estimation/volume.py

JessieRamaux/Food-Volume-Estimation

260b0e78a3b6a7b8bbe9daf98956502beea92552

[ "MIT" ]

null

Volume Estimation/volume.py

JessieRamaux/Food-Volume-Estimation

260b0e78a3b6a7b8bbe9daf98956502beea92552

[ "MIT" ]

3

2021-02-16T00:05:32.000Z

2021-06-11T13:37:10.000Z

import numpy as np import cv2 import os import json import glob from PIL import Image, ImageDraw plate_diameter = 25 #cm plate_depth = 1.5 #cm plate_thickness = 0.2 #cm img = cv2.imread("out.png",0) print(get_volume(img,"test.json"))

28.039604

116

0.604167

7cbe3198f6071ec0d541441f81f18f624a937b6f

5,044

py

Python

t2k/bin/cmttags.py

tianluyuan/pyutils

2cd3a90dbbd3d0eec3054fb9493ca0f6e0272e50

[ "MIT" ]

1

2019-02-22T10:57:13.000Z

t2k/bin/cmttags.py

tianluyuan/pyutils

2cd3a90dbbd3d0eec3054fb9493ca0f6e0272e50

[ "MIT" ]

null

t2k/bin/cmttags.py

tianluyuan/pyutils

2cd3a90dbbd3d0eec3054fb9493ca0f6e0272e50

[ "MIT" ]

null

#!/usr/bin/env python """ A script to create tags for CMT managed packages. Call from within cmt/ directory """ import subprocess import sys import os from optparse import OptionParser __author__ = 'Tianlu Yuan' __email__ = 'tianlu.yuan [at] colorado.edu' # Ignore large external packages for now IGNORES = ['CMT', 'EXTERN', 'GSL', 'MYSQL', 'GEANT', 'CLHEP'] # Extensions for finding src files, must satisfy unix wildcard rules EXTENSIONS = {'cpp': ('*.[hc]', '*.[hc]xx', '*.[hc]pp', '*.cc', '*.hh'), 'python':('*.py'), 'java':('*.java')} # Ignore these files and dirs, key specifies argument to find # (e.g. '-iname') PRUNE = {'iname':['*_Dict.[hc]*', '*linkdef.h']} def check_dir(): """ Are we inside cmt/ """ if os.path.basename(os.getcwd()) != 'cmt': sys.exit('Not inside cmt directory!') def check_requirements(): """ Ensure that requirements file exists in cmt dir """ if not os.path.isfile('requirements'): sys.exit('No requirements file!') def init_use_dict(): """Returns the initial use_dict which contains the current (cwd) package and its path. 'cmt show uses' does not include the package itself. """ # Must call os.path.dirname because the cwd should be inside a cmt # directory return {'this':os.path.dirname(os.getcwd())} def parse_uses(): """ Returns a dict of used packages and their root dir paths. e.g. {ROOT:/path/to/cmt/installed/ROOT/vXrY} """ check_dir() check_requirements() proc = subprocess.Popen(['cmt', 'show', 'uses'], stdout=subprocess.PIPE) use_dict = init_use_dict() for line in iter(proc.stdout.readline, ''): tokens = line.split() # ignore lines that start with '#' if line[0] != '#' and tokens[1] not in IGNORES: basepath = tokens[-1].strip('()') # highland and psyche do not strictly follow CMT path # organization. They have subpackages within a master, so # we need to take that into account relpath_list = [master for master in tokens[3:-1]] relpath_list.extend([tokens[1], tokens[2]]) use_dict[tokens[1]] = os.path.join(basepath, *relpath_list) return use_dict def build_find_args(exts): """ ext is a list of file extensions corresponding to the files we want to search. This will return a list of arguments that can be passed to `find` """ find_args = [] for a_ext in exts: # -o for "or" find_args.extend(['-o', '-iname']) find_args.append('{0}'.format(a_ext)) # replace first '-o' with '( for grouping matches find_args[0] = '(' # append parens for grouping negation find_args.extend([')', '(']) # Add prune files for match_type in PRUNE: for aprune in PRUNE[match_type]: find_args.append('-not') find_args.append('-'+match_type) find_args.append('{0}'.format(aprune)) find_args.append(')') return find_args def build_find_cmd(opts, paths): """ Builds teh cmd file using ctags. Returns cmd based on the following template: 'find {0} -type f {1} | etags -' """ find_args = build_find_args(get_exts(opts)) return ['find']+paths+['-type', 'f']+find_args def main(): """ Uses ctags to generate TAGS file in cmt directory based on cmt show uses """ parser = OptionParser() parser.add_option('--cpp', dest='cpp', action='store_true', default=False, help='tag only c/cpp files (default)') parser.add_option('--python', dest='python', action='store_true', default=False, help='tag only python files') parser.add_option('--java', dest='java', action='store_true', default=False, help='tag only java files') parser.add_option('-n', dest='dry_run', action='store_true', default=False, help='dry run') (opts, args) = parser.parse_args() # get the cmt show uses dictionary of programs and paths use_dict = parse_uses() # build the commands find_cmd = build_find_cmd(opts, list(use_dict.itervalues())) tags_cmd = build_tags_cmd() print 'Creating TAGS file based on dependencies:' print use_dict if not opts.dry_run: find_proc = subprocess.Popen(find_cmd, stdout=subprocess.PIPE) tags_proc = subprocess.Popen(tags_cmd, stdin=find_proc.stdout) tags_proc.communicate() if __name__ == '__main__': main()

28.822857

80

0.585052

7cbea5a7d278dcb466c16a1d3e035b7e14f3c77c

63,630

py

Python

salt/daemons/masterapi.py

rickh563/salt

02822d6466c47d0daafd6e98b4e767a396b0ed48

[ "Apache-2.0" ]

null

salt/daemons/masterapi.py

rickh563/salt

02822d6466c47d0daafd6e98b4e767a396b0ed48

[ "Apache-2.0" ]

null

salt/daemons/masterapi.py

rickh563/salt

02822d6466c47d0daafd6e98b4e767a396b0ed48

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' This module contains all of the routines needed to set up a master server, this involves preparing the three listeners and the workers needed by the master. ''' from __future__ import absolute_import # Import python libs import fnmatch import logging import os import re import time import stat import tempfile # Import salt libs import salt.crypt import salt.utils import salt.client import salt.payload import salt.pillar import salt.state import salt.runner import salt.auth import salt.wheel import salt.minion import salt.search import salt.key import salt.fileserver import salt.utils.atomicfile import salt.utils.event import salt.utils.verify import salt.utils.minions import salt.utils.gzip_util import salt.utils.jid from salt.pillar import git_pillar from salt.utils.event import tagify from salt.exceptions import SaltMasterError # Import 3rd-party libs import salt.ext.six as six try: import pwd HAS_PWD = True except ImportError: # pwd is not available on windows HAS_PWD = False log = logging.getLogger(__name__) # Things to do in lower layers: # only accept valid minion ids def init_git_pillar(opts): ''' Clear out the ext pillar caches, used when the master starts ''' pillargitfs = [] for opts_dict in [x for x in opts.get('ext_pillar', [])]: if 'git' in opts_dict: try: import git except ImportError: return pillargitfs parts = opts_dict['git'].strip().split() try: br = parts[0] loc = parts[1] except IndexError: log.critical( 'Unable to extract external pillar data: {0}' .format(opts_dict['git']) ) else: pillargitfs.append( git_pillar.GitPillar( br, loc, opts ) ) return pillargitfs def clean_fsbackend(opts): ''' Clean out the old fileserver backends ''' # Clear remote fileserver backend caches so they get recreated for backend in ('git', 'hg', 'svn'): if backend in opts['fileserver_backend']: env_cache = os.path.join( opts['cachedir'], '{0}fs'.format(backend), 'envs.p' ) if os.path.isfile(env_cache): log.debug('Clearing {0}fs env cache'.format(backend)) try: os.remove(env_cache) except OSError as exc: log.critical( 'Unable to clear env cache file {0}: {1}' .format(env_cache, exc) ) file_lists_dir = os.path.join( opts['cachedir'], 'file_lists', '{0}fs'.format(backend) ) try: file_lists_caches = os.listdir(file_lists_dir) except OSError: continue for file_lists_cache in fnmatch.filter(file_lists_caches, '*.p'): cache_file = os.path.join(file_lists_dir, file_lists_cache) try: os.remove(cache_file) except OSError as exc: log.critical( 'Unable to file_lists cache file {0}: {1}' .format(cache_file, exc) ) def clean_expired_tokens(opts): ''' Clean expired tokens from the master ''' serializer = salt.payload.Serial(opts) for (dirpath, dirnames, filenames) in os.walk(opts['token_dir']): for token in filenames: token_path = os.path.join(dirpath, token) with salt.utils.fopen(token_path) as token_file: token_data = serializer.loads(token_file.read()) if 'expire' not in token_data or token_data.get('expire', 0) < time.time(): try: os.remove(token_path) except (IOError, OSError): pass def clean_old_jobs(opts): ''' Clean out the old jobs from the job cache ''' # TODO: better way to not require creating the masterminion every time? mminion = salt.minion.MasterMinion( opts, states=False, rend=False, ) # If the master job cache has a clean_old_jobs, call it fstr = '{0}.clean_old_jobs'.format(opts['master_job_cache']) if fstr in mminion.returners: mminion.returners[fstr]() def access_keys(opts): ''' A key needs to be placed in the filesystem with permissions 0400 so clients are required to run as root. ''' users = [] keys = {} acl_users = set(opts['client_acl'].keys()) if opts.get('user'): acl_users.add(opts['user']) acl_users.add(salt.utils.get_user()) if HAS_PWD: for user in pwd.getpwall(): users.append(user.pw_name) for user in acl_users: log.info( 'Preparing the {0} key for local communication'.format( user ) ) if HAS_PWD: if user not in users: try: user = pwd.getpwnam(user).pw_name except KeyError: log.error('ACL user {0} is not available'.format(user)) continue keyfile = os.path.join( opts['cachedir'], '.{0}_key'.format(user) ) if os.path.exists(keyfile): log.debug('Removing stale keyfile: {0}'.format(keyfile)) os.unlink(keyfile) key = salt.crypt.Crypticle.generate_key_string() cumask = os.umask(191) with salt.utils.fopen(keyfile, 'w+') as fp_: fp_.write(key) os.umask(cumask) # 600 octal: Read and write access to the owner only. # Write access is necessary since on subsequent runs, if the file # exists, it needs to be written to again. Windows enforces this. os.chmod(keyfile, 0o600) if HAS_PWD: try: os.chown(keyfile, pwd.getpwnam(user).pw_uid, -1) except OSError: # The master is not being run as root and can therefore not # chown the key file pass keys[user] = key return keys def fileserver_update(fileserver): ''' Update the fileserver backends, requires that a built fileserver object be passed in ''' try: if not fileserver.servers: log.error( 'No fileservers loaded, the master will not be able to ' 'serve files to minions' ) raise SaltMasterError('No fileserver backends available') fileserver.update() except Exception as exc: log.error( 'Exception {0} occurred in file server update'.format(exc), exc_info_on_loglevel=logging.DEBUG )

39.253547

173

0.49604

7cbf3fcf677b8e93a5ef2be1bcf1c650636a93f5

2,003

py

Python

core/domain/role_services_test.py

Mohitbalwani26/oppia

a3d1de8b428b8216bb61ba70315583fe077f5b8a

[ "Apache-2.0" ]

null

core/domain/role_services_test.py

Mohitbalwani26/oppia

a3d1de8b428b8216bb61ba70315583fe077f5b8a

[ "Apache-2.0" ]

null

core/domain/role_services_test.py

Mohitbalwani26/oppia

a3d1de8b428b8216bb61ba70315583fe077f5b8a

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2017 The Oppia 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. """Test functions relating to roles and actions.""" from __future__ import absolute_import # pylint: disable=import-only-modules from __future__ import unicode_literals # pylint: disable=import-only-modules from core.domain import role_services from core.tests import test_utils import feconf import python_utils

39.27451

79

0.734898

7cbf5d867e83bab7776ed420c8f1d228f4f2244d

82,473

py

Python

deep_learning/keras/keras/backend/cntk_backend.py

xpennec/applications

50aefdf14de308fc3c132784ebba9d329e47b087

[ "MIT" ]

21

2019-01-12T17:59:41.000Z

2022-03-08T17:42:56.000Z

deep_learning/keras/keras/backend/cntk_backend.py

farrell236/applications

0e1ab139ade2a0b3ba6f04f6fd93822b1dd5ae2f

[ "MIT" ]

7

2019-01-24T11:44:58.000Z

2020-04-21T21:13:37.000Z

deep_learning/keras/keras/backend/cntk_backend.py

farrell236/applications

0e1ab139ade2a0b3ba6f04f6fd93822b1dd5ae2f

[ "MIT" ]

8

2019-01-24T11:36:05.000Z

2021-06-15T20:59:50.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import cntk as C import numpy as np from .common import floatx, epsilon, image_dim_ordering, image_data_format from collections import defaultdict from contextlib import contextmanager import warnings C.set_global_option('align_axis', 1) b_any = any dev = C.device.use_default_device() if dev.type() == 0: warnings.warn( 'CNTK backend warning: GPU is not detected. ' 'CNTK\'s CPU version is not fully optimized,' 'please run with GPU to get better performance.') # A learning phase is a bool tensor used to run Keras models in # either train mode (learning_phase == 1) or test mode (learning_phase == 0). # LEARNING_PHASE_PLACEHOLDER is the placeholder for dynamic learning phase _LEARNING_PHASE_PLACEHOLDER = C.constant(shape=(), dtype=np.float32, value=1.0, name='_keras_learning_phase') # static learning phase flag, if it is not 0 or 1, we will go with dynamic learning phase tensor. _LEARNING_PHASE = -1 _UID_PREFIXES = defaultdict(int) # cntk doesn't support gradient as symbolic op, to hook up with keras model, # we will create gradient as a constant placeholder, here use this global # map to keep the mapping from grad placeholder to parameter grad_parameter_dict = {} NAME_SCOPE_STACK = [] def clear_session(): """Reset learning phase flag for cntk backend. """ global _LEARNING_PHASE global _LEARNING_PHASE_PLACEHOLDER _LEARNING_PHASE = -1 _LEARNING_PHASE_PLACEHOLDER.value = np.asarray(1.0) def variable(value, dtype=None, name=None, constraint=None): """Instantiates a variable and returns it. # Arguments value: Numpy array, initial value of the tensor. dtype: Tensor type. name: Optional name string for the tensor. constraint: Optional projection function to be applied to the variable after an optimizer update. # Returns A variable instance (with Keras metadata included). """ if dtype is None: dtype = floatx() if name is None: name = '' if isinstance( value, C.variables.Constant) or isinstance( value, C.variables.Parameter): value = value.value # we don't support init parameter with symbolic op, so eval it first as # workaround if isinstance(value, C.cntk_py.Function): value = eval(value) shape = value.shape if hasattr(value, 'shape') else () if hasattr(value, 'dtype') and value.dtype != dtype and len(shape) > 0: value = value.astype(dtype) # TODO: remove the conversion when cntk supports int32, int64 # https://docs.microsoft.com/en-us/python/api/cntk.variables.parameter dtype = 'float32' if 'int' in str(dtype) else dtype v = C.parameter(shape=shape, init=value, dtype=dtype, name=_prepare_name(name, 'variable')) v._keras_shape = v.shape v._uses_learning_phase = False v.constraint = constraint return v def is_placeholder(x): """Returns whether `x` is a placeholder. # Arguments x: A candidate placeholder. # Returns Boolean. """ return hasattr(x, '_cntk_placeholder') and x._cntk_placeholder

32.153216

109

0.584361

7cbfb5620b9999ebfec8396e7e566e9eef183412

6,946

py

Python

Project Files/Prebuilt tools/twitter/Twitter/pylib/oauthlib/oauth1/rfc5849/endpoints/resource.py

nVoid/Yale-TouchDesigner-April2016

40eb36f515fa3935f3e9ddaa923664e88308262c

[ "MIT" ]

39

2015-06-10T23:18:07.000Z

2021-10-21T04:29:06.000Z

Project Files/Prebuilt tools/twitter/Twitter/pylib/oauthlib/oauth1/rfc5849/endpoints/resource.py

nVoid/Yale-TouchDesigner-April2016

40eb36f515fa3935f3e9ddaa923664e88308262c

[ "MIT" ]

13

2020-10-28T16:02:09.000Z

2020-11-16T13:30:05.000Z

Project Files/Prebuilt tools/twitter/Twitter/pylib/oauthlib/oauth1/rfc5849/endpoints/resource.py

nVoid/Yale-TouchDesigner-April2016

40eb36f515fa3935f3e9ddaa923664e88308262c

[ "MIT" ]

26

2015-06-10T22:09:15.000Z

2021-06-27T15:45:15.000Z

# -*- coding: utf-8 -*- """ oauthlib.oauth1.rfc5849.endpoints.resource ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This module is an implementation of the resource protection provider logic of OAuth 1.0 RFC 5849. """ from __future__ import absolute_import, unicode_literals from oauthlib.common import log from .base import BaseEndpoint from .. import errors

44.525641

84

0.6444

7cbffc6e03738d28e2329b530ca6fb3c25fe1127

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py

Python

python/ex_1.py

AymenSe/Geometric-operations-DIP

ef0b0bc86210a8da5e63136bf5a239179b869722

[ "MIT" ]

null

python/ex_1.py

AymenSe/Geometric-operations-DIP

ef0b0bc86210a8da5e63136bf5a239179b869722

[ "MIT" ]

null

python/ex_1.py

AymenSe/Geometric-operations-DIP

ef0b0bc86210a8da5e63136bf5a239179b869722

[ "MIT" ]

null

#################################################### # # @ Authors : SEKHRI Aymen # MOHAMMED HACENE Tarek # # @ Hint: you have to install all requirements # from requirements.txt # #################################################### import numpy as np import cv2 as cv import matplotlib.pyplot as plt # load the image onion_img = cv.imread("onion.png") # Store height and width and channels of the image row, col, chs = onion_img.shape # Store the spectral resolution dtype_img = onion_img.dtype # This will give you: uint8 def translation(img, trans): """ args: - img: absolute path to the image - trans: must be a tuple (row_trans, col_trans) """ # read the image image = cv.imread(img) # retrieve the height and the width height, width = image.shape[:2] # retrieve the params of translation row_trans, col_trans = trans # Create the translation matrix T = np.float32([[1, 0, col_trans], [0, 1, row_trans]]) # Apply the T matrix: T*M img_translation = cv.warpAffine(image, T, (width, height)) # show the images cv.imshow("Original Image", image) cv.imshow('Translation Image', img_translation) # Don't destroy the images until the user do cv.waitKey() cv.destroyAllWindows() # translation 20 pixel to the right translation("onion.png", (0, 20)) # translation 50 lines and 100 cols to the right translation("onion.png", (50, 100)) # remove the peper from the image using translations translation("onion.png", (40, 40))

24.883333

59

0.649029

7cc0266db2f787f19a55358bfe261dafe0201d9d

3,999

py

Python

utils/hit_rate_utils.py

h-zcc/ref-nms

8f83f350c497d0ef875c778a8ce76725552abb3c

[ "MIT" ]

19

2020-12-14T13:53:10.000Z

2022-02-27T09:46:15.000Z

utils/hit_rate_utils.py

h-zcc/ref-nms

8f83f350c497d0ef875c778a8ce76725552abb3c

[ "MIT" ]

3

2021-01-16T11:41:07.000Z

2021-08-06T08:21:42.000Z

utils/hit_rate_utils.py

h-zcc/ref-nms

8f83f350c497d0ef875c778a8ce76725552abb3c

[ "MIT" ]

3

2021-01-10T15:25:29.000Z

2021-09-26T01:38:16.000Z

from utils.misc import calculate_iou, xywh_to_xyxy __all__ = ['NewHitRateEvaluator', 'CtxHitRateEvaluator']

36.688073

105

0.565141

7cc16e64c487a1ae6266b04c47e0496bada66d00

905

py

Python

LeetCode_ReorderDataLogFiles.py

amukher3/Problem_solutions

8fa6014a91f295d08cafb989024caa91d99211d9

[ "Apache-2.0" ]

1

2021-12-28T08:58:51.000Z

LeetCode_ReorderDataLogFiles.py

amukher3/Coding

a330cb04b5dd5cc1c3cf69249417a71586441bc7

[ "Apache-2.0" ]

null

LeetCode_ReorderDataLogFiles.py

amukher3/Coding

a330cb04b5dd5cc1c3cf69249417a71586441bc7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Aug 22 19:07:30 2020 @author: Abhishek Mukherjee """

27.424242

76

0.438674

7cc17f1b77efcc568026cf1d93c6a6ded983ab6a

475

py

Python

saleor/core/transactions.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

15,337

2015-01-12T02:11:52.000Z

2021-10-05T19:19:29.000Z

saleor/core/transactions.py

fairhopeweb/saleor

9ac6c22652d46ba65a5b894da5f1ba5bec48c019

[ "CC-BY-4.0" ]

7,486

2015-02-11T10:52:13.000Z

2021-10-06T09:37:15.000Z

saleor/core/transactions.py

aminziadna/saleor

2e78fb5bcf8b83a6278af02551a104cfa555a1fb

[ "CC-BY-4.0" ]

5,864

2015-01-16T14:52:54.000Z

2021-10-05T23:01:15.000Z

from contextlib import contextmanager from django.db import DatabaseError from ..core.tracing import traced_atomic_transaction

22.619048

65

0.669474

7cc1ccd2747eb46713eaccaf9ca6dc49d25b3128

3,314

py

Python

src/command_modules/azure-cli-policyinsights/azure/cli/command_modules/policyinsights/tests/latest/test_policyinsights_scenario.py

diberry/azure-cli

302999245cbb13b890b0a74f03443c577bd4bfae

[ "MIT" ]

1

2019-03-30T20:49:32.000Z

src/command_modules/azure-cli-policyinsights/azure/cli/command_modules/policyinsights/tests/latest/test_policyinsights_scenario.py

diberry/azure-cli

302999245cbb13b890b0a74f03443c577bd4bfae

[ "MIT" ]

4

2018-08-08T20:01:17.000Z

2018-09-17T15:20:06.000Z

src/command_modules/azure-cli-policyinsights/azure/cli/command_modules/policyinsights/tests/latest/test_policyinsights_scenario.py

diberry/azure-cli

302999245cbb13b890b0a74f03443c577bd4bfae

[ "MIT" ]

1

2018-04-14T01:46:00.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from azure.cli.testsdk import ScenarioTest, record_only

48.028986

194

0.545866

7cc20f1f6a53dbfc79dbca785199d6d05868daf1

25,440

py

Python

tests/prep_post/test.py

Aslic/rmats_turbo_4.1.0

c651509a5d32799315054fa37a2210fab2aae5e5

[ "BSD-2-Clause-FreeBSD" ]

null

tests/prep_post/test.py

Aslic/rmats_turbo_4.1.0

c651509a5d32799315054fa37a2210fab2aae5e5

[ "BSD-2-Clause-FreeBSD" ]

null

tests/prep_post/test.py

Aslic/rmats_turbo_4.1.0

c651509a5d32799315054fa37a2210fab2aae5e5

[ "BSD-2-Clause-FreeBSD" ]

null

import glob import os.path import subprocess import sys import unittest import tests.bam import tests.base_test import tests.gtf import tests.output_parser as output_parser import tests.test_config import tests.util if __name__ == '__main__': unittest.main(verbosity=2)

39.75

83

0.553263

7cc57c915f6cace046e0bbe739957206038f009f

1,527

py

Python

nltk/align/util.py

kruskod/nltk

dba7b5431b1d57a75d50e048961c1a203b98c3da

[ "Apache-2.0" ]

1

2015-11-25T00:47:58.000Z

nltk/align/util.py

kruskod/nltk

dba7b5431b1d57a75d50e048961c1a203b98c3da

[ "Apache-2.0" ]

null

nltk/align/util.py

kruskod/nltk

dba7b5431b1d57a75d50e048961c1a203b98c3da

[ "Apache-2.0" ]

null

# Natural Language Toolkit: Aligner Utilities # # Copyright (C) 2001-2015 NLTK Project # Author: Anna Garbar # URL: <http://www.nltk.org/> # For license information, see LICENSE.TXT from nltk.align.api import Alignment def pharaohtext2tuples(pharaoh_text): """ Converts pharaoh text format into an Alignment object (a list of tuples). >>> pharaoh_text = '0-0 2-1 9-2 21-3 10-4 7-5' >>> pharaohtext2tuples(pharaoh_text) Alignment([(0, 0), (2, 1), (7, 5), (9, 2), (10, 4), (21, 3)]) :type pharaoh_text: str :param pharaoh_text: the word alignment outputs in the pharaoh output format :rtype: Alignment :return: An Alignment object that contains a list of integer tuples """ # Converts integers to strings for a word alignment point. list_of_tuples = [tuple(map(int,a.split('-'))) for a in pharaoh_text.split()] return Alignment(list_of_tuples) def alignment2pharaohtext(alignment): """ Converts an Alignment object (a list of tuples) into pharaoh text format. >>> alignment = [(0, 0), (2, 1), (9, 2), (21, 3), (10, 4), (7, 5)] >>> alignment2pharaohtext(alignment) '0-0 2-1 9-2 21-3 10-4 7-5' :type alignment: Alignment :param alignment: An Alignment object that contains a list of integer tuples :rtype: str :return: the word alignment outputs in the pharaoh output format """ pharaoh_text = ' '.join(str(i) + "-" + str(j) for i,j in alignment) return pharaoh_text

33.933333

81

0.642436

7cc70ea72109b3602fc21ef2eb53e2e3c1469770

1,461

py

Python

grr/server/grr_response_server/databases/db_yara_test_lib.py

khanhgithead/grr

8ad8a4d2c5a93c92729206b7771af19d92d4f915

[ "Apache-2.0" ]

4,238

2015-01-01T15:34:50.000Z

2022-03-31T08:18:05.000Z

grr/server/grr_response_server/databases/db_yara_test_lib.py

khanhgithead/grr

8ad8a4d2c5a93c92729206b7771af19d92d4f915

[ "Apache-2.0" ]

787

2015-01-02T21:34:24.000Z

2022-03-02T13:26:38.000Z

grr/server/grr_response_server/databases/db_yara_test_lib.py

khanhgithead/grr

8ad8a4d2c5a93c92729206b7771af19d92d4f915

[ "Apache-2.0" ]

856

2015-01-02T02:50:11.000Z

2022-03-31T11:11:53.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- """A module with test cases for the YARA database method.""" import os from grr_response_server.databases import db from grr_response_server.rdfvalues import objects as rdf_objects

33.976744

75

0.776181

7cc7a37d4874c578241d8fb555c025d8c962058b

4,912

py

Python

gpytorch/kernels/inducing_point_kernel.py

4aHxKzD/gpytorch

7193545f88820ea04588b983f1d7ed603a59a27c

[ "MIT" ]

1

2021-03-05T07:20:58.000Z

gpytorch/kernels/inducing_point_kernel.py

4aHxKzD/gpytorch

7193545f88820ea04588b983f1d7ed603a59a27c

[ "MIT" ]

1

2021-02-24T14:01:43.000Z

gpytorch/kernels/inducing_point_kernel.py

syncrostone/gpytorch

4d33fbf64594aab2dd6e0cfcb3242510231b3e0e

[ "MIT" ]

1

2021-03-15T12:32:24.000Z

#!/usr/bin/env python3 import copy import math import torch from ..distributions import MultivariateNormal from ..lazy import DiagLazyTensor, LowRankRootAddedDiagLazyTensor, LowRankRootLazyTensor, MatmulLazyTensor, delazify from ..mlls import InducingPointKernelAddedLossTerm from ..models import exact_prediction_strategies from ..utils.cholesky import psd_safe_cholesky from .kernel import Kernel

36.656716

116

0.659202

7cc9967d7946d0cff670ce2e551feabb3ef304ce

891

py

Python

app/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

app/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

app/__init__.py

Jotasenpai/DigitalMediaStoreRESTfull

bb776d398e1756b1ff2fd4f392b80479ae29847d

[ "MIT" ]

null

import logging import os from flask import Flask from flask_cors import CORS from app.extensions import api from app.extensions.database import db from app.extensions.schema import ma from app.views import albums, artists, hello, tracks

21.214286

54

0.679012

7cc9e6223af3f0ca91fd050679827da65d115102

18,053

py

Python

app.py

SASHA-PAIS/A-Flask-web-app-for-inventory-management

e6ed1b0d1d06ba04f9930f7653ce0504ecf81dd3

[ "MIT" ]

null

app.py

SASHA-PAIS/A-Flask-web-app-for-inventory-management

e6ed1b0d1d06ba04f9930f7653ce0504ecf81dd3

[ "MIT" ]

null

app.py

SASHA-PAIS/A-Flask-web-app-for-inventory-management

e6ed1b0d1d06ba04f9930f7653ce0504ecf81dd3

[ "MIT" ]

null

from flask import Flask, url_for, request, redirect from flask import render_template as render from flask_mysqldb import MySQL import yaml import json import MySQLdb import decimal # Setting up the flask instance app = Flask(__name__) # Configure the database db = yaml.load(open('db.yaml')) app.config['MYSQL_HOST'] = db['mysql_host'] app.config['MYSQL_USER'] = db['mysql_user'] app.config['MYSQL_PASSWORD'] = db['mysql_password'] app.config['MYSQL_DB'] = db['mysql_db'] mysql = MySQL(app) link = {x:x for x in ["location", "product", "movement"]} link["index"] = '/' if __name__ == '__main__': app.run(debug=True)

37.146091

218

0.585831

7ccbe673fd6019f10368e191ac41278443f5c053

9,554

py

Python

python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_mean_op.py

zmxdream/Paddle

04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c

[ "Apache-2.0" ]

8

2016-08-15T07:02:27.000Z

2016-08-24T09:34:00.000Z

python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_mean_op.py

zmxdream/Paddle

04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c

[ "Apache-2.0" ]

1

2021-11-01T06:28:16.000Z

python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_mean_op.py

zmxdream/Paddle

04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c

[ "Apache-2.0" ]

5

2021-12-10T11:20:06.000Z

2022-02-18T05:18:12.000Z

# Copyright (c) 2021 PaddlePaddle 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. from __future__ import print_function import unittest import numpy as np from inference_pass_test import InferencePassTest import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.core import PassVersionChecker from paddle.fluid.core import AnalysisConfig if __name__ == "__main__": unittest.main()

40.483051

82

0.626125

7ccc10fc8c636712784281edcf93b9e16ef2ae97

2,202

py

Python

configs/vinbig/detectors_resnext.py

SeHwanJoo/mmdetection_vinbig

9a27d2b5cd8b3ec9ed1a94e4704a7c883f15dce3

[ "Apache-2.0" ]

2

2021-04-01T08:17:08.000Z

2021-07-12T11:53:53.000Z

configs/vinbig/detectors_resnext.py

SeHwanJoo/mmdetection_vinbig

9a27d2b5cd8b3ec9ed1a94e4704a7c883f15dce3

[ "Apache-2.0" ]

null

configs/vinbig/detectors_resnext.py

SeHwanJoo/mmdetection_vinbig

9a27d2b5cd8b3ec9ed1a94e4704a7c883f15dce3

[ "Apache-2.0" ]

null

_base_ = [ '../_base_/models/cascade_rcnn_r50_fpn.py', './dataset_base.py', './scheduler_base.py', '../_base_/default_runtime.py' ] model = dict( pretrained='open-mmlab://resnext101_32x4d', backbone=dict( type='DetectoRS_ResNeXt', pretrained='open-mmlab://resnext101_32x4d', depth=101, groups=32, base_width=4, conv_cfg=dict(type='ConvAWS'), sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), output_img=True, plugins=[ dict( cfg=dict( type='GeneralizedAttention', spatial_range=-1, num_heads=8, attention_type='0010', kv_stride=2), stages=(False, False, True, True), in_channels=512, position='after_conv2') ] ), neck=dict( type='RFP', rfp_steps=2, aspp_out_channels=64, aspp_dilations=(1, 3, 6, 1), rfp_backbone=dict( rfp_inplanes=256, type='DetectoRS_ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, conv_cfg=dict(type='ConvAWS'), sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), pretrained='open-mmlab://resnext101_32x4d', style='pytorch')), roi_head=dict( bbox_head=[ dict( type='Shared2FCBBoxHead', num_classes=14 ), dict( type='Shared2FCBBoxHead', num_classes=14 ), dict( type='Shared2FCBBoxHead', num_classes=14 ) ] ), test_cfg=dict( rpn=dict( nms_thr=0.7 ), rcnn=dict( score_thr=0.0, nms=dict(type='nms', iou_threshold=0.4) ) ) )

27.525

57

0.475931

7cccbb5b10c9e4406bbef811b8c0c86a34ddfd24

26,701

py

Python

skbio/draw/tests/test_distributions.py

johnchase/scikit-bio

340e6153b6c93053d923d344e63481860e03731e

[ "BSD-3-Clause" ]

null

skbio/draw/tests/test_distributions.py

johnchase/scikit-bio

340e6153b6c93053d923d344e63481860e03731e

[ "BSD-3-Clause" ]

null

skbio/draw/tests/test_distributions.py

johnchase/scikit-bio

340e6153b6c93053d923d344e63481860e03731e

[ "BSD-3-Clause" ]

null

# ---------------------------------------------------------------------------- # Copyright (c) 2013--, scikit-bio development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file COPYING.txt, distributed with this software. # ---------------------------------------------------------------------------- from __future__ import absolute_import, division, print_function from unittest import TestCase, main import numpy as np import numpy.testing as npt import matplotlib.pyplot as plt from skbio.draw import boxplots, grouped_distributions from skbio.draw._distributions import ( _calc_data_point_locations, _calc_data_point_ticks, _color_box_plot, _create_legend, _get_distribution_markers, _is_single_matplotlib_color, _plot_bar_data, _plot_box_data, _plot_scatter_data, _set_axes_options, _set_figure_size, _validate_input, _validate_x_values) if __name__ == '__main__': main()

46.275563

79

0.562638

7ccec8a64f1094a2aaa4d1c42f4858ca203734a3

122

py

Python

packages/gtmapi/lmsrvcore/api/interfaces/__init__.py

jjwatts/gigantum-client

88ce0475fb6880322bdd06d987c494e29064f278

[ "MIT" ]

60

2018-09-26T15:46:00.000Z

2021-10-10T02:37:14.000Z

packages/gtmapi/lmsrvcore/api/interfaces/__init__.py

jjwatts/gigantum-client

88ce0475fb6880322bdd06d987c494e29064f278

[ "MIT" ]

1,706

2018-09-26T16:11:22.000Z

2021-08-20T13:37:59.000Z

packages/gtmapi/lmsrvcore/api/interfaces/__init__.py

jjwatts/gigantum-client

88ce0475fb6880322bdd06d987c494e29064f278

[ "MIT" ]

11

2019-03-14T13:23:51.000Z

2022-01-25T01:29:16.000Z

from lmsrvcore.api.interfaces.user import User from lmsrvcore.api.interfaces.git import GitCommit, GitRef, GitRepository

30.5

73

0.844262

7cced65964aa995783474d3ea16a3fdb37a88182

3,570

py

Python

tensorflow_probability/python/bijectors/invert_test.py

matthieucoquet/probability

2426f4fc4743ceedc1a638a03d19ce6654ebff76

[ "Apache-2.0" ]

null

tensorflow_probability/python/bijectors/invert_test.py

matthieucoquet/probability

2426f4fc4743ceedc1a638a03d19ce6654ebff76

[ "Apache-2.0" ]

null

tensorflow_probability/python/bijectors/invert_test.py

matthieucoquet/probability

2426f4fc4743ceedc1a638a03d19ce6654ebff76

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Probability 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. # ============================================================================ """Tests for Bijector.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow.compat.v2 as tf from tensorflow_probability.python import bijectors as tfb from tensorflow_probability.python import distributions as tfd from tensorflow_probability.python.bijectors import bijector_test_util from tensorflow_probability.python.internal import tensorshape_util from tensorflow_probability.python.internal import test_util as tfp_test_util from tensorflow.python.framework import test_util # pylint: disable=g-direct-tensorflow-import if __name__ == "__main__": tf.test.main()

38.387097

95

0.693557

7ccf2b0c1cc9f5a9318ca8b0e302ba7e965fbb1e

4,394

py

Python

dayu_widgets/alert.py

ZSD-tim/dayu_widgets

31c2530bdc4161d9311574d9850c2e9471e53072

[ "MIT" ]

null

dayu_widgets/alert.py

ZSD-tim/dayu_widgets

31c2530bdc4161d9311574d9850c2e9471e53072

[ "MIT" ]

null

dayu_widgets/alert.py

ZSD-tim/dayu_widgets

31c2530bdc4161d9311574d9850c2e9471e53072

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################### # Author: Mu yanru # Date : 2019.2 # Email : [email protected] ################################################################### """ MAlert class. """ import six import functools from dayu_widgets.avatar import MAvatar from dayu_widgets.label import MLabel from dayu_widgets import dayu_theme from dayu_widgets.tool_button import MToolButton from dayu_widgets.mixin import property_mixin from dayu_widgets.qt import QWidget, QHBoxLayout, MPixmap, Qt, MIcon, Property

32.072993

98

0.62244

7cd0a40cffa8dae1e2b1f6c63ccd1a5c2242724b

86

py

Python

week03/code05.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week03/code05.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week03/code05.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

1

2019-11-27T20:28:19.000Z

input_str = input(" . >> ") print(" ", len(input_str), ".")

28.666667

46

0.639535

7cd14d3a6d6b9088b4271089222cd9080f058243

5,664

py

Python

jobs/SCH/JB_SALES_HIERARCHY_FLAG_N_SR.py

bibinvasudev/EBI_Project

df2560139e463d68a37e67e0bb683c06fa9ef91b

[ "CNRI-Python" ]

null

jobs/SCH/JB_SALES_HIERARCHY_FLAG_N_SR.py

bibinvasudev/EBI_Project

df2560139e463d68a37e67e0bb683c06fa9ef91b

[ "CNRI-Python" ]

null

jobs/SCH/JB_SALES_HIERARCHY_FLAG_N_SR.py

bibinvasudev/EBI_Project

df2560139e463d68a37e67e0bb683c06fa9ef91b

[ "CNRI-Python" ]

null

# SCH1101.sh --> JB_SALES_HIERARCHY_FLAG_N_SR.py #************************************************************************************************************** # # Created by : bibin # Version : 1.0 # # Description : # 1. This script will load the data into 'SALES_HIERARCHY' table based on stream lookups. # # # Initial Creation: # # Date (YYYY-MM-DD) Change Description # ----------------- ------------------ # 2018-11-02 Initial creation # #************************************************************************************************************** # Importing required Lib from dependencies.spark import start_spark from dependencies.EbiReadWrite import EbiReadWrite import logging import sys from time import gmtime, strftime import cx_Oracle import py4j import pyspark # Spark logging logger = logging.getLogger(__name__) # Date Formats start_date = "'"+strftime("%Y-%m-%d %H:%M:%S", gmtime())+"'" log_date =strftime("%Y%m%d", gmtime()) # Job Naming Details script_name = "SCH1101.SH" app_name = "JB_SALES_HIERARCHY_FLAG_N_SR" log_filename = app_name + '_' + log_date + '.log' # Query for loading invoice table # Main method # Entry point for script if __name__ == "__main__": # Calling main() method main()

38.794521

218

0.625883

7cd1e3bcc66dd50fd9167cfd73166db8b21f6910

670

py

Python

myth/util.py

amanbhandari2002/mythproto

b03764485dad5178127307a3b3e4ddc508158143

[ "BSD-3-Clause" ]

1

2020-10-01T09:17:00.000Z

myth/util.py

amanbhandari2002/mythproto

b03764485dad5178127307a3b3e4ddc508158143

[ "BSD-3-Clause" ]

null

myth/util.py

amanbhandari2002/mythproto

b03764485dad5178127307a3b3e4ddc508158143

[ "BSD-3-Clause" ]

2

2020-09-30T19:53:40.000Z

2020-10-01T09:13:08.000Z

# t is a nine item tuple returned by the time module. This method converts it to # MythTV's standard representation used on filenames

18.108108

80

0.631343

7cd283a215a5ab2f5c601f954e24742216c659e4

14,208

py

Python

scripts/tator_tracker.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

10

2019-01-23T23:58:01.000Z

2021-08-30T19:42:35.000Z

scripts/tator_tracker.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

3

2020-03-20T15:21:41.000Z

2020-09-18T18:49:38.000Z

scripts/tator_tracker.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

2

2020-05-08T17:39:12.000Z

2020-10-09T01:27:17.000Z

#!/usr/bin/env python3 import argparse import openem import os import cv2 import numpy as np from openem.tracking import * import json import sys import datetime import tator from pprint import pprint from collections import defaultdict import yaml import math import subprocess import sys if __name__=="__main__": parser = argparse.ArgumentParser(description=__doc__) tator.get_parser(parser) parser.add_argument("--detection-type-id", type=int, required=True) parser.add_argument("--tracklet-type-id", type=int, required=True) parser.add_argument("--version-id", type=int) parser.add_argument("--input-version-id", type=int) parser.add_argument("--strategy-config", type=str) parser.add_argument("--dry-run", action='store_true') parser.add_argument('media_files', type=str, nargs='*') args = parser.parse_args() # Weight methods methods = ['hybrid', 'iou', 'iou-motion', 'iou-global-motion'] # Weight methods that require the video visual_methods = ['hybrid', 'iou-global-motion'] api = tator.get_api(args.host, args.token) detection_type = api.get_localization_type(args.detection_type_id) project = detection_type.project version_id = args.version_id default_strategy = {"method": "hybrid", "frame-diffs": [1,2,4,8,16,32,64,128,256], "args": {}, "extension": {'method' : None}, "max-length": {}, "min-length": 0} if args.strategy_config: strategy = {**default_strategy} with open(args.strategy_config, "r") as strategy_file: strategy.update(yaml.load(strategy_file)) else: strategy = default_strategy if strategy['method'] == 'hybrid': model_file = strategy['args']['model_file'] batch_size = strategy['args'].get('batch_size', 4) comparator=FeaturesComparator(model_file) #extractor=FeaturesExtractor(args.model_file) class_method = strategy.get('class-method',None) classify_function = None classify_args = {} if class_method: pip_package=class_method.get('pip',None) if pip_package: p = subprocess.run([sys.executable, "-m", "pip", "install", pip_package]) print("Finished process.", flush=True) function_name = class_method.get('function',None) classify_args = class_method.get('args',None) names = function_name.split('.') module = __import__(names[0]) for name in names[1:-1]: module = getattr(module,name) classify_function = getattr(module,names[-1]) print("Strategy: ", flush=True) pprint(strategy) print(args.media_files, flush=True) optional_fetch_args = {} if args.input_version_id: optional_fetch_args['version'] = [args.input_version_id] for media_file in args.media_files: comps=os.path.splitext(os.path.basename(media_file))[0] media_id=comps.split('_')[0] media = api.get_media(media_id) if media.attributes.get("Tracklet Generator Processed") != "No": print(f"Skipping media ID {media.id}, name {media.name} due to " f"'Tracklet Generator Processed' attribute being set to " f"something other than 'No'!") continue media_shape = (media.height, media.width) fps = media.fps localizations_by_frame = {} localizations = api.get_localization_list(project, type=args.detection_type_id, media_id=[media_id], **optional_fetch_args) localizations = [l.to_dict() for l in localizations] if len(localizations) == 0: print(f"No localizations present in media {media_file}", flush=True) continue print(f"Processing {len(localizations)} detections", flush=True) # Group by localizations by frame for lid, local in enumerate(localizations): frame = local['frame'] if frame in localizations_by_frame: localizations_by_frame[frame].append(local) else: localizations_by_frame[frame] = [local] detections=[] track_ids=[] track_id=1 # If media does not exist, download it. if strategy['method'] == 'iou-global-motion': if not os.path.exists(media_file): temp_path = f'/tmp/{os.path.basename(media_file)}' for progress in tator.util.download_media(api, media, temp_path): print(f"Downloading {media_file}, {progress}%...") print("Download finished!") # Unfrag the file subprocess.run(["ffmpeg", '-i', temp_path, '-c:v', 'copy', media_file]) os.remove(temp_path) if strategy['method'] == 'hybrid': # Not all visual methods need detection images vid=cv2.VideoCapture(media_file) ok=True frame = 0 while ok: ok,frame_bgr = vid.read() if frame in localizations_by_frame: for l in localizations_by_frame[frame]: l['bgr'] = crop_localization(frame_bgr, l) if l['attributes']['Confidence'] < 0.50: continue detections.append(l) track_ids.append(track_id) track_id += 1 frame+=1 else: # The method is analytical on the detections coordinates # and does not require processing the video for frame,frame_detections in localizations_by_frame.items(): for det in frame_detections: detections.append(det) track_ids.append(track_id) track_id += 1 print("Loaded all detections", flush=True) track_ids = renumber_track_ids(track_ids) if strategy['method'] == 'hybrid': weights_strategy = HybridWeights(comparator, None, None, media_shape, fps, 0.0, batch_size) elif strategy['method'] == 'iou': weights_strategy = IoUWeights(media_shape, **strategy['args']) elif strategy['method'] == 'iou-motion': weights_strategy = IoUMotionWeights(media_shape, **strategy['args']) elif strategy['method'] == 'iou-global-motion': weights_strategy = IoUGlobalMotionWeights(media_shape, media_file, **strategy['args']) # Generate localization bgr based on grouped localizations for x in strategy['frame-diffs']: print(f"Started {x}", flush=True) detections, track_ids, pairs, weights, is_cut, constraints = join_tracklets( detections, track_ids, x, weights_strategy) if x in strategy['max-length']: trim_to = strategy['max-length'][x] print(f"Trimming track to max length of {trim_to}") detections, track_ids = trim_tracklets(detections, track_ids, trim_to) _,det_counts_per_track=np.unique(track_ids,return_counts=True) print(f"frame-diff {x}: {len(detections)} to {len(det_counts_per_track)}", flush=True) if x > 1 and strategy['extension']['method'] == 'linear-motion': ext_frames=x print(f"Extending by linear motion, {ext_frames}") tracklets = join_up_iteration(detections,track_ids) tracklets = extend_tracklets(tracklets, ext_frames) detections, track_ids = split_tracklets(tracklets) # Now we make new track objects based on the result # from the graph solver # [ detection, detection, detection, ...] # [ track#, track#, track#,...] # [ 133, 33, 13, 133,] # [ 0,0,1,1] # TODO: Handle is_cut? tracklets = join_up_final(detections, track_ids) new_objs=[make_object(tracklet) for tracklet in tracklets.values()] new_objs=[x for x in new_objs if x is not None] print(f"New objects = {len(new_objs)}") with open(f"/work/{media_id}.json", "w") as f: json.dump(new_objs,f) if not args.dry_run: for response in tator.util.chunked_create(api.create_state_list,project, state_spec=new_objs): pass try: api.update_media(int(media_id), {"attributes":{"Tracklet Generator Processed": str(datetime.datetime.now())}}) except: print("WARNING: Unable to set 'Tracklet Generator Processed' attribute")

39.248619

126

0.551943

7cd30f449f940b3e03ca41f6babd9a375fe19ebf

1,167

py

Python

hypergan/losses/multi_loss.py

Darkar25/HyperGAN

76ef7e0c20569ceece88dc76396d92c77050692b

[ "MIT" ]

1

2019-05-29T14:24:04.000Z

hypergan/losses/multi_loss.py

KonradLinkowski/HyperGAN

3153daee838dbb8e8d8926b1e81419682a24f2fe

[ "MIT" ]

218

2021-05-25T01:46:15.000Z

2022-02-11T01:08:52.000Z

hypergan/losses/multi_loss.py

KonradLinkowski/HyperGAN

3153daee838dbb8e8d8926b1e81419682a24f2fe

[ "MIT" ]

null

import tensorflow as tf import numpy as np import hyperchamber as hc from hypergan.losses.base_loss import BaseLoss from hypergan.multi_component import MultiComponent TINY=1e-8

26.522727

99

0.61868

7cd33c8d7f17b4aa6fc5f6d3f2701686f2ce01a4

13,643

py

Python

src/fidesops/api/v1/endpoints/policy_endpoints.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

[ "Apache-2.0" ]

null

src/fidesops/api/v1/endpoints/policy_endpoints.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

[ "Apache-2.0" ]

null

src/fidesops/api/v1/endpoints/policy_endpoints.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

[ "Apache-2.0" ]

null

import logging from typing import Any, Dict, List from fastapi import APIRouter, Body, Depends, Security from fastapi_pagination import ( Page, Params, ) from fastapi_pagination.bases import AbstractPage from fastapi_pagination.ext.sqlalchemy import paginate from fidesops.schemas.shared_schemas import FidesOpsKey from pydantic import conlist from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import Session from starlette.exceptions import HTTPException from starlette.status import HTTP_404_NOT_FOUND from fidesops.api import deps from fidesops.api.v1 import scope_registry as scopes from fidesops.api.v1 import urn_registry as urls from fidesops.common_exceptions import ( DataCategoryNotSupported, PolicyValidationError, RuleValidationError, RuleTargetValidationError, KeyOrNameAlreadyExists, ) from fidesops.models.client import ClientDetail from fidesops.models.policy import ( ActionType, Policy, Rule, RuleTarget, ) from fidesops.models.storage import StorageConfig from fidesops.schemas import policy as schemas from fidesops.schemas.api import BulkUpdateFailed from fidesops.util.oauth_util import verify_oauth_client router = APIRouter(tags=["Policy"], prefix=urls.V1_URL_PREFIX) logger = logging.getLogger(__name__) def get_policy_or_error(db: Session, policy_key: FidesOpsKey) -> Policy: """Helper method to load Policy or throw a 404""" logger.info(f"Finding policy with key '{policy_key}'") policy = Policy.get_by(db=db, field="key", value=policy_key) if not policy: raise HTTPException( status_code=HTTP_404_NOT_FOUND, detail=f"No Policy found for key {policy_key}.", ) return policy

31.95082

113

0.609543

7cd398bd2b3034834a61ea93e2ee16c8e1011acb

7,425

py

Python

engage-analytics/sentiment_analysis/src/report/interface_report.py

oliveriopt/mood-analytics

c98eb8c483a05af938a2f6f49d8ea803f5711572

[ "Apache-2.0" ]

null

engage-analytics/sentiment_analysis/src/report/interface_report.py

oliveriopt/mood-analytics

c98eb8c483a05af938a2f6f49d8ea803f5711572

[ "Apache-2.0" ]

2

2020-03-27T19:14:44.000Z

engage-analytics/sentiment_analysis/src/report/interface_report.py

oliveriopt/mood-analytics

c98eb8c483a05af938a2f6f49d8ea803f5711572

[ "Apache-2.0" ]

null

import emoji import sentiment_analysis.src.report.cons_report as cons import sentiment_analysis.src.constants as global_cons from utils.data_connection.api_data_manager import APISourcesFetcher from utils.utilities import read_json_file, CUSTOM_YEAR_WEEK_AGG, extract_dimension, extract_question from sentiment_analysis.src.word_cloud import words_clouds from sentiment_analysis.src.clients_language_sentiments_entity import ClientsLanguageSentiment from nested_lookup import nested_lookup

40.135135

116

0.644175

7cd4c163b81a2a9f7a9f4fb51454b97b7933bffd

1,565

py

Python

dwh_analytic/dags/data_warehouse_prod/schema/dim_process.py

dnguyenngoc/analytic

d609a93e96e7c546ad3ee3ebd4e13309ddf575f8

[ "MIT" ]

null

dwh_analytic/dags/data_warehouse_prod/schema/dim_process.py

dnguyenngoc/analytic

d609a93e96e7c546ad3ee3ebd4e13309ddf575f8

[ "MIT" ]

null

dwh_analytic/dags/data_warehouse_prod/schema/dim_process.py

dnguyenngoc/analytic

d609a93e96e7c546ad3ee3ebd4e13309ddf575f8

[ "MIT" ]

null

resource ='human ad machime'

27.946429

62

0.548882

7cd59f2bf170f30d86846848bf3c6c4bf7b96d9c

2,491

py

Python

lino/modlib/gfks/mixins.py

NewRGB/lino

43799e42107169ff173d3b8bc0324d5773471499

[ "BSD-2-Clause" ]

1

2019-11-13T19:38:50.000Z

lino/modlib/gfks/mixins.py

NewRGB/lino

43799e42107169ff173d3b8bc0324d5773471499

[ "BSD-2-Clause" ]

null

lino/modlib/gfks/mixins.py

NewRGB/lino

43799e42107169ff173d3b8bc0324d5773471499

[ "BSD-2-Clause" ]

null

# -*- coding: UTF-8 -*- # Copyright 2010-2018 Rumma & Ko Ltd # License: BSD (see file COPYING for details) from builtins import object from django.contrib.contenttypes.models import * from django.conf import settings from django.utils.translation import ugettext_lazy as _ from django.utils.text import format_lazy from lino.api import dd from lino.core.gfks import gfk2lookup from .fields import GenericForeignKey, GenericForeignKeyIdField

30.753086

71

0.635488

7cd5ad9a803e1cac21f7d6ba2961e58bea3c98da

21,926

py

Python

optical_form_reader/main.py

1enes/optical_form_reader

fab99f2403c25f84fcb5bdac50148ab248432516

[ "MIT" ]

null

optical_form_reader/main.py

1enes/optical_form_reader

fab99f2403c25f84fcb5bdac50148ab248432516

[ "MIT" ]

null

optical_form_reader/main.py

1enes/optical_form_reader

fab99f2403c25f84fcb5bdac50148ab248432516

[ "MIT" ]

null

import cv2 import numpy as np from imutils import contours from imutils.perspective import four_point_transform import imutils import cv2 import matplotlib.pyplot as plt import numpy as np from imutils import contours from imutils.perspective import four_point_transform,order_points import imutils cevap_anahtar={0:2,1:1,2:2,3:3,4:1,5:4,6:4,7:3,8:1,9:1,10:0,11:0,12:2,13:1,14:2,15:3,16:4,17:4,18:4,19:3,20:2,21:1,22:0,23:0,24:0,25:4,26:2,27:3,28:4,29:4,30:4,31:3,32:2,33:1,34:0,35:0,36:1,37:2,38:3,39:4} #, alfabe={0:'A',1:'B',2:'C',3:'',4:'D',5:'E',6:'F',7:'G',8:'',9:'H',10:'I',11:'',12:'J',13:'K',14:'L',15:'M',16:'N',17:'O',18:'',19:'P',20:'Q',21:'R',22:'S',23:'',24:'T',25:'U',26:'',27:'V',28:'W',29:'Y',30:'Z',31:'X'} def cevap_gri(col1,col2,col3,col4): ''' KOLONLARI GR YAPMAK N,MANDE YER KAPLAMASIN ''' col1_gri=cv2.cvtColor(col1,cv2.COLOR_BGR2GRAY) col2_gri=cv2.cvtColor(col2,cv2.COLOR_BGR2GRAY) col3_gri=cv2.cvtColor(col3,cv2.COLOR_BGR2GRAY) col4_gri=cv2.cvtColor(col4,cv2.COLOR_BGR2GRAY) return col1_gri,col2_gri,col3_gri,col4_gri #################################################################### if __name__ == '__main__': bos_kagit="optic_empty.jpg" dolu_kagit="optic_marked.jpg" main_starter(bos_kagit,dolu_kagit)

31.100709

223

0.622457

7cd6ff8a4443655a42df05eccf62b0e804763fb0

2,898

py

Python

service.py

Tigge/script.filmtipset-grade

a5b438dc478d6ef40f611585e9cd196c2ff49cf6

[ "BSD-2-Clause" ]

1

2015-02-19T08:45:57.000Z

service.py

Tigge/script.filmtipset-grade

a5b438dc478d6ef40f611585e9cd196c2ff49cf6

[ "BSD-2-Clause" ]

1

2015-02-01T19:28:17.000Z

2015-03-18T22:27:14.000Z

service.py

Tigge/script.filmtipset-grade

a5b438dc478d6ef40f611585e9cd196c2ff49cf6

[ "BSD-2-Clause" ]

null

# Copyright (c) 2013, Gustav Tiger # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import xbmc import xbmcaddon import xbmcgui import filmtipset FILMTIPSET_ACCESS_KEY = "7ndg3Q3qwW8dPzbJMrB5Rw" player = XBMCPlayer() while(not xbmc.abortRequested): if player.isPlayingVideo(): player.update() xbmc.sleep(1000)

34.094118

82

0.670807

7cd8fbdd89ede56684cdd39b8bd8583e3ed86ea6

16,528

py

Python

test/testMatrix.py

turkeydonkey/nzmath3

a48ae9efcf0d9ad1485c2e9863c948a7f1b20311

[ "BSD-3-Clause" ]

1

2021-05-26T19:22:17.000Z

test/testMatrix.py

turkeydonkey/nzmath3

a48ae9efcf0d9ad1485c2e9863c948a7f1b20311

[ "BSD-3-Clause" ]

null

test/testMatrix.py

turkeydonkey/nzmath3

a48ae9efcf0d9ad1485c2e9863c948a7f1b20311

[ "BSD-3-Clause" ]

null

import unittest from nzmath.matrix import * import nzmath.vector as vector import nzmath.rational as rational import nzmath.poly.uniutil as uniutil Ra = rational.Rational Poly = uniutil.polynomial Int = rational.theIntegerRing # sub test try: from test.testMatrixFiniteField import * except: try: from nzmath.test.testMatrixFiniteField import * except: from .testMatrixFiniteField import * ## for RingMatrix a1 = createMatrix(1, 2, [3, 2]) a2 = Matrix(1, 2, [5, -6]) a3 = createMatrix(3, 2, [7, 8]+[3, -2]+[0, 10]) a4 = Matrix(3, 2, [21, -12]+[1, -1]+[0, 0]) a5 = createMatrix(1, 2, [Poly({0:3, 1:5}, Int), Poly({1:2}, Int)]) ## for RingSquareMatrix b1 = createMatrix(2, 2, [1, 2]+[3, 4]) b2 = Matrix(2, 2, [0, -1]+[1, -2]) b3 = createMatrix(3, 3, [0, 1, 2]+[5, 4, 6]+[7, 9, 8]) b4 = Matrix(3, 3, [1, 2, 3]+[0, 5, -2]+[7, 1, 9]) b5 = createMatrix(3, 3, [1, 3, 2, 4, 6, 5, 6, 8, 9]) b6 = createMatrix(3, 3, [1, 2, 4, 0, 3, 5, 0, 0, 0]) b7 = createMatrix(3, 3, [1, 0, 0, 9, 1, 0, 5, 6, 1]) b8 = Matrix(3, 3, [3, 15, 12]+[2,7,5]+[1,-4,-2]) ## for FieldMatrix c1 = createMatrix(1, 2, [Ra(3), Ra(2)]) c2 = createMatrix(4, 5, \ [Ra(0), 0, 1, 2, -1]+[0, 0, 5, 12, -2]+[0, 0, 1, 3, -1]+[0, 0, 1, 2, 0]) c3 = createMatrix(3, 2, [Ra(1), 2]+[2, 5]+[6, 7]) ## for FieldSquareMatrix d1 = createMatrix(2, 2, [Ra(1), Ra(2)]+[Ra(3), Ra(4)]) d2 = createMatrix(3, 3, [Ra(1), 2, 3]+[4, 5, 6]+[5, 7, 9]) d3 = Matrix(3, 3, \ [Ra(1), Ra(2), Ra(3)]+[Ra(0), Ra(5), Ra(-2)]+[7, 1, 9]) d4 = createMatrix(6, 6, \ [Ra(4), 2, 5, 0, 2, 1]+[5, 1, 2, 5, 1, 1]+[90, 7, 54, 8, 4, 6]+\ [7, 5, 0, 8, 2, 5]+[8, 2, 6, 5, -4, 2]+[4, 1, 5, 6, 3, 1]) d5 = createMatrix(4, 4, \ [Ra(2), -1, 0, 0]+[-1, 2, -1, 0]+[0, -1, 2, -1]+[0, 0, -1, 2]) d6 = createMatrix(4, 4, \ [Ra(1), 2, 3, 4]+[2, 3, 4, 5]+[3, 4, 5, 6]+[4, 5, 6, 7]) d7 = Matrix(3, 3, \ [Ra(1, 2), Ra(2, 3), Ra(1, 5)]+[Ra(3, 2), Ra(1, 3), Ra(2, 5)]+[Ra(-1, 2), Ra(4, 3), Ra(3, 5)]) ## other objects v1 = vector.Vector([1, 4]) v2 = vector.Vector([8]) v3 = vector.Vector([0, 0, 1]) def suite(suffix="Test"): suite = unittest.TestSuite() all_names = globals() for name in all_names: if name.endswith(suffix): suite.addTest(unittest.makeSuite(all_names[name], "test")) return suite if __name__ == '__main__': runner = unittest.TextTestRunner() runner.run(suite())

35.165957

94

0.575811

7cd9e1b23df0b17b32715fc652a510c5c85b28ff

372

py

Python

python/test-nose-3.py

li-ma/homework

d75b1752a02bd028af0806683abe079c7b0a9b29

[ "Apache-2.0" ]

null

python/test-nose-3.py

li-ma/homework

d75b1752a02bd028af0806683abe079c7b0a9b29

[ "Apache-2.0" ]

null

python/test-nose-3.py

li-ma/homework

d75b1752a02bd028af0806683abe079c7b0a9b29

[ "Apache-2.0" ]

null

# Module Level # Function Level # Target Func test_func_1.setUp = func_1_setup test_func_1.tearDown = func_1_teardown

15.5

38

0.72043

7cda6328ac58b61f05923cca8623aa6b42f94561

3,591

py

Python

lib/reindex/reporting.py

scality/utapi

29475f1b9aa25cf3c883262bfb6f4573f846a5b7

[ "Apache-2.0" ]

13

2016-10-07T20:25:11.000Z

2022-02-23T06:33:59.000Z

lib/reindex/reporting.py

scality/utapi

29475f1b9aa25cf3c883262bfb6f4573f846a5b7

[ "Apache-2.0" ]

427

2016-08-17T18:03:32.000Z

2022-03-31T10:46:12.000Z

lib/reindex/reporting.py

scality/utapi

29475f1b9aa25cf3c883262bfb6f4573f846a5b7

[ "Apache-2.0" ]

5

2017-04-25T21:13:03.000Z

2018-01-23T00:21:06.000Z

import requests import redis import json import ast import sys import time import urllib import re import sys from threading import Thread from concurrent.futures import ThreadPoolExecutor import argparse if __name__ == '__main__': options = get_options() redis_conf = dict( ip=options.sentinel_ip, port=options.sentinel_port, sentinel_cluster_name=options.sentinel_cluster_name, password=options.redis_password ) P = S3ListBuckets(options.bucketd_addr) listbuckets = P.run() userids = set([x for x, y in listbuckets]) executor = ThreadPoolExecutor(max_workers=1) for userid, bucket in listbuckets: U = askRedis(**redis_conf) data = U.read('buckets', bucket) content = "Account:%s|Bucket:%s|NumberOFfiles:%s|StorageCapacity:%s " % ( userid, bucket, data["files"], data["total_size"]) executor.submit(safe_print, content) data = U.read('buckets', 'mpuShadowBucket'+bucket) content = "Account:%s|Bucket:%s|NumberOFfiles:%s|StorageCapacity:%s " % ( userid, 'mpuShadowBucket'+bucket, data["files"], data["total_size"]) executor.submit(safe_print, content) executor.submit(safe_print, "") for userid in sorted(userids): U = askRedis(**redis_conf) data = U.read('accounts', userid) content = "Account:%s|NumberOFfiles:%s|StorageCapacity:%s " % ( userid, data["files"], data["total_size"]) executor.submit(safe_print, content)

35.554455

114

0.634085

7cdbf4b64b3ba075f66b013f73d0f70e791b05ee

52,272

py

Python

src/skim/modeling/skim_attention/modeling_skim.py

recitalAI/skim-attention

a37a277072d1f70ea615cfd19e5b84a6effd2464

[ "Apache-2.0" ]

4

2021-09-08T17:20:59.000Z

2021-12-08T07:49:24.000Z

src/skim/modeling/skim_attention/modeling_skim.py

recitalAI/skim-attention

a37a277072d1f70ea615cfd19e5b84a6effd2464

[ "Apache-2.0" ]

null

src/skim/modeling/skim_attention/modeling_skim.py

recitalAI/skim-attention

a37a277072d1f70ea615cfd19e5b84a6effd2464

[ "Apache-2.0" ]

1

2021-12-09T00:02:23.000Z

from collections import namedtuple import logging from dataclasses import dataclass from typing import Optional, Tuple import math import torch from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.autograd.function import Function from transformers.file_utils import ( ModelOutput, ) from transformers.modeling_utils import ( PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.modeling_outputs import ( BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, TokenClassifierOutput, ) from transformers.models.bert.modeling_bert import ( BertConfig, BertEmbeddings, BertIntermediate, BertOutput, BertPooler, BertEncoder, BertOnlyMLMHead, ) from transformers.models.layoutlm.modeling_layoutlm import LayoutLMEmbeddings from .configuration_skim import ( SkimformerConfig, BertWithSkimEmbedConfig, SkimmingMaskConfig, ) logger = logging.getLogger(__name__) SkimformerEncoderOutput = namedtuple( "SkimformerEncoderOutput", ["hidden_states", "all_hidden_states"], )

39.540091

168

0.661674

7cdcd3e9c2d7e86acfb845c8d72f0dc9c0f23f7d

2,290

py

Python

api/routers/dashboard.py

xming521/coco_API

51d7ac3141e58f1d6a5438af135fba3ea101bd53

[ "MIT" ]

null

api/routers/dashboard.py

xming521/coco_API

51d7ac3141e58f1d6a5438af135fba3ea101bd53

[ "MIT" ]

null

api/routers/dashboard.py

xming521/coco_API

51d7ac3141e58f1d6a5438af135fba3ea101bd53

[ "MIT" ]

null

import time import psutil import pymysql from fastapi import APIRouter from api.utils import response_code router = APIRouter()

30.533333

79

0.620524

7cdd5ddf7b7d2568fd208a60927251ae8e3ac857

10,399

py

Python

retargeting/models/Kinematics.py

yujiatay/deep-motion-editing

0a6fc5fd20059c5074f68a452cd49cf6ede36ea8

[ "BSD-2-Clause" ]

1

2021-07-06T14:34:12.000Z

retargeting/models/Kinematics.py

bmd080/deep-motion-editing

19604abdc0ead66f8c82d9211b8c5862c6a68089

[ "BSD-2-Clause" ]

null

retargeting/models/Kinematics.py

bmd080/deep-motion-editing

19604abdc0ead66f8c82d9211b8c5862c6a68089

[ "BSD-2-Clause" ]

null

import torch import torch.nn as nn import numpy as np import math

36.233449

131

0.521877

7cdd5ff32b1c2238dcf9d02a8e0c07b84239dfc5

13,145

py

Python

tests/operators/test_hive_operator.py

Ryan-Miao/airflow

a2aca8714fac014ed7da97229d7877f1bc6e5a59

[ "Apache-2.0" ]

null

tests/operators/test_hive_operator.py

Ryan-Miao/airflow

a2aca8714fac014ed7da97229d7877f1bc6e5a59

[ "Apache-2.0" ]

null

tests/operators/test_hive_operator.py

Ryan-Miao/airflow

a2aca8714fac014ed7da97229d7877f1bc6e5a59

[ "Apache-2.0" ]

1

2020-09-29T05:26:34.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import datetime import os import unittest from unittest import mock import nose from airflow import DAG, configuration, operators from airflow.models import TaskInstance from airflow.operators.hive_operator import HiveOperator from airflow.utils import timezone DEFAULT_DATE = datetime.datetime(2015, 1, 1) DEFAULT_DATE_ISO = DEFAULT_DATE.isoformat() DEFAULT_DATE_DS = DEFAULT_DATE_ISO[:10] if 'AIRFLOW_RUNALL_TESTS' in os.environ: import airflow.hooks.hive_hooks import airflow.operators.presto_to_mysql

39.005935

88

0.617117

7cdde129ca347d44c4fc15ae483831b97628b1d6

4,553

py

Python

main.py

OrionDark7/Alakajam12

4f9f8f87a05feb718baddb12aa8cbbed3e36a071

[ "MIT" ]

null

main.py

OrionDark7/Alakajam12

4f9f8f87a05feb718baddb12aa8cbbed3e36a071

[ "MIT" ]

null

main.py

OrionDark7/Alakajam12

4f9f8f87a05feb718baddb12aa8cbbed3e36a071

[ "MIT" ]

null

import pygame, math from game import map, ui window = pygame.display.set_mode([800, 600]) ui.window = window screen = "game" s = {"fullscreen": False} running = True gamedata = {"level": 0, "coal": 0, "iron": 1, "copper":0} tiles = pygame.sprite.Group() rails = pygame.sprite.Group() carts = pygame.sprite.Group() interactables = pygame.sprite.Group() listmap = [] clock = pygame.time.Clock() selected = pygame.image.load("./resources/images/selected.png") selected2 = pygame.image.load("./resources/images/selected2.png") box = pygame.image.load("./resources/images/box.png") uibox = pygame.image.load("./resources/images/ui box.png") m = Mouse() loadlevel(0) while running: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False elif event.type == pygame.MOUSEBUTTONDOWN: m.pos(pygame.mouse.get_pos()) if screen == "game": if pygame.sprite.spritecollide(m, carts, False) and m.mode == "select": carts.update("select", m, listmap) if m.clickedcart != None: m.mode = "action" elif m.mode == "action" and m.clickedcart != None and listmap[snaptogrid(m.tl)[0]][snaptogrid(m.tl)[1]] > 0: m.clickedcart.pathfind(listmap, snaptogrid(m.tl)) m.clickedcart = None m.mode = "select" elif event.type == pygame.MOUSEMOTION: m.pos(pygame.mouse.get_pos()) if screen == "game": m.hoveritem = None if len(pygame.sprite.spritecollide(m, carts, False)) > 0: m.hoveritem = pygame.sprite.spritecollide(m, carts, False)[0] elif len(pygame.sprite.spritecollide(m, interactables, False)) > 0: m.hoveritem = pygame.sprite.spritecollide(m, interactables, False)[0] elif event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: carts.add(map.Cart(snaptogrid(m.tl), "miner")) if screen == "game": window.fill([100, 100, 100]) tiles.draw(window) carts.draw(window) carts.update("update", m, listmap) if not m.hoveritem == None and not m.mode == "action": window.blit(box, [m.rect.left+10, m.rect.top+10]) ui.Resize(30) ui.Text(m.hoveritem.type.upper(), [m.rect.left+27, m.rect.top+25]) if m.hoveritem.type.startswith("mine") and m.hoveritem not in carts: ui.Resize(18) ui.Text("Carts Inside: " + str(m.hoveritem.data["carts"]), [m.rect.left+27, m.rect.top+47]) ui.Text("Max Carts: " + str(m.hoveritem.data["max"]), [m.rect.left+27, m.rect.top+60]) if not m.clickedcart == None: window.blit(selected2, [m.clickedcart.rect.left-2, m.clickedcart.rect.top-2]) if m.mode == "action": window.blit(box, [m.rect.left+10, m.rect.top+10]) ui.Resize(30) try: ui.Text(m.hoveritem.type.upper(), [m.rect.left+27, m.rect.top+25]) except: ui.Text(m.clickedcart.type.upper(), [m.rect.left+27, m.rect.top+25]) if listmap[snaptogrid(m.tl)[0]][snaptogrid(m.tl)[1]] > 0: ui.Resize(22) ui.Text("Click to move", [m.rect.left+27, m.rect.top+45]) ui.Text("Cart Here", [m.rect.left+27, m.rect.top+60]) window.blit(selected, [snaptogrid(m.tl)[0]*40-2, snaptogrid(m.tl)[1]*40-2]) window.blit(uibox, [555, 475]) pygame.display.flip() clock.tick(60) fps = clock.get_fps() pygame.quit()

40.292035

124

0.573468

7cde0e155e222f52e34bae521e25a21b28caf52a

550

py

Python

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

# Written by Kamran Bigdely # Example for Compose Methods: Extract Method. import math print('distance', get_distance()) # *** somewhere else in your program *** print('length', get_length())

26.190476

88

0.670909

7cde5911adb7d9da7046ae21614759503f243fc8

51,158

py

Python

sympy/integrals/prde.py

Abhi58/sympy

5ca228b17a7d44ef08a268ba1fa959d5763634af

[ "BSD-3-Clause" ]

2

2019-06-12T16:15:39.000Z

2019-10-06T10:40:59.000Z

sympy/integrals/prde.py

Abhi58/sympy

5ca228b17a7d44ef08a268ba1fa959d5763634af

[ "BSD-3-Clause" ]

null

sympy/integrals/prde.py

Abhi58/sympy

5ca228b17a7d44ef08a268ba1fa959d5763634af

[ "BSD-3-Clause" ]

1

2019-10-02T10:47:13.000Z

""" Algorithms for solving Parametric Risch Differential Equations. The methods used for solving Parametric Risch Differential Equations parallel those for solving Risch Differential Equations. See the outline in the docstring of rde.py for more information. The Parametric Risch Differential Equation problem is, given f, g1, ..., gm in K(t), to determine if there exist y in K(t) and c1, ..., cm in Const(K) such that Dy + f*y == Sum(ci*gi, (i, 1, m)), and to find such y and ci if they exist. For the algorithms here G is a list of tuples of factions of the terms on the right hand side of the equation (i.e., gi in k(t)), and Q is a list of terms on the right hand side of the equation (i.e., qi in k[t]). See the docstring of each function for more information. """ from __future__ import print_function, division from sympy.core import Dummy, ilcm, Add, Mul, Pow, S from sympy.core.compatibility import reduce, range from sympy.integrals.rde import (order_at, order_at_oo, weak_normalizer, bound_degree) from sympy.integrals.risch import (gcdex_diophantine, frac_in, derivation, residue_reduce, splitfactor, residue_reduce_derivation, DecrementLevel, recognize_log_derivative) from sympy.matrices import zeros, eye from sympy.polys import Poly, lcm, cancel, sqf_list from sympy.polys.polymatrix import PolyMatrix as Matrix from sympy.solvers import solve def prde_normal_denom(fa, fd, G, DE): """ Parametric Risch Differential Equation - Normal part of the denominator. Given a derivation D on k[t] and f, g1, ..., gm in k(t) with f weakly normalized with respect to t, return the tuple (a, b, G, h) such that a, h in k[t], b in k<t>, G = [g1, ..., gm] in k(t)^m, and for any solution c1, ..., cm in Const(k) and y in k(t) of Dy + f*y == Sum(ci*gi, (i, 1, m)), q == y*h in k<t> satisfies a*Dq + b*q == Sum(ci*Gi, (i, 1, m)). """ dn, ds = splitfactor(fd, DE) Gas, Gds = list(zip(*G)) gd = reduce(lambda i, j: i.lcm(j), Gds, Poly(1, DE.t)) en, es = splitfactor(gd, DE) p = dn.gcd(en) h = en.gcd(en.diff(DE.t)).quo(p.gcd(p.diff(DE.t))) a = dn*h c = a*h ba = a*fa - dn*derivation(h, DE)*fd ba, bd = ba.cancel(fd, include=True) G = [(c*A).cancel(D, include=True) for A, D in G] return (a, (ba, bd), G, h) def real_imag(ba, bd, gen): """ Helper function, to get the real and imaginary part of a rational function evaluated at sqrt(-1) without actually evaluating it at sqrt(-1) Separates the even and odd power terms by checking the degree of terms wrt mod 4. Returns a tuple (ba[0], ba[1], bd) where ba[0] is real part of the numerator ba[1] is the imaginary part and bd is the denominator of the rational function. """ bd = bd.as_poly(gen).as_dict() ba = ba.as_poly(gen).as_dict() denom_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in bd.items()] denom_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in bd.items()] bd_real = sum(r for r in denom_real) bd_imag = sum(r for r in denom_imag) num_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in ba.items()] num_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in ba.items()] ba_real = sum(r for r in num_real) ba_imag = sum(r for r in num_imag) ba = ((ba_real*bd_real + ba_imag*bd_imag).as_poly(gen), (ba_imag*bd_real - ba_real*bd_imag).as_poly(gen)) bd = (bd_real*bd_real + bd_imag*bd_imag).as_poly(gen) return (ba[0], ba[1], bd) def prde_special_denom(a, ba, bd, G, DE, case='auto'): """ Parametric Risch Differential Equation - Special part of the denominator. case is one of {'exp', 'tan', 'primitive'} for the hyperexponential, hypertangent, and primitive cases, respectively. For the hyperexponential (resp. hypertangent) case, given a derivation D on k[t] and a in k[t], b in k<t>, and g1, ..., gm in k(t) with Dt/t in k (resp. Dt/(t**2 + 1) in k, sqrt(-1) not in k), a != 0, and gcd(a, t) == 1 (resp. gcd(a, t**2 + 1) == 1), return the tuple (A, B, GG, h) such that A, B, h in k[t], GG = [gg1, ..., ggm] in k(t)^m, and for any solution c1, ..., cm in Const(k) and q in k<t> of a*Dq + b*q == Sum(ci*gi, (i, 1, m)), r == q*h in k[t] satisfies A*Dr + B*r == Sum(ci*ggi, (i, 1, m)). For case == 'primitive', k<t> == k[t], so it returns (a, b, G, 1) in this case. """ # TODO: Merge this with the very similar special_denom() in rde.py if case == 'auto': case = DE.case if case == 'exp': p = Poly(DE.t, DE.t) elif case == 'tan': p = Poly(DE.t**2 + 1, DE.t) elif case in ['primitive', 'base']: B = ba.quo(bd) return (a, B, G, Poly(1, DE.t)) else: raise ValueError("case must be one of {'exp', 'tan', 'primitive', " "'base'}, not %s." % case) nb = order_at(ba, p, DE.t) - order_at(bd, p, DE.t) nc = min([order_at(Ga, p, DE.t) - order_at(Gd, p, DE.t) for Ga, Gd in G]) n = min(0, nc - min(0, nb)) if not nb: # Possible cancellation. if case == 'exp': dcoeff = DE.d.quo(Poly(DE.t, DE.t)) with DecrementLevel(DE): # We are guaranteed to not have problems, # because case != 'base'. alphaa, alphad = frac_in(-ba.eval(0)/bd.eval(0)/a.eval(0), DE.t) etaa, etad = frac_in(dcoeff, DE.t) A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE) if A is not None: Q, m, z = A if Q == 1: n = min(n, m) elif case == 'tan': dcoeff = DE.d.quo(Poly(DE.t**2 + 1, DE.t)) with DecrementLevel(DE): # We are guaranteed to not have problems, # because case != 'base'. betaa, alphaa, alphad = real_imag(ba, bd*a, DE.t) betad = alphad etaa, etad = frac_in(dcoeff, DE.t) if recognize_log_derivative(2*betaa, betad, DE): A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE) B = parametric_log_deriv(betaa, betad, etaa, etad, DE) if A is not None and B is not None: Q, s, z = A # TODO: Add test if Q == 1: n = min(n, s/2) N = max(0, -nb) pN = p**N pn = p**-n # This is 1/h A = a*pN B = ba*pN.quo(bd) + Poly(n, DE.t)*a*derivation(p, DE).quo(p)*pN G = [(Ga*pN*pn).cancel(Gd, include=True) for Ga, Gd in G] h = pn # (a*p**N, (b + n*a*Dp/p)*p**N, g1*p**(N - n), ..., gm*p**(N - n), p**-n) return (A, B, G, h) def prde_linear_constraints(a, b, G, DE): """ Parametric Risch Differential Equation - Generate linear constraints on the constants. Given a derivation D on k[t], a, b, in k[t] with gcd(a, b) == 1, and G = [g1, ..., gm] in k(t)^m, return Q = [q1, ..., qm] in k[t]^m and a matrix M with entries in k(t) such that for any solution c1, ..., cm in Const(k) and p in k[t] of a*Dp + b*p == Sum(ci*gi, (i, 1, m)), (c1, ..., cm) is a solution of Mx == 0, and p and the ci satisfy a*Dp + b*p == Sum(ci*qi, (i, 1, m)). Because M has entries in k(t), and because Matrix doesn't play well with Poly, M will be a Matrix of Basic expressions. """ m = len(G) Gns, Gds = list(zip(*G)) d = reduce(lambda i, j: i.lcm(j), Gds) d = Poly(d, field=True) Q = [(ga*(d).quo(gd)).div(d) for ga, gd in G] if not all([ri.is_zero for _, ri in Q]): N = max([ri.degree(DE.t) for _, ri in Q]) M = Matrix(N + 1, m, lambda i, j: Q[j][1].nth(i)) else: M = Matrix(0, m, []) # No constraints, return the empty matrix. qs, _ = list(zip(*Q)) return (qs, M) def poly_linear_constraints(p, d): """ Given p = [p1, ..., pm] in k[t]^m and d in k[t], return q = [q1, ..., qm] in k[t]^m and a matrix M with entries in k such that Sum(ci*pi, (i, 1, m)), for c1, ..., cm in k, is divisible by d if and only if (c1, ..., cm) is a solution of Mx = 0, in which case the quotient is Sum(ci*qi, (i, 1, m)). """ m = len(p) q, r = zip(*[pi.div(d) for pi in p]) if not all([ri.is_zero for ri in r]): n = max([ri.degree() for ri in r]) M = Matrix(n + 1, m, lambda i, j: r[j].nth(i)) else: M = Matrix(0, m, []) # No constraints. return q, M def constant_system(A, u, DE): """ Generate a system for the constant solutions. Given a differential field (K, D) with constant field C = Const(K), a Matrix A, and a vector (Matrix) u with coefficients in K, returns the tuple (B, v, s), where B is a Matrix with coefficients in C and v is a vector (Matrix) such that either v has coefficients in C, in which case s is True and the solutions in C of Ax == u are exactly all the solutions of Bx == v, or v has a non-constant coefficient, in which case s is False Ax == u has no constant solution. This algorithm is used both in solving parametric problems and in determining if an element a of K is a derivative of an element of K or the logarithmic derivative of a K-radical using the structure theorem approach. Because Poly does not play well with Matrix yet, this algorithm assumes that all matrix entries are Basic expressions. """ if not A: return A, u Au = A.row_join(u) Au = Au.rref(simplify=cancel, normalize_last=False)[0] # Warning: This will NOT return correct results if cancel() cannot reduce # an identically zero expression to 0. The danger is that we might # incorrectly prove that an integral is nonelementary (such as # risch_integrate(exp((sin(x)**2 + cos(x)**2 - 1)*x**2), x). # But this is a limitation in computer algebra in general, and implicit # in the correctness of the Risch Algorithm is the computability of the # constant field (actually, this same correctness problem exists in any # algorithm that uses rref()). # # We therefore limit ourselves to constant fields that are computable # via the cancel() function, in order to prevent a speed bottleneck from # calling some more complex simplification function (rational function # coefficients will fall into this class). Furthermore, (I believe) this # problem will only crop up if the integral explicitly contains an # expression in the constant field that is identically zero, but cannot # be reduced to such by cancel(). Therefore, a careful user can avoid this # problem entirely by being careful with the sorts of expressions that # appear in his integrand in the variables other than the integration # variable (the structure theorems should be able to completely decide these # problems in the integration variable). Au = Au.applyfunc(cancel) A, u = Au[:, :-1], Au[:, -1] for j in range(A.cols): for i in range(A.rows): if A[i, j].has(*DE.T): # This assumes that const(F(t0, ..., tn) == const(K) == F Ri = A[i, :] # Rm+1; m = A.rows Rm1 = Ri.applyfunc(lambda x: derivation(x, DE, basic=True)/ derivation(A[i, j], DE, basic=True)) Rm1 = Rm1.applyfunc(cancel) um1 = cancel(derivation(u[i], DE, basic=True)/ derivation(A[i, j], DE, basic=True)) for s in range(A.rows): # A[s, :] = A[s, :] - A[s, i]*A[:, m+1] Asj = A[s, j] A.row_op(s, lambda r, jj: cancel(r - Asj*Rm1[jj])) # u[s] = u[s] - A[s, j]*u[m+1 u.row_op(s, lambda r, jj: cancel(r - Asj*um1)) A = A.col_join(Rm1) u = u.col_join(Matrix([um1])) return (A, u) def prde_spde(a, b, Q, n, DE): """ Special Polynomial Differential Equation algorithm: Parametric Version. Given a derivation D on k[t], an integer n, and a, b, q1, ..., qm in k[t] with deg(a) > 0 and gcd(a, b) == 1, return (A, B, Q, R, n1), with Qq = [q1, ..., qm] and R = [r1, ..., rm], such that for any solution c1, ..., cm in Const(k) and q in k[t] of degree at most n of a*Dq + b*q == Sum(ci*gi, (i, 1, m)), p = (q - Sum(ci*ri, (i, 1, m)))/a has degree at most n1 and satisfies A*Dp + B*p == Sum(ci*qi, (i, 1, m)) """ R, Z = list(zip(*[gcdex_diophantine(b, a, qi) for qi in Q])) A = a B = b + derivation(a, DE) Qq = [zi - derivation(ri, DE) for ri, zi in zip(R, Z)] R = list(R) n1 = n - a.degree(DE.t) return (A, B, Qq, R, n1) def prde_no_cancel_b_large(b, Q, n, DE): """ Parametric Poly Risch Differential Equation - No cancellation: deg(b) large enough. Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with b != 0 and either D == d/dt or deg(b) > max(0, deg(D) - 1), returns h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and Dq + b*q == Sum(ci*qi, (i, 1, m)), then q = Sum(dj*hj, (j, 1, r)), where d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0. """ db = b.degree(DE.t) m = len(Q) H = [Poly(0, DE.t)]*m for N in range(n, -1, -1): # [n, ..., 0] for i in range(m): si = Q[i].nth(N + db)/b.LC() sitn = Poly(si*DE.t**N, DE.t) H[i] = H[i] + sitn Q[i] = Q[i] - derivation(sitn, DE) - b*sitn if all(qi.is_zero for qi in Q): dc = -1 M = zeros(0, 2) else: dc = max([qi.degree(DE.t) for qi in Q]) M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i)) A, u = constant_system(M, zeros(dc + 1, 1), DE) c = eye(m) A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c)) return (H, A) def prde_no_cancel_b_small(b, Q, n, DE): """ Parametric Poly Risch Differential Equation - No cancellation: deg(b) small enough. Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with deg(b) < deg(D) - 1 and either D == d/dt or deg(D) >= 2, returns h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and Dq + b*q == Sum(ci*qi, (i, 1, m)) then q = Sum(dj*hj, (j, 1, r)) where d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0. """ m = len(Q) H = [Poly(0, DE.t)]*m for N in range(n, 0, -1): # [n, ..., 1] for i in range(m): si = Q[i].nth(N + DE.d.degree(DE.t) - 1)/(N*DE.d.LC()) sitn = Poly(si*DE.t**N, DE.t) H[i] = H[i] + sitn Q[i] = Q[i] - derivation(sitn, DE) - b*sitn if b.degree(DE.t) > 0: for i in range(m): si = Poly(Q[i].nth(b.degree(DE.t))/b.LC(), DE.t) H[i] = H[i] + si Q[i] = Q[i] - derivation(si, DE) - b*si if all(qi.is_zero for qi in Q): dc = -1 M = Matrix() else: dc = max([qi.degree(DE.t) for qi in Q]) M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i)) A, u = constant_system(M, zeros(dc + 1, 1), DE) c = eye(m) A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c)) return (H, A) # else: b is in k, deg(qi) < deg(Dt) t = DE.t if DE.case != 'base': with DecrementLevel(DE): t0 = DE.t # k = k0(t0) ba, bd = frac_in(b, t0, field=True) Q0 = [frac_in(qi.TC(), t0, field=True) for qi in Q] f, B = param_rischDE(ba, bd, Q0, DE) # f = [f1, ..., fr] in k^r and B is a matrix with # m + r columns and entries in Const(k) = Const(k0) # such that Dy0 + b*y0 = Sum(ci*qi, (i, 1, m)) has # a solution y0 in k with c1, ..., cm in Const(k) # if and only y0 = Sum(dj*fj, (j, 1, r)) where # d1, ..., dr ar in Const(k) and # B*Matrix([c1, ..., cm, d1, ..., dr]) == 0. # Transform fractions (fa, fd) in f into constant # polynomials fa/fd in k[t]. # (Is there a better way?) f = [Poly(fa.as_expr()/fd.as_expr(), t, field=True) for fa, fd in f] else: # Base case. Dy == 0 for all y in k and b == 0. # Dy + b*y = Sum(ci*qi) is solvable if and only if # Sum(ci*qi) == 0 in which case the solutions are # y = d1*f1 for f1 = 1 and any d1 in Const(k) = k. f = [Poly(1, t, field=True)] # r = 1 B = Matrix([[qi.TC() for qi in Q] + [S(0)]]) # The condition for solvability is # B*Matrix([c1, ..., cm, d1]) == 0 # There are no constraints on d1. # Coefficients of t^j (j > 0) in Sum(ci*qi) must be zero. d = max([qi.degree(DE.t) for qi in Q]) if d > 0: M = Matrix(d, m, lambda i, j: Q[j].nth(i + 1)) A, _ = constant_system(M, zeros(d, 1), DE) else: # No constraints on the hj. A = Matrix(0, m, []) # Solutions of the original equation are # y = Sum(dj*fj, (j, 1, r) + Sum(ei*hi, (i, 1, m)), # where ei == ci (i = 1, ..., m), when # A*Matrix([c1, ..., cm]) == 0 and # B*Matrix([c1, ..., cm, d1, ..., dr]) == 0 # Build combined constraint matrix with m + r + m columns. r = len(f) I = eye(m) A = A.row_join(zeros(A.rows, r + m)) B = B.row_join(zeros(B.rows, m)) C = I.row_join(zeros(m, r)).row_join(-I) return f + H, A.col_join(B).col_join(C) def prde_cancel_liouvillian(b, Q, n, DE): """ Pg, 237. """ H = [] # Why use DecrementLevel? Below line answers that: # Assuming that we can solve such problems over 'k' (not k[t]) if DE.case == 'primitive': with DecrementLevel(DE): ba, bd = frac_in(b, DE.t, field=True) for i in range(n, -1, -1): if DE.case == 'exp': # this re-checking can be avoided with DecrementLevel(DE): ba, bd = frac_in(b + i*derivation(DE.t, DE)/DE.t, DE.t, field=True) with DecrementLevel(DE): Qy = [frac_in(q.nth(i), DE.t, field=True) for q in Q] fi, Ai = param_rischDE(ba, bd, Qy, DE) fi = [Poly(fa.as_expr()/fd.as_expr(), DE.t, field=True) for fa, fd in fi] ri = len(fi) if i == n: M = Ai else: M = Ai.col_join(M.row_join(zeros(M.rows, ri))) Fi, hi = [None]*ri, [None]*ri # from eq. on top of p.238 (unnumbered) for j in range(ri): hji = fi[j]*DE.t**i hi[j] = hji # building up Sum(djn*(D(fjn*t^n) - b*fjnt^n)) Fi[j] = -(derivation(hji, DE) - b*hji) H += hi # in the next loop instead of Q it has # to be Q + Fi taking its place Q = Q + Fi return (H, M) def param_poly_rischDE(a, b, q, n, DE): """Polynomial solutions of a parametric Risch differential equation. Given a derivation D in k[t], a, b in k[t] relatively prime, and q = [q1, ..., qm] in k[t]^m, return h = [h1, ..., hr] in k[t]^r and a matrix A with m + r columns and entries in Const(k) such that a*Dp + b*p = Sum(ci*qi, (i, 1, m)) has a solution p of degree <= n in k[t] with c1, ..., cm in Const(k) if and only if p = Sum(dj*hj, (j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm, d1, ..., dr) is a solution of Ax == 0. """ m = len(q) if n < 0: # Only the trivial zero solution is possible. # Find relations between the qi. if all([qi.is_zero for qi in q]): return [], zeros(1, m) # No constraints. N = max([qi.degree(DE.t) for qi in q]) M = Matrix(N + 1, m, lambda i, j: q[j].nth(i)) A, _ = constant_system(M, zeros(M.rows, 1), DE) return [], A if a.is_ground: # Normalization: a = 1. a = a.LC() b, q = b.quo_ground(a), [qi.quo_ground(a) for qi in q] if not b.is_zero and (DE.case == 'base' or b.degree() > max(0, DE.d.degree() - 1)): return prde_no_cancel_b_large(b, q, n, DE) elif ((b.is_zero or b.degree() < DE.d.degree() - 1) and (DE.case == 'base' or DE.d.degree() >= 2)): return prde_no_cancel_b_small(b, q, n, DE) elif (DE.d.degree() >= 2 and b.degree() == DE.d.degree() - 1 and n > -b.as_poly().LC()/DE.d.as_poly().LC()): raise NotImplementedError("prde_no_cancel_b_equal() is " "not yet implemented.") else: # Liouvillian cases if DE.case == 'primitive' or DE.case == 'exp': return prde_cancel_liouvillian(b, q, n, DE) else: raise NotImplementedError("non-linear and hypertangent " "cases have not yet been implemented") # else: deg(a) > 0 # Iterate SPDE as long as possible cumulating coefficient # and terms for the recovery of original solutions. alpha, beta = 1, [0]*m while n >= 0: # and a, b relatively prime a, b, q, r, n = prde_spde(a, b, q, n, DE) beta = [betai + alpha*ri for betai, ri in zip(beta, r)] alpha *= a # Solutions p of a*Dp + b*p = Sum(ci*qi) correspond to # solutions alpha*p + Sum(ci*betai) of the initial equation. d = a.gcd(b) if not d.is_ground: break # a*Dp + b*p = Sum(ci*qi) may have a polynomial solution # only if the sum is divisible by d. qq, M = poly_linear_constraints(q, d) # qq = [qq1, ..., qqm] where qqi = qi.quo(d). # M is a matrix with m columns an entries in k. # Sum(fi*qi, (i, 1, m)), where f1, ..., fm are elements of k, is # divisible by d if and only if M*Matrix([f1, ..., fm]) == 0, # in which case the quotient is Sum(fi*qqi). A, _ = constant_system(M, zeros(M.rows, 1), DE) # A is a matrix with m columns and entries in Const(k). # Sum(ci*qqi) is Sum(ci*qi).quo(d), and the remainder is zero # for c1, ..., cm in Const(k) if and only if # A*Matrix([c1, ...,cm]) == 0. V = A.nullspace() # V = [v1, ..., vu] where each vj is a column matrix with # entries aj1, ..., ajm in Const(k). # Sum(aji*qi) is divisible by d with exact quotient Sum(aji*qqi). # Sum(ci*qi) is divisible by d if and only if ci = Sum(dj*aji) # (i = 1, ..., m) for some d1, ..., du in Const(k). # In that case, solutions of # a*Dp + b*p = Sum(ci*qi) = Sum(dj*Sum(aji*qi)) # are the same as those of # (a/d)*Dp + (b/d)*p = Sum(dj*rj) # where rj = Sum(aji*qqi). if not V: # No non-trivial solution. return [], eye(m) # Could return A, but this has # the minimum number of rows. Mqq = Matrix([qq]) # A single row. r = [(Mqq*vj)[0] for vj in V] # [r1, ..., ru] # Solutions of (a/d)*Dp + (b/d)*p = Sum(dj*rj) correspond to # solutions alpha*p + Sum(Sum(dj*aji)*betai) of the initial # equation. These are equal to alpha*p + Sum(dj*fj) where # fj = Sum(aji*betai). Mbeta = Matrix([beta]) f = [(Mbeta*vj)[0] for vj in V] # [f1, ..., fu] # # Solve the reduced equation recursively. # g, B = param_poly_rischDE(a.quo(d), b.quo(d), r, n, DE) # g = [g1, ..., gv] in k[t]^v and and B is a matrix with u + v # columns and entries in Const(k) such that # (a/d)*Dp + (b/d)*p = Sum(dj*rj) has a solution p of degree <= n # in k[t] if and only if p = Sum(ek*gk) where e1, ..., ev are in # Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0. # The solutions of the original equation are then # Sum(dj*fj, (j, 1, u)) + alpha*Sum(ek*gk, (k, 1, v)). # Collect solution components. h = f + [alpha*gk for gk in g] # Build combined relation matrix. A = -eye(m) for vj in V: A = A.row_join(vj) A = A.row_join(zeros(m, len(g))) A = A.col_join(zeros(B.rows, m).row_join(B)) return h, A def param_rischDE(fa, fd, G, DE): """ Solve a Parametric Risch Differential Equation: Dy + f*y == Sum(ci*Gi, (i, 1, m)). Given a derivation D in k(t), f in k(t), and G = [G1, ..., Gm] in k(t)^m, return h = [h1, ..., hr] in k(t)^r and a matrix A with m + r columns and entries in Const(k) such that Dy + f*y = Sum(ci*Gi, (i, 1, m)) has a solution y in k(t) with c1, ..., cm in Const(k) if and only if y = Sum(dj*hj, (j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm, d1, ..., dr) is a solution of Ax == 0. Elements of k(t) are tuples (a, d) with a and d in k[t]. """ m = len(G) q, (fa, fd) = weak_normalizer(fa, fd, DE) # Solutions of the weakly normalized equation Dz + f*z = q*Sum(ci*Gi) # correspond to solutions y = z/q of the original equation. gamma = q G = [(q*ga).cancel(gd, include=True) for ga, gd in G] a, (ba, bd), G, hn = prde_normal_denom(fa, fd, G, DE) # Solutions q in k<t> of a*Dq + b*q = Sum(ci*Gi) correspond # to solutions z = q/hn of the weakly normalized equation. gamma *= hn A, B, G, hs = prde_special_denom(a, ba, bd, G, DE) # Solutions p in k[t] of A*Dp + B*p = Sum(ci*Gi) correspond # to solutions q = p/hs of the previous equation. gamma *= hs g = A.gcd(B) a, b, g = A.quo(g), B.quo(g), [gia.cancel(gid*g, include=True) for gia, gid in G] # a*Dp + b*p = Sum(ci*gi) may have a polynomial solution # only if the sum is in k[t]. q, M = prde_linear_constraints(a, b, g, DE) # q = [q1, ..., qm] where qi in k[t] is the polynomial component # of the partial fraction expansion of gi. # M is a matrix with m columns and entries in k. # Sum(fi*gi, (i, 1, m)), where f1, ..., fm are elements of k, # is a polynomial if and only if M*Matrix([f1, ..., fm]) == 0, # in which case the sum is equal to Sum(fi*qi). M, _ = constant_system(M, zeros(M.rows, 1), DE) # M is a matrix with m columns and entries in Const(k). # Sum(ci*gi) is in k[t] for c1, ..., cm in Const(k) # if and only if M*Matrix([c1, ..., cm]) == 0, # in which case the sum is Sum(ci*qi). ## Reduce number of constants at this point V = M.nullspace() # V = [v1, ..., vu] where each vj is a column matrix with # entries aj1, ..., ajm in Const(k). # Sum(aji*gi) is in k[t] and equal to Sum(aji*qi) (j = 1, ..., u). # Sum(ci*gi) is in k[t] if and only is ci = Sum(dj*aji) # (i = 1, ..., m) for some d1, ..., du in Const(k). # In that case, # Sum(ci*gi) = Sum(ci*qi) = Sum(dj*Sum(aji*qi)) = Sum(dj*rj) # where rj = Sum(aji*qi) (j = 1, ..., u) in k[t]. if not V: # No non-trivial solution return [], eye(m) Mq = Matrix([q]) # A single row. r = [(Mq*vj)[0] for vj in V] # [r1, ..., ru] # Solutions of a*Dp + b*p = Sum(dj*rj) correspond to solutions # y = p/gamma of the initial equation with ci = Sum(dj*aji). try: # We try n=5. At least for prde_spde, it will always # terminate no matter what n is. n = bound_degree(a, b, r, DE, parametric=True) except NotImplementedError: # A temporary bound is set. Eventually, it will be removed. # the currently added test case takes large time # even with n=5, and much longer with large n's. n = 5 h, B = param_poly_rischDE(a, b, r, n, DE) # h = [h1, ..., hv] in k[t]^v and and B is a matrix with u + v # columns and entries in Const(k) such that # a*Dp + b*p = Sum(dj*rj) has a solution p of degree <= n # in k[t] if and only if p = Sum(ek*hk) where e1, ..., ev are in # Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0. # The solutions of the original equation for ci = Sum(dj*aji) # (i = 1, ..., m) are then y = Sum(ek*hk, (k, 1, v))/gamma. ## Build combined relation matrix with m + u + v columns. A = -eye(m) for vj in V: A = A.row_join(vj) A = A.row_join(zeros(m, len(h))) A = A.col_join(zeros(B.rows, m).row_join(B)) ## Eliminate d1, ..., du. W = A.nullspace() # W = [w1, ..., wt] where each wl is a column matrix with # entries blk (k = 1, ..., m + u + v) in Const(k). # The vectors (bl1, ..., blm) generate the space of those # constant families (c1, ..., cm) for which a solution of # the equation Dy + f*y == Sum(ci*Gi) exists. They generate # the space and form a basis except possibly when Dy + f*y == 0 # is solvable in k(t}. The corresponding solutions are # y = Sum(blk'*hk, (k, 1, v))/gamma, where k' = k + m + u. v = len(h) M = Matrix([wl[:m] + wl[-v:] for wl in W]) # excise dj's. N = M.nullspace() # N = [n1, ..., ns] where the ni in Const(k)^(m + v) are column # vectors generating the space of linear relations between # c1, ..., cm, e1, ..., ev. C = Matrix([ni[:] for ni in N]) # rows n1, ..., ns. return [hk.cancel(gamma, include=True) for hk in h], C def limited_integrate_reduce(fa, fd, G, DE): """ Simpler version of step 1 & 2 for the limited integration problem. Given a derivation D on k(t) and f, g1, ..., gn in k(t), return (a, b, h, N, g, V) such that a, b, h in k[t], N is a non-negative integer, g in k(t), V == [v1, ..., vm] in k(t)^m, and for any solution v in k(t), c1, ..., cm in C of f == Dv + Sum(ci*wi, (i, 1, m)), p = v*h is in k<t>, and p and the ci satisfy a*Dp + b*p == g + Sum(ci*vi, (i, 1, m)). Furthermore, if S1irr == Sirr, then p is in k[t], and if t is nonlinear or Liouvillian over k, then deg(p) <= N. So that the special part is always computed, this function calls the more general prde_special_denom() automatically if it cannot determine that S1irr == Sirr. Furthermore, it will automatically call bound_degree() when t is linear and non-Liouvillian, which for the transcendental case, implies that Dt == a*t + b with for some a, b in k*. """ dn, ds = splitfactor(fd, DE) E = [splitfactor(gd, DE) for _, gd in G] En, Es = list(zip(*E)) c = reduce(lambda i, j: i.lcm(j), (dn,) + En) # lcm(dn, en1, ..., enm) hn = c.gcd(c.diff(DE.t)) a = hn b = -derivation(hn, DE) N = 0 # These are the cases where we know that S1irr = Sirr, but there could be # others, and this algorithm will need to be extended to handle them. if DE.case in ['base', 'primitive', 'exp', 'tan']: hs = reduce(lambda i, j: i.lcm(j), (ds,) + Es) # lcm(ds, es1, ..., esm) a = hn*hs b -= (hn*derivation(hs, DE)).quo(hs) mu = min(order_at_oo(fa, fd, DE.t), min([order_at_oo(ga, gd, DE.t) for ga, gd in G])) # So far, all the above are also nonlinear or Liouvillian, but if this # changes, then this will need to be updated to call bound_degree() # as per the docstring of this function (DE.case == 'other_linear'). N = hn.degree(DE.t) + hs.degree(DE.t) + max(0, 1 - DE.d.degree(DE.t) - mu) else: # TODO: implement this raise NotImplementedError V = [(-a*hn*ga).cancel(gd, include=True) for ga, gd in G] return (a, b, a, N, (a*hn*fa).cancel(fd, include=True), V) def limited_integrate(fa, fd, G, DE): """ Solves the limited integration problem: f = Dv + Sum(ci*wi, (i, 1, n)) """ fa, fd = fa*Poly(1/fd.LC(), DE.t), fd.monic() # interpretting limited integration problem as a # parametric Risch DE problem Fa = Poly(0, DE.t) Fd = Poly(1, DE.t) G = [(fa, fd)] + G h, A = param_rischDE(Fa, Fd, G, DE) V = A.nullspace() V = [v for v in V if v[0] != 0] if not V: return None else: # we can take any vector from V, we take V[0] c0 = V[0][0] # v = [-1, c1, ..., cm, d1, ..., dr] v = V[0]/(-c0) r = len(h) m = len(v) - r - 1 C = list(v[1: m + 1]) y = -sum([v[m + 1 + i]*h[i][0].as_expr()/h[i][1].as_expr() \ for i in range(r)]) y_num, y_den = y.as_numer_denom() Ya, Yd = Poly(y_num, DE.t), Poly(y_den, DE.t) Y = Ya*Poly(1/Yd.LC(), DE.t), Yd.monic() return Y, C def parametric_log_deriv_heu(fa, fd, wa, wd, DE, c1=None): """ Parametric logarithmic derivative heuristic. Given a derivation D on k[t], f in k(t), and a hyperexponential monomial theta over k(t), raises either NotImplementedError, in which case the heuristic failed, or returns None, in which case it has proven that no solution exists, or returns a solution (n, m, v) of the equation n*f == Dv/v + m*Dtheta/theta, with v in k(t)* and n, m in ZZ with n != 0. If this heuristic fails, the structure theorem approach will need to be used. The argument w == Dtheta/theta """ # TODO: finish writing this and write tests c1 = c1 or Dummy('c1') p, a = fa.div(fd) q, b = wa.div(wd) B = max(0, derivation(DE.t, DE).degree(DE.t) - 1) C = max(p.degree(DE.t), q.degree(DE.t)) if q.degree(DE.t) > B: eqs = [p.nth(i) - c1*q.nth(i) for i in range(B + 1, C + 1)] s = solve(eqs, c1) if not s or not s[c1].is_Rational: # deg(q) > B, no solution for c. return None M, N = s[c1].as_numer_denom() nfmwa = N*fa*wd - M*wa*fd nfmwd = fd*wd Qv = is_log_deriv_k_t_radical_in_field(N*fa*wd - M*wa*fd, fd*wd, DE, 'auto') if Qv is None: # (N*f - M*w) is not the logarithmic derivative of a k(t)-radical. return None Q, v = Qv if Q.is_zero or v.is_zero: return None return (Q*N, Q*M, v) if p.degree(DE.t) > B: return None c = lcm(fd.as_poly(DE.t).LC(), wd.as_poly(DE.t).LC()) l = fd.monic().lcm(wd.monic())*Poly(c, DE.t) ln, ls = splitfactor(l, DE) z = ls*ln.gcd(ln.diff(DE.t)) if not z.has(DE.t): # TODO: We treat this as 'no solution', until the structure # theorem version of parametric_log_deriv is implemented. return None u1, r1 = (fa*l.quo(fd)).div(z) # (l*f).div(z) u2, r2 = (wa*l.quo(wd)).div(z) # (l*w).div(z) eqs = [r1.nth(i) - c1*r2.nth(i) for i in range(z.degree(DE.t))] s = solve(eqs, c1) if not s or not s[c1].is_Rational: # deg(q) <= B, no solution for c. return None M, N = s[c1].as_numer_denom() nfmwa = N.as_poly(DE.t)*fa*wd - M.as_poly(DE.t)*wa*fd nfmwd = fd*wd Qv = is_log_deriv_k_t_radical_in_field(nfmwa, nfmwd, DE) if Qv is None: # (N*f - M*w) is not the logarithmic derivative of a k(t)-radical. return None Q, v = Qv if Q.is_zero or v.is_zero: return None return (Q*N, Q*M, v) def is_deriv_k(fa, fd, DE): r""" Checks if Df/f is the derivative of an element of k(t). a in k(t) is the derivative of an element of k(t) if there exists b in k(t) such that a = Db. Either returns (ans, u), such that Df/f == Du, or None, which means that Df/f is not the derivative of an element of k(t). ans is a list of tuples such that Add(*[i*j for i, j in ans]) == u. This is useful for seeing exactly which elements of k(t) produce u. This function uses the structure theorem approach, which says that for any f in K, Df/f is the derivative of a element of K if and only if there are ri in QQ such that:: --- --- Dt \ r * Dt + \ r * i Df / i i / i --- = --. --- --- t f i in L i in E i K/C(x) K/C(x) Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of hyperexponential monomials of K over C(x)). If K is an elementary extension over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the transcendence degree of K over C(x). Furthermore, because Const_D(K) == Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x) and L_K/C(x) are disjoint. The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed recursively using this same function. Therefore, it is required to pass them as indices to D (or T). E_args are the arguments of the hyperexponentials indexed by E_K (i.e., if i is in E_K, then T[i] == exp(E_args[i])). This is needed to compute the final answer u such that Df/f == Du. log(f) will be the same as u up to a additive constant. This is because they will both behave the same as monomials. For example, both log(x) and log(2*x) == log(x) + log(2) satisfy Dt == 1/x, because log(2) is constant. Therefore, the term const is returned. const is such that log(const) + f == u. This is calculated by dividing the arguments of one logarithm from the other. Therefore, it is necessary to pass the arguments of the logarithmic terms in L_args. To handle the case where we are given Df/f, not f, use is_deriv_k_in_field(). See also ======== is_log_deriv_k_t_radical_in_field, is_log_deriv_k_t_radical """ # Compute Df/f dfa, dfd = (fd*derivation(fa, DE) - fa*derivation(fd, DE)), fd*fa dfa, dfd = dfa.cancel(dfd, include=True) # Our assumption here is that each monomial is recursively transcendental if len(DE.exts) != len(DE.D): if [i for i in DE.cases if i == 'tan'] or \ (set([i for i in DE.cases if i == 'primitive']) - set(DE.indices('log'))): raise NotImplementedError("Real version of the structure " "theorems with hypertangent support is not yet implemented.") # TODO: What should really be done in this case? raise NotImplementedError("Nonelementary extensions not supported " "in the structure theorems.") E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')] L_part = [DE.D[i].as_expr() for i in DE.indices('log')] lhs = Matrix([E_part + L_part]) rhs = Matrix([dfa.as_expr()/dfd.as_expr()]) A, u = constant_system(lhs, rhs, DE) if not all(derivation(i, DE, basic=True).is_zero for i in u) or not A: # If the elements of u are not all constant # Note: See comment in constant_system # Also note: derivation(basic=True) calls cancel() return None else: if not all(i.is_Rational for i in u): raise NotImplementedError("Cannot work with non-rational " "coefficients in this case.") else: terms = ([DE.extargs[i] for i in DE.indices('exp')] + [DE.T[i] for i in DE.indices('log')]) ans = list(zip(terms, u)) result = Add(*[Mul(i, j) for i, j in ans]) argterms = ([DE.T[i] for i in DE.indices('exp')] + [DE.extargs[i] for i in DE.indices('log')]) l = [] ld = [] for i, j in zip(argterms, u): # We need to get around things like sqrt(x**2) != x # and also sqrt(x**2 + 2*x + 1) != x + 1 # Issue 10798: i need not be a polynomial i, d = i.as_numer_denom() icoeff, iterms = sqf_list(i) l.append(Mul(*([Pow(icoeff, j)] + [Pow(b, e*j) for b, e in iterms]))) dcoeff, dterms = sqf_list(d) ld.append(Mul(*([Pow(dcoeff, j)] + [Pow(b, e*j) for b, e in dterms]))) const = cancel(fa.as_expr()/fd.as_expr()/Mul(*l)*Mul(*ld)) return (ans, result, const) def is_log_deriv_k_t_radical(fa, fd, DE, Df=True): r""" Checks if Df is the logarithmic derivative of a k(t)-radical. b in k(t) can be written as the logarithmic derivative of a k(t) radical if there exist n in ZZ and u in k(t) with n, u != 0 such that n*b == Du/u. Either returns (ans, u, n, const) or None, which means that Df cannot be written as the logarithmic derivative of a k(t)-radical. ans is a list of tuples such that Mul(*[i**j for i, j in ans]) == u. This is useful for seeing exactly what elements of k(t) produce u. This function uses the structure theorem approach, which says that for any f in K, Df is the logarithmic derivative of a K-radical if and only if there are ri in QQ such that:: --- --- Dt \ r * Dt + \ r * i / i i / i --- = Df. --- --- t i in L i in E i K/C(x) K/C(x) Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of hyperexponential monomials of K over C(x)). If K is an elementary extension over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the transcendence degree of K over C(x). Furthermore, because Const_D(K) == Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x) and L_K/C(x) are disjoint. The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed recursively using this same function. Therefore, it is required to pass them as indices to D (or T). L_args are the arguments of the logarithms indexed by L_K (i.e., if i is in L_K, then T[i] == log(L_args[i])). This is needed to compute the final answer u such that n*f == Du/u. exp(f) will be the same as u up to a multiplicative constant. This is because they will both behave the same as monomials. For example, both exp(x) and exp(x + 1) == E*exp(x) satisfy Dt == t. Therefore, the term const is returned. const is such that exp(const)*f == u. This is calculated by subtracting the arguments of one exponential from the other. Therefore, it is necessary to pass the arguments of the exponential terms in E_args. To handle the case where we are given Df, not f, use is_log_deriv_k_t_radical_in_field(). See also ======== is_log_deriv_k_t_radical_in_field, is_deriv_k """ H = [] if Df: dfa, dfd = (fd*derivation(fa, DE) - fa*derivation(fd, DE)).cancel(fd**2, include=True) else: dfa, dfd = fa, fd # Our assumption here is that each monomial is recursively transcendental if len(DE.exts) != len(DE.D): if [i for i in DE.cases if i == 'tan'] or \ (set([i for i in DE.cases if i == 'primitive']) - set(DE.indices('log'))): raise NotImplementedError("Real version of the structure " "theorems with hypertangent support is not yet implemented.") # TODO: What should really be done in this case? raise NotImplementedError("Nonelementary extensions not supported " "in the structure theorems.") E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')] L_part = [DE.D[i].as_expr() for i in DE.indices('log')] lhs = Matrix([E_part + L_part]) rhs = Matrix([dfa.as_expr()/dfd.as_expr()]) A, u = constant_system(lhs, rhs, DE) if not all(derivation(i, DE, basic=True).is_zero for i in u) or not A: # If the elements of u are not all constant # Note: See comment in constant_system # Also note: derivation(basic=True) calls cancel() return None else: if not all(i.is_Rational for i in u): # TODO: But maybe we can tell if they're not rational, like # log(2)/log(3). Also, there should be an option to continue # anyway, even if the result might potentially be wrong. raise NotImplementedError("Cannot work with non-rational " "coefficients in this case.") else: n = reduce(ilcm, [i.as_numer_denom()[1] for i in u]) u *= n terms = ([DE.T[i] for i in DE.indices('exp')] + [DE.extargs[i] for i in DE.indices('log')]) ans = list(zip(terms, u)) result = Mul(*[Pow(i, j) for i, j in ans]) # exp(f) will be the same as result up to a multiplicative # constant. We now find the log of that constant. argterms = ([DE.extargs[i] for i in DE.indices('exp')] + [DE.T[i] for i in DE.indices('log')]) const = cancel(fa.as_expr()/fd.as_expr() - Add(*[Mul(i, j/n) for i, j in zip(argterms, u)])) return (ans, result, n, const) def is_log_deriv_k_t_radical_in_field(fa, fd, DE, case='auto', z=None): """ Checks if f can be written as the logarithmic derivative of a k(t)-radical. It differs from is_log_deriv_k_t_radical(fa, fd, DE, Df=False) for any given fa, fd, DE in that it finds the solution in the given field not in some (possibly unspecified extension) and "in_field" with the function name is used to indicate that. f in k(t) can be written as the logarithmic derivative of a k(t) radical if there exist n in ZZ and u in k(t) with n, u != 0 such that n*f == Du/u. Either returns (n, u) or None, which means that f cannot be written as the logarithmic derivative of a k(t)-radical. case is one of {'primitive', 'exp', 'tan', 'auto'} for the primitive, hyperexponential, and hypertangent cases, respectively. If case is 'auto', it will attempt to determine the type of the derivation automatically. See also ======== is_log_deriv_k_t_radical, is_deriv_k """ fa, fd = fa.cancel(fd, include=True) # f must be simple n, s = splitfactor(fd, DE) if not s.is_one: pass z = z or Dummy('z') H, b = residue_reduce(fa, fd, DE, z=z) if not b: # I will have to verify, but I believe that the answer should be # None in this case. This should never happen for the # functions given when solving the parametric logarithmic # derivative problem when integration elementary functions (see # Bronstein's book, page 255), so most likely this indicates a bug. return None roots = [(i, i.real_roots()) for i, _ in H] if not all(len(j) == i.degree() and all(k.is_Rational for k in j) for i, j in roots): # If f is the logarithmic derivative of a k(t)-radical, then all the # roots of the resultant must be rational numbers. return None # [(a, i), ...], where i*log(a) is a term in the log-part of the integral # of f respolys, residues = list(zip(*roots)) or [[], []] # Note: this might be empty, but everything below should work find in that # case (it should be the same as if it were [[1, 1]]) residueterms = [(H[j][1].subs(z, i), i) for j in range(len(H)) for i in residues[j]] # TODO: finish writing this and write tests p = cancel(fa.as_expr()/fd.as_expr() - residue_reduce_derivation(H, DE, z)) p = p.as_poly(DE.t) if p is None: # f - Dg will be in k[t] if f is the logarithmic derivative of a k(t)-radical return None if p.degree(DE.t) >= max(1, DE.d.degree(DE.t)): return None if case == 'auto': case = DE.case if case == 'exp': wa, wd = derivation(DE.t, DE).cancel(Poly(DE.t, DE.t), include=True) with DecrementLevel(DE): pa, pd = frac_in(p, DE.t, cancel=True) wa, wd = frac_in((wa, wd), DE.t) A = parametric_log_deriv(pa, pd, wa, wd, DE) if A is None: return None n, e, u = A u *= DE.t**e elif case == 'primitive': with DecrementLevel(DE): pa, pd = frac_in(p, DE.t) A = is_log_deriv_k_t_radical_in_field(pa, pd, DE, case='auto') if A is None: return None n, u = A elif case == 'base': # TODO: we can use more efficient residue reduction from ratint() if not fd.is_sqf or fa.degree() >= fd.degree(): # f is the logarithmic derivative in the base case if and only if # f = fa/fd, fd is square-free, deg(fa) < deg(fd), and # gcd(fa, fd) == 1. The last condition is handled by cancel() above. return None # Note: if residueterms = [], returns (1, 1) # f had better be 0 in that case. n = reduce(ilcm, [i.as_numer_denom()[1] for _, i in residueterms], S(1)) u = Mul(*[Pow(i, j*n) for i, j in residueterms]) return (n, u) elif case == 'tan': raise NotImplementedError("The hypertangent case is " "not yet implemented for is_log_deriv_k_t_radical_in_field()") elif case in ['other_linear', 'other_nonlinear']: # XXX: If these are supported by the structure theorems, change to NotImplementedError. raise ValueError("The %s case is not supported in this function." % case) else: raise ValueError("case must be one of {'primitive', 'exp', 'tan', " "'base', 'auto'}, not %s" % case) common_denom = reduce(ilcm, [i.as_numer_denom()[1] for i in [j for _, j in residueterms]] + [n], S(1)) residueterms = [(i, j*common_denom) for i, j in residueterms] m = common_denom//n if common_denom != n*m: # Verify exact division raise ValueError("Inexact division") u = cancel(u**m*Mul(*[Pow(i, j) for i, j in residueterms])) return (common_denom, u)

40.123922

110

0.561222

7ce07b8311ef90c93682c15fc681abf9e95c0bb7

1,076

py

Python

ssh_telnet/netmiko/ex07_netmiko_command_mult_prompts.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-05T09:30:23.000Z

2022-03-09T13:27:56.000Z

ssh_telnet/netmiko/ex07_netmiko_command_mult_prompts.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

null

ssh_telnet/netmiko/ex07_netmiko_command_mult_prompts.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-06T03:44:35.000Z

2022-03-04T21:20:40.000Z

from pprint import pprint import yaml import netmiko import paramiko if __name__ == "__main__": with open("devices.yaml") as f: devices = yaml.safe_load(f) r1 = devices[0] out = send_cmd_with_prompt( r1, "copy run start", wait_for="Destination filename", confirmation="\n" ) print(out) """ R1#copy run start Destination filename [startup-config]? Building configuration... [OK] R1# """

25.619048

84

0.616171

7ce146b894402021fe89e46e79f310a76ff9ef08

2,479

py

Python

LightTestLoop.py

Growing-Beyond-Earth/GettingStarted

04c2fd5fa36224ac25a6c6c62c4d6e558b27e700

[ "Apache-2.0" ]

null

LightTestLoop.py

Growing-Beyond-Earth/GettingStarted

04c2fd5fa36224ac25a6c6c62c4d6e558b27e700

[ "Apache-2.0" ]

null

LightTestLoop.py

Growing-Beyond-Earth/GettingStarted

04c2fd5fa36224ac25a6c6c62c4d6e558b27e700

[ "Apache-2.0" ]

null

# GROWNG BEYOND EARTH CONTROL BOX Traning # RASPBERRY PI PICO / MICROPYTHON # FAIRCHILD TROPICAL BOTANIC GARDEN, Oct 18, 2021 # The Growing Beyond Earth (GBE) control box is a device that controls # the LED lights and fan in a GBE growth chamber. It can also control # accessories including a 12v water pump and environmental sensors. # The device is based on a Raspberry Pi Pico microcontroller running # Micropython. # lesson Written by @MarioTheMaker from sys import stdin, stdout, exit import machine import time #Set the brightness for each color red_brightness = 100 green_brightness = 100 blue_brightness = 100 white_brightness = 100 # Pulse width modulation (PWM) is a way to get an artificial analog output on a digital pin. # It achieves this by rapidly toggling the pin from low to high. There are two parameters # associated with this: the frequency of the toggling, and the duty cycle. # The duty cycle is defined to be how long the pin is high compared with the length of a # single period (low plus high time). Maximum duty cycle is when the pin is high all of the # time, and minimum is when it is low all of the time. # https://projects.raspberrypi.org/en/projects/getting-started-with-the-pico/7#: # control I/O pins # machine.Pin(id, mode=- 1, pull=- 1, *, value, drive, alt) # Access the pin peripheral (GPIO pin) associated with the given id. # If additional arguments are given in the constructor then they are used to initialise # the pin. Any settings that are not specified will remain in their previous state. # More info https://docs.micropython.org/en/latest/library/machine.Pin.html r=machine.PWM(machine.Pin(0)); r.freq(20000) # Red channel g=machine.PWM(machine.Pin(2)); g.freq(20000) # Green channel b=machine.PWM(machine.Pin(1)); b.freq(20000) # Blue channel w=machine.PWM(machine.Pin(3)); w.freq(20000) # White channel # More info https://docs.micropython.org/en/latest/library/machine.PWM.html # Start a loop and change the brightness multiplier "n" # PWM.duty_u16([value]) Get the current duty cycle of the PWM output, # as an unsigned 16-bit value in the range 0 to 65535 inclusive. n = 100 while n > 0: print("Power Level ",n) r.duty_u16(int(red_brightness)*n) g.duty_u16(int(green_brightness)*n) b.duty_u16(int(blue_brightness)*n) w.duty_u16(int(white_brightness)*n) time.sleep(.3) n = n - 5 #Turn all the lights off time.sleep(3) r.duty_u16(0) g.duty_u16(0) b.duty_u16(0) w.duty_u16(0)

38.138462

92

0.745058

7ce15c82ddc26277baddffb09d13b58c226ab5d6

3,409

py

Python

core/known_bugs_utils.py

nicolasbock/hotsos

6a0d650a8d76b5a5f85f4ddc8c0a9f8939e1de7a

[ "Apache-2.0" ]

null

core/known_bugs_utils.py

nicolasbock/hotsos

6a0d650a8d76b5a5f85f4ddc8c0a9f8939e1de7a

[ "Apache-2.0" ]

null

core/known_bugs_utils.py

nicolasbock/hotsos

6a0d650a8d76b5a5f85f4ddc8c0a9f8939e1de7a

[ "Apache-2.0" ]

null

import os import yaml from core import plugintools from core import constants from core.searchtools import SearchDef from core.issues.issue_utils import IssueEntry LAUNCHPAD = "launchpad" MASTER_YAML_KNOWN_BUGS_KEY = "bugs-detected" KNOWN_BUGS = {MASTER_YAML_KNOWN_BUGS_KEY: []} def _get_known_bugs(): """ Fetch the current plugin known_bugs.yaml if it exists and return its contents or None if it doesn't exist yet. """ if not os.path.isdir(constants.PLUGIN_TMP_DIR): raise Exception("plugin tmp dir '{}' not found". format(constants.PLUGIN_TMP_DIR)) known_bugs_yaml = os.path.join(constants.PLUGIN_TMP_DIR, "known_bugs.yaml") if not os.path.exists(known_bugs_yaml): return {} bugs = yaml.safe_load(open(known_bugs_yaml)) if bugs and bugs.get(MASTER_YAML_KNOWN_BUGS_KEY): return bugs return {} def add_known_bug(bug_id, description=None, type=LAUNCHPAD): """ Fetch the current plugin known_bugs.yaml if it exists and add new bug with description of the bug. """ if not os.path.isdir(constants.PLUGIN_TMP_DIR): raise Exception("plugin tmp dir '{}' not found". format(constants.PLUGIN_TMP_DIR)) if type == LAUNCHPAD: new_bug = "https://bugs.launchpad.net/bugs/{}".format(bug_id) if description is None: description = "no description provided" entry = IssueEntry(new_bug, description, key="id") current = _get_known_bugs() if current and current.get(MASTER_YAML_KNOWN_BUGS_KEY): current[MASTER_YAML_KNOWN_BUGS_KEY].append(entry.data) else: current = {MASTER_YAML_KNOWN_BUGS_KEY: [entry.data]} known_bugs_yaml = os.path.join(constants.PLUGIN_TMP_DIR, "known_bugs.yaml") with open(known_bugs_yaml, 'w') as fd: fd.write(yaml.dump(current)) def add_known_bugs_to_master_plugin(): """ Fetch the current plugin known_bugs.yaml and add it to the master yaml. Note that this can only be called once per plugin and is typically performed as a final part after all others have executed. """ bugs = _get_known_bugs() if bugs and bugs.get(MASTER_YAML_KNOWN_BUGS_KEY): plugintools.save_part(bugs, priority=99)

33.097087

79

0.680845

7ce16cb7ee2c1e090289468a70fd88401aba8ddc

339

py

Python

examples/xml-rpc/echoserver.py

keobox/yap101

26913da9f61ef3d0d9cb3ef54bbfc451a9ef9de9

[ "MIT" ]

null

examples/xml-rpc/echoserver.py

keobox/yap101

26913da9f61ef3d0d9cb3ef54bbfc451a9ef9de9

[ "MIT" ]

null

examples/xml-rpc/echoserver.py

keobox/yap101

26913da9f61ef3d0d9cb3ef54bbfc451a9ef9de9

[ "MIT" ]

null

import SimpleXMLRPCServer as xmls server = echoserver(('127.0.0.1', 8001)) server.register_function(echo, 'echo') print 'Listening on port 8001' try: server.serve_forever() except: server.server_close()

19.941176

42

0.728614

7ce1993cbdc65a6d053c9478a3f9b9475d29bb5c

7,083

py

Python

tf_pose/slim/nets/mobilenet/mobilenet_v2_test.py

gpspelle/pose-estimation

b817dcc120092002984d8a41431046f323bc02c8

[ "Apache-2.0" ]

862

2019-12-11T18:40:48.000Z

2022-03-29T15:23:58.000Z

tf_pose/slim/nets/mobilenet/mobilenet_v2_test.py

bvanelli/tf-pose-estimation

1dec506ac8abf00616dc0fe76bf476ccdfd6b93e

[ "Apache-2.0" ]

72

2019-05-07T18:33:32.000Z

2022-03-10T07:48:39.000Z

tf_pose/slim/nets/mobilenet/mobilenet_v2_test.py

bvanelli/tf-pose-estimation

1dec506ac8abf00616dc0fe76bf476ccdfd6b93e

[ "Apache-2.0" ]

165

2019-12-11T20:04:22.000Z

2022-03-29T06:18:12.000Z

# Copyright 2018 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. # ============================================================================== """Tests for mobilenet_v2.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import tensorflow as tf from nets.mobilenet import conv_blocks as ops from nets.mobilenet import mobilenet from nets.mobilenet import mobilenet_v2 slim = tf.contrib.slim def find_ops(optype): """Find ops of a given type in graphdef or a graph. Args: optype: operation type (e.g. Conv2D) Returns: List of operations. """ gd = tf.get_default_graph() return [var for var in gd.get_operations() if var.type == optype] if __name__ == '__main__': tf.test.main()

37.278947

80

0.674432

7ce2ce6e7522a59e86a553aeb0f5ee90bd00e269

1,402

py

Python

firebase-gist.py

darwin/firebase-gist

5aa4eb89e82fbf2971d7afca07471e1f51ff6e51

[ "MIT" ]

1

2017-08-15T15:37:21.000Z

firebase-gist.py

darwin/firebase-gist

5aa4eb89e82fbf2971d7afca07471e1f51ff6e51

[ "MIT" ]

null

firebase-gist.py

darwin/firebase-gist

5aa4eb89e82fbf2971d7afca07471e1f51ff6e51

[ "MIT" ]

null

from firebase import firebase import os import datetime import json import logging from boto.s3.connection import S3Connection from boto.s3.key import Key from github3 import login firebase_url = os.environ['FIREBASE_DB'] firebase_secret = os.environ['FIREBASE_SECRET'] firebase_path = os.environ['FIREBASE_PATH'] firebase_username = os.environ['FIREBASE_USERNAME'] # not checked ATM gh_token = os.environ['GH_TOKEN'] gh_gist = os.environ['GH_GIST'] gh_fname = os.environ['GH_FNAME'] logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) logger.info('==================================') logger.info('Fetching firebase data') f = connect_firebase() data = f.get(firebase_path, None) new_content = json.dumps(data, ensure_ascii=False, indent=2, sort_keys=True) logger.info('Reading existing gist') gh = login(token=gh_token) gist = gh.gist(gh_gist) old_content = "" for f in gist.iter_files(): if f.filename == gh_fname: old_content = f.content break if old_content == new_content: logger.info('No changes detected') else: logger.info('Updating gist with new content') gist.edit(files={ gh_fname: { "content": new_content } }) logger.info('Done.')

25.962963

100

0.738231

7ce32e38118a236e7f22400e28b670e7f2079e82

869

py

Python

practice/2008/qualification/C-Fly_swatter/c.py

victorWeiFreelancer/CodeJam

edb8f921860a35985823cb3dbd3ebec8a8f3c12f

[ "MIT" ]

null

practice/2008/qualification/C-Fly_swatter/c.py

victorWeiFreelancer/CodeJam

edb8f921860a35985823cb3dbd3ebec8a8f3c12f

[ "MIT" ]

null

practice/2008/qualification/C-Fly_swatter/c.py

victorWeiFreelancer/CodeJam

edb8f921860a35985823cb3dbd3ebec8a8f3c12f

[ "MIT" ]

null

import sys sys.dont_write_bytecode = True if __name__ == '__main__': main()

25.558824

69

0.537399

7ce34380af3cdc654ec22dc00486fd1079b00edb

25,614

py

Python

synapse/notifier.py

rkfg/synapse

0b3112123da5fae4964db784e3bab0c4d83d9d62

[ "Apache-2.0" ]

1

2021-09-09T08:50:13.000Z

synapse/notifier.py

rkfg/synapse

0b3112123da5fae4964db784e3bab0c4d83d9d62

[ "Apache-2.0" ]

null

synapse/notifier.py

rkfg/synapse

0b3112123da5fae4964db784e3bab0c4d83d9d62

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2014 - 2016 OpenMarket Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from collections import namedtuple from typing import ( Awaitable, Callable, Dict, Iterable, List, Optional, Set, Tuple, TypeVar, Union, ) import attr from prometheus_client import Counter from twisted.internet import defer import synapse.server from synapse.api.constants import EventTypes, HistoryVisibility, Membership from synapse.api.errors import AuthError from synapse.events import EventBase from synapse.handlers.presence import format_user_presence_state from synapse.logging.context import PreserveLoggingContext from synapse.logging.opentracing import log_kv, start_active_span from synapse.logging.utils import log_function from synapse.metrics import LaterGauge from synapse.streams.config import PaginationConfig from synapse.types import ( Collection, PersistedEventPosition, RoomStreamToken, StreamToken, UserID, ) from synapse.util.async_helpers import ObservableDeferred, timeout_deferred from synapse.util.metrics import Measure from synapse.visibility import filter_events_for_client logger = logging.getLogger(__name__) notified_events_counter = Counter("synapse_notifier_notified_events", "") users_woken_by_stream_counter = Counter( "synapse_notifier_users_woken_by_stream", "", ["stream"] ) T = TypeVar("T") # TODO(paul): Should be shared somewhere def count(func: Callable[[T], bool], it: Iterable[T]) -> int: """Return the number of items in it for which func returns true.""" n = 0 for x in it: if func(x): n += 1 return n def notify_replication(self) -> None: """Notify the any replication listeners that there's a new event""" for cb in self.replication_callbacks: cb() def notify_remote_server_up(self, server: str): """Notify any replication that a remote server has come back up""" # We call federation_sender directly rather than registering as a # callback as a) we already have a reference to it and b) it introduces # circular dependencies. if self.federation_sender: self.federation_sender.wake_destination(server)

36.025316

87

0.605216

7ce35b3e99fb727d7c2e93f64173851978f39e8c

30,955

py

Python

saleor/checkout/tests/test_base_calculations.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

null

saleor/checkout/tests/test_base_calculations.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

76

2021-11-01T04:53:42.000Z

2022-03-28T04:51:25.000Z

saleor/checkout/tests/test_base_calculations.py

nestfiy/saleor

6fce3bc5c0ca72ac28db99553e6d2b49249c6dac

[ "CC-BY-4.0" ]

null

from decimal import Decimal from prices import Money, TaxedMoney from ...discount import DiscountValueType, VoucherType from ...discount.utils import get_product_discount_on_sale from ..base_calculations import ( base_checkout_total, base_tax_rate, calculate_base_line_total_price, calculate_base_line_unit_price, ) from ..fetch import fetch_checkout_lines

37.475787

88

0.772767

7ce3c5ddd55aea55c48d0f942c54f7645b346e45

24,690

py

Python

tests/test_date.py

andy-z/ged4py

2270bd8366174dcc98424cc6671bdaecf770fda0

[ "MIT" ]

10

2017-07-25T22:39:34.000Z

2022-03-01T04:40:38.000Z

tests/test_date.py

andy-z/ged4py

2270bd8366174dcc98424cc6671bdaecf770fda0

[ "MIT" ]

20

2018-03-25T10:25:40.000Z

2021-05-02T20:38:48.000Z

tests/test_date.py

andy-z/ged4py

2270bd8366174dcc98424cc6671bdaecf770fda0

[ "MIT" ]

6

2018-04-29T12:45:34.000Z

2021-09-14T14:30:52.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `ged4py.date` module.""" import unittest from ged4py.calendar import ( CalendarType, CalendarDate, FrenchDate, GregorianDate, HebrewDate, JulianDate, CalendarDateVisitor ) from ged4py.date import ( DateValue, DateValueAbout, DateValueAfter, DateValueBefore, DateValueCalculated, DateValueEstimated, DateValueFrom, DateValueInterpreted, DateValuePeriod, DateValuePhrase, DateValueRange, DateValueSimple, DateValueTo, DateValueTypes, DateValueVisitor )

41.356784

108

0.637708

7ce3f2fa9ab64a2e056aae0886c6829b2b5285e6

7,722

py

Python

src/quart/local.py

Dunkledore/quart

803c8678b083895f4ece35fccb6aca56e189ee0a

[ "MIT" ]

3

2020-03-31T10:36:31.000Z

2020-04-23T12:01:10.000Z

venv/lib/python3.9/site-packages/quart/local.py

ryanwwest/kademlia

e1e5b84db0a7710cf372663325041850802d55f1

[ "MIT" ]

6

2020-09-05T01:40:23.000Z

2022-03-12T00:40:58.000Z

src/quart/local.py

ccns1/ccns11

d6edfac34fbee06fe974cda007d24a088d31ad30

[ "MIT" ]

1

2020-09-05T00:19:03.000Z

from __future__ import annotations import asyncio import copy from contextvars import ContextVar # noqa # contextvars not understood as stdlib from typing import Any # noqa # contextvars not understood as stdlib from typing import Callable, Dict, Optional

41.740541

95

0.630536

7ce4682d4472c3403cd709b201e4107d5de073fb

20,891

py

Python

pytorch3dunet/unet3d/predictor.py

searobbersduck/pytorch-3dunet

5bb8ed2b6966b2cd06b1dc676b62d1ad98329305

[ "MIT" ]

null

pytorch3dunet/unet3d/predictor.py

searobbersduck/pytorch-3dunet

5bb8ed2b6966b2cd06b1dc676b62d1ad98329305

[ "MIT" ]

null

pytorch3dunet/unet3d/predictor.py

searobbersduck/pytorch-3dunet

5bb8ed2b6966b2cd06b1dc676b62d1ad98329305

[ "MIT" ]

null

import time import h5py import hdbscan import numpy as np import torch from sklearn.cluster import MeanShift from pytorch3dunet.datasets.hdf5 import SliceBuilder from pytorch3dunet.unet3d.utils import get_logger from pytorch3dunet.unet3d.utils import unpad logger = get_logger('UNet3DPredictor') class StandardPredictor(_AbstractPredictor): """ Applies the model on the given dataset and saves the result in the `output_file` in the H5 format. Predictions from the network are kept in memory. If the results from the network don't fit in into RAM use `LazyPredictor` instead. The output dataset names inside the H5 is given by `des_dataset_name` config argument. If the argument is not present in the config 'predictions{n}' is used as a default dataset name, where `n` denotes the number of the output head from the network. Args: model (Unet3D): trained 3D UNet model used for prediction data_loader (torch.utils.data.DataLoader): input data loader output_file (str): path to the output H5 file config (dict): global config dict """ class LazyPredictor(StandardPredictor): """ Applies the model on the given dataset and saves the result in the `output_file` in the H5 format. Predicted patches are directly saved into the H5 and they won't be stored in memory. Since this predictor is slower than the `StandardPredictor` it should only be used when the predicted volume does not fit into RAM. The output dataset names inside the H5 is given by `des_dataset_name` config argument. If the argument is not present in the config 'predictions{n}' is used as a default dataset name, where `n` denotes the number of the output head from the network. Args: model (Unet3D): trained 3D UNet model used for prediction data_loader (torch.utils.data.DataLoader): input data loader output_file (str): path to the output H5 file config (dict): global config dict """ class EmbeddingsPredictor(_AbstractPredictor): """ Applies the embedding model on the given dataset and saves the result in the `output_file` in the H5 format. The resulting volume is the segmentation itself (not the embedding vectors) obtained by clustering embeddings with HDBSCAN or MeanShift algorithm patch by patch and then stitching the patches together. """ def _embeddings_to_segmentation(self, embeddings): """ Cluster embeddings vectors with HDBSCAN and return the segmented volume. Args: embeddings (ndarray): 4D (CDHW) embeddings tensor Returns: 3D (DHW) segmentation """ # shape of the output segmentation output_shape = embeddings.shape[1:] # reshape (C, D, H, W) -> (C, D * H * W) and transpose -> (D * H * W, C) flattened_embeddings = embeddings.reshape(embeddings.shape[0], -1).transpose() logger.info('Clustering embeddings...') # perform clustering and reshape in order to get the segmentation volume start = time.time() clusters = self.clustering.fit_predict(flattened_embeddings).reshape(output_shape) logger.info( f'Number of clusters found by {self.clustering}: {np.max(clusters)}. Duration: {time.time() - start} sec.') return clusters def _merge_segmentation(self, segmentation, index, output_segmentation, visited_voxels_array): """ Given the `segmentation` patch, its `index` in the `output_segmentation` array and the array visited voxels merge the segmented patch (`segmentation`) into the `output_segmentation` Args: segmentation (ndarray): segmented patch index (tuple): position of the patch inside `output_segmentation` volume output_segmentation (ndarray): current state of the output segmentation visited_voxels_array (ndarray): array of voxels visited so far (same size as `output_segmentation`); visited voxels will be marked by a number greater than 0 """ index = tuple(index) # get new unassigned label max_label = np.max(output_segmentation) + 1 # make sure there are no clashes between current segmentation patch and the output_segmentation # but keep the noise label noise_mask = segmentation == self.noise_label segmentation += int(max_label) segmentation[noise_mask] = self.noise_label # get the overlap mask in the current patch overlap_mask = visited_voxels_array[index] > 0 # get the new labels inside the overlap_mask new_labels = np.unique(segmentation[overlap_mask]) merged_labels = self._merge_labels(output_segmentation[index], new_labels, segmentation) # relabel new segmentation with the merged labels for current_label, new_label in merged_labels: segmentation[segmentation == new_label] = current_label # update the output_segmentation output_segmentation[index] = segmentation # visit the patch visited_voxels_array[index] += 1

49.387707

134

0.631372

7ce4ea0979a0d8bcdfade749e59f8ad94da264f2

3,487

py

Python

var/spack/repos/builtin/packages/visionary-dev-tools/package.py

electronicvisions/spack

d6121eb35b4948f7d8aef7ec7a305a5123a7439e

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-10T13:47:48.000Z

2019-04-17T13:05:17.000Z

var/spack/repos/builtin/packages/visionary-dev-tools/package.py

einc-eu/spack

15468b92ed21d970c0111ae19144e85e66746433

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

8

2021-05-28T06:39:59.000Z

2022-03-30T15:12:35.000Z

var/spack/repos/builtin/packages/visionary-dev-tools/package.py

einc-eu/spack

15468b92ed21d970c0111ae19144e85e66746433

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2018-04-06T09:04:11.000Z

2020-01-24T12:52:12.000Z

# Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import os.path as osp

33.854369

124

0.677086

7ce65373a75b86fe5dcecdc0e2146bc5ea3033e1

3,593

py

Python

extra/convertBAMtoPILFER.py

MartaLoBalastegui/XICRA

74a7e74379c7e1b3fc1360d2c609994e884ee37a

[ "MIT" ]

3

2021-05-16T21:13:22.000Z

2022-01-23T08:47:48.000Z

extra/convertBAMtoPILFER.py

MartaLoBalastegui/XICRA

74a7e74379c7e1b3fc1360d2c609994e884ee37a

[ "MIT" ]

16

2021-03-11T10:51:25.000Z

2022-03-12T01:02:00.000Z

extra/convertBAMtoPILFER.py

MartaLoBalastegui/XICRA

74a7e74379c7e1b3fc1360d2c609994e884ee37a

[ "MIT" ]

3

2021-03-05T10:07:38.000Z

2022-01-23T08:48:06.000Z

#usr/bin/env python ## useful imports import time import io import os import re import sys from sys import argv import subprocess ## ARGV if len (sys.argv) < 5: print ("\nUsage:") print ("python3 %s bam_file folder bedtools_bin samtools_bin logfile\n" %os.path.realpath(__file__)) exit() bam_file = os.path.abspath(argv[1]) folder = argv[2] bedtools_exe = argv[3] samtools_exe = argv[4] logFile = argv[5] # start output_file = open(logFile, 'a') output_file.write("\nConvert BAM to Pilfer Input file:\n") ## Variables dirname_name = os.path.dirname(bam_file) split_name = os.path.splitext( os.path.basename(bam_file) ) bed_file = folder + '/' + split_name[0] + '.bed' sam_file = folder + '/' + split_name[0] + '.sam' pilfer_tmp = folder + '/' + split_name[0] + '.tmp.pilfer.bed' pilfer_file = folder + '/' + split_name[0] + '.pilfer.bed' ## START print ("\n+ Converting BAM file into PILFER input file") ## generate bed file with bedtools bamtobed -i bam_file if (os.path.isfile(bed_file)): print ("\t+ File %s already exists" %bed_file) else: cmd_bedtools = "%s bamtobed -i %s > %s" %(bedtools_exe, bam_file, bed_file) output_file.write(cmd_bedtools) output_file.write("\n") try: subprocess.check_output(cmd_bedtools, shell = True) except Exception as exc: print ('***ERROR:') print (cmd_bedtools) print('bedtools command generated an exception: %s' %exc) exit() ## generate samtools if (os.path.isfile(sam_file)): print ("\t+ File %s already exists" %sam_file) else: cmd_samtools = "%s view %s > %s" %(samtools_exe, bam_file, sam_file) output_file.write(cmd_samtools) output_file.write("\n") try: subprocess.check_output(cmd_samtools, shell = True) except Exception as exc: print ('***ERROR:') print (cmd_samtools) print('samtools view command generated an exception: %s' %exc) exit() ## generate paste filter tmp file if (os.path.isfile(pilfer_tmp)): print ("\t+ File %s already exists" %pilfer_tmp) else: ## paste Aligned.sortedByCoord.out.bed Aligned.sortedByCoord.out.sam | awk -v "OFS=\t" '{print $1, $2, $3, $16, $6}' cmd_paste = "paste %s %s | awk -v \"OFS=\t\" \'{print $1, $2, $3, $16, $6}\' > %s" %(bed_file, sam_file, pilfer_tmp) output_file.write(cmd_paste) output_file.write("\n") try: subprocess.check_output(cmd_paste, shell = True) except Exception as exc: print ('***ERROR:') print (cmd_paste) print('paste bed sam command generated an exception: %s' %exc) exit() ## parse pilfer tmp file counter = 1 previous_line = () # Open file OUT output_file = open(pilfer_file, 'w') # Open file IN fileHandler = open (pilfer_tmp, "r") while True: # Get next line from file line = fileHandler.readline().strip() # If line is empty then end of file reached if not line : break; seq = line.split('\t')[3] real_seq = seq.split('::PU') seq_len = len(str(real_seq[0])) ## Discard smaller if (previous_line): if (previous_line == line): line = previous_line counter += 1 else: line_split = previous_line.split('\t') output_file.write('%s\t%s\t%s\t%s::PI\t%s\t%s\n' %(line_split[0], line_split[1], line_split[2], line_split[3], counter, line_split[4])) #counter += 1 while True: #get next line next_line = fileHandler.readline().strip() if (next_line == line): counter += 1 else: line_split = line.split('\t') output_file.write('%s\t%s\t%s\t%s::PI\t%s\t%s\n' %(line_split[0], line_split[1], line_split[2], line_split[3], counter, line_split[4])) previous_line = next_line counter = 1 break; ## close and finish fileHandler.close() output_file.close()

27.219697

138

0.680768

7ce786042f57d81a5644876d6657fee00934ca96

154

py

Python

day7/main5list.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day7/main5list.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day7/main5list.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

a="Betty Bought a butter the butter was bitter so betty bought a better butter which was not bitter" v=[a[-1] for a in a.split() if(len(a)%2==0)] print(v)

51.333333

100

0.707792

7ce89a8d2a94f66d0921f4dfd7dff6f5d544c025

2,727

py

Python

app/reader.py

lcarnevale/proxy-mqtt2influx

89b3cd354b465d7451556a2d2ec49ac8688b4f17

[ "MIT" ]

null

app/reader.py

lcarnevale/proxy-mqtt2influx

89b3cd354b465d7451556a2d2ec49ac8688b4f17

[ "MIT" ]

null

app/reader.py

lcarnevale/proxy-mqtt2influx

89b3cd354b465d7451556a2d2ec49ac8688b4f17

[ "MIT" ]

null

# -*- coding: utf-8 -*- #!/usr/bin/env python """Writer class based on InfluxDB This implementation does its best to follow the Robert Martin's Clean code guidelines. The comments follows the Google Python Style Guide: https://github.com/google/styleguide/blob/gh-pages/pyguide.md """ __copyright__ = 'Copyright 2021, FCRlab at University of Messina' __author__ = 'Lorenzo Carnevale <[email protected]>' __credits__ = '' __description__ = 'Writer class based on InfluxDB' import time import logging import threading import persistqueue from datetime import datetime from influxdb_client.client.write_api import SYNCHRONOUS from influxdb_client import InfluxDBClient, Point, WritePrecision

32.464286

105

0.604694

7ce89c46f636fde71ee0a887ac7403a640c90ce5

1,781

py

Python

example_problems/tutorial/tiling_mxn-boards_with_1x2-boards/services/tell_if_tilable/tell_if_tilable_server.py

DottaPaperella/TALight

580322c3121c9acde9827f996fd4e39e31d93a6f

[ "MIT" ]

null

example_problems/tutorial/tiling_mxn-boards_with_1x2-boards/services/tell_if_tilable/tell_if_tilable_server.py

DottaPaperella/TALight

580322c3121c9acde9827f996fd4e39e31d93a6f

[ "MIT" ]

null

example_problems/tutorial/tiling_mxn-boards_with_1x2-boards/services/tell_if_tilable/tell_if_tilable_server.py

DottaPaperella/TALight

580322c3121c9acde9827f996fd4e39e31d93a6f

[ "MIT" ]

null

#!/usr/bin/env python3 from sys import stderr, exit, argv from random import randrange #from TALinputs import TALinput from multilanguage import Env, Lang, TALcolors # METADATA OF THIS TAL_SERVICE: problem="tiling_mxn-boards_with_1x2-boards" service="is_tilable" args_list = [ ('m',int), ('n',int), ('my_conjecture',str), ('h',int), ('k',int), ('lang',str), ('ISATTY',bool), ] ENV =Env(problem, service, args_list) TAc =TALcolors(ENV) LANG=Lang(ENV, TAc, lambda fstring: eval(f"f'{fstring}'")) TAc.print(LANG.opening_msg, "green") # START CODING YOUR SERVICE: assert ENV['h']==1 assert ENV['k']==2 print() if (ENV['m'] * ENV['n']) % 2 == 1: if ENV['my_conjecture'] == "yes": TAc.NO() print(LANG.render_feedback("FALSE-is-not-tilable", f"Contrary to what you have asserted, the {ENV['m']}x{ENV['n']}-grid is NOT tilable. If you are not convinced you can submit a tiling of that grid to the service 'check_my_tiling'.")) if ENV['my_conjecture'] == "no": TAc.OK() print(LANG.render_feedback("TRUE-is-not-tilable", f"You are perfecty right: the {ENV['m']}x{ENV['n']}-grid is NOT tilable.")) if (ENV['m'] * ENV['n']) % 2 == 0: if ENV['my_conjecture'] == "yes": TAc.OK() print(LANG.render_feedback("TRUE-is-tilable", f"We agree on the fact that the {ENV['m']}x{ENV['n']}-grid is tilable. If you want to exhibit us a tiling for this grid you can submit it to the service 'check_my_tiling'.")) if ENV['my_conjecture'] == "no": TAc.NO() print(LANG.render_feedback("FALSE-is-tilable", f"No, the {ENV['m']}x{ENV['n']}-grid is tilable. If you can not believe a tiling of the {ENV['m']}x{ENV['n']}-grid exists try the service 'gimme_hints_on_a_tiling'.")) exit(0)

35.62

242

0.64009

7ceab378038506dba92e4b8d3ecd8a07fc74f4a2

1,469

py

Python

tests/unit/peapods/runtimes/remote/ssh/test_ssh_remote.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

1

2020-12-23T12:34:00.000Z

tests/unit/peapods/runtimes/remote/ssh/test_ssh_remote.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

tests/unit/peapods/runtimes/remote/ssh/test_ssh_remote.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

import pytest from jina.enums import RemoteAccessType from jina.flow import Flow from jina.parser import set_pea_parser, set_pod_parser from jina.peapods.pods import BasePod from jina.peapods.runtimes.remote.ssh import SSHRuntime from jina.proto import jina_pb2

32.644444

96

0.701157

7ceacff0df961aecfe4df023a30c5731860a145d

181

py

Python

waio/factory/models/basic.py

dotX12/waio

6bc41df2d650f31fdb11a1a2b67c6149afa0e11a

[ "MIT" ]

24

2021-11-08T13:37:05.000Z

2022-03-24T12:49:54.000Z

waio/factory/models/basic.py

dotX12/waio

6bc41df2d650f31fdb11a1a2b67c6149afa0e11a

[ "MIT" ]

1

2022-01-19T03:11:58.000Z

2022-01-19T03:13:30.000Z

waio/factory/models/basic.py

dotX12/waio

6bc41df2d650f31fdb11a1a2b67c6149afa0e11a

[ "MIT" ]

5

2021-11-11T04:11:12.000Z

2022-02-15T10:41:58.000Z

from dataclasses import dataclass

12.066667

33

0.740331

7cec971c07d5ed98dc62f84b80e44472db92d7d3

531

py

Python

Uber/validExpression.py

Nithanaroy/random_scripts

908e539e2b7050a09e03b4fc0d2621b23733d65a

[ "MIT" ]

null

Uber/validExpression.py

Nithanaroy/random_scripts

908e539e2b7050a09e03b4fc0d2621b23733d65a

[ "MIT" ]

null

Uber/validExpression.py

Nithanaroy/random_scripts

908e539e2b7050a09e03b4fc0d2621b23733d65a

[ "MIT" ]

null

if __name__ =='__main__': print main('{(abc})')

25.285714

57

0.551789

7cee12e1d9ee7123f1ed98d591b1f1a9ee9c89f2

10,845

py

Python

sgains/tool.py

KrasnitzLab/sgains

501c42bfdad4542725f00ca8199983eccf8c0b3f

[ "MIT" ]

1

2017-09-08T05:09:59.000Z

sgains/tool.py

KrasnitzLab/sgains

501c42bfdad4542725f00ca8199983eccf8c0b3f

[ "MIT" ]

35

2017-07-31T04:13:40.000Z

2019-09-06T13:32:17.000Z

sgains/tool.py

KrasnitzLab/sgains

501c42bfdad4542725f00ca8199983eccf8c0b3f

[ "MIT" ]

3

2017-09-08T05:10:34.000Z

2019-06-11T09:06:41.000Z

import os import sys from copy import deepcopy import traceback import functools from collections import defaultdict import yaml from argparse import ArgumentParser,\ RawDescriptionHelpFormatter, ArgumentDefaultsHelpFormatter from sgains.configuration.parser import SgainsValidator, Config from sgains.configuration.schema import sgains_schema from sgains.executor import Executor from sgains.pipelines.mappableregions_pipeline import MappableRegionsPipeline from sgains.pipelines.genomeindex_pipeline import GenomeIndexPipeline from sgains.pipelines.bins_pipeline import BinsPipeline from sgains.pipelines.mapping_pipeline import MappingPipeline from sgains.pipelines.extract_10x_pipeline import Extract10xPipeline from sgains.pipelines.varbin_10x_pipeline import Varbin10xPipeline from sgains.pipelines.varbin_pipeline import VarbinPipeline from sgains.pipelines.r_pipeline import Rpipeline from sgains.pipelines.composite_pipeline import CompositePipeline SGAINS_COMMANDS = { "genomeindex": { "config_groups": ["aligner", "genome"], "help": "builds appropriate hisat2 or bowtie index for the " "reference genome", }, "mappable_regions": { "config_groups": ["aligner", "genome", "mappable_regions", "sge"], "help": "finds all mappable regions in specified genome", }, "bins": { "config_groups": ["genome", "mappable_regions", "bins", "sge"], "help": "calculates all bins boundaries for specified bins count " "and read length", }, "prepare": { "config_groups": [ "aligner", "genome", "mappable_regions", "bins", "sge"], "help": "combines all preparation steps ('genome', 'mappable-regions' " "and 'bins') into single command", }, "mapping": { "config_groups": ["aligner", "genome", "reads", "mapping", "sge"], "help": "performs mapping of cells reads to the reference genome", }, "extract_10x": { "config_groups": [ "data_10x", "reads", "sge"], "help": "extracts cells reads from 10x Genomics datasets", }, "varbin": { "config_groups": ["bins", "mapping", "varbin", "sge"], "help": "applies varbin algorithm to count read mappings in each bin", }, "varbin_10x": { "config_groups": [ "data_10x", "bins", "varbin", "sge"], "help": "applies varbin algorithm to count read mappings in each bin " "to 10x Genomics datasets without realigning", }, "scclust": { "config_groups": ["bins", "varbin", "scclust"], "help": "segmentation and clustering based bin counts and " "preparation of the SCGV input data" }, "process": { "config_groups": [ "aligner", "genome", "reads", "mapping", "bins", "varbin", "scclust", "sge"], "help": "combines all process steps ('mapping', 'varbin' " "and 'scclust') into single command" }, } if __name__ == "__main__": sys.exit(main())

30.635593

79

0.616136

7cee8f95a77e8d2ded7b9467b41b6c25c5fb7cdf

3,135

py

Python

lib/modeling/VGG16.py

rsumner31/Detectron

021685d42f7e8ac097e2bcf79fecb645f211378e

[ "Apache-2.0" ]

429

2018-04-28T00:01:57.000Z

2021-12-18T12:53:22.000Z

lib/modeling/VGG16.py

absorbguo/Detectron

2f8161edc3092b0382cab535c977a180a8b3cc4d

[ "Apache-2.0" ]

54

2018-12-26T13:04:32.000Z

2020-04-24T04:09:30.000Z

lib/modeling/VGG16.py

absorbguo/Detectron

2f8161edc3092b0382cab535c977a180a8b3cc4d

[ "Apache-2.0" ]

96

2018-12-24T05:12:36.000Z

2021-04-23T15:51:21.000Z

# Copyright (c) 2017-present, Facebook, Inc. # # 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. ############################################################################## """VGG16 from https://arxiv.org/abs/1409.1556.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from core.config import cfg

41.25

78

0.648166

7ceed4646921c1456f0b28f435da564f3dae7896

2,913

py

Python

setup.py

yangjing1127/xmind2testcase

49a581159a0d8e028f89939777399493662df111

[ "MIT" ]

537

2018-12-26T03:02:54.000Z

2022-03-30T17:41:53.000Z

setup.py

yangjing1127/xmind2testcase

49a581159a0d8e028f89939777399493662df111

[ "MIT" ]

49

2019-01-08T09:59:15.000Z

2022-03-30T00:58:47.000Z

setup.py

yangjing1127/xmind2testcase

49a581159a0d8e028f89939777399493662df111

[ "MIT" ]

190

2018-12-29T07:09:48.000Z

2022-03-31T01:55:02.000Z

#!/usr/env/bin python # -*- coding: utf-8 -*- import io import os import sys from shutil import rmtree from setuptools import setup, find_packages, Command about = {} here = os.path.abspath(os.path.dirname(__file__)) with io.open(os.path.join(here, 'xmind2testcase', '__about__.py'), encoding='utf-8') as f: # custom exec(f.read(), about) with io.open('README.md', encoding='utf-8') as f: long_description = f.read() install_requires = [ # custom "xmind", "flask", "arrow", ] setup( name=about['__title__'], version=about['__version__'], description=about['__description__'], long_description=long_description, long_description_content_type='text/markdown', keywords=about['__keywords__'], author=about['__author__'], author_email=about['__author_email__'], url=about['__url__'], license=about['__license__'], packages=find_packages(exclude=['tests', 'test.*', 'docs']), # custom package_data={ # custom '': ['README.md'], 'webtool': ['static/*', 'static/css/*', 'static/guide/*', 'templates/*', 'schema.sql'], }, install_requires=install_requires, extras_require={}, python_requires='>=3.0, <4', # custom classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], entry_points={ # custom 'console_scripts': [ 'xmind2testcase=xmind2testcase.cli:cli_main', ] }, cmdclass={ # python3 setup.py pypi 'pypi': PyPiCommand } )

28.281553

100

0.604875

7cef0095a10852826052b744b28e1db78c985b8d

2,670

py

Python

skultrafast/styles.py

Tillsten/skultrafast

778eaf1539b6d85f21ac53b011472605673ef7e8

[ "BSD-3-Clause" ]

10

2019-02-17T15:57:51.000Z

2021-11-15T02:00:33.000Z

skultrafast/styles.py

cZahn/skultrafast

23572ba9ea32238f34a8a15390fb572ecd8bc6fa

[ "BSD-3-Clause" ]

1

2019-01-17T11:56:38.000Z

2019-07-11T15:30:58.000Z

skultrafast/styles.py

cZahn/skultrafast

23572ba9ea32238f34a8a15390fb572ecd8bc6fa

[ "BSD-3-Clause" ]

6

2018-11-08T14:11:06.000Z

2021-09-01T14:53:02.000Z

# -*- coding: utf-8 -*- """ Created on Thu Sep 17 21:33:24 2015 @author: Tillsten """ import matplotlib import matplotlib.pyplot as plt import numpy as np tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] tableau20 = [(r/255., g/255., b/255.) for r,g,b, in tableau20] #plt.rcParams['savefig.dpi'] = 110 #plt.rcParams['font.family'] = 'Vera Sans' out_ticks = {'xtick.direction': 'out', 'xtick.major.width': 1.5, 'xtick.minor.width': 1, 'xtick.major.size': 6, 'xtick.minor.size': 3, 'xtick.minor.visible': True, 'ytick.direction': 'out', 'ytick.major.width': 1.5, 'ytick.minor.width': 1, 'ytick.major.size': 6, 'ytick.minor.size': 3, 'ytick.minor.visible': True, 'axes.spines.top': False, 'axes.spines.right': False, 'text.hinting': True, 'axes.titlesize': 'xx-large', 'axes.titleweight': 'semibold', } plt.figure(figsize=(6,4)) with plt.style.context(out_ticks): ax = plt.subplot(111) x = np.linspace(0, 7, 1000) y = np.exp(-x/1.5)*np.cos(x/1*(2*np.pi))#*np.cos(x/0.05*(2*np.pi)) l, = plt.plot(x, np.exp(-x/1.5), lw=0.5, color='grey') l, = plt.plot(x, -np.exp(-x/1.5), lw=0.5, color='grey') l, = plt.plot(x, y, lw=1.1) #l.set_clip_on(0) plt.tick_params(which='both', top=False, right=False) plt.margins(0.01) ax.text(7, 1, r'$y(t)=\exp\left(-t/1.5\right)\cos(\omega_1t)\cos(\omega_2t)$', fontsize=18, va='top', ha='right') #plt.title("Hallo") plt.setp(plt.gca(), xlabel='Time [s]', ylabel='Amplitude') ax = plt.axes([0.57, 0.25, 0.3, .2]) #ax.plot(np.fft.fftfreq(x.size)[:y.size/2], abs(np.fft.fft(y))[:y.size/2]) ax.fill_between(np.fft.fftfreq(x.size, x[1]-x[0])[:y.size/2], abs(np.fft.fft(y))[:y.size/2], alpha=0.2, color='r') ax.set_xlim(0, 10) ax.set_xlabel("Frequency") ax.xaxis.labelpad = 1 plt.locator_params(nbins=4) plt.tick_params(which='both', top=False, right=False) plt.tick_params(which='minor', bottom=False, left=False) #plt.grid(1, axis='y', linestyle='-', alpha=0.3, lw=.5) plt.show()

37.083333

83

0.522846

7cef6acdfa1f2191c94118bdb071a657a3a738d4

3,634

py

Python

src/oci/devops/models/github_build_run_source.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

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

3

2020-09-10T22:09:45.000Z

2021-12-24T17:00:07.000Z

src/oci/devops/models/github_build_run_source.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

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

null

src/oci/devops/models/github_build_run_source.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

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

null

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from .build_run_source import BuildRunSource from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs

33.648148

245

0.660704

7cf24c69a41740779ba55ee7c2d11c15c8feec7e

12,133

py

Python

aiida_fleur/tests/tools/test_common_fleur_wf.py

anoopkcn/aiida-fleur

5d4cc2092b7c3ce5402f1d4b89787eae53b2e60f

[ "MIT" ]

null

aiida_fleur/tests/tools/test_common_fleur_wf.py

anoopkcn/aiida-fleur

5d4cc2092b7c3ce5402f1d4b89787eae53b2e60f

[ "MIT" ]

null

aiida_fleur/tests/tools/test_common_fleur_wf.py

anoopkcn/aiida-fleur

5d4cc2092b7c3ce5402f1d4b89787eae53b2e60f

[ "MIT" ]

null

from __future__ import absolute_import import pytest import os # is_code def test_get_inputs_fleur(): ''' Tests if get_inputs_fleur assembles inputs correctly. Note it is the work of FleurCalculation to check if input types are correct i.e. 'code' is a Fleur code etc. ''' from aiida_fleur.tools.common_fleur_wf import get_inputs_fleur from aiida.orm import Dict inputs = {'code': 'code', 'remote': 'remote', 'fleurinp': 'fleurinp', 'options': {'custom_scheduler_commands': 'test_command'}, 'label': 'label', 'description': 'description', 'settings': {'test': 1}, 'serial': False} results = get_inputs_fleur(**inputs) out_options = results['options'].get_dict() out_settings = results['settings'].get_dict() assert results['code'] == 'code' assert results['fleurinpdata'] == 'fleurinp' assert results['parent_folder'] == 'remote' assert results['description'] == 'description' assert results['label'] == 'label' assert out_options == {'custom_scheduler_commands': 'test_command', 'withmpi': True} assert out_settings == {'test': 1} inputs = {'code': 'code', 'remote': 'remote', 'fleurinp': 'fleurinp', 'options': {'custom_scheduler_commands': 'test_command'}, 'serial': True} results = get_inputs_fleur(**inputs) out_options = results['options'].get_dict() assert results['description'] == '' assert results['label'] == '' assert out_options == {'custom_scheduler_commands': 'test_command', 'withmpi': False, 'resources': {"num_machines": 1}} def test_get_inputs_inpgen(fixture_code, generate_structure): ''' Tests if get_inputs_fleur assembles inputs correctly. Note it is the work of FleurinputgenCalculation to check if input types are correct i.e. 'code' is a Fleur code etc. ''' from aiida_fleur.tools.common_fleur_wf import get_inputs_inpgen from aiida.orm import Dict code = fixture_code('fleur.inpgen') structure = generate_structure() params = Dict(dict={'test': 1}) inputs = {'structure': structure, 'inpgencode': code, 'options': {}, 'label': 'label', 'description': 'description', 'params': params} returns = {'metadata': { 'options': {'withmpi': False, 'resources': {'num_machines': 1}}, 'description': 'description', 'label': 'label'}, 'code': code, 'parameters': params, 'structure': structure } assert get_inputs_inpgen(**inputs) == returns # repeat without a label and description inputs = {'structure': structure, 'inpgencode': code, 'options': {}, 'params': params} returns = {'metadata': { 'options': {'withmpi': False, 'resources': {'num_machines': 1}}, 'description': '', 'label': ''}, 'code': code, 'parameters': params, 'structure': structure} assert get_inputs_inpgen(**inputs) == returns # test_and_get_codenode def test_test_and_get_codenode_inpgen(fixture_code): from aiida_fleur.tools.common_fleur_wf import test_and_get_codenode from aiida.orm import Code from aiida.common.exceptions import NotExistent # install code setup code code = fixture_code('fleur.inpgen') code_fleur = fixture_code('fleur.fleur') code_fleur.label = 'fleur_test' code_fleur.store() expected = 'fleur.inpgen' nonexpected = 'fleur.fleur' not_existing = 'fleur.not_existing' assert isinstance(test_and_get_codenode(code, expected), Code) with pytest.raises(ValueError) as msg: test_and_get_codenode(code, nonexpected, use_exceptions=True) assert str(msg.value) == ("Given Code node is not of expected code type.\n" "Valid labels for a fleur.fleur executable are:\n" "* fleur_test@localhost-test") with pytest.raises(ValueError) as msg: test_and_get_codenode(code, not_existing, use_exceptions=True) assert str(msg.value) == ("Code not valid, and no valid codes for fleur.not_existing.\n" "Configure at least one first using\n" " verdi code setup") def test_get_kpoints_mesh_from_kdensity(generate_structure): from aiida_fleur.tools.common_fleur_wf import get_kpoints_mesh_from_kdensity from aiida.orm import KpointsData a, b = get_kpoints_mesh_from_kdensity(generate_structure(), 0.1) assert a == ([21, 21, 21], [0.0, 0.0, 0.0]) assert isinstance(b, KpointsData) # @pytest.mark.skip(reason="There seems to be now way to add outputs to CalcJobNode") def test_performance_extract_calcs(fixture_localhost, generate_calc_job_node): from aiida_fleur.tools.common_fleur_wf import performance_extract_calcs from aiida.common.links import LinkType from aiida.orm import Dict out = Dict(dict={'title': 'A Fleur input generator calculation with aiida', 'energy': -138529.7052157, 'bandgap': 6.0662e-06, 'end_date': {'date': '2019/11/12', 'time': '16:12:08'}, 'unparsed': [], 'walltime': 43, 'warnings': {'info': {}, 'debug': {}, 'error': {}, 'warning': {}}, 'start_date': {'date': '2019/11/12', 'time': '16:11:25'}, 'parser_info': 'AiiDA Fleur Parser v0.2beta', 'CalcJob_uuid': '3dc62d43-b607-4415-920f-e0d34e805711', 'creator_name': 'fleur 30', 'energy_units': 'eV', 'kmax': 4.2, 'fermi_energy': 0.0605833326, 'spin_density': 0.0792504665, 'bandgap_units': 'eV', 'force_largest': 0.0, 'energy_hartree': -5090.8728101494, 'walltime_units': 'seconds', 'charge_density1': 0.0577674505, 'charge_density2': 0.0461840944, 'number_of_atoms': 4, 'parser_warnings': [], 'magnetic_moments': [3.3720063737, 3.3719345944, 3.3719329177, 3.3719329162], 'number_of_kpoints': 8, 'number_of_species': 1, 'fermi_energy_units': 'Htr', 'sum_of_eigenvalues': -2973.4129786677, 'output_file_version': '0.27', 'energy_hartree_units': 'Htr', 'number_of_atom_types': 4, 'number_of_iterations': 11, 'number_of_symmetries': 8, 'energy_core_electrons': -2901.8120489845, 'magnetic_moment_units': 'muBohr', 'overall_charge_density': 0.0682602474, 'creator_target_structure': ' ', 'energy_valence_electrons': -71.6009296831, 'magnetic_spin_up_charges': [9.1494766577, 9.1494806151, 9.1494806833, 9.1494806834], 'orbital_magnetic_moments': [], 'density_convergence_units': 'me/bohr^3', 'number_of_spin_components': 2, 'charge_den_xc_den_integral': -223.295208608, 'magnetic_spin_down_charges': [5.777470284, 5.7775460208, 5.7775477657, 5.7775477672], 'number_of_iterations_total': 11, 'creator_target_architecture': 'GEN', 'orbital_magnetic_moment_units': 'muBohr', 'orbital_magnetic_spin_up_charges': [], 'orbital_magnetic_spin_down_charges': []}) out.store() node = generate_calc_job_node('fleur.fleur', fixture_localhost) node.store() out.add_incoming(node, link_type=LinkType.CREATE, link_label='output_parameters') result = performance_extract_calcs([node.pk]) assert result == {'n_symmetries': [8], 'n_spin_components': [2], 'n_kpoints': [8], 'n_iterations': [11], 'walltime_sec': [43], 'walltime_sec_per_it': [3.909090909090909], 'n_iterations_total': [11], 'density_distance': [0.0682602474], 'computer': ['localhost-test'], 'n_atoms': [4], 'kmax': [4.2], 'cost': [75866.11200000001], 'costkonstant': [147.02734883720933], 'walltime_sec_cor': [43], 'total_cost': [834527.2320000001], 'fermi_energy': [0.0605833326], 'bandgap': [6.0662e-06], 'energy': [-138529.7052157], 'force_largest': [0.0], 'ncores': [12], 'pk': [node.pk], 'uuid': [node.uuid], 'serial': [False], 'resources': [{'num_machines': 1, 'num_mpiprocs_per_machine': 1}]} inputs_optimize = [(4, 8, 3, True, 0.5, None, 720), (4, 8, 3, True, 2, None, 720), (4, 8, 3, True, 100, None, 720), (4, 8, 3, True, 100, None, 720, 0.5), (4, 8, 3, False, 0.5, None, 720)] results_optimize = [ (4, 3, 8, 'Computational setup is perfect! Nodes: 4, MPIs per node 3, OMP per MPI 8. Number of k-points is 720'), (4, 6, 4, 'Computational setup is perfect! Nodes: 4, MPIs per node 6, OMP per MPI 4. Number of k-points is 720'), (4, 12, 2, 'Computational setup is perfect! Nodes: 4, MPIs per node 12, OMP per MPI 2. Number of k-points is 720'), (3, 24, 1, 'WARNING: Changed the number of nodes from 4 to 3'), (4, 20, 1, 'WARNING: Changed the number of MPIs per node from 8 to 20 an OMP from 3 to 1. Changed the number of nodes from 4 to 4. Number of k-points is 720.')]

43.332143

164

0.597461

7cf2ae07e37425db960a133be2b5c330c6ba9916

36,957

py

Python

src/probnum/random_variables/_random_variable.py

admdev8/probnum

792b6299bac247cf8b1b5056756f0f078855d83a

[ "MIT" ]

null

src/probnum/random_variables/_random_variable.py

admdev8/probnum

792b6299bac247cf8b1b5056756f0f078855d83a

[ "MIT" ]

2

2020-12-28T19:37:16.000Z

2020-12-28T19:37:31.000Z

src/probnum/random_variables/_random_variable.py

admdev8/probnum

792b6299bac247cf8b1b5056756f0f078855d83a

[ "MIT" ]

null

""" Random Variables. This module implements random variables. Random variables are the main in- and outputs of probabilistic numerical methods. """ from typing import Any, Callable, Dict, Generic, Optional, Tuple, TypeVar, Union import numpy as np from probnum import utils as _utils from probnum.type import ( ArrayLikeGetitemArgType, DTypeArgType, FloatArgType, RandomStateArgType, RandomStateType, ShapeArgType, ShapeType, ) try: # functools.cached_property is only available in Python >=3.8 from functools import cached_property except ImportError: from cached_property import cached_property _ValueType = TypeVar("ValueType") def sample(self, size: ShapeArgType = ()) -> _ValueType: """ Draw realizations from a random variable. Parameters ---------- size : tuple Size of the drawn sample of realizations. Returns ------- sample : array-like Sample of realizations with the given ``size`` and the inherent ``shape``. """ if self.__sample is None: raise NotImplementedError("No sampling method provided.") return self.__sample(size=_utils.as_shape(size)) def cdf(self, x: _ValueType) -> np.float_: """ Cumulative distribution function. Parameters ---------- x : array-like Evaluation points of the cumulative distribution function. The shape of this argument should be :code:`(..., S1, ..., SN)`, where :code:`(S1, ..., SN)` is the :attr:`shape` of the random variable. The cdf evaluation will be broadcast over all additional dimensions. Returns ------- q : array-like Value of the cumulative density function at the given points. """ if self.__cdf is not None: return RandomVariable._ensure_numpy_float( "cdf", self.__cdf(self._as_value_type(x)) ) elif self.__logcdf is not None: cdf = np.exp(self.logcdf(self._as_value_type(x))) assert isinstance(cdf, np.float_) return cdf else: raise NotImplementedError( f"Neither the `cdf` nor the `logcdf` of the random variable object " f"with type `{type(self).__name__}` is implemented." ) def logcdf(self, x: _ValueType) -> np.float_: """ Log-cumulative distribution function. Parameters ---------- x : array-like Evaluation points of the cumulative distribution function. The shape of this argument should be :code:`(..., S1, ..., SN)`, where :code:`(S1, ..., SN)` is the :attr:`shape` of the random variable. The logcdf evaluation will be broadcast over all additional dimensions. Returns ------- q : array-like Value of the log-cumulative density function at the given points. """ if self.__logcdf is not None: return RandomVariable._ensure_numpy_float( "logcdf", self.__logcdf(self._as_value_type(x)) ) elif self.__cdf is not None: logcdf = np.log(self.__cdf(x)) assert isinstance(logcdf, np.float_) return logcdf else: raise NotImplementedError( f"Neither the `logcdf` nor the `cdf` of the random variable object " f"with type `{type(self).__name__}` is implemented." ) def quantile(self, p: FloatArgType) -> _ValueType: """Quantile function. The quantile function :math:`Q \\colon [0, 1] \\to \\mathbb{R}` of a random variable :math:`X` is defined as :math:`Q(p) = \\inf\\{ x \\in \\mathbb{R} \\colon p \\le F_X(x) \\}`, where :math:`F_X \\colon \\mathbb{R} \\to [0, 1]` is the :meth:`cdf` of the random variable. From the definition it follows that the quantile function always returns values of the same dtype as the random variable. For instance, for a discrete distribution over the integers, the returned quantiles will also be integers. This means that, in general, :math:`Q(0.5)` is not equal to the :attr:`median` as it is defined in this class. See https://en.wikipedia.org/wiki/Quantile_function for more details and examples. """ if self.__shape != (): raise NotImplementedError( "The quantile function is only defined for scalar random variables." ) if self.__quantile is None: raise NotImplementedError try: p = _utils.as_numpy_scalar(p, dtype=np.floating) except TypeError as exc: raise TypeError( "The given argument `p` can not be cast to a `np.floating` object." ) from exc quantile = self.__quantile(p) if quantile.shape != self.__shape: raise ValueError( f"The quantile function should return values of the same shape as the " f"random variable, i.e. {self.__shape}, but it returned a value with " f"{quantile.shape}." ) if quantile.dtype != self.__dtype: raise ValueError( f"The quantile function should return values of the same dtype as the " f"random variable, i.e. `{self.__dtype.name}`, but it returned a value " f"with dtype `{quantile.dtype.name}`." ) return quantile def reshape(self, newshape: ShapeArgType) -> "RandomVariable": """ Give a new shape to a random variable. Parameters ---------- newshape : int or tuple of ints New shape for the random variable. It must be compatible with the original shape. Returns ------- reshaped_rv : ``self`` with the new dimensions of ``shape``. """ newshape = _utils.as_shape(newshape) return RandomVariable( shape=newshape, dtype=self.dtype, random_state=_utils.derive_random_seed(self.random_state), sample=lambda size: self.sample(size).reshape(size + newshape), mode=lambda: self.mode.reshape(newshape), median=lambda: self.median.reshape(newshape), mean=lambda: self.mean.reshape(newshape), cov=lambda: self.cov, var=lambda: self.var.reshape(newshape), std=lambda: self.std.reshape(newshape), entropy=lambda: self.entropy, as_value_type=self.__as_value_type, ) def transpose(self, *axes: int) -> "RandomVariable": """ Transpose the random variable. Parameters ---------- axes : None, tuple of ints, or n ints See documentation of numpy.ndarray.transpose. Returns ------- transposed_rv : The transposed random variable. """ return RandomVariable( shape=np.empty(shape=self.shape).transpose(*axes).shape, dtype=self.dtype, random_state=_utils.derive_random_seed(self.random_state), sample=lambda size: self.sample(size).transpose(*axes), mode=lambda: self.mode.transpose(*axes), median=lambda: self.median.transpose(*axes), mean=lambda: self.mean.transpose(*axes), cov=lambda: self.cov, var=lambda: self.var.transpose(*axes), std=lambda: self.std.transpose(*axes), entropy=lambda: self.entropy, as_value_type=self.__as_value_type, ) T = property(transpose) # Unary arithmetic operations # Binary arithmetic operations __array_ufunc__ = None """ This prevents numpy from calling elementwise arithmetic operations allowing expressions like: y = np.array([1, 1]) + RV to call the arithmetic operations defined by RandomVariable instead of elementwise. Thus no array of RandomVariables but a RandomVariable with the correct shape is returned. """ def _as_value_type(self, x: Any) -> _ValueType: if self.__as_value_type is not None: return self.__as_value_type(x) return x

34.668856

107

0.596152

7cf38c52d649f28843e5da3730409c34a52dc82f

8,026

py

Python

platform/gcutil/lib/google_compute_engine/gcutil_lib/address_cmds_test.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

platform/gcutil/lib/google_compute_engine/gcutil_lib/address_cmds_test.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

platform/gcutil/lib/google_compute_engine/gcutil_lib/address_cmds_test.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

2

2020-07-25T05:03:06.000Z

2020-11-04T04:55:57.000Z

# Copyright 2012 Google Inc. 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. """Unit tests for address collection commands.""" import path_initializer path_initializer.InitSysPath() import json import unittest import gflags as flags from gcutil_lib import address_cmds from gcutil_lib import gcutil_unittest from gcutil_lib import mock_api from gcutil_lib import mock_lists FLAGS = flags.FLAGS if __name__ == '__main__': unittest.main(testLoader=gcutil_unittest.GcutilLoader())

32.362903

75

0.705457

7cf46d5f8307606187101597e795384399b48446

804

py

Python

vote/migrations/0005_auto_20210204_1900.py

jnegrete2005/JuradoFMS

25848037e51de1781c419155615d0fb41edc07ec

[ "MIT" ]

2

2021-02-24T21:57:50.000Z

2021-03-15T08:44:09.000Z

vote/migrations/0005_auto_20210204_1900.py

jnegrete2005/JuradoFMS

25848037e51de1781c419155615d0fb41edc07ec

[ "MIT" ]

null

vote/migrations/0005_auto_20210204_1900.py

jnegrete2005/JuradoFMS

25848037e51de1781c419155615d0fb41edc07ec

[ "MIT" ]

null

# Generated by Django 3.1.5 on 2021-02-05 00:00 import django.contrib.postgres.fields from django.db import migrations, models

32.16

163

0.655473

7cf5ba1b8968aa69e6a1b87247368728da9bf55b

11,847

py

Python

tools/wasm-sourcemap.py

ngzhian/emscripten

94b1555a09f869d65354a2033da724ce77a43106

[ "MIT" ]

1

2019-08-16T23:42:09.000Z

tools/wasm-sourcemap.py

ngzhian/emscripten

94b1555a09f869d65354a2033da724ce77a43106

[ "MIT" ]

null

tools/wasm-sourcemap.py

ngzhian/emscripten

94b1555a09f869d65354a2033da724ce77a43106

[ "MIT" ]

1

2019-09-26T20:05:46.000Z

#!/usr/bin/env python # Copyright 2018 The Emscripten Authors. All rights reserved. # Emscripten is available under two separate licenses, the MIT license and the # University of Illinois/NCSA Open Source License. Both these licenses can be # found in the LICENSE file. """Utility tools that extracts DWARF information encoded in a wasm output produced by the LLVM tools, and encodes it as a wasm source map. Additionally, it can collect original sources, change files prefixes, and strip debug sections from a wasm file. """ import argparse from collections import OrderedDict, namedtuple import json import logging from math import floor, log import os import re from subprocess import Popen, PIPE import sys sys.path.insert(1, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from tools.shared import asstr logger = logging.getLogger('wasm-sourcemap') # SourceMapPrefixes contains resolver for file names that are: # - "sources" is for names that output to source maps JSON # - "load" is for paths that used to load source text SourceMapPrefixes = namedtuple('SourceMapPrefixes', 'sources, load') if __name__ == '__main__': logging.basicConfig(level=logging.DEBUG if os.environ.get('EMCC_DEBUG') else logging.INFO) sys.exit(main())

35.258929

171

0.653668

7cf6644c8071f50f1d98ec6acd6e40c622a3ce59

12,540

py

Python

pysnmp-with-texts/ENTERASYS-NAC-APPLIANCE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

8

2019-05-09T17:04:00.000Z

2021-06-09T06:50:51.000Z

pysnmp-with-texts/ENTERASYS-NAC-APPLIANCE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

4

2019-05-31T16:42:59.000Z

2020-01-31T21:57:17.000Z

pysnmp-with-texts/ENTERASYS-NAC-APPLIANCE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module ENTERASYS-NAC-APPLIANCE-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ENTERASYS-NAC-APPLIANCE-MIB # Produced by pysmi-0.3.4 at Wed May 1 13:04:09 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ValueSizeConstraint, ConstraintsUnion, ConstraintsIntersection, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ValueSizeConstraint", "ConstraintsUnion", "ConstraintsIntersection", "SingleValueConstraint") etsysModules, = mibBuilder.importSymbols("ENTERASYS-MIB-NAMES", "etsysModules") ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance") Bits, ObjectIdentity, MibIdentifier, Counter64, iso, NotificationType, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, IpAddress, Unsigned32, TimeTicks, Gauge32, Integer32, Counter32 = mibBuilder.importSymbols("SNMPv2-SMI", "Bits", "ObjectIdentity", "MibIdentifier", "Counter64", "iso", "NotificationType", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ModuleIdentity", "IpAddress", "Unsigned32", "TimeTicks", "Gauge32", "Integer32", "Counter32") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") etsysNacApplianceMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73)) etsysNacApplianceMIB.setRevisions(('2010-03-09 13:03',)) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): if mibBuilder.loadTexts: etsysNacApplianceMIB.setRevisionsDescriptions(('The initial version of this MIB module.',)) if mibBuilder.loadTexts: etsysNacApplianceMIB.setLastUpdated('201003091303Z') if mibBuilder.loadTexts: etsysNacApplianceMIB.setOrganization('Enterasys Networks, Inc') if mibBuilder.loadTexts: etsysNacApplianceMIB.setContactInfo('Postal: Enterasys Networks 50 Minuteman Rd. Andover, MA 01810-1008 USA Phone: +1 978 684 1000 E-mail: [email protected] WWW: http://www.enterasys.com') if mibBuilder.loadTexts: etsysNacApplianceMIB.setDescription("This MIB module defines a portion of the SNMP enterprise MIBs under Enterasys Networks' enterprise OID pertaining to NAC Appliance Status.") etsysNacApplianceObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1)) etsysNacApplAuthenticationRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 1), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationRequests.setDescription('Represents the number of authentication requests made since the NAC was started.') etsysNacApplAuthenticationSuccesses = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 2), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationSuccesses.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationSuccesses.setDescription('Represents the number of successful authentication requests made since the NAC was started.') etsysNacApplAuthenticationFailures = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 3), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationFailures.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationFailures.setDescription('Represents the number of failed authentication requests made since the NAC was started.') etsysNacApplRadiusChallenges = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 4), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplRadiusChallenges.setStatus('current') if mibBuilder.loadTexts: etsysNacApplRadiusChallenges.setDescription('Represents the number of Radius challenges made since the NAC was started.') etsysNacApplAuthenticationInvalidRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 5), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationInvalidRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationInvalidRequests.setDescription('Represents the number of invalid authentication requests made since the NAC was started.') etsysNacApplAuthenticationDuplicateRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 6), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationDuplicateRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationDuplicateRequests.setDescription('Represents the number of duplicate authentication requests made since the NAC was started.') etsysNacApplAuthenticationMalformedRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 7), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationMalformedRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationMalformedRequests.setDescription('Represents the number of malformed authentication requests made since the NAC was started.') etsysNacApplAuthenticationBadRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 8), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationBadRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationBadRequests.setDescription('Represents the number of bad authentication requests made since the NAC was started.') etsysNacApplAuthenticationDroppedPackets = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 9), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationDroppedPackets.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationDroppedPackets.setDescription('Represents the number of dropped authentication packets since the NAC was started.') etsysNacApplAuthenticationUnknownTypes = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 10), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAuthenticationUnknownTypes.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAuthenticationUnknownTypes.setDescription('Represents the number of unknown authentication types since the NAC was started.') etsysNacApplAssessmentRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 11), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplAssessmentRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplAssessmentRequests.setDescription('Represents the number of assessment requests made since the NAC was started.') etsysNacApplCaptivePortalRequests = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 12), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplCaptivePortalRequests.setStatus('current') if mibBuilder.loadTexts: etsysNacApplCaptivePortalRequests.setDescription('Represents the number of captive portal requests made since the NAC was started.') etsysNacApplContactLostSwitches = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 13), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplContactLostSwitches.setStatus('current') if mibBuilder.loadTexts: etsysNacApplContactLostSwitches.setDescription('Represents the number of configured switches with which the NAC has lost SNMP contact.') etsysNacApplIPResolutionFailures = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 14), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplIPResolutionFailures.setStatus('current') if mibBuilder.loadTexts: etsysNacApplIPResolutionFailures.setDescription('Represents the number of failed IP Resolution attempts made since the NAC was started.') etsysNacApplIPResolutionTimeouts = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 15), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplIPResolutionTimeouts.setStatus('current') if mibBuilder.loadTexts: etsysNacApplIPResolutionTimeouts.setDescription('Represents the number of IP Resolution attempts that timed out since the NAC was started.') etsysNacApplConnectedAgents = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 1, 16), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysNacApplConnectedAgents.setStatus('current') if mibBuilder.loadTexts: etsysNacApplConnectedAgents.setDescription('Represents the number of End-System Assessment Agents currently connected to the NAC.') etsysNacApplianceMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 2)) etsysNacApplianceMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 2, 1)) etsysNacApplianceMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 2, 2)) etsysNacApplianceMIBGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 2, 1, 1)).setObjects(("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationSuccesses"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationFailures"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplRadiusChallenges"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationInvalidRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationDuplicateRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationMalformedRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationBadRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationDroppedPackets"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAuthenticationUnknownTypes"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplAssessmentRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplCaptivePortalRequests"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplContactLostSwitches"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplIPResolutionFailures"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplIPResolutionTimeouts"), ("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplConnectedAgents")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysNacApplianceMIBGroup = etsysNacApplianceMIBGroup.setStatus('current') if mibBuilder.loadTexts: etsysNacApplianceMIBGroup.setDescription('The basic collection of objects providing status information about the NAC Appliance.') etsysNacApplianceMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 5624, 1, 2, 73, 2, 2, 1)).setObjects(("ENTERASYS-NAC-APPLIANCE-MIB", "etsysNacApplianceMIBGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysNacApplianceMIBCompliance = etsysNacApplianceMIBCompliance.setStatus('current') if mibBuilder.loadTexts: etsysNacApplianceMIBCompliance.setDescription('The compliance statement for clients implementing the NAC Appliance Status MIB.') mibBuilder.exportSymbols("ENTERASYS-NAC-APPLIANCE-MIB", etsysNacApplianceMIBCompliance=etsysNacApplianceMIBCompliance, etsysNacApplAuthenticationDuplicateRequests=etsysNacApplAuthenticationDuplicateRequests, etsysNacApplIPResolutionTimeouts=etsysNacApplIPResolutionTimeouts, etsysNacApplianceObjects=etsysNacApplianceObjects, etsysNacApplAuthenticationInvalidRequests=etsysNacApplAuthenticationInvalidRequests, etsysNacApplAuthenticationUnknownTypes=etsysNacApplAuthenticationUnknownTypes, etsysNacApplianceMIBCompliances=etsysNacApplianceMIBCompliances, etsysNacApplAssessmentRequests=etsysNacApplAssessmentRequests, etsysNacApplAuthenticationBadRequests=etsysNacApplAuthenticationBadRequests, etsysNacApplAuthenticationRequests=etsysNacApplAuthenticationRequests, etsysNacApplRadiusChallenges=etsysNacApplRadiusChallenges, etsysNacApplAuthenticationMalformedRequests=etsysNacApplAuthenticationMalformedRequests, etsysNacApplContactLostSwitches=etsysNacApplContactLostSwitches, etsysNacApplAuthenticationDroppedPackets=etsysNacApplAuthenticationDroppedPackets, etsysNacApplCaptivePortalRequests=etsysNacApplCaptivePortalRequests, etsysNacApplAuthenticationSuccesses=etsysNacApplAuthenticationSuccesses, etsysNacApplIPResolutionFailures=etsysNacApplIPResolutionFailures, etsysNacApplianceMIBConformance=etsysNacApplianceMIBConformance, PYSNMP_MODULE_ID=etsysNacApplianceMIB, etsysNacApplianceMIBGroups=etsysNacApplianceMIBGroups, etsysNacApplianceMIB=etsysNacApplianceMIB, etsysNacApplAuthenticationFailures=etsysNacApplAuthenticationFailures, etsysNacApplianceMIBGroup=etsysNacApplianceMIBGroup, etsysNacApplConnectedAgents=etsysNacApplConnectedAgents)

145.813953

1,653

0.817384

7cf7d0d22a5ee01c1d25faa33b9b8f99ef2f0210

3,300

py

Python

Unsupervised/pix2pixHD/extract_frames.py

Kebniss/AutoDetect

44ca4d6930ef5fbf044ebeed5c9fd925f04bc1a8

[ "MIT" ]

1

2019-07-25T02:16:32.000Z

Unsupervised/pix2pixHD/extract_frames.py

Kebniss/AutoDetect

44ca4d6930ef5fbf044ebeed5c9fd925f04bc1a8

[ "MIT" ]

null

Unsupervised/pix2pixHD/extract_frames.py

Kebniss/AutoDetect

44ca4d6930ef5fbf044ebeed5c9fd925f04bc1a8

[ "MIT" ]

null

import os import cv2 import argparse from utils import * from tqdm import tqdm from glob import glob from pathlib import Path parser = argparse.ArgumentParser( description='build a "frame dataset" from a given video') parser.add_argument('-input', dest="input", required=True, help='''Path to a single video or a folder. If path to folder the algorithm will extract frames from all files with extension defined in --extension and save them under separate folders under dest_folder. The frames from each video will be saved under a folder with its name. ''') parser.add_argument('--dest-folder', dest="dest_folder", default='./dataset/', help='''Path where to store frames. NB all files in this folder will be removed before adding the new frames''') parser.add_argument('--same-folder', dest="same_folder", default=False, help='''Set it to True if you want to save the frames of all videos to the same folder in ascending order going from the first frame of the first video to the last frame of the last video. If True frames will be saved in dest_folder/frames.''') parser.add_argument('--sampling', help='how many fps', default='3') parser.add_argument('--run-type', help='train or test', default='train') parser.add_argument('--extension', help='avi, mp4, mov...', default='mp4') parser.add_argument('-width', help='output width', default=640, type=int) parser.add_argument('-height', help='output height', default=480, type=int) args = parser.parse_args() mkdir(args.dest_folder) if (args.width % 32 != 0) or (args.height % 32 != 0): raise Exception("Please use width and height that are divisible by 32") if os.path.isdir(args.input): inp = str(Path(args.input) / f'*.{args.extension}') videos = [v for v in glob(inp)] if not videos: raise Exception(f'No {args.extension} files in input directory {args.input}') elif os.path.isfile(args.input): _, ext = get_filename_extension(args.input) if ext != args.extension: raise ValueError(f'Correct inputs: folder or path to {args.extension} file only') videos = [args.input] else: raise ValueError(f'Correct inputs: folder or path to {args.extension} file only') if args.same_folder: start = 0 dest_folder = str(Path(args.dest_folder) / f'{args.run_type}_frames') mkdir(dest_folder) for v in tqdm(videos): if not args.same_folder: start = 0 name, _ = get_filename_extension(v) dest_folder = str(Path(args.dest_folder) / name) mkdir(dest_folder) start = _extract_frames(v, dest_folder, start, sampling_f=int(args.sampling))

39.285714

89

0.677879

7cf872b37fe89e4b5c0f3a287b43439b1d433523

2,923

py

Python

burp-filter-options/filter-options.py

parsiya/Parsia-Code

e75bd9f7f295e6d8e584de67f90dd02cb75ae915

[ "MIT" ]

21

2018-09-10T03:09:17.000Z

2022-02-07T09:20:03.000Z

burp-filter-options/filter-options.py

parsiya/Parsia-Code

e75bd9f7f295e6d8e584de67f90dd02cb75ae915

[ "MIT" ]

1

2019-11-10T21:17:23.000Z

2020-01-19T04:36:19.000Z

burp-filter-options/filter-options.py

parsiya/Parsia-Code

e75bd9f7f295e6d8e584de67f90dd02cb75ae915

[ "MIT" ]

8

2019-10-11T23:29:58.000Z

2021-05-26T12:11:43.000Z

# modified "example traffic redirector" # https://raw.githubusercontent.com/PortSwigger/example-traffic-redirector/master/python/TrafficRedirector.py # Idea: https://github.com/pajswigger/filter-options/blob/master/src/filter-options.kt # Usage: Put both files in a directory and add filter-options.py to Burp. Nees Jython. # Blog post: https://parsiya.net/blog/2019-04-06-hiding-options-an-adventure-in-dealing-with-burp-proxy-in-an-extension/ # support for burp-exceptions - see https://github.com/securityMB/burp-exceptions try: from exceptions_fix import FixBurpExceptions import sys except ImportError: pass # support for burputils - https://github.com/parsiya/burputils try: from burputils import BurpUtils except ImportError: pass from burp import IBurpExtender from burp import IHttpListener # support for burp-exceptions try: FixBurpExceptions() except: pass

31.095745

121

0.659254

7cf8865345a71c46f4e1edec308e018d877fedb9

11,128

py

Python

AppServer/google/appengine/tools/devappserver2/login.py

loftwah/appscale

586fc1347ebc743d7a632de698f4dbfb09ae38d6

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

AppServer/google/appengine/tools/devappserver2/login.py

loftwah/appscale

586fc1347ebc743d7a632de698f4dbfb09ae38d6

[ "Apache-2.0" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

AppServer/google/appengine/tools/devappserver2/login.py

loftwah/appscale

586fc1347ebc743d7a632de698f4dbfb09ae38d6

[ "Apache-2.0" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

#!/usr/bin/env python # # Copyright 2007 Google Inc. # # 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. # """Handles login/logout pages and dealing with user cookies. Includes a WSGI application that serves the login page and handles login and logout HTTP requests. It accepts these GET query parameters: continue: URL to redirect to after a login or logout has completed. email: Email address to set for the client. admin: If 'True', the client should be logged in as an admin. action: What action to take ('Login' or 'Logout'). To view the current user information and a form for logging in and out, supply no parameters. """ import cgi import Cookie import hashlib import logging import os import sha import sys import urllib import uuid import webapp2 app_dashboard_lib = '/../../../../../AppDashboard/lib' sys.path.append(os.path.dirname(__file__) + app_dashboard_lib) from app_dashboard_helper import AppDashboardHelper # URL of the login page within the dev appserver. LOGIN_URL_RELATIVE = '_ah/login' # CGI parameter constants. CONTINUE_PARAM = 'continue' _EMAIL_PARAM = 'email' _ADMIN_PARAM = 'admin' ACTION_PARAM = 'action' # Values for the action parameter. LOGOUT_ACTION = 'logout' LOGIN_ACTION = 'login' # Name of the cookie that stores the user info. _COOKIE_NAME = 'dev_appserver_login' # Indicates that the user has admin access to all applications. CLOUD_ADMIN_MARKER = 'CLOUD_ADMIN' # The port that the AppDashboard serves HTTPS traffic on. DASHBOARD_HTTPS_PORT = "1443" def get_user_info(http_cookie, cookie_name=_COOKIE_NAME): """Gets the requestor's user info from an HTTP Cookie header. Args: http_cookie: The value of the 'Cookie' HTTP request header. cookie_name: The name of the cookie that stores the user info. Returns: A tuple (email, admin, user_id) where: email: The user's email address, if any. admin: True if the user is an admin; False otherwise. user_id: The user ID, if any. """ try: cookie = Cookie.SimpleCookie(http_cookie) except Cookie.CookieError: return '', False, '' cookie_dict = dict((k, v.value) for k, v in cookie.iteritems()) return _get_user_info_from_dict(cookie_dict, cookie_name) def _get_user_info_from_dict(cookie_dict, cookie_name=_COOKIE_NAME): """Gets the requestor's user info from a cookie dictionary. Args: cookie_dict: A dictionary mapping cookie names onto values. cookie_name: The name of the cookie that stores the user info. Returns: A tuple (email, admin, user_id) where: email: The user's email address, if any. admin: True if the user is an admin; False otherwise. user_id: The user ID, if any. """ cookie_secret = os.environ['COOKIE_SECRET'] cookie_value = cookie_dict.get(cookie_name, '') cookie_value = cookie_value.replace("%3A",":") cookie_value = cookie_value.replace("%40",'@') cookie_value = cookie_value.replace("%2C",",") email, nickname, admin, hsh = (cookie_value.split(':') + ['', '', '', ''])[:4] if email == '': nickname = '' admin = '' return '', False, '' else: vhsh = sha.new(email+nickname+admin+cookie_secret).hexdigest() if hsh != vhsh: logging.info("{0} has an invalid cookie, so ignoring it.".format(email)) return '', False, '' admin_apps = admin.split(',') current_app = os.environ['APPLICATION_ID'] is_admin = current_app in admin_apps or CLOUD_ADMIN_MARKER in admin_apps return email, is_admin, nickname def _create_cookie_data(email, admin): """Creates cookie payload data. Args: email: The user's email address. admin: True if the user is an admin; False otherwise. Returns: A string containing the cookie payload. """ if email: user_id_digest = hashlib.md5(email.lower()).digest() user_id = '1' + ''.join(['%02d' % ord(x) for x in user_id_digest])[:20] else: user_id = '' return '%s:%s:%s' % (email, admin, user_id) def _set_user_info_cookie(email, admin, cookie_name=_COOKIE_NAME): """Creates a cookie to set the user information for the requestor. Args: email: The email to set for the user. admin: True if the user should be admin; False otherwise. cookie_name: The name of the cookie that stores the user info. Returns: Set-Cookie value for setting the user info of the requestor. """ cookie_value = _create_cookie_data(email, admin) cookie = Cookie.SimpleCookie() cookie[cookie_name] = cookie_value cookie[cookie_name]['path'] = '/' return cookie[cookie_name].OutputString() def _clear_user_info_cookie(cookie_name=_COOKIE_NAME): """Clears the user info cookie from the requestor, logging them out. Args: cookie_name: The name of the cookie that stores the user info. Returns: A Set-Cookie value for clearing the user info of the requestor. """ cookie = Cookie.SimpleCookie() cookie[cookie_name] = '' cookie[cookie_name]['path'] = '/' cookie[cookie_name]['max-age'] = '0' if AppDashboardHelper.USE_SHIBBOLETH: cookie[cookie_name]['domain'] = AppDashboardHelper.\ SHIBBOLETH_COOKIE_DOMAIN return cookie[cookie_name].OutputString() _LOGIN_TEMPLATE = """<html> <head> <title>Login</title> </head> <body> <form method="get" action="%(login_url)s" style="text-align:center; font: 13px sans-serif"> <div style="width: 20em; margin: 1em auto; text-align:left; padding: 0 2em 1.25em 2em; background-color: #d6e9f8; border: 2px solid #67a7e3"> <h3>%(login_message)s</h3> <p style="padding: 0; margin: 0"> <label for="email" style="width: 3em">Email:</label> <input name="email" type="email" value="%(email)s" id="email"/> </p> <p style="margin: .5em 0 0 3em; font-size:12px"> <input name="admin" type="checkbox" value="True" %(admin_checked)s id="admin"/> <label for="admin">Sign in as Administrator</label> </p> <p style="margin-left: 3em"> <input name="action" value="Login" type="submit" id="submit-login" /> <input name="action" value="Logout" type="submit" id="submit-logout" /> </p> </div> <input name="continue" type="hidden" value="%(continue_url)s"/> </form> </body> </html> """ def _render_login_template(login_url, continue_url, email, admin): """Renders the login page. Args: login_url: The parameter to _login_response. continue_url: The parameter to _login_response. email: The email address of the current user, if any. admin: True if the user is currently an admin; False otherwise. Returns: A string containing the contents of the login page. """ if email: login_message = 'Logged in' else: login_message = 'Not logged in' email = 'test\x40example.com' admin_checked = 'checked' if admin else '' template_dict = { 'email': cgi.escape(email, quote=True), 'admin_checked': admin_checked, 'login_message': login_message, 'login_url': cgi.escape(login_url, quote=True), 'continue_url': cgi.escape(continue_url, quote=True), } return _LOGIN_TEMPLATE % template_dict def login_redirect(application_url, continue_url, start_response): """Writes a login redirection URL to a user. This redirects to login_url with a continue parameter to return to continue_url. The login_url should be on the canonical front-end server, regardless of the host:port the user connected to. Args: application_url: The URL of the dev appserver domain (e.g., 'http://localhost:8080'). continue_url: The URL to continue to after the user logs in. start_response: A WSGI start_response function. Returns: An (empty) iterable over strings containing the body of the HTTP response. """ if AppDashboardHelper.USE_SHIBBOLETH: redirect_url = '{0}:{1}/login?{2}={3}'.format( AppDashboardHelper.SHIBBOLETH_CONNECTOR, AppDashboardHelper.SHIBBOLETH_CONNECTOR_PORT, CONTINUE_PARAM, urllib.quote(continue_url) ) else: hostname = os.environ['NGINX_HOST'] redirect_url = 'https://{0}:{1}/login?{2}={3}'.format( hostname, DASHBOARD_HTTPS_PORT, CONTINUE_PARAM, urllib.quote(continue_url)) start_response('302 Requires login', [('Location', redirect_url)]) return [] def fake_admin(): """ Generate the fake admin login secret Returns: A string containing the fake login secret """ return hashlib.sha1('{}/{}'.format( os.environ.get('APPNAME', str(uuid.uuid4())), os.environ.get('COOKIE_SECRET', str(uuid.uuid4())))).hexdigest() application = webapp2.WSGIApplication([('/.*', Handler)], debug=True)

31.885387

80

0.689162

7cf8d4321937161cb10d000e0dbd87e721b04ad3

7,060

py

Python

sdks/python/apache_beam/runners/portability/job_server.py

noah-goodrich/beam

5a851b734f53206c20efe08d93d15760bbc15b0c

[ "Apache-2.0" ]

1

2019-12-05T04:36:46.000Z

sdks/python/apache_beam/runners/portability/job_server.py

noah-goodrich/beam

5a851b734f53206c20efe08d93d15760bbc15b0c

[ "Apache-2.0" ]

14

2020-02-12T22:20:41.000Z

2021-11-09T19:41:23.000Z

sdks/python/apache_beam/runners/portability/job_server.py

violalyu/beam

dd605e568d70b1a6ebea60c15b2aec3e240f3914

[ "Apache-2.0" ]

1

2021-03-21T23:28:23.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import absolute_import import atexit import os import shutil import signal import subprocess import sys import tempfile import threading import grpc from apache_beam.portability.api import beam_job_api_pb2_grpc from apache_beam.runners.portability import local_job_service from apache_beam.utils import subprocess_server from apache_beam.version import __version__ as beam_version class DockerizedJobServer(SubprocessJobServer): """ Spins up the JobServer in a docker container for local execution. """

33.619048

79

0.703258

7cf950bf294a91d7beefe8c59885eaed2c328e0e

14,856

py

Python

sympy/printing/pycode.py

tachycline/sympy

abf6fec12012852c7e6fae38461da9723cadc8b9

[ "BSD-3-Clause" ]

null

sympy/printing/pycode.py

tachycline/sympy

abf6fec12012852c7e6fae38461da9723cadc8b9

[ "BSD-3-Clause" ]

null

sympy/printing/pycode.py

tachycline/sympy

abf6fec12012852c7e6fae38461da9723cadc8b9

[ "BSD-3-Clause" ]

null

from collections import defaultdict from functools import wraps from itertools import chain from sympy.core import sympify from .precedence import precedence from .codeprinter import CodePrinter _kw_py2and3 = { 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield', 'None' # 'None' is actually not in Python 2's keyword.kwlist } _kw_only_py2 = {'exec', 'print'} _kw_only_py3 = {'False', 'nonlocal', 'True'} _known_functions = { 'Abs': 'abs', } _known_functions_math = { 'acos': 'acos', 'acosh': 'acosh', 'asin': 'asin', 'asinh': 'asinh', 'atan': 'atan', 'atan2': 'atan2', 'atanh': 'atanh', 'ceiling': 'ceil', 'cos': 'cos', 'cosh': 'cosh', 'erf': 'erf', 'erfc': 'erfc', 'exp': 'exp', 'expm1': 'expm1', 'factorial': 'factorial', 'floor': 'floor', 'gamma': 'gamma', 'hypot': 'hypot', 'loggamma': 'lgamma', 'log': 'log', 'log10': 'log10', 'log1p': 'log1p', 'log2': 'log2', 'sin': 'sin', 'sinh': 'sinh', 'Sqrt': 'sqrt', 'tan': 'tan', 'tanh': 'tanh' } # Not used from ``math``: [copysign isclose isfinite isinf isnan ldexp frexp pow modf # radians trunc fmod fsum gcd degrees fabs] _known_constants_math = { 'Exp1': 'e', 'Pi': 'pi', # Only in python >= 3.5: # 'Infinity': 'inf', # 'NaN': 'nan' } for k in PythonCodePrinter._kf: setattr(PythonCodePrinter, '_print_%s' % k, _print_known_func) for k in _known_constants_math: setattr(PythonCodePrinter, '_print_%s' % k, _print_known_const) _not_in_mpmath = 'log1p log2'.split() _in_mpmath = [(k, v) for k, v in _known_functions_math.items() if k not in _not_in_mpmath] _known_functions_mpmath = dict(_in_mpmath) _known_constants_mpmath = { 'Pi': 'pi' } for k in MpmathPrinter._kf: setattr(MpmathPrinter, '_print_%s' % k, _print_known_func) for k in _known_constants_mpmath: setattr(MpmathPrinter, '_print_%s' % k, _print_known_const) _not_in_numpy = 'erf erfc factorial gamma lgamma'.split() _in_numpy = [(k, v) for k, v in _known_functions_math.items() if k not in _not_in_numpy] _known_functions_numpy = dict(_in_numpy, **{ 'acos': 'arccos', 'acosh': 'arccosh', 'asin': 'arcsin', 'asinh': 'arcsinh', 'atan': 'arctan', 'atan2': 'arctan2', 'atanh': 'arctanh', 'exp2': 'exp2', }) for k in NumPyPrinter._kf: setattr(NumPyPrinter, '_print_%s' % k, _print_known_func) for k in NumPyPrinter._kc: setattr(NumPyPrinter, '_print_%s' % k, _print_known_const) _known_functions_scipy_special = { 'erf': 'erf', 'erfc': 'erfc', 'gamma': 'gamma', 'loggamma': 'gammaln' } _known_constants_scipy_constants = { 'GoldenRatio': 'golden_ratio' } for k in SciPyPrinter._kf: setattr(SciPyPrinter, '_print_%s' % k, _print_known_func) for k in SciPyPrinter._kc: setattr(SciPyPrinter, '_print_%s' % k, _print_known_const)

34.388889

119

0.601642