xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

8f57c53783255d968c61238c03513fb1829119ef

7,404

py

Python

gecosistema_lite/gdal_shape.py

valluzzi/libcore

1e714ed0df13000bf853696551ee109b3b65997a

[ "MIT" ]

null

gecosistema_lite/gdal_shape.py

valluzzi/libcore

1e714ed0df13000bf853696551ee109b3b65997a

[ "MIT" ]

null

gecosistema_lite/gdal_shape.py

valluzzi/libcore

1e714ed0df13000bf853696551ee109b3b65997a

[ "MIT" ]

null

# ------------------------------------------------------------------------------- # Licence: # Copyright (c) 2012-2017 Valerio for Gecosistema S.r.l. # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES # OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR # OTHER DEALINGS IN THE SOFTWARE. # # # Name: gdal_shape.py # Purpose: # # Author: Luzzi Valerio # # Created: 26/07/2017 # ------------------------------------------------------------------------------- """apt-get -y install gdal-bin libgdal-dev python-gdal""" import ogr from execution import * def GetFeatures(fileshp): """ GetFeatures """ res = [] dataset = ogr.OpenShared(fileshp) if dataset: layer = dataset.GetLayer(0) for feature in layer: res.append(feature) dataset = None return res def GetFeatureByFid(fileshp, fid): """ GetFeatureByFid """ feature = None dataset = ogr.OpenShared(fileshp) if dataset: layer = dataset.GetLayer(0) feature = layer.GetFeature(fid) dataset = None return feature def removeShape(filename): """ removeShape """ try: if file(filename): driver = ogr.GetDriverByName('ESRI Shapefile') driver.DeleteDataSource(filename) except Exception, ex: print ex return None def SaveFeature(feature, fileshp=""): """ SaveFeature """ fileshp = fileshp if fileshp else "%d.shp" % (feature.GetField("OBJECTID")) driver = ogr.GetDriverByName("ESRI Shapefile") if os.path.exists(fileshp): driver.DeleteDataSource(fileshp) ds = driver.CreateDataSource(fileshp) geom = feature.GetGeometryRef() layer = ds.CreateLayer(fileshp, srs=geom.GetSpatialReference(), geom_type=geom.GetGeometryType()) # create a field # idField = ogr.FieldDefn(fieldName, fieldType) # layer.CreateField(idField) # Create the feature and set values featureDefn = layer.GetLayerDefn() layer.CreateFeature(feature) feature = None ds = None return fileshp def Extent2shp(filename, fileout=""): """ Extent2shp """ fileout = fileout if fileout else forceext(filename, "ext.shp") layername, (minx, miny, maxx, maxy), proj4, geomtype, dontcare = GDAL_META(filename) rect = ogr.Geometry(ogr.wkbLinearRing) rect.AddPoint(minx, miny) rect.AddPoint(maxx, miny) rect.AddPoint(maxx, maxy) rect.AddPoint(minx, maxy) rect.AddPoint(minx, miny) # Create polygon poly = ogr.Geometry(ogr.wkbPolygon) poly.AddGeometry(rect) # Save extent to a new Shapefile driver = ogr.GetDriverByName("ESRI Shapefile") # Remove output shapefile if it already exists if os.path.exists(fileout): driver.DeleteDataSource(fileout) # Create the output shapefile ds = driver.CreateDataSource(fileout) srs = ogr.osr.SpatialReference() srs.ImportFromProj4(proj4) strtofile(srs.ExportToWkt(), forceext(fileout, "prj")) layer = ds.CreateLayer(layername, geom_type=ogr.wkbPolygon) feature = ogr.Feature(layer.GetLayerDefn()) feature.SetGeometry(poly) layer.CreateFeature(feature) feature, ds = None, None return fileout def XYZ2Shp(filecsv, t_srs="EPSG:4326", fileout=None): """ XYZ2Shp """ driver = ogr.GetDriverByName('ESRI Shapefile') fileout = forceext(filecsv, "shp") if not fileout else fileout remove(fileout) layername = juststem(fileout) print layername dataset = driver.CreateDataSource(fileout) # important fix layername!!!!! layername = layername.encode('utf-8') # end fix layer = dataset.CreateLayer(layername, None, ogr.wkbPoint) layer.CreateField(ogr.FieldDefn("VALUE", ogr.OFTReal)) srs = None try: # Set the SpatialReference srs = ogr.osr.SpatialReference() epsg = val(t_srs.upper().replace("EPSG:", "")) srs.ImportFromEPSG(epsg) strtofile(srs.ExportToWkt(), forceext(fileout, "prj")) except Exception, ex: print ex with open(filecsv, 'rb') as stream: line = stream.readline() while line: arr = line.strip(" \r\n").split(",") arr = [item for item in arr if len(item) > 0] if len(arr) >= 3 and val(arr[0]) != None and val(arr[1]) != None and val(arr[2]) != None: X, Y, value = val(arr[0]), val(arr[1]), val(arr[2]) # print X,Y,value # X,Y = X*1000,Y*1000 # from km to meters! feature = ogr.Feature(layer.GetLayerDefn()) geom = ogr.CreateGeometryFromWkt("POINT (%s %s)" % (X, Y)) if srs: geom.AssignSpatialReference(srs) feature.SetGeometry(geom) feature.SetField2("VALUE", value) # Save the feature on the layer layer.CreateFeature(feature) feature.Destroy() line = stream.readline() return fileout # ------------------------------------------------------------------------------- # XYZ2VRT # ------------------------------------------------------------------------------- def XYZ2VRT(filename, t_srs="EPSG:4326", fileout=None): """ XYZ2VRT """ fileout = fileout if fileout else forceext(filename, "csv") filevrt = forceext(fileout, "vrt") ws = open(fileout, "wb") # ws.write("""X,Y,Z\n""") with open(filename, 'rb') as stream: line = stream.readline() while line: line = line.strip(" \r\n") line = re.sub(r'\s+', ',', line) if len(line.split(",")) == 5: arr = line.split(",") arr = val(arr) arr[0] *= 1000 arr[1] *= 1000 arr = ["%g" % item for item in arr[:3]] line = ",".join(arr) ws.write(line + "\n") line = stream.readline() ws.close() ## srs = ogr.osr.SpatialReference() ## epsg= val(t_srs.upper().replace("EPSG:","")) ## srs.ImportFromEPSG(epsg) env = {"layername": juststem(fileout), "fileout": fileout, "t_srs": t_srs} text = """<OGRVRTDataSource> <OGRVRTLayer name="{layername}"> <SrcDataSource>{fileout}</SrcDataSource> <GeometryType>wkbPoint</GeometryType> <GeometryField encoding="PointFromColumns" x="field_1" y="field_2" z="field_3"/> <LayerSRS>{t_srs}</LayerSRS> </OGRVRTLayer> </OGRVRTDataSource>""" text = sformat(text, env) strtofile(text, filevrt) # ------------------------------------------------------------------------------- # Main loop # ------------------------------------------------------------------------------- if __name__ == '__main__': workdir = r"D:\EUDEM_GECO\Basins\Tevere\PERC" chdir(workdir) env = {"Tevere": r"Tevere.perc0.3.tif", "px": 1} # gdalwarp()

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null

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1

8f583755beca1d6f083766bc7e6fb3691cf83619

1,015

py

Python

pw22/__main__.py

paeronskruven/pyweek22

4657b03a49c011581af6ae460fd97b6d58d13ead

[ "MIT" ]

null

pw22/__main__.py

paeronskruven/pyweek22

4657b03a49c011581af6ae460fd97b6d58d13ead

[ "MIT" ]

null

pw22/__main__.py

paeronskruven/pyweek22

4657b03a49c011581af6ae460fd97b6d58d13ead

[ "MIT" ]

null

import pyglet import logging logging.basicConfig(format='%(asctime)s %(module)s %(levelname)s %(message)s', level=logging.DEBUG) # setup resource paths before importing any game code pyglet.resource.path = ['data', 'data/tiles'] pyglet.resource.reindex() from .scenes import SceneManager, GameScene window = pyglet.window.Window(width=1024, height=768, caption='The Nightmare') scene_manager = SceneManager(window) scene_manager.push(GameScene(scene_manager)) clock_display = pyglet.clock.ClockDisplay() pyglet.gl.glEnable(pyglet.gl.GL_BLEND) pyglet.gl.glBlendFunc(pyglet.gl.GL_SRC_ALPHA, pyglet.gl.GL_ONE_MINUS_SRC_ALPHA) @window.event def on_draw(): window.clear() try: scene_manager.on_draw() except IndexError: pyglet.app.exit() pyglet.gl.glLoadIdentity() clock_display.draw() def on_update(dt): try: scene_manager.on_update(dt) except IndexError: pyglet.app.exit() def main(): pyglet.clock.schedule(on_update) pyglet.app.run()

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null

0

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8f58b85680a38832bb5ae69272da473e0e9adc35

1,993

py

Python

src/web-scrapers/GetFixtures.py

CharlesFrankum/FF_Team_Selector

f230904faa6713dcec97e086e14eb7d841de9278

[ "Apache-2.0" ]

null

src/web-scrapers/GetFixtures.py

CharlesFrankum/FF_Team_Selector

f230904faa6713dcec97e086e14eb7d841de9278

[ "Apache-2.0" ]

3

2021-03-31T19:24:31.000Z

2021-12-13T20:07:43.000Z

src/web-scrapers/GetFixtures.py

CharlesFrankum/FF_Team_Selector

f230904faa6713dcec97e086e14eb7d841de9278

[ "Apache-2.0" ]

1

2019-08-08T06:46:13.000Z

import os import sys sys.path.insert(1, f'{os.path.dirname(os.getcwd())}\\models\\') from datetime import datetime from time import sleep import pandas as pd from Mapper import df_ISO3_mapper def get_fixture_data(url, driver): # Get Fixture data for gameweeks 1-38 home_teams = [] away_teams = [] date_times = [] gameweeks = [] gw_counter = 0 for i in range(1,39): gw_counter += 1 week = url+str(i) driver.get(week) sleep(1) game_days = driver.find_elements_by_css_selector('div.sc-bdVaJa.eIzRjw') for day in game_days: date = day.find_element_by_tag_name('h4').text game_day = day.find_element_by_tag_name('ul').text games = game_day.split('\n') if ':' in game_day: # work around to keep loop consistent with game updates n_games = [] for item in games: new_items = item.split(':') for i in new_items: n_games.append(i) for i in range(0, len(n_games), 4): home_teams.append(n_games[i]) away_teams.append(n_games[i+3]) date_time = datetime.strptime(date, '%A %d %B %Y') date_times.append(date_time) gameweeks.append(gw_counter) df = pd.DataFrame({'home_team':home_teams,'away_team':away_teams,'datetime':date_times,'gameweek':gameweeks}) return df[['home_team','away_team','gameweek','datetime']] def save_csv(data): path = f'{os.path.dirname(os.getcwd())}\\data\\Fixtures\\fixtures.csv' data.to_csv(path, index=0, sep=',') def collect(driver, mapper): print('Collecting fixtures...') fixtures_url = 'https://fantasy.premierleague.com/fixtures/' fixtures = get_fixture_data(fixtures_url, driver) fixtures = df_ISO3_mapper(fixtures, mapper) save_csv(fixtures)

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8f5b5e7cd66b9e8ad42fd7854dfddea7ba008c1d

1,097

py

Python

gicowa/impl/output.py

AurelienLourot/github-commit-watcher

ca4ea1ee8ebaefdf270e4d16735d563f23cf4833

[ "Unlicense" ]

22

2015-07-05T09:12:41.000Z

2022-01-12T00:09:38.000Z

gicowa/impl/output.py

AurelienLourot/github-commit-watcher

ca4ea1ee8ebaefdf270e4d16735d563f23cf4833

[ "Unlicense" ]

3

2015-10-17T15:26:08.000Z

2016-04-27T06:00:40.000Z

gicowa/impl/output.py

AurelienLourot/github-commit-watcher

ca4ea1ee8ebaefdf270e4d16735d563f23cf4833

[ "Unlicense" ]

4

2015-10-15T21:03:47.000Z

2020-09-09T21:54:30.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- class Output: def __init__(self, print_function): """ @param print_function: Dependency. Inject a function implementing the same interface as print(). """ self.__print_function = print_function self.colored = True # Contains at any time the whole text that has been echoed by this instance: self.echoed = "" def echo(self, text): self.__print_function(text) self.echoed += text + "\n" def red(self, text): return self.__colored(text, 31) def green(self, text): return self.__colored(text, 32) def blue(self, text): return self.__colored(text, 34) def __colored(self, text, color): """Returns 'text' with a color, i.e. bash and zsh would print the returned string in the given color. Returns 'text' with no color if not self.colored. """ text = unicode(text) return text if not self.colored else "\033[" + unicode(color) + "m" + text + "\033[0m"

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8f5cb3300698dddd958b6e5a7a02e3cb797a505c

463

py

Python

stackstats/settings.py

kapsali29/StackStatsAPI

5181bd5275129080206350e147ce6b1db18a0b69

[ "MIT" ]

null

stackstats/settings.py

kapsali29/StackStatsAPI

5181bd5275129080206350e147ce6b1db18a0b69

[ "MIT" ]

null

stackstats/settings.py

kapsali29/StackStatsAPI

5181bd5275129080206350e147ce6b1db18a0b69

[ "MIT" ]

null

# ================================= # STACKEXCHANGE APP SETTINGS # ================================= CLIENT_ID = "***" CLIENT_SECRET = "*****" KEY = "****" ACCESS_TOKEN = "*****" # ================================= # STACKEXCHANGE API SETTINGS # ================================= STACKEXCHANGE_URL = "api.stackexchange.com" API_VERSION = "2.2" ANSWERS_URL = "answers" QUESTIONS_URL = "questions" COMMENTS_URL = "answers/{answerID}/comments" SECONDS_TO_SLEEP = 10

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false

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null

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null

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1

0

8f5d18b826b9c188006c34d7c3b409c3f3d1575a

288

py

Python

Platzi_Python2022/Platzi_/for.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

9

2021-08-28T01:16:31.000Z

2022-02-23T15:07:48.000Z

Platzi_Python2022/Platzi_/for.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

null

Platzi_Python2022/Platzi_/for.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

3

2021-07-21T20:03:16.000Z

2021-07-23T15:04:19.000Z

# print(1) # print(2) # print(3) # print(4) # print(5) # contador = 1 # print(contador) # while contador < 1000: # contador += 1 # print(contador) # a = list(range(1000)) # print(a) # for contador in range(1, 1001): # print(contador) for i in range(10): print(11 * i)

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8f60c2158a21875e4f1814a10f74c2d6e01951da

410

py

Python

src/leetcode/1997/sol_0.py

kagemeka/competitive-programming

c70fe481bcd518f507b885fc9234691d8ce63171

[ "MIT" ]

1

2021-07-11T03:20:10.000Z

src/leetcode/1997/sol_0.py

kagemeka/competitive-programming

c70fe481bcd518f507b885fc9234691d8ce63171

[ "MIT" ]

39

2021-07-10T05:21:09.000Z

2021-12-15T06:10:12.000Z

src/leetcode/1997/sol_0.py

kagemeka/competitive-programming

c70fe481bcd518f507b885fc9234691d8ce63171

[ "MIT" ]

null

import typing import functools class Solution: def firstDayBeenInAllRooms( self, nx: typing.List[int], ) -> int: mod = 10 ** 9 + 7 @functools.lru_cache(maxsize=None) def dfs(i: int): if i == 0: return 0 res = 2 + dfs(i - 1) if nx[i - 1] != i - 1: res += dfs(i - 1) - dfs(nx[i - 1]) return res % mod return dfs(len(nx) - 1)

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null

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8f61f1856f16642c78fb503a5e3b2cbe7612d63a

1,278

py

Python

setup.py

EasyPost/ssh_certificate_parser

836d7df5c676659604a1d76f528aad5d35321803

[ "0BSD" ]

3

2017-10-09T13:50:16.000Z

2021-08-16T21:04:16.000Z

setup.py

EasyPost/ssh_certificate_parser

836d7df5c676659604a1d76f528aad5d35321803

[ "0BSD" ]

null

setup.py

EasyPost/ssh_certificate_parser

836d7df5c676659604a1d76f528aad5d35321803

[ "0BSD" ]

2

2017-07-20T16:30:44.000Z

2021-08-09T05:52:53.000Z

#!/usr/bin/env python from setuptools import setup, find_packages setup( name="ssh_certificate_parser", version="1.3.3", author="James Brown", author_email="jbrown@easypost.com", url="https://github.com/easypost/ssh_certificate_parser", license="ISC", packages=find_packages(exclude=['tests']), description="Python library for interacting with OpenSSH Certificates", long_description=open('README.md', 'r').read(), long_description_content_type='text/markdown', install_requires=[ 'attrs>=16', ], project_urls={ 'Docs': 'https://ssh-certificate-parser.readthedocs.io/', 'Tracker': 'https://github.com/EasyPost/ssh_certificate_parser/issues', 'Source': 'https://github.com/EasyPost/ssh_certificate_parser', }, classifiers=[ "Development Status :: 3 - Alpha", "Environment :: Console", "Programming Language :: Python", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Operating System :: POSIX", "Intended Audience :: Developers", "License :: OSI Approved :: ISC License (ISCL)", ] )

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null

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null

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8f639c82ba6fc3b596994756f2ed202a124ee6d6

472

py

Python

scripts/audio/replace.py

Y4SSIN/video-editor

879e53ee689e0085140d10f3c7b35a4048ca233b

[ "MIT" ]

8

2019-01-21T13:14:33.000Z

2020-10-02T14:40:21.000Z

scripts/audio/replace.py

Y4SSIN/video-editor

879e53ee689e0085140d10f3c7b35a4048ca233b

[ "MIT" ]

3

2021-06-08T21:30:11.000Z

2022-03-12T00:28:37.000Z

scripts/audio/replace.py

Y4SSIN/video-editor

879e53ee689e0085140d10f3c7b35a4048ca233b

[ "MIT" ]

2

2020-12-01T16:59:04.000Z

2021-02-01T03:31:21.000Z

''' This function gives you the possibility to replace the video audio. ''' import os def replace_audio(video_file_path, audio_file_path): old_filename = video_file_path.rsplit('\\', 1)[-1] old_extension = os.path.splitext(video_file_path)[1] new_filename = old_filename.replace(old_extension, '.mp4') os.system(f'ffmpeg -i {video_file_path} -i {audio_file_path} -map 0:0 -map 1:0 -c:v copy -c:a aac -b:a 256k -shortest assets/videos/{new_filename}')

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0.333333

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null

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null

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1

0

8f64fd79f6f4e0f16023d3c4112423cb2c29995a

405

py

Python

sims-g2/pos-adv/code/plot2D.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

1

2019-12-19T16:21:13.000Z

sims-g2/pos-adv/code/plot2D.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

null

sims-g2/pos-adv/code/plot2D.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

2

2020-01-08T06:23:33.000Z

2020-01-08T07:06:50.000Z

from pylab import * X = linspace(-1, 1, 50) XX, YY = meshgrid(X, X) def calcf(XX, YY, mu1): f1 = 0.5 f2 = 1/(2*sqrt(3)*mu1) f3 = 1/(2*sqrt(3)*mu1) f4 = 1/(6*mu1**2) return f1*0.5 + f2*sqrt(3)*XX/2 + f3*sqrt(3)*YY/2 + 3*XX*YY/2 mu1 = 3.0/5.0 f1 = calcf(XX, YY, mu1) pcolormesh(XX, YY, transpose(f1)) axis('image') colorbar() print("Max: %g. Min = %g" % (f1.max(), f1.min())) show()

19.285714

65

0.540741

84

405

2.607143

0.404762

0.091324

0.082192

0.109589

0.091324

0

0.137072

0.207407

405

20

66

20.25

0.545171

0

0.054321

0

1

0.0625

false

0

0.0625

0

0.1875

0.0625

0

null

0

null

0

1

0

8f651734783eec2d577591561e02a2c193bbe807

4,312

py

Python

cfn_custom_resources_backed_by_step_functions/cfn_custom_resources_backed_by_step_functions_stack.py

bitesizedserverless/cfn-custom-resources-backed-by-step-functions

45c424a9d6f491700e1729ef88c5fee36beb5e44

[ "MIT" ]

null

cfn_custom_resources_backed_by_step_functions/cfn_custom_resources_backed_by_step_functions_stack.py

bitesizedserverless/cfn-custom-resources-backed-by-step-functions

45c424a9d6f491700e1729ef88c5fee36beb5e44

[ "MIT" ]

null

cfn_custom_resources_backed_by_step_functions/cfn_custom_resources_backed_by_step_functions_stack.py

bitesizedserverless/cfn-custom-resources-backed-by-step-functions

45c424a9d6f491700e1729ef88c5fee36beb5e44

[ "MIT" ]

null

"""Module for the main CfnCustomResourcesBackedByStepFunctions Stack.""" # Standard library imports import time # Related third party imports # - # Local application/library specific imports from aws_cdk import ( core as cdk, aws_lambda as lambda_, aws_stepfunctions as sfn, aws_stepfunctions_tasks as sfn_tasks, ) class CfnCustomResourcesBackedByStepFunctionsStack(cdk.Stack): """The CfnCustomResourcesBackedByStepFunctions Stack.""" def __init__( self, scope: cdk.Construct, construct_id: str, **kwargs, ) -> None: """Construct a new CfnCustomResourcesBackedByStepFunctionsStack.""" super().__init__(scope, construct_id, **kwargs) # Define the Lambda functions for the state machine fail_50_percent_lambda = lambda_.Function( scope=self, id="Fail50PercentOfUpdates", code=lambda_.Code.from_asset("lambda/functions/fail_50_percent_of_updates"), handler="index.lambda_handler", runtime=lambda_.Runtime.PYTHON_3_9, ) requests_layer = lambda_.LayerVersion( scope=self, id="RequestsLayer", code=lambda_.Code.from_asset("lambda/layers/requests_layer/python.zip"), ) update_cfn_lambda = lambda_.Function( scope=self, id="UpdateCfnLambda", code=lambda_.Code.from_asset("lambda/functions/update_cfn_custom_resource"), handler="index.lambda_handler", runtime=lambda_.Runtime.PYTHON_3_9, layers=[requests_layer], ) # The State Machine looks like this: # Start # | # V # # Lambda (fails 50% of the time) # # | | # success \ / catch # V # # Lambda (update CFN) fail_50_percent_step = sfn_tasks.LambdaInvoke( scope=self, id="Lambda (Fail 50%)", lambda_function=fail_50_percent_lambda, retry_on_service_exceptions=False, ) update_cfn_step = sfn_tasks.LambdaInvoke( scope=self, id="Update CloudFormation", lambda_function=update_cfn_lambda, # We pass both the original execution input AND the lambda execution # results to the Update CloudFormation Lambda. The function will use # the Lambda execution results to determine success or failure, and will # use the original Step Functions Execution Input to fetch the CloudFormation # callback parameters (ResponseURL, StackId, RequestId and LogicalResourceId). payload=sfn.TaskInput.from_object( { "ExecutionInput": sfn.JsonPath.string_at("$$.Execution.Input"), "LambdaResults.$": "$", } ), ) # Make sure failures are also handled by the update_cfn_step fail_50_percent_step.add_catch(handler=update_cfn_step, errors=["States.ALL"]) # Create the state machine. state_machine = sfn.StateMachine( self, "StateMachine", definition=fail_50_percent_step.next(update_cfn_step), timeout=cdk.Duration.minutes(1), ) # The Lambda Function backing the custom resource custom_resource_handler_function = lambda_.Function( scope=self, id="CustomResourceHandler", code=lambda_.Code.from_asset("lambda/functions/custom_resource_handler"), handler="index.lambda_handler", runtime=lambda_.Runtime.PYTHON_3_9, environment={"STATE_MACHINE_ARN": state_machine.state_machine_arn}, ) state_machine.grant_start_execution(custom_resource_handler_function) # The CFN Custom Resource cdk.CustomResource( scope=self, id="CustomResource", service_token=custom_resource_handler_function.function_arn, # Passing the time as a parameter will trigger the custom # resource with every deployment. properties={"ExecutionTime": str(time.time())}, )

35.344262

90

0.606679

424

4,312

5.910377

0.351415

0.038308

0.030726

0.028731

0.226257

0.173184

0.136872

0.063448

0

0.008452

0.314007

4,312

121

91

35.636364

0.838742

0.265074

0

0.202703

0

0.143314

0.066539

0

1

0.013514

false

0

0.027027

0

0.054054

0

null

0

null

0

1

0

8f6591658c09575e183359943f78305804567803

590

py

Python

config/asgi.py

Jairoguo/django-sonsuz

118698bc3353a67c5af968c34431619d6e1211af

[ "MIT" ]

null

config/asgi.py

Jairoguo/django-sonsuz

118698bc3353a67c5af968c34431619d6e1211af

[ "MIT" ]

null

config/asgi.py

Jairoguo/django-sonsuz

118698bc3353a67c5af968c34431619d6e1211af

[ "MIT" ]

null

import os import sys import django from channels.routing import get_default_application # from django.core.asgi import get_asgi_application # os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sonsuz.config.settings.local') # application = get_asgi_application() # application加入查找路径中 app_path = os.path.abspath(os.path.join( os.path.dirname(os.path.abspath(__file__)), os.pardir)) sys.path.append(os.path.join(app_path, 'sonsuz')) # ../mydjango/mydjango os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.local") django.setup() application = get_default_application()

29.5

81

0.791525

79

590

5.683544

0.379747

0.066815

0.093541

0.111359

0.173719

0

0.083051

590

19

82

31.052632

0.829945

0.349153

0

0.129973

0.114058

0

1

0

false

0

0.4

0

0.4

0

null

0

null

0

1

0

1

8f660202be6af86137fb1acec31901c87e526857

1,113

py

Python

generators/app/templates/config.py

andybp85/generator-flask-api-dotenv

1da51618c07ee6989000fbcefc26a0df2ae426ea

[ "MIT" ]

1

2016-10-10T02:35:03.000Z

generators/app/templates/config.py

andybp85/generator-flask-api-dotenv

1da51618c07ee6989000fbcefc26a0df2ae426ea

[ "MIT" ]

null

generators/app/templates/config.py

andybp85/generator-flask-api-dotenv

1da51618c07ee6989000fbcefc26a0df2ae426ea

[ "MIT" ]

null

import os, tempfile from flask.ext.dotenv import DotEnv basedir = os.path.abspath(os.path.dirname(__file__)) class Config(object): DEBUG = False TESTING = False SQLALCHEMY_TRACK_MODIFICATIONS = False @classmethod def init_app(self, app): env = DotEnv() env.init_app(app, verbose_mode=True) <% if (databaseMapper === 'sqlalchemy') { -%> if self.__name__ != 'TestingConfig': prefix = self.__name__.replace('Config', '').upper() env.alias(maps={ '<%= appEnvVar %>_' + prefix + '_DATABASE_URI': 'SQLALCHEMY_DATABASE_URI' }) <% } -%> class ProductionConfig(Config): pass class DevelopmentConfig(Config): DEBUG = True class TestingConfig(Config): TESTING = True <% if (databaseMapper === 'sqlalchemy') { -%> db_file = tempfile.NamedTemporaryFile() SQLALCHEMY_DATABASE_URI = 'sqlite:///' + db_file.name SQLALCHEMY_ECHO = True <% } -%> config = { 'production': ProductionConfig, 'development': DevelopmentConfig, 'testing': TestingConfig, 'default': ProductionConfig, }

21.823529

89

0.634322

106

1,113

6.40566

0.490566

0.048601

0.05891

0.088365

0

0.229111

1,113

50

90

22.26

0.791375

0

0.114286

0

0.123091

0.020665

0

null

0.028571

0.057143

null

0

null

0

null

0

1

0

1

8f6693ddb85d3bb717698451379c61708255fc9d

9,726

py

Python

Pokemon/pokemon_item.py

V-FEXrt/Pokemon-Spoof-Plus

d397d680742496b7f64b401511da7eb57f63c973

[ "MIT" ]

2

2017-05-04T20:24:19.000Z

2017-05-04T20:58:07.000Z

Pokemon/pokemon_item.py

V-FEXrt/Pokemon-Spoof-Plus

d397d680742496b7f64b401511da7eb57f63c973

[ "MIT" ]

null

Pokemon/pokemon_item.py

V-FEXrt/Pokemon-Spoof-Plus

d397d680742496b7f64b401511da7eb57f63c973

[ "MIT" ]

null

import random class Item(): def __init__(self, name, hex): self.name = name self.hex = hex def __str__(self): return self.name @staticmethod def fromBytes(hex): return Item.reverse[hex] @staticmethod def buildReverse(): reverse = {} Item.members = [attr for attr in dir(Item) if not callable(getattr(Item, attr)) and not attr.startswith("__")] for member in Item.members: item = getattr(Item, member) reverse[item.hex] = item Item.reverse = reverse @staticmethod def rnd(): return getattr(Item, random.choice(Item.members)) Item.NOTHING = Item('Nothing', 0x00) Item.MASTER_BALL = Item("Master Ball", 0x01) Item.ULTRA_BALL = Item("Ultra Ball", 0x02) Item.BRIGHT_POWDER = Item("BrightPowder", 0x03) Item.GREAT_BALL = Item("Great Ball", 0x04) Item.POKE_BALL = Item("Poke Ball", 0x05) Item.BICYCLE = Item("Bicycle", 0x06) Item.MOON_STONE = Item("Moon Stone", 0x08) Item.ANTIDOTE = Item("Antidote", 0x09) Item.BURN_HEAL = Item("Burn Heal", 0x0A) Item.ICE_HEAL = Item("Ice Heal", 0x0B) Item.AWAKENING = Item("Awakening", 0x0C) Item.PARLYZ_HEAL = Item("Parlyz Heal", 0x0D) Item.FULL_RESTORE = Item("Full Restore", 0x0E) Item.MAX_POTION = Item("Max Potion", 0x0F) Item.HYPER_POTION = Item("Hyper Potion", 0x10) Item.SUPER_POTION = Item("Super Potion", 0x11) Item.POTION = Item("Potion", 0x12) Item.ESCAPE_ROPE = Item("Escape Rope", 0x13) Item.REPEL = Item("Repel", 0x14) Item.MAX_ELIXER = Item("Max Elixer", 0x15) Item.FIRE_STONE = Item("Fire Stone", 0x16) Item.THUNDER_STONE = Item("Thunder Stone", 0x17) Item.WATER_STONE = Item("Water Stone", 0x18) Item.HP_UP = Item("HP Up", 0x1A) Item.PROTEIN = Item("Protein", 0x1B) Item.IRON = Item("Iron", 0x1C) Item.CARBOS = Item("Carbos", 0x1D) Item.LUCKY_PUNCH = Item("Lucky Punch", 0x1E) Item.CALCIUM = Item("Calcium", 0x1F) Item.RARE_CANDY = Item("Rare Candy", 0x20) Item.X_ACCURACY = Item("X Accuracy", 0x21) Item.LEAF_STONE = Item("Leaf Stone", 0x22) Item.METAL_POWDER = Item("Metal Powder", 0x23) Item.NUGGET = Item("Nugget", 0x24) Item.POKE_DOLL = Item("Poke Doll", 0x25) Item.FULL_HEAL = Item("Full Heal", 0x26) Item.REVIVE = Item("Revive", 0x27) Item.MAX_REVIVE = Item("Max Revive", 0x28) Item.GUARD_SPEC = Item("Guard Spec.", 0x29) Item.SUPER_REPEL = Item("Super Repel", 0x2A) Item.MAX_REPEL = Item("Max Repel", 0x2B) Item.DIRE_HIT = Item("Dire Hit", 0x2C) Item.FRESH_WATER = Item("Fresh Water", 0x2E) Item.SODA_POP = Item("Soda Pop", 0x2F) Item.LEMONADE = Item("Lemonade", 0x30) Item.X_ATTACK = Item("X Attack", 0x31) Item.X_DEFEND = Item("X Defend", 0x33) Item.X_SPEED = Item("X Speed", 0x34) Item.X_SPECIAL = Item("X Special", 0x35) Item.COIN_CASE = Item("Coin Case", 0x36) Item.ITEMFINDER = Item("Itemfinder", 0x37) Item.EXP_SHARE = Item("Exp Share", 0x39) Item.OLD_ROD = Item("Old Rod", 0x3A) Item.GOOD_ROD = Item("Good Rod", 0x3B) Item.SILVER_LEAF = Item("Silver Leaf", 0x3C) Item.SUPER_ROD = Item("Super Rod", 0x3D) Item.PP_UP = Item("PP Up", 0x3E) Item.ETHER = Item("Ether", 0x3F) Item.MAX_ETHER = Item("Max Ether", 0x40) Item.ELIXER = Item("Elixer", 0x41) Item.RED_SCALE = Item("Red Scale", 0x42) Item.SECRET_POTION = Item("SecretPotion", 0x43) Item.SS_TICKET = Item("S.S. Ticket", 0x44) Item.MYSTERY_EGG = Item("Mystery Egg", 0x45) Item.CLEAR_BELL = Item("Clear Bell*", 0x46) Item.SILVER_WING = Item("Silver Wing", 0x47) Item.MOOMOO_MILK = Item("Moomoo Milk", 0x48) Item.QUICK_CLAW = Item("Quick Claw", 0x49) Item.PSN_CURE_BERRY = Item("PSNCureBerry", 0x4A) Item.GOLD_LEAF = Item("Gold Leaf", 0x4B) Item.SOFT_SAND = Item("Soft Sand", 0x4C) Item.SHARP_BEAK = Item("Sharp Beak", 0x4D) Item.PRZ_CURE_BERRY = Item("PRZCureBerry", 0x4E) Item.BURNT_BERRY = Item("Burnt Berry", 0x4F) Item.ICE_BERRY = Item("Ice Berry", 0x50) Item.POISON_BARB = Item("Poison Barb", 0x51) Item.KINGS_ROCK = Item("King's Rock", 0x52) Item.BITTER_BERRY = Item("Bitter Berry", 0x53) Item.MINT_BERRY = Item("Mint Berry", 0x54) Item.RED_APRICORN = Item("Red Apricorn", 0x55) Item.TINY_MUSHROOM = Item("TinyMushroom", 0x56) Item.BIG_MUSHROOM = Item("Big Mushroom", 0x57) Item.SILVER_POWDER = Item("SilverPowder", 0x58) Item.BLU_APRICORN = Item("Blu Apricorn", 0x59) Item.AMULET_COIN = Item("Amulet Coin", 0x5B) Item.YLW_APRICORN = Item("Ylw Apricorn", 0x5C) Item.GRN_APRICORN = Item("Grn Apricorn", 0x5D) Item.CLEANSE_TAG = Item("Cleanse Tag", 0x5E) Item.MYSTIC_WATER = Item("Mystic Water", 0x5F) Item.TWISTED_SPOON = Item("TwistedSpoon", 0x60) Item.WHT_APRICORN = Item("Wht Apricorn", 0x61) Item.BLACK_BELT = Item("Black Belt", 0x62) Item.BLK_APRICORN = Item("Blk Apricorn", 0x63) Item.PNK_APRICORN = Item("Pnk Apricorn", 0x65) Item.BLACK_GLASSES = Item("BlackGlasses", 0x66) Item.SLOWPOKE_TAIL = Item("SlowpokeTail", 0x67) Item.PINK_BOW = Item("Pink Bow", 0x68) Item.STICK = Item("Stick", 0x69) Item.SMOKE_BALL = Item("Smoke Ball", 0x6A) Item.NEVER_MELT_ICE = Item("NeverMeltIce", 0x6B) Item.MAGNET = Item("Magnet", 0x6C) Item.MIRACLE_BERRY = Item("MiracleBerry", 0x6D) Item.PEARL = Item("Pearl", 0x6E) Item.BIG_PEARL = Item("Big Pearl", 0x6F) Item.EVERSTONE = Item("Everstone", 0x70) Item.SPELL_TAG = Item("Spell Tag", 0x71) Item.RAGE_CANDY_BAR = Item("RageCandyBar", 0x72) Item.GS_BALL = Item("GS Ball*", 0x73) Item.BLUE_CARD = Item("Blue Card*", 0x74) Item.MIRACLE_SEED = Item("Miracle Seed", 0x75) Item.THICK_CLUB = Item("Thick Club", 0x76) Item.FOCUS_BAND = Item("Focus Band", 0x77) Item.ENERGY_POWDER = Item("EnergyPowder", 0x79) Item.ENERGY_ROOT = Item("Energy Root", 0x7A) Item.HEAL_POWDER = Item("Heal Powder", 0x7B) Item.REVIVAL_HERB = Item("Revival Herb", 0x7C) Item.HARD_STONE = Item("Hard Stone", 0x7D) Item.LUCKY_EGG = Item("Lucky Egg", 0x7E) Item.CARD_KEY = Item("Card Key", 0x7F) Item.MACHINE_PART = Item("Machine Part", 0x80) Item.EGG_TICKET = Item("Egg Ticket*", 0x81) Item.LOST_ITEM = Item("Lost Item", 0x82) Item.STARDUST = Item("Stardust", 0x83) Item.STAR_PIECE = Item("Star Piece", 0x84) Item.BASEMENT_KEY = Item("Basement Key", 0x85) Item.PASS = Item("Pass", 0x86) Item.CHARCOAL = Item("Charcoal", 0x8A) Item.BERRY_JUICE = Item("Berry Juice", 0x8B) Item.SCOPE_LENS = Item("Scope Lens", 0x8C) Item.METAL_COAT = Item("Metal Coat", 0x8F) Item.DRAGON_FANG = Item("Dragon Fang", 0x90) Item.LEFTOVERS = Item("Leftovers", 0x92) Item.MYSTERY_BERRY = Item("MysteryBerry", 0x96) Item.DRAGON_SCALE = Item("Dragon Scale", 0x97) Item.BERSERK_GENE = Item("Berserk Gene", 0x98) Item.SACRED_ASH = Item("Sacred Ash", 0x9C) Item.HEAVY_BALL = Item("Heavy Ball", 0x9D) Item.FLOWER_MAIL = Item("Flower Mail", 0x9E) Item.LEVEL_BALL = Item("Level Ball", 0x9F) Item.LURE_BALL = Item("Lure Ball", 0xA0) Item.FAST_BALL = Item("Fast Ball", 0xA1) Item.LIGHT_BALL = Item("Light Ball", 0xA3) Item.FRIEND_BALL = Item("Friend Ball", 0xA4) Item.MOON_BALL = Item("Moon Ball", 0xA5) Item.LOVE_BALL = Item("Love Ball", 0xA6) Item.NORMAL_BOX = Item("Normal Box", 0xA7) Item.GORGEOUS_BOX = Item("Gorgeous Box", 0xA8) Item.SUN_STONE = Item("Sun Stone", 0xA9) Item.POLKADOT_BOW = Item("Polkadot Bow", 0xAA) Item.UP_GRADE = Item("Up-Grade", 0xAC) Item.BERRY = Item("Berry", 0xAD) Item.GOLD_BERRY = Item("Gold Berry", 0xAE) Item.SQUIRT_BOTTLE = Item("SquirtBottle", 0xAF) Item.PARK_BALL = Item("Park Ball", 0xB1) Item.RAINBOW_WING = Item("Rainbow Wing", 0xB2) Item.BRICK_PIECE = Item("Brick Piece", 0xB4) Item.SURF_MAIL = Item("Surf Mail", 0xB5) Item.LITEBLUEMAIL = Item("Litebluemail", 0xB6) Item.PORTRAITMAIL = Item("Portraitmail", 0xB7) Item.LOVELY_MAIL = Item("Lovely Mail", 0xB8) Item.EON_MAIL = Item("Eon Mail", 0xB9) Item.MORPH_MAIL = Item("Morph Mail", 0xBA) Item.BLUESKY_MAIL = Item("Bluesky Mail", 0xBB) Item.MUSIC_MAIL = Item("Music Mail", 0xBC) Item.MIRAGE_MAIL = Item("Mirage Mail", 0xBD) Item.TM01 = Item("TM01", 0xBF) Item.TM02 = Item("TM02", 0xC0) Item.TM03 = Item("TM03", 0xC1) Item.TM04 = Item("TM04", 0xC2) Item.TM05 = Item("TM05", 0xC4) Item.TM06 = Item("TM06", 0xC5) Item.TM07 = Item("TM07", 0xC6) Item.TM08 = Item("TM08", 0xC7) Item.TM09 = Item("TM09", 0xC8) Item.TM10 = Item("TM10", 0xC9) Item.TM11 = Item("TM11", 0xCA) Item.TM12 = Item("TM12", 0xCB) Item.TM13 = Item("TM13", 0xCC) Item.TM14 = Item("TM14", 0xCD) Item.TM15 = Item("TM15", 0xCE) Item.TM16 = Item("TM16", 0xCF) Item.TM17 = Item("TM17", 0xD0) Item.TM18 = Item("TM18", 0xD1) Item.TM19 = Item("TM19", 0xD2) Item.TM20 = Item("TM20", 0xD3) Item.TM21 = Item("TM21", 0xD4) Item.TM22 = Item("TM22", 0xD5) Item.TM23 = Item("TM23", 0xD6) Item.TM24 = Item("TM24", 0xD7) Item.TM25 = Item("TM25", 0xD8) Item.TM26 = Item("TM26", 0xD9) Item.TM27 = Item("TM27", 0xDA) Item.TM28 = Item("TM28", 0xDB) Item.TM29 = Item("TM29", 0xDD) Item.TM30 = Item("TM30", 0xDE) Item.TM31 = Item("TM31", 0xDF) Item.TM32 = Item("TM32", 0xE0) Item.TM33 = Item("TM33", 0xE1) Item.TM34 = Item("TM34", 0xE2) Item.TM35 = Item("TM35", 0xE3) Item.TM36 = Item("TM36", 0xE4) Item.TM37 = Item("TM37", 0xE5) Item.TM38 = Item("TM38", 0xE6) Item.TM39 = Item("TM39", 0xE7) Item.TM40 = Item("TM40", 0xE8) Item.TM41 = Item("TM41", 0xE9) Item.TM42 = Item("TM42", 0xEA) Item.TM43 = Item("TM43", 0xEB) Item.TM44 = Item("TM44", 0xEC) Item.TM45 = Item("TM45", 0xED) Item.TM46 = Item("TM46", 0xEE) Item.TM47 = Item("TM47", 0xEF) Item.TM48 = Item("TM48", 0xF0) Item.TM49 = Item("TM49", 0xF1) Item.TM50 = Item("TM50", 0xF2) Item.HM01 = Item("HM01", 0xF3) Item.HM02 = Item("HM02", 0xF4) Item.HM03 = Item("HM03", 0xF5) Item.HM04 = Item("HM04", 0xF6) Item.HM05 = Item("HM05", 0xF7) Item.HM06 = Item("HM06", 0xF8) Item.HM07 = Item("HM07", 0xF9) Item.HM08 = Item("HM08", 0xFA) Item.HM09 = Item("HM09", 0xFB) Item.HM10 = Item("HM10", 0xFC) Item.HM11 = Item("HM11", 0xFD) Item.HM12 = Item("HM12", 0xFE) Item.UNKNOWN = Item("Unknown", 0xFF) Item.buildReverse()

36.980989

118

0.699054

1,493

9,726

4.45144

0.358339

0.018056

0.004514

0

0.089473

0.127802

9,726

262

119

37.122137

0.693976

0

0.011952

0

0.193728

0

0.09419

0

1

0.01992

false

0.003984

0.011952

0.039841

0

null

0

null

0

1

0

8f66a163bf1e5878e2474fa634b0488a8aa1b816

3,589

py

Python

train.py

Markus-Goetz/block-prediction

3f89d17d449f023d60fae5ec6bd712cb6cc8cb50

[ "MIT" ]

5

2018-11-28T22:18:29.000Z

2021-08-16T22:09:35.000Z

train.py

Markus-Goetz/block-prediction

3f89d17d449f023d60fae5ec6bd712cb6cc8cb50

[ "MIT" ]

null

train.py

Markus-Goetz/block-prediction

3f89d17d449f023d60fae5ec6bd712cb6cc8cb50

[ "MIT" ]

5

2018-12-03T08:40:46.000Z

2022-02-21T14:21:52.000Z

#!/usr/bin/env python import argparse import pickle import h5py from keras import optimizers from keras.callbacks import ModelCheckpoint from keras.layers import Activation, add, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Input, ZeroPadding2D from keras.models import load_model, Model from keras.regularizers import l2 from keras.utils import plot_model import numpy as np def positive_int(value): try: parsed = int(value) if not parsed > 0: raise ValueError() return parsed except ValueError: raise argparse.ArgumentTypeError('value must be an positive integer') def parse_cli(): parser = argparse.ArgumentParser() parser.add_argument( '-e', '--epochs', nargs='?', type=positive_int, action='store', default=10, help='number of training epochs' ) parser.add_argument( metavar='TRAIN', type=str, dest='train', help='path to the HDF5 file with the training data' ) parser.add_argument( metavar='MODEL', type=str, dest='model', help='path where to store the model' ) return parser.parse_args() def load_data(path): with h5py.File(path, 'r') as handle: data = np.array(handle['diagonalset']) labels = np.array(handle['vectorset']) return data, labels def preprocess(data, labels): # simply add an additional dimension for the channels for data # swap axis of the label set return np.expand_dims(data, axis=3), np.moveaxis(labels, 0, -1) def build_model(input_shape): input_img = Input(shape=input_shape) # first bottleneck unit bn_1 = BatchNormalization()(input_img) activation_1 = Activation('selu')(bn_1) conv_1 = Conv2D(32, kernel_size=(5, 5,), padding='same', kernel_regularizer=l2(0.02))(activation_1) bn_2 = BatchNormalization()(conv_1) activation_2 = Activation('selu')(bn_2) conv_2 = Conv2D(128, kernel_size=(3, 3,), padding='same', kernel_regularizer=l2(0.02))(activation_2) merged = add([input_img, conv_2]) # corner detection bn_3 = BatchNormalization()(merged) padding = ZeroPadding2D(padding=(0, 3))(bn_3) conv_3 = Conv2D( 32, kernel_size=(21, 7,), padding='valid', activation='tanh')(padding) conv_4 = Conv2D(128, kernel_size=( 1, 3,), padding='same', activation='tanh')(conv_3) # fully-connected predictor flat = Flatten()(conv_4) classify = Dense(512, activation='sigmoid')(flat) dropout = Dropout(0.1)(classify) result = Dense(input_shape[1], activation='sigmoid')(dropout) model = Model(inputs=input_img, outputs=result) model.compile(optimizer=optimizers.Nadam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy']) return model def train_network(model, data, labels, model_file, epochs): plot_model(model, to_file='{}.png'.format(model_file), show_shapes=True) checkpoint = ModelCheckpoint(model_file, monitor='val_loss', verbose=True, save_best_only=True, save_weights_only=False, mode='auto') training = model.fit(data, labels, epochs=epochs, batch_size=8, validation_split=1.0/5.0, class_weight={0: 0.1, 1: 0.9}, callbacks=[checkpoint]) with open('{}.history'.format(model_file), 'wb') as handle: pickle.dump(training.history, handle) if __name__ == '__main__': arguments = parse_cli() data, labels = preprocess(*load_data(arguments.train)) model = build_model(input_shape=data.shape[1:]) train_network(model, data, labels, arguments.model, arguments.epochs)

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0.021574

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null

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null

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0

8f68e760e629e6c9289a968b529c8bbdbfa66448

1,281

py

Python

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_Flat_Pbr_02_benchvise.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_Flat_Pbr_02_benchvise.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_Flat_Pbr_02_benchvise.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = ["./FlowNet512_1.5AugCosyAAEGray_Flat_Pbr_01_ape.py"] OUTPUT_DIR = "output/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_Flat_lmPbr_SO/benchvise" DATASETS = dict(TRAIN=("lm_pbr_benchvise_train",), TEST=("lm_real_benchvise_test",)) # bbnc7 # objects benchvise Avg(1) # ad_2 9.12 9.12 # ad_5 44.52 44.52 # ad_10 90.69 90.69 # rete_2 45.97 45.97 # rete_5 99.71 99.71 # rete_10 100.00 100.00 # re_2 63.43 63.43 # re_5 99.71 99.71 # re_10 100.00 100.00 # te_2 77.40 77.40 # te_5 100.00 100.00 # te_10 100.00 100.00 # proj_2 75.75 75.75 # proj_5 99.22 99.22 # proj_10 100.00 100.00 # re 1.80 1.80 # te 0.01 0.01 # init by mlBCE # objects benchvise Avg(1) # ad_2 9.21 9.21 # ad_5 44.52 44.52 # ad_10 90.79 90.79 # rete_2 46.46 46.46 # rete_5 99.52 99.52 # rete_10 100.00 100.00 # re_2 64.31 64.31 # re_5 99.52 99.52 # re_10 100.00 100.00 # te_2 77.50 77.50 # te_5 100.00 100.00 # te_10 100.00 100.00 # proj_2 75.85 75.85 # proj_5 99.22 99.22 # proj_10 100.00 100.00 # re 1.80 1.80 # te 0.01 0.01

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0.540984

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1,281

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0.155039

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0.489922

0.415504

0.356589

0.244961

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0.347385

1,281

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0

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1

0

1

null

0

3

8f6a4ac0f3616dd43ef5ba0483ceab37ae337f14

2,489

py

Python

DataCrawl/crawler/lib/browser.py

Haiduongcable/BigData-Computing

917210a6254b5e656a98af9056a0c975d2dfef26

[ "Apache-2.0" ]

null

DataCrawl/crawler/lib/browser.py

Haiduongcable/BigData-Computing

917210a6254b5e656a98af9056a0c975d2dfef26

[ "Apache-2.0" ]

null

DataCrawl/crawler/lib/browser.py

Haiduongcable/BigData-Computing

917210a6254b5e656a98af9056a0c975d2dfef26

[ "Apache-2.0" ]

null

import sys import os from pathlib import Path import json import pickle from selenium import webdriver from selenium.webdriver.chrome.options import Options import selenium.webdriver.support.ui as ui class Browser: def __init__(self): chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.add_argument('--window-size=1920,1080') if sys.platform == 'linux': self.driver = webdriver.Chrome(os.path.abspath('./crawler/driver/linux/chromedriver'), desired_capabilities = chrome_options.to_capabilities()) elif sys.platform == 'win32': self.driver = webdriver.Chrome(os.path.abspath('./crawler/driver/win/chromedriver.exe'), desired_capabilities = chrome_options.to_capabilities()) elif sys.platform == 'darwin': self.driver = webdriver.Chrome(os.path.abspath('./crawler/driver/mac/chromedriver'), desired_capabilities = chrome_options.to_capabilities()) self.wait = ui.WebDriverWait(self.driver,30) def go(self, url): self.driver.get(url) def load_cookie_from(self, cookie_file): if(Path(cookie_file).exists()): for cookie in pickle.load(open(cookie_file, "rb")): #TODO it's a workaround if 'SPC_CDS' in json.dumps(cookie): continue self.driver.add_cookie(cookie) #print('cookie loaded') def wait_for(self, method): self.wait.until(method) def find_by_css(self, path): self.wait_for(lambda driver: driver.find_element_by_css_selector(path)) return self.driver.find_element_by_css_selector(path) def find_by_xpath(self, path): self.wait_for(lambda driver: driver.find_element_by_xpath(path)) return self.driver.find_element_by_xpath(path) def send_by_css(self, path, *keys): el = self.find_by_css(path) el.send_keys(*keys) def send_by_xpath(self, path, *keys): el = self.find_by_xpath(path) el.send_keys(*keys) def click_by_css(self, path): el = self.find_by_css(path) el.click() def click_by_xpath(self, path): el = self.find_by_xpath(path) el.click() def get_cookies(self): return self.driver.get_cookies() def dump_cookie(self, cookie_file): pickle.dump( self.driver.get_cookies() , open(cookie_file,"wb")) def quit(self): self.driver.quit()

34.569444

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2,489

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0.415759

0.240231

0.064062

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2,489

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34.569444

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null

0

null

0

1

0

1

0

2

8f6a5d9899e0976ac95b7796e1458b1bd050494d

3,001

py

Python

selfdrive/car/subaru/values.py

Richardrgc74/openpilot

70b15580966a6c7f8d25eca51c912c37904cc461

[ "MIT" ]

null

selfdrive/car/subaru/values.py

Richardrgc74/openpilot

70b15580966a6c7f8d25eca51c912c37904cc461

[ "MIT" ]

null

selfdrive/car/subaru/values.py

Richardrgc74/openpilot

70b15580966a6c7f8d25eca51c912c37904cc461

[ "MIT" ]

null

# flake8: noqa from selfdrive.car import dbc_dict from cereal import car Ecu = car.CarParams.Ecu class CarControllerParams: STEER_MAX = 2047 # max_steer 4095 STEER_STEP = 2 # how often we update the steer cmd STEER_DELTA_UP = 50 # torque increase per refresh, 0.8s to max STEER_DELTA_DOWN = 70 # torque decrease per refresh STEER_DRIVER_ALLOWANCE = 60 # allowed driver torque before start limiting STEER_DRIVER_MULTIPLIER = 10 # weight driver torque heavily STEER_DRIVER_FACTOR = 1 # from dbc #SUBARU STOP AND GO - Global SNG_DISTANCE_LIMIT = 120 # distance trigger value limit for stop and go (0-255) SNG_DISTANCE_DEADBAND = 10 # deadband for SNG lead car refence distance to cater for Close_Distance sensor noises THROTTLE_TAP_LIMIT = 5 # send a maximum of 5 throttle tap messages (trial and error) THROTTLE_TAP_LEVEL = 5 # send a throttle message with value of 5 (trial and error) SNG_DISTANCE_THRESHOLD = 150 #SUBARU STOP AND GO - Pre-Global SNG_DISTANCE_THRESHOLD_PREGLOBAL = 3 #SnG trigger when lead car distance > 3m SNG_DISTANCE_LIMIT_PREGLOBAL = 4 #SnG only trigger if close distance is less than 4 #SUBARU NON-EPB NON_EPB_STANDSTILL_THRESHOLD = 1000000000 #1 second NON_EPB_FAKE_SPEED = 3 #km/h class CAR: ASCENT = "SUBARU ASCENT LIMITED 2019" IMPREZA = "SUBARU IMPREZA LIMITED 2019" FORESTER = "SUBARU FORESTER 2019" FORESTER_PREGLOBAL = "SUBARU FORESTER 2017 - 2018" LEGACY_PREGLOBAL = "SUBARU LEGACY 2015 - 2018" OUTBACK_PREGLOBAL = "SUBARU OUTBACK 2015 - 2017" OUTBACK_PREGLOBAL_2018 = "SUBARU OUTBACK 2018 - 2019" FINGERPRINTS = { CAR.OUTBACK_PREGLOBAL_2018: [{ # OUTBACK 2.0D 2018 ADM 2: 8, 208: 8, 209: 4, 210: 8, 211: 7, 212: 8, 316: 8, 320: 8, 321: 8, 324: 8, 328: 8, 329: 8, 336: 2, 338: 8, 342: 8, 352: 8, 353: 8, 354: 8, 356: 8, 358: 8, 359: 8, 392: 8, 554: 8, 640: 8, 642: 8, 805: 8, 864: 8, 865: 8, 872: 8, 880: 8, 881: 8, 882: 8, 884: 8, 885: 8, 977: 8, 1614: 8, 1632: 8, 1657: 8, 1658: 8, 1672: 8, 1722: 8, 1745: 8, 1786: 5, 1787: 5, 1968: 8, 1976: 8, 2015: 8, 2016: 8, 2017: 8, 2024: 8, 2025: 8 }], } STEER_THRESHOLD = { CAR.ASCENT: 80, CAR.IMPREZA: 80, CAR.FORESTER: 80, CAR.FORESTER_PREGLOBAL: 75, CAR.LEGACY_PREGLOBAL: 75, CAR.OUTBACK_PREGLOBAL: 75, CAR.OUTBACK_PREGLOBAL_2018: 75, } DBC = { CAR.ASCENT: dbc_dict('subaru_global_2017_generated', None), CAR.IMPREZA: dbc_dict('subaru_global_2017_generated', None), CAR.FORESTER: dbc_dict('subaru_global_2017_generated', None), CAR.FORESTER_PREGLOBAL: dbc_dict('subaru_forester_2017_generated', None), CAR.LEGACY_PREGLOBAL: dbc_dict('subaru_outback_2015_generated', None), CAR.OUTBACK_PREGLOBAL: dbc_dict('subaru_outback_2015_generated', None), CAR.OUTBACK_PREGLOBAL_2018: dbc_dict('subaru_outback_2019_generated', None), } PREGLOBAL_CARS = [CAR.FORESTER_PREGLOBAL, CAR.LEGACY_PREGLOBAL, CAR.OUTBACK_PREGLOBAL, CAR.OUTBACK_PREGLOBAL_2018]

44.132353

424

0.71043

464

3,001

4.403017

0.375

0.070485

0.0651

0.045032

0.14929

0.124327

0.105237

0.059227

0

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0.193602

3,001

67

425

44.791045

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0

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1

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0

null

0

null

0

1

0

1

8f6b4fb8b5826570c194c749e3b2f1b2eb2295e0

391

py

Python

Fletnix/apps/profiles/migrations/0002_profiles_user_name.py

FuryAndRage/Fletnix

99cc015c799eda24d605ecb9706f809fa6a05392

[ "MIT" ]

null

Fletnix/apps/profiles/migrations/0002_profiles_user_name.py

FuryAndRage/Fletnix

99cc015c799eda24d605ecb9706f809fa6a05392

[ "MIT" ]

1

2021-02-21T11:08:36.000Z

2021-02-24T20:42:01.000Z

Fletnix/apps/profiles/migrations/0002_profiles_user_name.py

FuryAndRage/Fletnix

99cc015c799eda24d605ecb9706f809fa6a05392

[ "MIT" ]

null

# Generated by Django 3.1.2 on 2021-02-14 10:56 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('profiles', '0001_initial'), ] operations = [ migrations.AddField( model_name='profiles', name='user_name', field=models.CharField(max_length=255, null=True), ), ]

20.578947

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391

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0.333333

0

null

0

1

0

null

0

1

8f6cda2b221292f939019bccf17b0c0c955ce9d9

492

py

Python

options/test_options.py

salfamusic/encoder4editing

8263cb9d42cd4811f4ab2768dfcc9085259fc251

[ "MIT" ]

null

options/test_options.py

salfamusic/encoder4editing

8263cb9d42cd4811f4ab2768dfcc9085259fc251

[ "MIT" ]

null

options/test_options.py

salfamusic/encoder4editing

8263cb9d42cd4811f4ab2768dfcc9085259fc251

[ "MIT" ]

null

from .base_options import BaseOptions class TestOptions(BaseOptions): def initialize(self, parser): BaseOptions.initialize(self, parser) parser.set_defaults(phase='test') parser.add_argument('--only_for_test', type=str, default='...') parser.add_argument('--network_pkl', type=str, default='gdrive:networks/stylegan2-ffhq-config-f.pkl') parser.add_argument('--max_result_snapshots', default=30, help='max result snapshots') return parser

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492

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110

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false

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0.444444

0

null

0

null

0

1

0

8f6d9715bc92a8e85ce12c2c520af4e5db0b1a7c

3,071

py

Python

prepomm/tools.py

dwhswenson/prepomm

1109c4ae03f13f2c111e1d243243f45d0e28ceb2

[ "MIT" ]

null

prepomm/tools.py

dwhswenson/prepomm

1109c4ae03f13f2c111e1d243243f45d0e28ceb2

[ "MIT" ]

null

prepomm/tools.py

dwhswenson/prepomm

1109c4ae03f13f2c111e1d243243f45d0e28ceb2

[ "MIT" ]

null

""" Tools used elsewhere in this package """ import os import collections import mdtraj as md from simtk import openmm as mm from simtk.openmm import app from simtk import unit as u try: import openmmtools except ImportError: HAS_OPENMMTOOLS = False else: HAS_OPENMMTOOLS = True def _traj_from_file_or_traj(file_or_traj): if isinstance(file_or_traj, md.Trajectory): traj = file_or_traj elif os.path.isfile(file_or_traj): traj = md.load(file_or_traj) else: raise TypeError("%s is neither a trajectory nor a filename", file_or_traj) return traj def steps_for_duration(duration, simulation): if isinstance(duration, u.Quantity): return int(duration / simulation.integrator.getStepSize()) elif isinstance(duration, int): return duration else: raise RuntimeError("Unable to treat duration: %s", duration) def simulation_write_pdb(simulation, pdb_outfile): """Write out the current state of the simulation as a PDB""" positions = simulation.context.getState(getPositions=True).getPositions() with open(pdb_outfile, 'w') as pdb_out: app.PDBFile.writeFile(simulation.topology, positions, pdb_out) def simulation_serialize_parts(simulation, basename): def serialize_part(part, part_name): with open(basename + '_' + part_name + '.xml', 'w') as f: f.write(mm.XmlSerializer.serialize(part)) serialize_part(simulation.system, 'sys') serialize_part(simulation.integrator, 'integ') simulation.saveState(basename + '_state.xml') def simulation_from_parts(basename, pdb): topology = mm.app.PDBFile(pdb).topology system = basename + '_sys.xml' integrator = basename + '_integ.xml' state = basename + '_state.xml' sim = mm.app.Simulation(topology, system, integrator, state=state) if HAS_OPENMMTOOLS: integ = sim.integrator openmmtools.utils.RestorableOpenMMObject.restore_interface(integ) return sim def simulation_to_mdtraj(simulation): topology, positions = _topology_and_positions(simulation) md_topology = md.Topology.from_openmm(topology) xyz = np.array([positions.value_in_unit(u.nanometer)]) trajectory = md.Trajectory(xyz, topology) # TODO unitcells return trajectory # with tempfile.NamedTemporaryFile(suffix=".pdb") as tmp: # app.PDBFile.writeFile(topology, positions, tmp) # trajectory = md.load(tmp.name) return trajectory def residue_type(res): if res.is_protein: return 'protein' elif res.is_nucleic: return 'nucleic' elif res.is_water: return 'water' else: return 'other' def topology_describe(topology): total_str = "" for chain in topology.chains: restypes = collections.Counter([residue_type(res) for res in chain.residues]) mystr = ", ".join([str(v) + " " + k + " residues" for k, v in restypes.items()]) total_str += mystr + "\n" return total_str

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0.020813

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94

78

32.670213

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1

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false

0

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0.378378

0

null

0

null

0

1

0

1

8f6e45249501a9eeced6ad9317380712aa8a2e41

1,746

py

Python

ansible/module_utils/facts/hardware/base.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

1

2022-01-25T22:52:58.000Z

ansible/module_utils/facts/hardware/base.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

ansible/module_utils/facts/hardware/base.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. from __future__ import (absolute_import, division, print_function) __metaclass__ = type from ansible.module_utils.facts.collector import BaseFactCollector class Hardware: platform = 'Generic' # FIXME: remove load_on_init when we can def __init__(self, module, load_on_init=False): self.module = module def populate(self, collected_facts=None): return {} class HardwareCollector(BaseFactCollector): name = 'hardware' _fact_ids = set(['processor', 'processor_cores', 'processor_count', # TODO: mounts isnt exactly hardware 'mounts', 'devices']) _fact_class = Hardware def collect(self, module=None, collected_facts=None): collected_facts = collected_facts or {} if not module: return {} # Network munges cached_facts by side effect, so give it a copy facts_obj = self._fact_class(module) facts_dict = facts_obj.populate(collected_facts=collected_facts) return facts_dict

32.333333

72

0.683276

225

1,746

5.133333

0.542222

0.072727

0.033766

0.049351

0.070996

0.048485

0

0.000763

0.249141

1,746

53

73

32.943396

0.880244

0.434708

0

0.083333

0

0.069143

0

0.018868

0

1

0.125

false

0

0.083333

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0.583333

0.041667

0

null

0

null

0

1

0

1

8f71414f26283f636acf131540cc80063b73c4d4

691

py

Python

components/amp-utility/python/Snd.py

ekmixon/AliOS-Things

00334295af8aa474d818724149726ca93da4645d

[ "Apache-2.0" ]

4,538

2017-10-20T05:19:03.000Z

2022-03-30T02:29:30.000Z

components/amp-utility/python/Snd.py

ekmixon/AliOS-Things

00334295af8aa474d818724149726ca93da4645d

[ "Apache-2.0" ]

1,088

2017-10-21T07:57:22.000Z

2022-03-31T08:15:49.000Z

components/amp-utility/python/Snd.py

willianchanlovegithub/AliOS-Things

637c0802cab667b872d3b97a121e18c66f256eab

[ "Apache-2.0" ]

1,860

2017-10-20T05:22:35.000Z

2022-03-27T10:54:14.000Z

# * coding: UTF8 * """ 这里所有的的接口仅需要调用一次即可，具体接口和参数如下所示。 ================================================================================================= """ def install_codec_driver(): """ 声卡安装，仅需要调用一次。 :param 空: :returns: 0: 成功，其他: 失败 :raises OSError: EINVAL """ pass def uninstall_codec_driver(): """ 声卡卸载，仅需要调用一次。 :param 空: :returns: 0: 成功，其他: 失败 :raises OSError: EINVAL """ pass def init(): """ 初始化uVoice功能组件，仅需要调用一次。 :param 空: :returns: 0: 成功，其他: 失败 :raises OSError: EINVAL """ pass def deinit(): """ 取消初始化uVoice功能组件，仅需要调用一次。 :param 空: :returns: 0: 成功，其他: 失败 :raises OSError: EINVAL """ pass

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5

8f72691d634c7185886e3950715ddd4866514065

919

py

Python

2018/day3/claim.py

scrubskip/adventofcode2018

8149908d1239759597fda575432cf3ec99019dc0

[ "Apache-2.0" ]

null

2018/day3/claim.py

scrubskip/adventofcode2018

8149908d1239759597fda575432cf3ec99019dc0

[ "Apache-2.0" ]

null

2018/day3/claim.py

scrubskip/adventofcode2018

8149908d1239759597fda575432cf3ec99019dc0

[ "Apache-2.0" ]

null

#!/usr/bin/env python # from parse import compile def main(): file = open("day3input.txt", "r") fabric = [[0]*1000 for _ in range(1000)] for claim_str in file: claim = Claim(claim_str) #print claim.data claim_fabric(fabric, claim) contested = 0 for x in range(1000): for y in range(1000): if fabric[x][y] > 1: contested += 1 return contested def claim_fabric(fabric, claim): for x in range(claim.data['x'], claim.data['x'] + claim.data['width']): for y in range(claim.data['y'], claim.data['y'] + claim.data['height']): fabric[x][y] += 1 class Claim: # sample: #1 @ 257,829: 10x23 parser = compile("#{claim_id:d} @ {x:d},{y:d}: {width:d}x{height:d}") def __init__(self, claim_str): self.data = self.parser.parse(claim_str, True) if __name__== "__main__": print main()

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null

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0

1

8f72fbd93f21f83f4ef3662e0b27d9a6cf567288

735

py

Python

lognotif/alertmgmt/migrations/0009_newassignment.py

subhamoykarmakar224/Django-LiveLogNotifier

15f36048f3eb8d43d9b58b04c660bcb7fa005451

[ "MIT" ]

null

lognotif/alertmgmt/migrations/0009_newassignment.py

subhamoykarmakar224/Django-LiveLogNotifier

15f36048f3eb8d43d9b58b04c660bcb7fa005451

[ "MIT" ]

null

lognotif/alertmgmt/migrations/0009_newassignment.py

subhamoykarmakar224/Django-LiveLogNotifier

15f36048f3eb8d43d9b58b04c660bcb7fa005451

[ "MIT" ]

null

# Generated by Django 2.2.12 on 2020-05-06 12:27 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('alertmgmt', '0008_logfilterfields_log_src_url'), ] operations = [ migrations.CreateModel( name='NewAssignment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('assignment_name', models.CharField(max_length=128)), ('assignee', models.CharField(max_length=100)), ('assignto', models.CharField(max_length=100)), ('ackstatus', models.IntegerField(default=0)), ], ), ]

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null

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8f745bc0d930118b19141f5dfac7b6915950c7e9

1,772

py

Python

f5/bigiq/cm/device/licensing/pool/initial_activation.py

nghia-tran/f5-common-python

acb23a6e5830a119b460c19a578654113419f5c3

[ "Apache-2.0" ]

272

2016-02-23T06:05:44.000Z

2022-02-20T02:09:32.000Z

f5/bigiq/cm/device/licensing/pool/initial_activation.py

nghia-tran/f5-common-python

acb23a6e5830a119b460c19a578654113419f5c3

[ "Apache-2.0" ]

1,103

2016-02-11T17:48:03.000Z

2022-02-15T17:13:37.000Z

f5/bigiq/cm/device/licensing/pool/initial_activation.py

nghia-tran/f5-common-python

acb23a6e5830a119b460c19a578654113419f5c3

[ "Apache-2.0" ]

167

2016-02-11T17:48:21.000Z

2022-01-17T20:13:05.000Z

# coding=utf-8 # # Copyright 2017 F5 Networks 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. # """BIG-IQ® license pool regkeys. REST URI ``http://localhost/mgmt/cm/device/licensing/pool/initial-activation`` REST Kind ``cm:device:licensing:pool:initial-activation:*`` """ from f5.bigiq.resource import Collection from f5.bigiq.resource import Resource class Initial_Activations(Collection): def __init__(self, pool): super(Initial_Activations, self).__init__(pool) self._meta_data['required_json_kind'] = \ 'cm:device:licensing:pool:initial-activation:initialactivationworkercollectionstate' # NOQA self._meta_data['allowed_lazy_attributes'] = [Initial_Activation] self._meta_data['attribute_registry'] = { 'cm:device:licensing:pool:initial-activation:initialactivationworkeritemstate': Initial_Activation # NOQA } class Initial_Activation(Resource): def __init__(self, initial_activations): super(Initial_Activation, self).__init__(initial_activations) self._meta_data['required_creation_parameters'] = {'name', 'regKey'} self._meta_data['required_json_kind'] = \ 'cm:device:licensing:pool:initial-activation:initialactivationworkeritemstate'

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219

1,772

5.780822

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0.104265

0

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1,772

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119

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0

0.352941

0

null

0

null

0

1

0

8f758e5cc6da0001620edeb8cb1d368674c76e41

373

py

Python

run.py

myncow/compositing

7b79011d39f42594144581143ee8e7fd3a6669a3

[ "MIT" ]

null

run.py

myncow/compositing

7b79011d39f42594144581143ee8e7fd3a6669a3

[ "MIT" ]

null

run.py

myncow/compositing

7b79011d39f42594144581143ee8e7fd3a6669a3

[ "MIT" ]

2

2021-11-05T17:06:12.000Z

2021-11-17T04:15:29.000Z

import argparse from generator import logic parser = argparse.ArgumentParser() parser.add_argument('--mode', type=str, required=True) args = parser.parse_args() logic_main=logic.composite_probabilistically if args.mode == "permute": pass elif args.mode == "logic": logic_main() else: print("Specify a flag: either --mode permute or --mode probabilistic")

19.631579

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0.645833

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0

0.144772

373

18

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0

1

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false

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0.166667

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0.083333

0

null

0

null

0

1

0

1

8f783f3db1a5b9213fb73a1f97a44919aad8d34c

175

py

Python

RecommenderSystem/Book.py

RowanOmar/Recommender-System

60c8299610f4bc5ccc7254b1e05ed6228947118c

[ "MIT" ]

null

RecommenderSystem/Book.py

RowanOmar/Recommender-System

60c8299610f4bc5ccc7254b1e05ed6228947118c

[ "MIT" ]

null

RecommenderSystem/Book.py

RowanOmar/Recommender-System

60c8299610f4bc5ccc7254b1e05ed6228947118c

[ "MIT" ]

null

class Book: Title = "" Rate = 0 def __init__(self, title, rate): self.Title = title self.Rate = rate # print ("I am new ", self.Title)

14.583333

41

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175

3.863636

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0.317647

0

0.009091

0.371429

175

11

42

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0.763636

0.177143

0

1

0.166667

false

0

0.666667

0

1

0

null

1

0

null

0

1

0

3

8f787cfd457d65b0d4fa5d03424c02511129986f

109

py

Python

fluxio_parser/__init__.py

NarrativeScience/fluxio-parser

bddd6b86a550ec87a58a2d854978d559e29cf3f4

[ "BSD-3-Clause" ]

1

2021-06-09T20:22:38.000Z

fluxio_parser/__init__.py

NarrativeScience/fluxio-parser

bddd6b86a550ec87a58a2d854978d559e29cf3f4

[ "BSD-3-Clause" ]

null

fluxio_parser/__init__.py

NarrativeScience/fluxio-parser

bddd6b86a550ec87a58a2d854978d559e29cf3f4

[ "BSD-3-Clause" ]

1

2021-06-09T20:22:39.000Z

"""Main exports""" from fluxio_parser.parser import parse_project_tree # noqa: F401 __version__ = "0.3.1"

18.166667

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0.733945

16

109

4.5625

0.9375

0

0.06383

0.137615

109

5

66

21.8

0.712766

0.220183

0

0.063291

0

1

0

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0

0.5

0

0.5

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null

0

1

0

null

0

1

0

3

8f78b69e4772845ec293a193b3ef41aeb3b1c4fc

1,491

py

Python

prophet_gcp/utils.py

SpikeLab-CL/paralell_prophet

c04b069ae27eb8645dd10e0cf9992415e585ba62

[ "WTFPL" ]

7

2018-10-18T18:06:27.000Z

2021-11-02T19:53:31.000Z

prophet_gcp/utils.py

SpikeLab-CL/paralell_prophet

c04b069ae27eb8645dd10e0cf9992415e585ba62

[ "WTFPL" ]

null

prophet_gcp/utils.py

SpikeLab-CL/paralell_prophet

c04b069ae27eb8645dd10e0cf9992415e585ba62

[ "WTFPL" ]

5

2020-01-23T22:03:00.000Z

2022-02-17T08:28:51.000Z

import dask.dataframe as dd import pandas as pd def load_parse_file(file_path, date_column="date"): """Loads a file into Pandas dataframe, and parse the datetime columns Arguments: file_path: string path to the input file. Returns: Dataframe: dask.dataframe from the file """ data = dd.read_csv(file_path) data[date_column] = dd.to_datetime(data[date_column], format='%Y-%m-%d') return data def get_frames_by_id(dataframe, index_col=None): """Group by the dataframe by index Arguments: dataframe: dask.dataframe. index_col: string with the index_col to order Returns: list: list of dask.dataframe with the data filtered """ assert index_col != None, "Must specify and index column" indexs_vals = dataframe[index_col].unique().compute() dfs = [] for index in indexs_vals: print("Doing ",index) d = dataframe[(dataframe[index_col] == index)] d = d.compute(scheduler='processes') dfs.append(d) return dfs def write_results(dataframes=None, file_name=None): """Group by the dataframe by index Arguments: dataframes: pandas.dataframe. Returns: string: path to the output file """ file_name = "output.csv" if file_name == None else file_name dataframe_ = pd.concat(dataframes, axis=0, copy=False, sort=False) dataframe_.to_csv(file_name) return file_name

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null

0

null

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1

0

8f79be8eb8ccee8775fd0b6dbe06883ce3e72270

4,756

py

Python

eva-accession-release-automation/run_release_in_embassy/analyze_vcf_validation_results.py

sundarvenkata-EBI/eva-accession

b26f0b5e5acaafe63d0755bad81837b9a5976237

[ "Apache-2.0" ]

3

2018-02-28T17:14:53.000Z

2020-03-17T17:19:45.000Z

eva-accession-release-automation/run_release_in_embassy/analyze_vcf_validation_results.py

sundarvenkata-EBI/eva-accession

b26f0b5e5acaafe63d0755bad81837b9a5976237

[ "Apache-2.0" ]

52

2018-03-29T15:44:23.000Z

2022-02-16T00:54:28.000Z

eva-accession-release-automation/run_release_in_embassy/analyze_vcf_validation_results.py

sundarvenkata-EBI/eva-accession

b26f0b5e5acaafe63d0755bad81837b9a5976237

[ "Apache-2.0" ]

15

2018-03-02T13:34:19.000Z

2021-06-22T15:54:59.000Z

#!/usr/bin/env python3 # Copyright 2019 EMBL - European Bioinformatics Institute # # 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 click import glob import logging import sys from ebi_eva_common_pyutils.command_utils import run_command_with_output from ebi_eva_common_pyutils.logger import logging_config as log_cfg from run_release_in_embassy.release_metadata import vcf_validation_output_file_pattern, asm_report_output_file_pattern logger = log_cfg.get_logger(__name__) def analyze_vcf_validation_files(vcf_validation_report_files): exit_code = 0 vcf_validation_report_error_classes_to_ignore = ["Error: Duplicated variant", "Warning: Reference and alternate alleles " "do not share the first nucleotide", "the input file is not valid", "the input file is valid", "not listed in a valid meta-data ALT entry"] vcf_validation_error_grep_command_chain = " | ".join(['grep -v "{0}"'.format(error_class) for error_class in vcf_validation_report_error_classes_to_ignore]) for vcf_validation_report_file in vcf_validation_report_files: logger.info("Analyzing file {0} ....".format(vcf_validation_report_file)) command_to_run = "cat {0} | {1} | wc -l".format(vcf_validation_report_file, vcf_validation_error_grep_command_chain) number_of_lines_with_unusual_errors = \ int(run_command_with_output("Checking unusual errors in {0}".format(vcf_validation_report_file), command_to_run, return_process_output=True)) if number_of_lines_with_unusual_errors > 0: logger.error("Unusual error(s) found in VCF validation log: {0}. \nRun command\n {1} \nfor details." .format(vcf_validation_report_file, command_to_run)) exit_code = -1 return exit_code def analyze_asm_report_files(asm_report_files): exit_code = 0 assembly_report_error_classes_to_ignore = ["not present in FASTA file", "does not match the reference sequence"] asm_report_error_grep_command_chain = " | ".join(['grep -v "{0}"'.format(error_class) for error_class in assembly_report_error_classes_to_ignore]) for asm_report_file in asm_report_files: logger.info("Analyzing file {0} ....".format(asm_report_file)) command_to_run = "cat {0} | {1} | wc -l".format(asm_report_file, asm_report_error_grep_command_chain) number_of_lines_with_unusual_errors = \ int(run_command_with_output("Checking unusual errors in {0}".format(asm_report_file), command_to_run, return_process_output=True)) if number_of_lines_with_unusual_errors > 0: logger.error("Unusual error(s) found in assembly report log: {0}. \nRun command\n {1} \nfor details." .format(asm_report_file, command_to_run)) exit_code = -1 return exit_code def analyze_vcf_validation_results(species_release_folder, assembly_accession): vcf_validation_report_files = glob.glob("{0}/{1}/{2}".format(species_release_folder, assembly_accession, vcf_validation_output_file_pattern)) exit_code = analyze_vcf_validation_files(vcf_validation_report_files) asm_report_files = glob.glob("{0}/{1}/{2}".format(species_release_folder, assembly_accession, asm_report_output_file_pattern)) exit_code = exit_code or analyze_asm_report_files(asm_report_files) sys.exit(exit_code) @click.option("--species-release-folder", required=True) @click.option("--assembly-accession", required=True) @click.command() def main(species_release_folder, assembly_accession): analyze_vcf_validation_results(species_release_folder, assembly_accession) if __name__ == '__main__': main()

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0

null

0

null

0

1

0

8f7aae12183d525e6d6a2892950c3be3ddc8cab8

4,115

py

Python

include/vortex.py

wheelerMT/two-component_BEC

3c7ddee98d2dd49b07705cb343536f8a68006d7f

[ "MIT" ]

1

2020-12-16T00:05:38.000Z

include/vortex.py

wheelerMT/twoComponent-BEC

3c7ddee98d2dd49b07705cb343536f8a68006d7f

[ "MIT" ]

null

include/vortex.py

wheelerMT/twoComponent-BEC

3c7ddee98d2dd49b07705cb343536f8a68006d7f

[ "MIT" ]

null

import numpy as np import uuid class Vortex: """ Vortex class to categorize vortices within a BEC.""" def __init__(self, position, winding, component, v_type=None): self.x, self.y = position self.winding = winding self.v_type = v_type # String: type of vortex (i.e. SQV or HQV) self.uid = '{}_{}'.format(v_type, uuid.uuid1()) # Unique identifier string self.isTracked = True # Tracking argument for vortex self.component = component # Which component of the wavefunction the vortex is in def get_coords(self): return self.x, self.y def get_uid(self): return self.uid def get_v_type(self): return self.v_type def get_distance(self, vortex): # Calculate distance between two vortices: return np.sqrt((self.x - vortex.x) ** 2 + (self.y - vortex.y) ** 2) def update_type(self, vortex_type): self.v_type = vortex_type def update_uid(self): self.uid = '{}_{}'.format(self.v_type, self.uid) def update_coords(self, pos_x, pos_y): self.x, self.y = pos_x, pos_y class VortexMap: """Map that keeps track of all vortices within a condensate.""" def __init__(self): self.vortices_unid = [] # Unidentified vortices self.vortices_sqv = [] self.vortices_hqv = [] def add_vortex(self, vortex): # * Adds a vortex to the unidentified pool of the vortexMap if vortex.v_type == 'SQV': self.vortices_sqv.append(vortex) elif vortex.v_type == 'HQV': self.vortices_hqv.append(vortex) else: self.vortices_unid.append(vortex) def sort_vortices(self, vortex): # * Function that sorts all identified vortices into their respective pools if vortex.v_type == 'SQV': self.vortices_sqv.append(vortex) if vortex.v_type == 'HQV': self.vortices_hqv.append(vortex) def total_vortices(self): return len(self.vortices_sqv) + len(self.vortices_hqv) def sqv_number(self, component): sqv_list = [vortex for vortex in self.vortices_sqv if vortex.component == component] return len(sqv_list) def hqv_number(self, component): hqv_list = [vortex for vortex in self.vortices_hqv if vortex.component == component] return len(hqv_list) def identify_vortices(self, threshold): # * Finds SQVs by finding overlapping vortices in the components # * Threshold determines the maximum distance between to cores to be classed as a SQV vortices_1 = [vortex for vortex in self.vortices_unid if vortex.component == '1'] vortices_2 = [vortex for vortex in self.vortices_unid if vortex.component == '2'] for vortex_1 in vortices_1: for vortex_2 in vortices_2: if abs(vortex_1.x - vortex_2.x) < threshold: if abs(vortex_1.y - vortex_2.y) < threshold: # * If this evaluates to true, the two vortices are within the threshold # * Firstly, get the average of the positions of the two overlapping vortices sqv_pos = (vortex_1.x + vortex_2.x) / 2, (vortex_1.y + vortex_2.y) / 2 # * Generate new SQV vortex that gets added to the SQV pool self.add_vortex(Vortex(sqv_pos, vortex_1.winding, component='both', v_type='SQV')) # * Remove the corresponding vortex_plus and vortex_minus from the unid pool if vortex_1 in self.vortices_unid: self.vortices_unid.remove(vortex_1) if vortex_2 in self.vortices_unid: self.vortices_unid.remove(vortex_2) break # * Determines HQVs by setting all remaining unidentified vortices to HQVs for vortex in self.vortices_unid: vortex.update_type('HQV') vortex.update_uid() self.vortices_hqv.append(vortex) self.vortices_unid = [] # Empties unid list

39.567308

106

0.616525

544

4,115

4.492647

0.224265

0.10311

0.065466

0.030687

0.251228

0.24018

0.174304

0.1473

0.076105

0

0.008993

0.297448

4,115

103

107

39.951456

0.836389

0.23548

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0.132353

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0.010901

0

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0.220588

false

0

0.029412

0.073529

0.382353

0

null

0

null

0

1

0

1

8f7b47f31150135672ffa4570db36af3997c8bdf

47

py

Python

biker_vasek.py

nikitadragaa/---

61cfdd2c078e221e2412a1e776ae8e9afb840562

[ "MIT" ]

1

2020-11-26T19:12:09.000Z

biker_vasek.py

nikitadragaa/informatics_first_module

61cfdd2c078e221e2412a1e776ae8e9afb840562

[ "MIT" ]

null

biker_vasek.py

nikitadragaa/informatics_first_module

61cfdd2c078e221e2412a1e776ae8e9afb840562

[ "MIT" ]

null

a=int(input()) b=int(input()) print((a*b)%109)

11.75

16

0.595745

10

47

2.8

0.6

0.571429

0

0.068182

0.06383

47

3

17

15.666667

0.568182

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

4

8f7c0232a0c1a08c2f41b65eb62c5d3ff5bd11ae

3,562

py

Python

tests/test_subnet.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

6

2021-04-18T19:46:40.000Z

2021-06-28T22:03:25.000Z

tests/test_subnet.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

10

2021-05-01T19:46:35.000Z

2021-07-04T08:39:35.000Z

tests/test_subnet.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

4

2021-05-01T22:04:24.000Z

2021-06-13T14:29:20.000Z

"""Unit tests for iplib3.subnet""" import pytest from iplib3.subnet import ( # pylint: disable=import-error,no-name-in-module SubnetMask, PureSubnetMask, ) from iplib3.constants import ( # pylint: disable=import-error,no-name-in-module IPV4_MIN_SUBNET_VALUE, IPV4_MAX_SUBNET_VALUE, IPV6_MAX_SUBNET_VALUE, ) def test_pure_subnet_mask(): """Test the PureSubnetMask base class""" _ = PureSubnetMask() def test_pure_subnet_mask_prefix_length(): """Test PureSubnetMask prefix length""" subnet = PureSubnetMask() another = PureSubnetMask() another._prefix_length = None assert subnet._prefix_length == IPV4_MIN_SUBNET_VALUE assert another._prefix_length is None def test_pure_subnet_mask_string(): """Test PureSubnetMask string represesetation""" subnet = PureSubnetMask() assert str(subnet) == '0' assert repr(subnet) == "iplib3.PureSubnetMask('0')" def test_pure_subnet_mask_equality(): """Test PureSubnetMask equality""" subnet = PureSubnetMask() assert subnet == PureSubnetMask() assert subnet == IPV4_MIN_SUBNET_VALUE assert subnet == '0' def test_pure_subnet_mask_inequality(): """Test PureSubnetMask inequality""" subnet = PureSubnetMask() another = PureSubnetMask() another._prefix_length = None assert subnet != 3.14 assert subnet != another def test_subnet_mask_subnet_type(): """Test SubnetMask subnet type""" assert SubnetMask()._subnet_type == 'ipv6' assert SubnetMask('255.255.255.0')._subnet_type == 'ipv4' def test_subnet_mask_string(): """Test SubnetMask string representation""" assert ( repr(SubnetMask(24, subnet_type='ipv4')) == "iplib3.SubnetMask('255.255.255.0')") assert repr(SubnetMask(24)) == "iplib3.SubnetMask('24')" def test_subnet_mask_subnet_to_num(): """Test SubnetMask subnet to number converter""" assert SubnetMask._subnet_to_num(None) is None assert SubnetMask._subnet_to_num(24) == 24 assert SubnetMask._subnet_to_num('24') == 24 assert SubnetMask._subnet_to_num(None, subnet_type='ipv4') is None assert SubnetMask._subnet_to_num(24, subnet_type='ipv4') == 24 assert SubnetMask._subnet_to_num('24', subnet_type='ipv4') == 24 assert SubnetMask._subnet_to_num('255.255.128.0', subnet_type='ipv4') == 17 def test_subnet_mask_subnet_to_num_errors(): """Test SubnetMask subnet to number converter errors""" with pytest.raises(TypeError): SubnetMask._subnet_to_num([255, 255, 255, 0]) with pytest.raises(ValueError): SubnetMask._subnet_to_num('255.255.255.0') with pytest.raises(ValueError): SubnetMask._subnet_to_num('3e2') with pytest.raises(ValueError): SubnetMask._subnet_to_num(IPV4_MAX_SUBNET_VALUE+1, subnet_type='ipv4') with pytest.raises(ValueError): SubnetMask._subnet_to_num(IPV6_MAX_SUBNET_VALUE+1) with pytest.raises(ValueError): SubnetMask._subnet_to_num('255.6.0.0', subnet_type='ipv4') def test_subnet_mask_prefix_to_subnet_mask(): """Test SubnetMask number to mask converter""" assert ( SubnetMask._prefix_to_subnet_mask(24, subnet_type='ipv4') == '255.255.255.0' ) def test_subnet_mask_prefix_to_subnet_mask_errors(): """Test SubnetMask number to mask converter""" with pytest.raises(ValueError): SubnetMask._prefix_to_subnet_mask(24, subnet_type='ipv6') with pytest.raises(ValueError): SubnetMask._prefix_to_subnet_mask(IPV4_MAX_SUBNET_VALUE+1, subnet_type='ipv4')

29.683333

86

0.714486

455

3,562

5.265934

0.138462

0.113523

0.068865

0.11394

0.641903

0.5697

0.474124

0.450751

0.308431

0.20576

0

0.044302

0.169848

3,562

119

87

29.932773

0.765979

0.150477

0

0.236111

0

0.067814

0.028003

0

0.291667

1

0.152778

false

0

0.041667

0

0.194444

0

null

0

null

0

1

0

8f7e37d4906c116b6d7ca399d7e8dabb52aaae91

2,570

py

Python

examples/linear_regression_with_database.py

facebookresearch/svinfer

14edce1af6c91e622b8691f5d78a490a8585e7b5

[ "Apache-2.0" ]

14

2020-05-29T18:45:16.000Z

2022-03-21T03:30:27.000Z

examples/linear_regression_with_database.py

facebookresearch/svinfer

14edce1af6c91e622b8691f5d78a490a8585e7b5

[ "Apache-2.0" ]

null

examples/linear_regression_with_database.py

facebookresearch/svinfer

14edce1af6c91e622b8691f5d78a490a8585e7b5

[ "Apache-2.0" ]

1

2020-07-30T17:01:20.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # 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. """ Illustrate how to run linear model (y ~ x1 + x2) with statistically valid inference when x1, x2 contains designed noise, when training data is stored as a table in SQLite database. """ from svinfer.processor import DatabaseProcessor from svinfer.linear_model import LinearRegression import sqlite3 from linear_regression_with_dataframe import simulate_training_data if __name__ == "__main__": # get training data # assume the variance of the added noise are 4 and 1 for each predictor # assume the training data is stored as a table called my_data in SQLite database x_s2 = [4, 1] data = simulate_training_data(x_s2) connection = sqlite3.connect(":memory:") data.to_sql("my_data", con=connection) # fit y ~ x1 + x2, where x1 and x2 have added noise db_data = DatabaseProcessor(connection, "my_data") model = LinearRegression( ["x1", "x2"], # column names for predictors "y", # column name for the response x_s2, # variances of the added noises to each predictor random_state=123, # optional, to ensure reproducibility ).fit(db_data) # check result print("beta_tilde is: \n{}".format(model.beta)) # expect results to be close to # beta_tilde is: # [10.53475783 12.26662045 -3.11457588] print("beta_tilde's standard error is: \n{}".format(model.beta_standarderror)) # expect results to be close to # beta_tilde's standard error is: # [1.28940235 0.45779356 0.17814397] print("beta_tile's variance-covariance matrix: \n{}".format(model.beta_vcov)) # expect results to be close to # beta_tile's variance-covariance matrix: # [[1.66255843 0.35312458 -0.17656444] # [0.35312458 0.20957495 -0.07915853] # [-0.17656444 -0.07915853 0.03173527]] print("estimated residual variance is {}".format(model.sigma_sq)) # expect results to be close to # estimated residual variance is 0.5136891806650965

39.538462

85

0.716342

374

2,570

4.828877

0.470588

0.033223

0.037652

0.172204

0.153378

0.083056

0.036545

0

0.087567

0.19572

2,570

64

86

40.15625

0.786164

0.618677

0

0.17766

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

8f808924f32b0bba54dcbd5d9c58b33439b7f83b

2,705

py

Python

sightpy/backgrounds/skybox.py

ulises1229/Python-Raytracer

ad89b9dabda1c3eeb68af2d3578c3f38dee9f5b9

[ "MIT" ]

326

2020-08-14T07:29:40.000Z

2022-03-30T11:13:32.000Z

sightpy/backgrounds/skybox.py

ulises1229/Python-Raytracer

ad89b9dabda1c3eeb68af2d3578c3f38dee9f5b9

[ "MIT" ]

7

2020-08-14T21:57:56.000Z

2021-06-09T00:53:04.000Z

sightpy/backgrounds/skybox.py

ulises1229/Python-Raytracer

ad89b9dabda1c3eeb68af2d3578c3f38dee9f5b9

[ "MIT" ]

37

2020-08-14T17:37:56.000Z

2022-03-30T09:37:22.000Z

from ..geometry import Cuboid_Collider, Primitive from ..materials import Material from ..utils.vector3 import vec3 from ..utils.constants import SKYBOX_DISTANCE from ..utils.image_functions import load_image, load_image_as_linear_sRGB from .util.blur_background import blur_skybox class SkyBox(Primitive): def __init__(self, cubemap, center = vec3(0.,0.,0.), light_intensity = 0.0, blur = 0.0): super().__init__(center, SkyBox_Material(cubemap, light_intensity, blur), shadow = False) l = SKYBOX_DISTANCE self.light_intensity = light_intensity #BOTTOM self.collider_list += [Cuboid_Collider(assigned_primitive = self, center = center, width = 2*l, height =2*l ,length =2*l )] def get_uv(self, hit): u,v = hit.collider.get_uv(hit) u,v = u/4,v/3 return u,v class SkyBox_Material(Material): def __init__(self, cubemap, light_intensity, blur): self.texture = load_image_as_linear_sRGB("sightpy/backgrounds/" + cubemap) if light_intensity != 0.0: self.lightmap = load_image("sightpy/backgrounds/lightmaps/" + cubemap) if blur != 0.0: self.blur_image = blur_skybox(load_image("sightpy/backgrounds/" + cubemap), blur, cubemap) self.blur = blur self.light_intensity = light_intensity self.repeat = 1.0 def get_texture_color(self, hit, ray): u,v = hit.get_uv() if (self.blur != 0.0) : im = self.blur_image[-((v * self.blur_image.shape[0]*self.repeat ).astype(int)% self.blur_image.shape[0]) , (u * self.blur_image.shape[1]*self.repeat).astype(int) % self.blur_image.shape[1] ].T else: im = self.texture[-((v * self.texture.shape[0]*self.repeat ).astype(int)% self.texture.shape[0]) , (u * self.texture.shape[1]*self.repeat).astype(int) % self.texture.shape[1] ].T if (ray.depth != 0) and (self.light_intensity != 0.0): ls = self.lightmap[-((v * self.texture.shape[0]*self.repeat ).astype(int)% self.texture.shape[0]) , (u * self.texture.shape[1]*self.repeat).astype(int) % self.texture.shape[1] ].T color = vec3(im[0] + self.light_intensity * ls[0], im[1] + self.light_intensity * ls[1], im[2] + self.light_intensity * ls[2]) else: color = vec3(im[0] , im[1] , im[2] ) return color def get_color(self, scene, ray, hit): hit.point = (ray.origin + ray.dir * hit.distance) return hit.material.get_texture_color(hit,ray)

46.637931

209

0.598521

361

2,705

4.315789

0.204986

0.107831

0.082157

0.073171

0.28113

0.195122

0.18742

0.139923

0

0.024723

0.267283

2,705

57

210

47.45614

0.761352

0.002218

0

0.097561

0

0.026515

0.011364

0

1

0.121951

false

0

0.146341

0

0.390244

0

null

0

null

0

1

0

8f8117fc5388acb6f8832bf7311ec1881c023df3

81

py

Python

pks/apps.py

xingyifei2016/clusterCAD

fb139edc90e3b963ac6bfc9f6890f0a4e4f356d6

[ "BSD-3-Clause-LBNL" ]

7

2018-11-06T00:04:47.000Z

2021-08-05T04:37:12.000Z

pks/apps.py

xingyifei2016/clusterCAD

fb139edc90e3b963ac6bfc9f6890f0a4e4f356d6

[ "BSD-3-Clause-LBNL" ]

26

2017-08-11T21:51:46.000Z

2022-03-11T23:18:25.000Z

pks/apps.py

xingyifei2016/clusterCAD

fb139edc90e3b963ac6bfc9f6890f0a4e4f356d6

[ "BSD-3-Clause-LBNL" ]

7

2017-08-16T17:28:40.000Z

2022-03-02T00:07:00.000Z

from django.apps import AppConfig class PksConfig(AppConfig): name = 'pks'

13.5

33

0.728395

10

81

5.9

0.9

0

0.185185

81

5

34

16.2

0.893939

0

0.037037

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

56a8d54ad528be2aaf7182e51f33608226c5e2df

45,398

py

Python

CGATPipelines/pipeline_exome_cancer.py

cdrakesmith/CGATPipelines

3c94ae4f9d87d51108255dc405c4b95af7c8b694

[ "MIT" ]

null

CGATPipelines/pipeline_exome_cancer.py

cdrakesmith/CGATPipelines

3c94ae4f9d87d51108255dc405c4b95af7c8b694

[ "MIT" ]

null

CGATPipelines/pipeline_exome_cancer.py

cdrakesmith/CGATPipelines

3c94ae4f9d87d51108255dc405c4b95af7c8b694

[ "MIT" ]

null

""" ====================== Exome Cancer pipeline ====================== .. todo:: *Final filtering if SNPs/INDELs is currently done in the reporting. This should be handled by the pipeline. The SNP output would also then be passed to the mutational signature task *Document *fully make phone home/key option work - GATK public key? Summarise *Indel calling (size of indels called) Example The exome cancer pipeline imports unmapped reads from matched sample fastqs or sra files and aligns them to the genome using BWA. Post alignment quality control is performed using Picard. The pipeline then performs local realignment around indels and base quality score recalibration using GATK. Next variants (SNVs and indels) are called and filtered 1. Align to genome using gapped alignment (BWA) 2. Check alignment quality and target region coverage (Picard) 3. Local realignment and BQSR in GATK 4. Variant calling (SNPs) on control samples using muTect to generate a "panel of normal" variants 5a. Variant calling (SNPs) with tumour samples using muTect including filtering 5b. Variant calling (indels) using Strelka 6a. Variant annotation using SNPeff, GATK VariantAnnotator, and SnpSift 6b. Variant annotation with data from eBIO 6c. Load Network of Cancer Genes (NCG) for Variant annotation in reporting .. note:: An optional downsampling analysis can also be performed to assess how coverage a control sample affects the called variants 1. Currently the pipeline is not able to deal with replicates, i.e replicates will be treated seperately. Usage ===== See :ref:`PipelineSettingUp` and :ref:`PipelineRunning` on general information how to use CGAT pipelines. Configuration ------------- Input ----- Reads are imported by placing files or linking to files in the :term:`working directory`. The default file format assumes the following convention: <patientID>-<tissue>-<replicate>.<suffix> ``patientID`` and ``tissue`` make up an :term:`experiment`, while ``replicate`` denotes the :term:`replicate` within an :term:`experiment`. The ``suffix`` determines the file type. The following suffixes/file types are possible: sra Short-Read Archive format. Reads will be extracted using the :file:`fastq-dump` tool. fastq.gz Single-end reads in fastq format. fastq.1.gz, fastq.2.gz Paired-end reads in fastq format. The two fastq files must be sorted by read-pair. .. note:: Quality scores need to be of the same scale for all input files. Thus it might be difficult to mix different formats. Documentation ------------- If you would like the genes of interest to be flagged in your vcf, make add_genes_of_interest=1 (default=0) and provide a list of comma separated genes (without spaces) in the ini file. If you would like to annotate genes of interest with a particular value in the results table, create a file call [label]_annotations.tsv in your working directory listing all the genes. For example, to annotate all genes identified in a previous shRNA screen, add a file called shRNA_annoations.tsv listing the genes and the results table will contain a column called "shRNA" with values "shRNA" and "null". Requirements ------------ On top of the default CGAT setup, the pipeline requires the following software to be in the path: +--------------------+------------+-------------------------------------------+ |*Program* |*Version* |*Purpose* | +--------------------+------------+-------------------------------------------+ |Stampy |>=0.9.0 |read mapping | +--------------------+------------+-------------------------------------------+ |BWA | |read mapping | +--------------------+------------+-------------------------------------------+ |SAMtools | |filtering, SNV / indel calling | +--------------------+------------+-------------------------------------------+ |BEDTools | |filtering | +--------------------+------------+-------------------------------------------+ |sra-tools | |extracting reads from .sra files | +--------------------+------------+-------------------------------------------+ |picard |>=1.38 |bam/sam files. The .jar files need to be in| | | |your CLASSPATH environment variable. | +--------------------+------------+-------------------------------------------+ |vcf-tools | |VCF filtering | +--------------------+------------+-------------------------------------------+ |GATK | 2.5-2 |local realignment, BQSR, variant calling | +--------------------+------------+-------------------------------------------+ |SNPeff | 3.3 | | +--------------------+------------+-------------------------------------------+ Pipeline output =============== The major output is a csvdb containing quality control information and variant information by patientID and an html report with similar information. Example ======= Code ==== """ # load modules from ruffus import * # from rpy2.robjects import r as R import numpy import CGAT.Experiment as E import sys import os import sqlite3 import CGAT.IOTools as IOTools import CGATPipelines.PipelineMapping as PipelineMapping import CGATPipelines.PipelineMappingQC as PipelineMappingQC import CGATPipelines.Pipeline as P import re import CGATPipelines.PipelineExome as PipelineExome USECLUSTER = True ######################################################################### ######################################################################### def connect(): '''connect to database. Use this method to connect to additional databases. Returns a database connection. ''' dbh = sqlite3.connect(PARAMS["database_name"]) return dbh ######################################################################### P.getParameters( ["%s/pipeline.ini" % os.path.splitext(__file__)[0], "../pipeline.ini", "pipeline.ini"], defaults={ 'paired_end': False}, only_import=__name__ != "__main__") PARAMS = P.PARAMS PipelineMapping.PARAMS = PARAMS PipelineMappingQC.PARAMS = PARAMS PipelineExome.PARAMS = PARAMS ######################################################################### ######################################################################### # Load manual annotations ######################################################################### @transform("*_annotations.tsv", suffix(".tsv"), ".load") def loadManualAnnotations(infile, outfile): tmp = P.getTempFilename(".") annotation = P.snip(infile, "_annotations.tsv") with IOTools.openFile(tmp, "w") as outf: outf.write("%s\tgene_id\n" % annotation) with IOTools.openFile(infile, "r") as inf: for line in inf: outf.write("%s\t%s" % (annotation, line)) P.load(tmp, outfile, options="--add-index=gene_id") os.unlink(tmp) ######################################################################### # Alignment to a reference genome ######################################################################### @follows(mkdir("bam")) @transform(("*.fastq.1.gz", "*.fastq.gz", "*.sra"), regex(r"(\S+).(fastq.1.gz|fastq.gz|sra)"), r"bam/\1.bam") def mapReads(infile, outfile): '''Map reads to the genome using BWA, sort and index BAM file, generate alignment statistics and deduplicate using Picard''' job_threads = PARAMS["bwa_threads"] job_memory = PARAMS["bwa_memory"] if PARAMS["bwa_algorithm"] == "aln": m = PipelineMapping.BWA( remove_non_unique=PARAMS["bwa_remove_non_unique"], strip_sequence=False) elif PARAMS["bwa_algorithm"] == "mem": m = PipelineMapping.BWAMEM( remove_non_unique=PARAMS["bwa_remove_non_unique"], strip_sequence=False) else: raise ValueError("bwa algorithm '%s' not known" % algorithm) statement = m.build((infile,), outfile) print(statement) P.run() @merge(mapReads, "picard_duplicate_stats.load") def loadPicardDuplicateStats(infiles, outfile): '''Merge Picard duplicate stats into single table and load into SQLite.''' PipelineMappingQC.loadPicardDuplicateStats(infiles, outfile) ######################################################################### # Post-alignment QC ######################################################################### @follows(mapReads) @merge("bam/*.picard_stats", "picard_stats.load") def loadPicardAlignStats(infiles, outfile): '''Merge Picard alignment stats into single table and load into SQLite.''' PipelineMappingQC.loadPicardAlignmentStats(infiles, outfile) ######################################################################### @transform(mapReads, regex(r"bam/(\S+).bam"), r"bam/\1.cov") def buildCoverageStats(infile, outfile): '''Generate coverage statistics for regions of interest from a bed file using Picard''' # TS check whether this is always required or specific to current baits # file # baits file requires modification to make picard accept it # this is performed before CalculateHsMetrics to_cluster = USECLUSTER baits = PARAMS["roi_baits"] modified_baits = infile + "_temp_baits_final.bed" regions = PARAMS["roi_regions"] statement = '''samtools view -H %(infile)s > %(infile)s_temp_header.txt; awk 'NR>2' %(baits)s | awk -F '\\t' 'BEGIN { OFS="\\t" } {print $1,$2,$3,"+",$4;}' > %(infile)s_temp_baits.bed; cat %(infile)s_temp_header.txt %(infile)s_temp_baits.bed > %(modified_baits)s; checkpoint ; rm -rf %(infile)s_temp_baits.bed %(infile)s_temp_header.txt ''' P.run() PipelineMappingQC.buildPicardCoverageStats( infile, outfile, modified_baits, modified_baits) IOTools.zapFile(modified_baits) @follows(buildCoverageStats) @merge(buildCoverageStats, "coverage_stats.load") def loadCoverageStats(infiles, outfile): PipelineMappingQC.loadPicardCoverageStats(infiles, outfile) ######################################################################### ######################################################################### ######################################################################### # GATK realign bams ######################################################################### @transform(mapReads, regex(r"bam/(\S+).bam"), r"bam/\1.bqsr.bam") def GATKpreprocessing(infile, outfile): '''Reorders BAM according to reference fasta and add read groups using SAMtools, realigns around indels and recalibrates base quality scores using GATK''' to_cluster = USECLUSTER track = P.snip(os.path.basename(infile), ".bam") tmpdir_gatk = P.getTempDir() job_memory = PARAMS["gatk_memory"] genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) outfile1 = outfile.replace(".bqsr", ".readgroups.bqsr") outfile2 = outfile.replace(".bqsr", ".realign.bqsr") PipelineExome.GATKReadGroups(infile, outfile1, genome, PARAMS["readgroup_library"], PARAMS["readgroup_platform"], PARAMS["readgroup_platform_unit"]) PipelineExome.GATKIndelRealign(outfile1, outfile2, genome, PARAMS["gatk_threads"]) IOTools.zapFile(outfile1) PipelineExome.GATKBaseRecal(outfile2, outfile, genome, PARAMS["gatk_dbsnp"], PARAMS["gatk_solid_options"]) IOTools.zapFile(outfile2) @transform(GATKpreprocessing, regex("bam/(\S+)-%s-(\d+).bqsr.bam" % PARAMS["sample_control"]), r"bam/\1-%s-\2.merged.bam" % PARAMS["sample_control"]) def mergeSampleBams(infile, outfile): '''merge control and tumor bams''' # Note: need to change readgroup headers for merge and subsequent # splitting of bam files to_cluster = USECLUSTER job_memory = PARAMS["gatk_memory"] tmpdir_gatk = P.getTempDir(shared=True) outfile_tumor = outfile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) infile_tumor = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) infile_base = os.path.basename(infile) infile_tumor_base = infile_base.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) track = P.snip(os.path.basename(infile), ".bam") track_tumor = track.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) library = PARAMS["readgroup_library"] platform = PARAMS["readgroup_platform"] platform_unit = PARAMS["readgroup_platform_unit"] control_id = "Control.bam" tumor_id = control_id.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) statement = '''picard AddOrReplaceReadGroups INPUT=%(infile)s OUTPUT=%(tmpdir_gatk)s/%(infile_base)s RGLB=%(library)s RGPL=%(platform)s RGPU=%(platform_unit)s RGSM=%(track)s ID=%(track)s VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' statement += '''picard AddOrReplaceReadGroups INPUT=%(infile_tumor)s OUTPUT=%(tmpdir_gatk)s/%(infile_tumor_base)s RGLB=%(library)s RGPL=%(platform)s RGPU=%(platform_unit)s RGSM=%(track_tumor)s ID=%(track_tumor)s VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' statement += '''samtools merge -rf %(outfile)s %(tmpdir_gatk)s/%(infile_base)s %(tmpdir_gatk)s/%(infile_tumor_base)s ; checkpoint ;''' statement += '''samtools index %(outfile)s ; checkpoint ;''' statement += '''rm -rf %(tmpdir_gatk)s ; checkpoint ; ''' P.run() IOTools.zapFile(infile) IOTools.zapFile(infile_tumor) @transform(mergeSampleBams, regex("bam/(\S+)-%s-(\d+).merged.bam" % PARAMS["sample_control"]), r"bam/\1-%s-\2.realigned.bqsr.bam" % PARAMS["sample_control"]) def realignMatchedSample(infile, outfile): ''' repeat realignments with merged bam of control and tumor this should help avoid problems with sample-specific realignments''' genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.GATKIndelRealign(infile, outfile, genome) IOTools.zapFile(infile) @transform(realignMatchedSample, regex("bam/(\S+)-%s-(\d+).realigned.bqsr.bam" % PARAMS["sample_control"]), r"bam/\1-%s-\2.realigned.split.bqsr.bam" % PARAMS["sample_control"]) def splitMergedRealigned(infile, outfile): ''' split realignment file and truncate intermediate bams''' track = P.snip(os.path.basename(infile), ".realigned.bqsr.bam") + ".bqsr" track_tumor = track.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) outfile_tumor = outfile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) statement = '''samtools view -hb %(infile)s -r %(track)s > %(outfile)s; samtools view -hb %(infile)s -r %(track_tumor)s > %(outfile_tumor)s; checkpoint ; samtools index %(outfile)s; samtools index %(outfile_tumor)s; checkpoint;''' P.run() IOTools.zapFile(infile) @transform(splitMergedRealigned, regex("bam/(\S+)-%s-(\S+).realigned.split.bqsr.bam" % PARAMS["sample_control"]), r"bam/\1-%s-\2.realigned.picard_stats" % PARAMS["sample_control"]) def runPicardOnRealigned(infile, outfile): to_cluster = USECLUSTER job_memory = PARAMS["gatk_memory"] tmpdir_gatk = P.getTempDir() outfile_tumor = outfile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) infile_tumor = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) track = P.snip(os.path.basename(infile), ".bam") track_tumor = track.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineMappingQC.buildPicardAlignmentStats(infile, outfile, genome) PipelineMappingQC.buildPicardAlignmentStats(infile_tumor, outfile_tumor, genome) @follows(runPicardOnRealigned) @merge("bam/*.realigned.picard_stats", "realigned_picard_stats.load") def loadPicardRealigenedAlignStats(infiles, outfile): '''Merge Picard alignment stats into single table and load into SQLite.''' PipelineMappingQC.loadPicardAlignmentStats(infiles, outfile) ######################################################################### ######################################################################### ######################################################################### # Variant Calling ######################################################################### @follows(mkdir("normal_panel_variants")) @transform(splitMergedRealigned, regex(r"bam/(\S+)-%s-(\S).realigned.split.bqsr.bam" % PARAMS["sample_control"]), r"normal_panel_variants/\1_normal_mutect.vcf") def callControlVariants(infile, outfile): '''run mutect to call snps in control sample''' basename = P.snip(outfile, "_normal_mutect.vcf") call_stats_out = basename + "_call_stats.out" mutect_log = basename + ".log" cosmic, dbsnp, = (PARAMS["mutect_cosmic"], PARAMS["gatk_dbsnp"]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.mutectSNPCaller(infile, outfile, mutect_log, genome, cosmic, dbsnp, call_stats_out, PARAMS[ 'mutect_memory'], PARAMS['mutect_threads'], artifact=True) @transform(callControlVariants, suffix(".vcf"), "_slim.vcf.gz") def indexControlVariants(infile, outfile): '''index control vcf for intersection by vcftools''' outfile = P.snip(outfile, ".gz") statement = '''cut -f1-8 %(infile)s > %(outfile)s; bgzip -f %(outfile)s; tabix -f %(outfile)s.gz''' P.run() # paramaterise vcf intersection (number of req. observations - currently 1) @merge(indexControlVariants, "normal_panel_variants/combined.vcf") def mergeControlVariants(infiles, outfile): ''' intersect control vcfs to generate a panel of normals for mutect''' infiles = " ".join(infiles) # remove module command when Sebastian has made latest version executable statement = '''module load bio/vcftools/0.1.08a; vcf-isec -o -n +1 %(infiles)s > %(outfile)s''' P.run() @follows(mkdir("variants"), callControlVariants) @transform(splitMergedRealigned, regex(r"bam/(\S+)-%s-(\S).realigned.split.bqsr.bam" % PARAMS["sample_control"]), add_inputs(mergeControlVariants), r"variants/\1.mutect.snp.vcf") def runMutect(infiles, outfile): '''calls somatic SNPs using MuTect''' infile, normal_panel = infiles infile_tumour = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) basename = P.snip(outfile, ".mutect.snp.vcf") call_stats_out = basename + "_call_stats.out" mutect_log = basename + ".log" (cosmic, dbsnp, quality, max_alt_qual, max_alt, max_fraction, tumor_LOD, strand_LOD) = ( PARAMS["mutect_cosmic"], PARAMS["gatk_dbsnp"], PARAMS["mutect_quality"], PARAMS["mutect_max_alt_qual"], PARAMS["mutect_max_alt"], PARAMS["mutect_max_fraction"], PARAMS["mutect_lod"], PARAMS["mutect_strand_lod"]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.mutectSNPCaller( infile_tumour, outfile, mutect_log, genome, cosmic, dbsnp, call_stats_out, PARAMS['mutect_memory'], PARAMS['mutect_threads'], quality, max_alt_qual, max_alt, max_fraction, tumor_LOD, strand_LOD, normal_panel, infile) @transform(runMutect, regex(r"variants/(\S+).mutect.snp.vcf"), r"variants/\1_call_stats.load") def loadMutectExtendedOutput(infile, outfile): '''Load mutect extended output into database''' infile = infile.replace(".mutect.snp.vcf", "_call_stats.out") indices = "contig,position" P.load(infile, outfile, options="--add-index=%(indices)s" % locals()) @transform(splitMergedRealigned, regex(r"bam/(\S+)-%s-(\S).realigned.split.bqsr.bam" % PARAMS["sample_control"]), r"variants/\1/results/all.somatic.indels.vcf") def indelCaller(infile, outfile): '''Call somatic indels using Strelka''' infile_tumour = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) outdir = "/".join(outfile.split("/")[0:2]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.strelkaINDELCaller(infile, infile_tumour, outfile, genome, PARAMS['strelka_config'], outdir, PARAMS['strelka_memory'], PARAMS['strelka_threads']) ########################################################################## ########################################################################## ########################################################################## # repeat mutect in reverse and on subsampled control bam as quality control ########################################################################## # this analysis should be part of an optional check of mutect parameters # mutect paramters should be identical to the runMutect function above @follows(mergeControlVariants) @transform(splitMergedRealigned, regex(r"bam/(\S+)-%s-(\S).realigned.split.bqsr.bam" % PARAMS["sample_control"]), add_inputs(mergeControlVariants), r"variants/\1.mutect.reverse.snp.vcf") def runMutectReverse(infiles, outfile): '''Use control as tumor and vis versa to estimate false positive rate''' infile, normal_panel = infiles infile_tumour = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) basename = P.snip(outfile, "_normal_mutect.vcf") call_stats_out = basename + "_call_stats.out" mutect_log = basename + ".log" basename = P.snip(outfile, ".mutect.reverse.snp.vcf") call_stats_out = basename + "_call_stats.reverse.out" coverage_wig_out = basename + "_coverage.reverse.wig" mutect_log = basename + ".reverse.log" (cosmic, dbsnp, quality, max_alt_qual, max_alt, max_fraction, tumor_LOD) = ( PARAMS["mutect_cosmic"], PARAMS["gatk_dbsnp"], PARAMS["mutect_quality"], PARAMS["mutect_max_alt_qual"], PARAMS["mutect_max_alt"], PARAMS["mutect_max_fraction"], PARAMS["mutect_LOD"]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.mutectSNPCaller(infile, outfile, mutect_log, genome, cosmic, dbsnp, call_stats_out, PARAMS['mutect_memory'], PARAMS['mutect_threads'], quality, max_alt_qual, max_alt, max_fraction, tumor_LOD, normal_panel, infile_tumour) # generalise the functions below # 1. identify sample with highest coverage in control # - should this check coverage in tumour also? # 2. subset control bam # 3. run mutect calling function with subset against unsubsetted tumour # 4. summary table adeno_bam = "bam/NU16C-Control-1.realigned.bqsr.bam" @subdivide(adeno_bam, regex("(\S+).bqsr.bam"), [r"\1.0.1.bqsr.bam", r"\1.0.2.bqsr.bam", r"\1.0.3.bqsr.bam", r"\1.0.4.bqsr.bam", r"\1.0.5.bqsr.bam", r"\1.0.6.bqsr.bam", r"\1.0.7.bqsr.bam", r"\1.0.8.bqsr.bam", r"\1.0.9.bqsr.bam", r"\1.1.0.bqsr.bam"]) def subsetControlBam(infile, outfiles): statements = [] n = 0 for fraction in numpy.arange(0.1, 1.1, 0.1): outfile = outfiles[n] n += 1 statement = '''samtools view -s %(fraction)s -b %(infile)s > %(outfile)s''' P.run() @transform(subsetControlBam, suffix(".bam"), ".bam.bai") def indexSubsets(infile, outfile): statement = '''samtools index %(infile)s''' P.run() @follows(indexSubsets) @transform(subsetControlBam, regex(r"bam/(\S+)-%s-1.realigned.(\S+).bqsr.bam" % PARAMS["sample_control"]), add_inputs(mergeControlVariants), r"variants/\1-downsampled-\2.mutect.snp.vcf") def runMutectOnDownsampled(infiles, outfile): '''call somatic SNPs using MuTect on downsampled bams''' infile, normal_panel = infiles infile_tumour = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) basename = P.snip(outfile, "_normal_mutect.vcf") call_stats_out = basename + "_call_stats.out" mutect_log = basename + ".log" (cosmic, dbsnp, quality, max_alt_qual, max_alt, max_fraction, tumor_LOD) = ( PARAMS["mutect_cosmic"], PARAMS["gatk_dbsnp"], PARAMS["mutect_quality"], PARAMS["mutect_max_alt_qual"], PARAMS["mutect_max_alt"], PARAMS["mutect_max_fraction"], PARAMS["mutect_LOD"]) genome = "%s/%s.fa" % (PARAMS["bwa_index_dir"], PARAMS["genome"]) PipelineExome.mutectSNPCaller(infile_tumour, outfile, mutect_log, genome, cosmic, dbsnp, call_stats_out, PARAMS['mutect_memory'], PARAMS[ 'mutect_threads'], quality, max_alt_qual, max_alt, max_fraction, tumor_LOD, normal_panel, infile) ############################################################################## ############################################################################## ############################################################################## # Variant Annotation and Recalibration ############################################################################## @collate(splitMergedRealigned, regex(r"bam/(\S+)-(\S+)-(\S+).realigned.split.bqsr.bam"), r"bam/\1.list") def listOfBAMs(infiles, outfile): '''generates a file containing a list of BAMs for each patient, for use in variant calling''' with IOTools.openFile(outfile, "w") as outf: for infile in infiles: infile_tumour = infile.replace( PARAMS["sample_control"], PARAMS["sample_tumour"]) outf.write(infile + '\n') outf.write(infile_tumour + '\n') @transform(runMutect, regex(r"variants/(\S+).mutect.snp.vcf"), r"variants/\1.mutect.snp.snpeff.vcf") def annotateVariantsSNPeff(infile, outfile): '''Annotate SNP variants using SNPeff''' to_cluster = USECLUSTER job_memory = "4G" job_threads = 2 snpeff_genome = PARAMS["annotation_snpeff_genome"] config = PARAMS["annotation_snpeff_config"] statement = '''java -Xmx4G -jar /ifs/apps/bio/snpEff-3.3-dev/snpEff.jar -c %(config)s -v %(snpeff_genome)s -o gatk %(infile)s > %(outfile)s''' P.run() @transform(indelCaller, regex("variants/(\S+)/results/all.somatic.indels.vcf"), r"variants/\1.indels.snpeff.vcf") def annotateVariantsINDELsSNPeff(infile, outfile): '''Annotate INDEL variants using SNPeff''' to_cluster = USECLUSTER job_memory = "4G" job_threads = 2 snpeff_genome = PARAMS["annotation_snpeff_genome"] config = PARAMS["annotation_snpeff_config"] statement = '''java -Xmx4G -jar /ifs/apps/bio/snpEff-3.3-dev/snpEff.jar -c %(config)s -v %(snpeff_genome)s -o gatk %(infile)s > %(outfile)s''' P.run() ######################################################################### # Annotate SNP and INDEL variants ######################################################################### # Need to check whether variant annotatot is using both bams # from a single patient? # should just be the tumour bam or else scores will be wrong! @follows(annotateVariantsSNPeff, listOfBAMs) @transform(runMutect, regex(r"variants/(\S+).mutect.snp.vcf"), add_inputs(r"bam/\1.list", r"variants/\1.mutect.snp.snpeff.vcf"), r"variants/\1.mutect.snp.annotated.vcf") def variantAnnotator(infiles, outfile): '''Annotate variant file using GATK VariantAnnotator''' to_cluster = USECLUSTER infile, bamlist, effFile = infiles dbsnp = PARAMS["gatk_dbsnp"] statement = '''GenomeAnalysisTK -T VariantAnnotator -R %(bwa_index_dir)s/%(genome)s.fa -I %(bamlist)s -A SnpEff --snpEffFile %(effFile)s -o %(outfile)s --variant %(infile)s -L %(infile)s --dbsnp %(dbsnp)s -A HaplotypeScore -A MappingQualityRankSumTest -A ReadPosRankSumTest -A AlleleBalanceBySample''' P.run() @follows(annotateVariantsINDELsSNPeff, listOfBAMs) @transform(indelCaller, regex("variants/(\S+)/results/all.somatic.indels.vcf"), add_inputs(r"bam/\1.list", r"variants/\1.indels.snpeff.vcf"), r"variants/\1.indels.annotated.vcf") def variantAnnotatorIndels(infiles, outfile): '''Annotate variant file using GATK VariantAnnotator''' to_cluster = USECLUSTER infile, bamlist, effFile = infiles statement = '''GenomeAnalysisTK -T VariantAnnotator -R %(bwa_index_dir)s/%(genome)s.fa -I %(bamlist)s -A SnpEff --snpEffFile %(effFile)s -o %(outfile)s --variant %(infile)s -L %(infile)s -A Coverage -A FisherStrand -A HaplotypeScore -A MappingQualityRankSumTest -A ReadPosRankSumTest -A AlleleBalanceBySample -A RMSMappingQuality''' P.run() ###################################################################### # this does not work - insufficient number of indels in mills+ # therefore this task is not a dependency of task full @transform(variantAnnotatorIndels, suffix(".annotated.vcf"), ".annotated.recalibrated.vcf") def variantRecalibrator(infile, outfile): '''Create variant recalibration file for indels''' to_cluster = USECLUSTER job_memory = PARAMS["gatk_memory"] job_threads = 6 track = P.snip(os.path.basename(outfile), ".annotated.recalibrated.vcf") mills = PARAMS["gatk_mills"] statement = '''GenomeAnalysisTK -T VariantRecalibrator -R %(bwa_index_dir)s/%(genome)s.fa -input %(infile)s -resource:mills,known=true,training=true,truth=true,prior=12.0 %(mills)s -an DP -an MQRankSum -an ReadPosRankSum -mode INDEL -tranche 100.0 -tranche 99.9 -tranche 99.0 -tranche 90.0 --maxGaussians 4 -recalFile %(outfile)s -tranchesFile variants/%(track)s.tranches -rscriptFile variants/%(track)s.plots.R''' P.run() ############################################################################## # Filter SNPs and INDELs ############################################################################## @transform(variantAnnotatorIndels, suffix(".annotated.vcf"), ".annotated.filtered.vcf") def filterIndels(infile, outfile): ''' use SnpSift to filter INDELS using VCF fields''' statement = '''cat %(infile)s | java -Xmx2g -jar /ifs/apps/bio/snpEff-3.1/SnpSift.jar filter "(QSI_NT>%(filter_indel_nt)s & IHP<%(filter_indel_ihp)s & RC<%(filter_indel_rc)s & IC<%(filter_indel_rc)s) " > %(outfile)s ''' P.run() @transform(variantAnnotator, regex("variants/(\S+).mutect.snp.annotated.vcf"), r"variants/\1.mutect.snp.annotated.filtered.vcf") def filterMutect(infile, outfile): ''' filter mutect snps using allele frequencies ''' logfile = outfile.replace(".vcf", ".log") min_t_alt = PARAMS["filter_minimum_tumor_allele"] min_t_alt_freq = PARAMS["filter_minimum_tumor_allele_frequency"] min_n_depth = PARAMS["filter_minimum_normal_depth"] max_n_alt_freq = PARAMS["filter_maximum_normal_allele_frequency"] min_ratio = PARAMS["filter_minimum_ratio"] PipelineExome.filterMutect( infile, outfile, logfile, PARAMS["sample_control"], PARAMS["sample_tumour"], min_t_alt, min_n_depth, max_n_alt_freq, min_t_alt_freq, min_ratio) ############################################################################## # Intersect filtered SNPs and INDELs ############################################################################## @mkdir("intersection.dir") @collate((filterIndels, filterMutect), regex(r"variants/(\S+)\.(\S+).annotated.filtered.vcf"), r"intersection.dir/overlap_\2_heatmap.png") def intersectHeatmap(infiles, outfile): ''' intersect DE test_ids across the different quantifiers''' PipelineExome.intersectionHeatmap(infiles, outfile) ######################################################################### ######################################################################### # convert vcf to tsv files and load into database @transform(filterMutect, regex("variants/(\S+).annotated.filtered.vcf"), r"variants/\1.annotated.filtered.tsv") def snpvcfToTable(infile, outfile): '''Converts vcf to tab-delimited file''' to_cluster = USECLUSTER statement = '''GenomeAnalysisTK -T VariantsToTable -R %(bwa_index_dir)s/%(genome)s.fa -V %(infile)s --showFiltered --allowMissingData -F CHROM -F POS -F ID -F REF -F ALT -F QUAL -F FILTER -F INFO -F BaseQRankSum -F HaplotypeScore -F MQRankSum -F ReadPosRankSum -F SNPEFF_EFFECT -F SNPEFF_IMPACT -F SNPEFF_FUNCTIONAL_CLASS -F SNPEFF_CODON_CHANGE -F SNPEFF_AMINO_ACID_CHANGE -F SNPEFF_GENE_NAME -F SNPEFF_GENE_BIOTYPE -F SNPEFF_TRANSCRIPT_ID -F SNPEFF_EXON_ID -GF GT -GF AD -GF SS -GF FA -GF AB -GF DP -o %(outfile)s''' P.run() @transform(filterIndels, regex("variants/(\S+).annotated.filtered.vcf"), r"variants/\1.annotated.filtered.tsv") def indelvcfToTable(infile, outfile): '''Converts vcf to tab-delimited file''' to_cluster = USECLUSTER statement = '''GenomeAnalysisTK -T VariantsToTable -R %(bwa_index_dir)s/%(genome)s.fa -V %(infile)s --showFiltered --allowMissingData -F CHROM -F POS -F ID -F REF -F ALT -F QUAL -F FILTER -F INFO -F BaseQRankSum -F HaplotypeScore -F MQRankSum -F ReadPosRankSum -F SNPEFF_EFFECT -F SNPEFF_IMPACT -F SNPEFF_FUNCTIONAL_CLASS -F SNPEFF_CODON_CHANGE -F SNPEFF_AMINO_ACID_CHANGE -F SNPEFF_GENE_NAME -F SNPEFF_GENE_BIOTYPE -F SNPEFF_TRANSCRIPT_ID -F SNPEFF_EXON_ID -F TQSI -F TSQI_NT -F DP -F IC -F IHP -F NT -F QSI -F QSI_NT -F RC -F RU -F SGT -GF DP -GF DP2 -GF DP50 -GF SUBDP50 -GF TAR -GF TIR -GF TOR -o %(outfile)s''' P.run() @transform([snpvcfToTable, indelvcfToTable], regex(r"variants/(\S+).annotated.filtered.tsv"), r"variants/\1_annotated.load") def loadVariantAnnotation(infile, outfile): '''Load VCF annotations into database''' if infile.endswith("indels.annotated.filtered.tsv"): indices = "CHROM,POS,SNPEFF_GENE_NAME" elif infile.endswith("mutect.snp.annotated.filtered.tsv"): indices = "CHROM,POS,SNPEFF_GENE_NAME" P.load(infile, outfile, options="--add-index=%(indices)s" % locals()) ######################################################################### # Genes of interest # check this will run in the correct position if option selected # @active_if(PARAMS["annotation_add_genes_of_interest"] == 1) # @transform((annotateVariantsSNPsift), # regex(r"variants/(\S+).haplotypeCaller.snpsift.vcf"), # r"variants/\1.genes.vcf") # def findGenes(infile, outfile): # '''Adds expression "GENE_OF_INTEREST" to the FILTER column of the vcf # if variant is within a gene of interest as defined in the ini # file''' # # geneList = P.asList(PARAMS["annotation_genes_of_interest"]) # expression = '\'||SNPEFF_GENE_NAME==\''.join(geneList) # statement = '''GenomeAnalysisTK -T VariantFiltration # -R %%(bwa_index_dir)s/%%(genome)s.fa # --variant %(infile)s # --filterExpression "SNPEFF_GENE_NAME=='%(expression)s'" # --filterName "GENE_OF_INTEREST" -o %(outfile)s''' % locals() # P.run() ######################################################################### ######################################################################### ######################################################################### # vcf statistics - this only summarises the nucleotide changes # this currently does not provide useful output! @transform((variantAnnotator, variantAnnotatorIndels), regex(r"variants/(\S+).vcf"), r"variants/\1.vcfstats") def buildVCFstats(infile, outfile): '''Calculate statistics on VCF file''' to_cluster = USECLUSTER statement = '''vcf-stats %(infile)s > %(outfile)s 2>>%(outfile)s.log;''' P.run() @merge(buildVCFstats, "vcf_stats.load") def loadVCFstats(infiles, outfile): '''Import variant statistics into SQLite''' filenames = " ".join(infiles) tablename = P.toTable(outfile) csv2db_options = PARAMS["csv2db_options"] E.info("Loading vcf stats...") statement = '''cgat vcfstats2db %(filenames)s >> %(outfile)s; ''' statement += '''cat vcfstats.txt | cgat csv2db %(csv2db_options)s --allow-empty-file --add-index=track --table=vcf_stats >> %(outfile)s; ''' P.run() ######################################################################### @transform(runMutect, suffix(".mutect.snp.vcf"), "_mutect_filtering_summary.tsv") def summariseFiltering(infile, outfile): infile = infile.replace(".mutect.snp.vcf", "_call_stats.out") PipelineExome.parseMutectCallStats(infile, outfile, submit=True) @transform(summariseFiltering, regex(r"variants/(\S+)_mutect_filtering_summary.tsv"), r"variants/\1_mutect_filtering_summary.load") def loadMutectFilteringSummary(infile, outfile): '''Load mutect extended output into database''' dbh = connect() tablename = P.toTable(outfile) statement = '''cat %(infile)s | cgat csv2db --table %(tablename)s --retry --ignore-empty > %(outfile)s''' P.run() ######################################################################### ######################################################################### ######################################################################### @originate("eBio_studies.tsv") def defineEBioStudies(outfile): ''' For the cancer types specified in pipeline.ini, identify the relevent studies in eBio ''' cancer_types = PARAMS["annotation_ebio_cancer_types"] PipelineExome.defineEBioStudies(cancer_types, outfile, submit=False) @transform(defineEBioStudies, suffix("eBio_studies.tsv"), add_inputs(filterIndels, filterMutect), "eBio_studies_gene_frequencies.tsv") def extractEBioinfo(infiles, outfile): '''find the number of mutations identitified in previous studies (ebio_ids) for the mutated genes in the annotated vcfs''' eBio_ids = infiles[0] vcfs = infiles[1:] PipelineExome.extractEBioinfo(eBio_ids, vcfs, outfile, submit=False) @transform(extractEBioinfo, suffix(".tsv"), ".load") def loadEBioInfo(infile, outfile): '''load the frequencies from the eBIO portal''' P.load(infile, outfile, options="--add-index=gene") ######################################################################### ######################################################################### ######################################################################### # load Network of Cancer Genes table # parameterise file location: @originate("cancergenes.load") def loadNCG(outfile): '''Load NCG into database''' infile = PARAMS["cancergenes_table"] # infile = "/ifs/projects/proj053/backup/NCG/cancergenes2016.tsv" P.load(infile, outfile, options="--add-index=symbol") ######################################################################### ######################################################################### ######################################################################### # analyse mutational siganture of filtered variants @merge(filterMutect, ["variants/mutational_signature.tsv", "variants/mutational_signature_table.tsv"]) def mutationalSignature(infiles, outfiles): PipelineExome.compileMutationalSignature( infiles, outfiles) @transform(mutationalSignature, suffix(".tsv"), ".load") def loadMutationalSignature(infiles, outfile): outfile2 = re.sub(".load", "_table.load", outfile) P.load(infiles[0], outfile) P.load(infiles[1], outfile2) ######################################################################### ######################################################################### ######################################################################### @follows(loadManualAnnotations, loadMutectFilteringSummary, loadMutectExtendedOutput, loadVariantAnnotation, loadCoverageStats, loadPicardRealigenedAlignStats, loadPicardAlignStats, loadNCG, loadMutationalSignature, loadEBioInfo, intersectHeatmap) def full(): pass @follows(defineEBioStudies) def test(): pass @follows(runMutectOnDownsampled, runMutectReverse) def TestMutect(): '''This target runs function which can be used to assess the chosen mutect parameters''' # @follows(loadROI, # loadROI2Gene) # def loadMetadata(): # pass @follows(mapReads) def mapping(): pass @follows(loadPicardDuplicateStats, loadPicardAlignStats, buildCoverageStats, loadCoverageStats) def postMappingQC(): pass @follows(GATKpreprocessing, runPicardOnRealigned) def gatk(): pass @follows(runMutect, indelCaller) def callVariants(): pass @follows(loadVariantAnnotation) def tabulation(): pass @follows(buildVCFstats, loadVCFstats) def vcfstats(): pass ######################################################################### ######################################################################### ######################################################################### @follows() def publish(): '''publish files.''' P.publish_report() @follows(mkdir("report")) def build_report(): '''build report from scratch.''' E.info("starting documentation build process from scratch") P.run_report(clean=True) @follows(mkdir("report")) def update_report(): '''update report.''' E.info("updating documentation") P.run_report(clean=False) def main(argv=None): if argv is None: argv = sys.argv P.main(argv) if __name__ == "__main__": sys.exit(P.main(sys.argv))

35.973059

81

0.552998

4,554

45,398

5.390426

0.170839

0.021998

0.022446

0.016295

0.373676

0.337176

0.314201

0.296969

0.277049

0.265643

0

0.005522

0.230076

45,398

1,261

82

36.001586

0.696793

0.22675

0

0.392908

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0.011348

0.389166

0.121364

0.001418

0

0.000793

0

1

0.08227

false

0.011348

0.01844

0

0.102128

0.002837

0

null

0

null

0

1

0

56aceb9473dca4ac2d68368a3618cbfcb8694e5a

7,581

py

Python

tests/snuba/api/endpoints/test_project_event_details.py

kinghuang/sentry

5c22673994a62f54a782d1c595852986ccc51ae9

[ "BSD-3-Clause" ]

1

2019-10-17T17:46:16.000Z

tests/snuba/api/endpoints/test_project_event_details.py

kinghuang/sentry

5c22673994a62f54a782d1c595852986ccc51ae9

[ "BSD-3-Clause" ]

null

tests/snuba/api/endpoints/test_project_event_details.py

kinghuang/sentry

5c22673994a62f54a782d1c595852986ccc51ae9

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import import six from django.core.urlresolvers import reverse from sentry.testutils import APITestCase, SnubaTestCase from sentry.testutils.helpers.datetime import before_now, iso_format class ProjectEventDetailsTest(APITestCase, SnubaTestCase): def setUp(self): super(ProjectEventDetailsTest, self).setUp() self.login_as(user=self.user) project = self.create_project() one_min_ago = iso_format(before_now(minutes=1)) two_min_ago = iso_format(before_now(minutes=2)) three_min_ago = iso_format(before_now(minutes=3)) four_min_ago = iso_format(before_now(minutes=4)) self.prev_event = self.store_event( data={"event_id": "a" * 32, "timestamp": four_min_ago, "fingerprint": ["group-1"]}, project_id=project.id, ) self.cur_event = self.store_event( data={"event_id": "b" * 32, "timestamp": three_min_ago, "fingerprint": ["group-1"]}, project_id=project.id, ) self.next_event = self.store_event( data={ "event_id": "c" * 32, "timestamp": two_min_ago, "fingerprint": ["group-1"], "environment": "production", "tags": {"environment": "production"}, }, project_id=project.id, ) # Event in different group self.store_event( data={ "event_id": "d" * 32, "timestamp": one_min_ago, "fingerprint": ["group-2"], "environment": "production", "tags": {"environment": "production"}, }, project_id=project.id, ) def test_simple(self): url = reverse( "sentry-api-0-project-event-details", kwargs={ "event_id": self.cur_event.event_id, "project_slug": self.cur_event.project.slug, "organization_slug": self.cur_event.project.organization.slug, }, ) response = self.client.get(url, format="json") assert response.status_code == 200, response.content assert response.data["id"] == six.text_type(self.cur_event.event_id) assert response.data["nextEventID"] == six.text_type(self.next_event.event_id) assert response.data["previousEventID"] == six.text_type(self.prev_event.event_id) assert response.data["groupID"] == six.text_type(self.cur_event.group.id) def test_snuba_no_prev(self): url = reverse( "sentry-api-0-project-event-details", kwargs={ "event_id": self.prev_event.event_id, "project_slug": self.prev_event.project.slug, "organization_slug": self.prev_event.project.organization.slug, }, ) response = self.client.get(url, format="json") assert response.status_code == 200, response.content assert response.data["id"] == six.text_type(self.prev_event.event_id) assert response.data["previousEventID"] is None assert response.data["nextEventID"] == self.cur_event.event_id assert response.data["groupID"] == six.text_type(self.prev_event.group.id) def test_snuba_with_environment(self): url = reverse( "sentry-api-0-project-event-details", kwargs={ "event_id": self.cur_event.event_id, "project_slug": self.cur_event.project.slug, "organization_slug": self.cur_event.project.organization.slug, }, ) response = self.client.get( url, format="json", data={"enable_snuba": "1", "environment": ["production", "staging"]} ) response = self.client.get( url, format="json", data={"environment": ["production", "staging"]} ) assert response.status_code == 200, response.content assert response.data["id"] == six.text_type(self.cur_event.event_id) assert response.data["previousEventID"] is None assert response.data["nextEventID"] == self.next_event.event_id assert response.data["groupID"] == six.text_type(self.prev_event.group.id) def test_ignores_different_group(self): url = reverse( "sentry-api-0-project-event-details", kwargs={ "event_id": self.next_event.event_id, "project_slug": self.next_event.project.slug, "organization_slug": self.next_event.project.organization.slug, }, ) response = self.client.get(url, format="json") assert response.status_code == 200, response.content assert response.data["id"] == six.text_type(self.next_event.event_id) assert response.data["nextEventID"] is None class ProjectEventJsonEndpointTest(APITestCase, SnubaTestCase): def setUp(self): super(ProjectEventJsonEndpointTest, self).setUp() self.login_as(user=self.user) self.event_id = "c" * 32 self.fingerprint = ["group_2"] self.min_ago = iso_format(before_now(minutes=1)) self.event = self.store_event( data={ "event_id": self.event_id, "timestamp": self.min_ago, "fingerprint": self.fingerprint, "user": {"email": self.user.email}, }, project_id=self.project.id, ) self.url = reverse( "sentry-api-0-event-json", kwargs={ "organization_slug": self.organization.slug, "project_slug": self.project.slug, "event_id": self.event_id, }, ) def assert_event(self, data): assert data["event_id"] == self.event_id assert data["user"]["email"] == self.user.email assert data["datetime"][:19] == self.min_ago assert data["fingerprint"] == self.fingerprint def test_simple(self): response = self.client.get(self.url, format="json") assert response.status_code == 200, response.content self.assert_event(response.data) def test_event_does_not_exist(self): self.url = reverse( "sentry-api-0-event-json", kwargs={ "organization_slug": self.organization.slug, "project_slug": self.project.slug, "event_id": "no" * 16, }, ) response = self.client.get(self.url, format="json") assert response.status_code == 404, response.content assert response.data == {"detail": "Event not found"} def test_user_unauthorized(self): user = self.create_user() self.login_as(user) response = self.client.get(self.url, format="json") assert response.status_code == 403, response.content assert response.data == {"detail": "You do not have permission to perform this action."} def test_project_not_associated_with_event(self): project2 = self.create_project(organization=self.organization) url = reverse( "sentry-api-0-event-json", kwargs={ "organization_slug": self.organization.slug, "project_slug": project2.slug, "event_id": self.event_id, }, ) response = self.client.get(url, format="json") assert response.status_code == 404, response.content assert response.data == {"detail": "Event not found"}

39.07732

100

0.590687

842

7,581

5.122328

0.135392

0.04869

0.070948

0.043821

0.748435

0.727336

0.641549

0.583585

0.542546

0.512868

0

0.010701

0.285055

7,581

193

101

39.279793

0.785055

0.003166

0

0.449704

0

0.146128

0.027134

0

0.183432

1

0.065089

false

0

0.029586

0

0.106509

0.04142

0

null

0

1

0

null

0

1

56ad3f65e7e326b6c8e24ade681a2bdad38713f8

61

py

Python

je_auto_control/windows/screen/__init__.py

JE-Chen/AutoControl

c2d78f0b428d27aef2ea27f210d11c6dc1144221

[ "MIT" ]

1

2022-03-27T14:59:45.000Z

je_auto_control/windows/screen/__init__.py

JE-Chen/AutoControl

c2d78f0b428d27aef2ea27f210d11c6dc1144221

[ "MIT" ]

2

2021-11-19T13:45:37.000Z

2021-12-03T12:25:28.000Z

je_auto_control/windows/screen/__init__.py

JE-Chen/AutoControl

c2d78f0b428d27aef2ea27f210d11c6dc1144221

[ "MIT" ]

null

from je_auto_control.windows.screen.win32_screen import size

30.5

60

0.885246

10

61

5.1

0.9

0

0.035088

0.065574

61

1

61

0.859649

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

56af02a969b9dab95dd47f1d92c922008e2433c4

435

py

Python

guestbook/models.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

guestbook/models.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

guestbook/models.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

import reprlib from django.db import models class Message(models.Model): """留言消息类 """ name = models.CharField('用户名', max_length=20) email = models.EmailField('邮箱', max_length=200) message = models.TextField('留言') active = models.BooleanField('有效', default=True) posted = models.DateTimeField('发布时间', auto_now_add=True) def __str__(self): return f'{self.name}{reprlib.repr(self.message)}'

27.1875

60

0.671264

55

435

5.163636

0.709091

0.091549

0

0.014085

0.183908

435

15

61

29

0.785915

0.011494

0

0.124105

0.093079

0

1

0.1

false

0

0.2

0.1

1

0

null

0

null

0

1

0

56b027366621352ff84a9bd75357a7f9c2bdede8

293

py

Python

rationalratio/rationalratio.py

omarchehab98/open.kattis.com-problems

0523e2e641151dad719ef05cc9811a8ef5c6a278

[ "MIT" ]

1

2020-10-04T22:41:04.000Z

rationalratio/rationalratio.py

omarchehab98/open.kattis.com-problems

0523e2e641151dad719ef05cc9811a8ef5c6a278

[ "MIT" ]

null

rationalratio/rationalratio.py

omarchehab98/open.kattis.com-problems

0523e2e641151dad719ef05cc9811a8ef5c6a278

[ "MIT" ]

null

from fractions import Fraction x, d = input().split(' ') d = int(d) k = len(x) - x.index('.') - d - 1 a, b = x[0:-d].replace('.', ''), 10 ** k ab = Fraction(int(a), b) rd = Fraction(int(x[-d:]), (10 ** d - 1) * b) result = ab + rd print(str(result.numerator) + '/' + str(result.denominator))

26.636364

60

0.546075

50

293

3.2

0.5

0.025

0

0.029289

0.1843

293

10

61

29.3

0.640167

0

0.013652

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

56b18a5e976b97460394eeab951ee9f6df83fd21

3,636

py

Python

examples/doq_server.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

examples/doq_server.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

examples/doq_server.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

import argparse import asyncio import logging import struct from typing import Dict, Optional from dnslib.dns import DNSRecord from aioquic.asyncio import QuicConnectionProtocol, serve from aioquic.quic.configuration import QuicConfiguration from aioquic.quic.events import QuicEvent, StreamDataReceived from aioquic.quic.logger import QuicFileLogger from aioquic.tls import SessionTicket class DnsServerProtocol(QuicConnectionProtocol): def quic_event_received(self, event: QuicEvent): if isinstance(event, StreamDataReceived): # parse query length = struct.unpack("!H", bytes(event.data[:2]))[0] query = DNSRecord.parse(event.data[2 : 2 + length]) # perform lookup and serialize answer data = query.send(args.resolver, 53) data = struct.pack("!H", len(data)) + data # send answer self._quic.send_stream_data(event.stream_id, data, end_stream=True) class SessionTicketStore: """ Simple in-memory store for session tickets. """ def __init__(self) -> None: self.tickets: Dict[bytes, SessionTicket] = {} def add(self, ticket: SessionTicket) -> None: self.tickets[ticket.ticket] = ticket def pop(self, label: bytes) -> Optional[SessionTicket]: return self.tickets.pop(label, None) if __name__ == "__main__": parser = argparse.ArgumentParser(description="DNS over QUIC server") parser.add_argument( "--host", type=str, default="::", help="listen on the specified address (defaults to ::)", ) parser.add_argument( "--port", type=int, default=4784, help="listen on the specified port (defaults to 4784)", ) parser.add_argument( "-k", "--private-key", type=str, help="load the TLS private key from the specified file", ) parser.add_argument( "-c", "--certificate", type=str, required=True, help="load the TLS certificate from the specified file", ) parser.add_argument( "--resolver", type=str, default="8.8.8.8", help="Upstream Classic DNS resolver to use", ) parser.add_argument( "--retry", action="store_true", help="send a retry for new connections", ) parser.add_argument( "-q", "--quic-log", type=str, help="log QUIC events to QLOG files in the specified directory", ) parser.add_argument( "-v", "--verbose", action="store_true", help="increase logging verbosity" ) args = parser.parse_args() logging.basicConfig( format="%(asctime)s %(levelname)s %(name)s %(message)s", level=logging.DEBUG if args.verbose else logging.INFO, ) if args.quic_log: quic_logger = QuicFileLogger(args.quic_log) else: quic_logger = None configuration = QuicConfiguration( alpn_protocols=["doq-i03"], is_client=False, quic_logger=quic_logger, ) configuration.load_cert_chain(args.certificate, args.private_key) ticket_store = SessionTicketStore() loop = asyncio.get_event_loop() loop.run_until_complete( serve( args.host, args.port, configuration=configuration, create_protocol=DnsServerProtocol, session_ticket_fetcher=ticket_store.pop, session_ticket_handler=ticket_store.add, retry=args.retry, ) ) try: loop.run_forever() except KeyboardInterrupt: pass

27.338346

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1

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false

0.009524

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0.009524

0.171429

0

null

0

null

0

1

0

56b2d0fc3f97f8a7563d2f632e5448b894ac8ef4

9,483

py

Python

extended_templates/backends/pdf.py

knivets/djaodjin-extended-templates

71bc725b3900fc45968e5a625d72dc0931561856

[ "BSD-2-Clause" ]

null

extended_templates/backends/pdf.py

knivets/djaodjin-extended-templates

71bc725b3900fc45968e5a625d72dc0931561856

[ "BSD-2-Clause" ]

null

extended_templates/backends/pdf.py

knivets/djaodjin-extended-templates

71bc725b3900fc45968e5a625d72dc0931561856

[ "BSD-2-Clause" ]

null

# Copyright (c) 2018, Djaodjin Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY 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. from __future__ import unicode_literals import logging, re, subprocess, io, warnings from bs4 import BeautifulSoup from django.conf import settings as django_settings from django.core.exceptions import ImproperlyConfigured from django.template import TemplateDoesNotExist from django.template.exceptions import TemplateSyntaxError from django.template.response import TemplateResponse from django.utils.module_loading import import_string from django.utils import six from django.utils.functional import cached_property import weasyprint from .. import settings from ..compat import BaseEngine, _dirs_undefined, RemovedInDjango110Warning from ..helpers import build_absolute_uri LOGGER = logging.getLogger(__name__) class PdfTemplateResponse(TemplateResponse): """ Response as PDF content. """ #pylint:disable=too-many-arguments def __init__(self, request, template, context=None, content_type=None, status=None, **kwargs): # Django 1.9 added (charset=None, using=None) to the prototype. # Django 1.10 removed (current_app=None) to the prototype. # We donot declare them explicitely but through **kwargs instead # so that our prototype is compatible with from Django 1.7 # through to Django 1.10. super(PdfTemplateResponse, self).__init__(request, template, context=context, content_type='application/pdf', status=status, **kwargs) @property def rendered_content(self): """ Converts the HTML content generated from the template as a Pdf document on the fly. """ html_content = super(PdfTemplateResponse, self).rendered_content soup = BeautifulSoup(html_content.encode('utf-8'), 'html.parser') for lnk in soup.find_all('a'): href = lnk.get('href') if href and href.startswith('/'): lnk['href'] = build_absolute_uri(self._request, href) html_content = soup.prettify() cstr = io.BytesIO() try: doc = weasyprint.HTML(string=html_content) doc.write_pdf(cstr) except RuntimeError as _: raise return cstr.getvalue() class PdfTemplateError(Exception): pass class PdfEngine(BaseEngine): #pylint: disable=no-member app_dirname = 'pdf' def __init__(self, params): params = params.copy() options = params.pop('OPTIONS').copy() super(PdfEngine, self).__init__(params) self.file_charset = options.get( 'file_charset', django_settings.FILE_CHARSET) self.loaders = options.get('loaders', []) # This is an ugly way to add the search paths for .pdf template files. @cached_property def template_loaders(self): return self.get_template_loaders(self.loaders) def get_template_loaders(self, template_loaders): loaders = [] for loader in template_loaders: if isinstance(loader, (tuple, list)): args = list(loader[1:]) loader = loader[0] else: args = [] if isinstance(loader, six.string_types): loader_class = import_string(loader) if getattr(loader_class, '_accepts_engine_in_init', False): args.insert(0, self) loader = loader_class(self, *args) if loader is not None: loaders.append(loader) else: raise ImproperlyConfigured( "Invalid value in template loaders configuration: %r" % loader) return loaders def find_template(self, template_name, dirs=None, skip=None): tried = [] # if dirs is None: # dirs = self.dirs # for search_dir in dirs: for loader in self.template_loaders: if hasattr(loader, 'get_contents'): # From Django 1.9, this is the code that should be executed. for origin in loader.get_template_sources( template_name, template_dirs=dirs): if skip is not None and origin in skip: tried.append((origin, 'Skipped')) continue try: contents = loader.get_contents(origin) except TemplateDoesNotExist: tried.append((origin, 'Source does not exist')) continue else: template = Template( contents, origin, origin.template_name) return template, template.origin else: # This code is there to support Django 1.8 only. try: source, template_path = loader.load_template_source( template_name, template_dirs=dirs) origin = self.make_origin( template_path, loader.load_template_source, template_name, dirs) template = Template(source, origin, template_path) return template, template.origin except TemplateDoesNotExist: pass raise TemplateDoesNotExist(template_name, tried=tried) def from_string(self, template_code): raise TemplateSyntaxError( "The from_string() method is not implemented") def get_template(self, template_name, dirs=_dirs_undefined): #pylint:disable=arguments-differ if template_name and template_name.endswith('.pdf'): if dirs is _dirs_undefined: dirs = None else: warnings.warn( "The dirs argument of get_template is deprecated.", RemovedInDjango110Warning, stacklevel=2) template, origin = self.find_template(template_name, dirs) if not hasattr(template, 'render'): # template needs to be compiled template = Template(template, origin, template_name) return template raise TemplateDoesNotExist(template_name) class Template(object): """ Fills a PDF template """ def __init__(self, template_string, origin=None, name=None): #pylint:disable=unused-argument self.name = name self.origin = origin def render(self, context=None, request=None): #pylint:disable=unused-argument if self.origin: template_path = self.origin.name else: template_path = self.name output, err = self.fill_form(context, template_path) if err: raise PdfTemplateError(err) return output @staticmethod def fill_form(fields, src, pdf_flatform_bin=None): if pdf_flatform_bin is None: assert hasattr(settings, 'PDF_FLATFORM_BIN'), "PDF generation"\ " requires podofo-flatform (https://github.com/djaodjin/podofo-flatform)."\ " Edit your PDF_FLATFORM_BIN settings accordingly." pdf_flatform_bin = settings.PDF_FLATFORM_BIN cmd = [pdf_flatform_bin] for key, value in six.iteritems(fields): if not isinstance(value, six.string_types): value = str(value) # We substitute non-standard whitespaces here because # they interact poorly with the Python utf-8 encoder. value = re.sub(r"\s", ' ', value) if len(value) > 0: # We don't want to end-up with ``--fill key=`` cmd += ["--fill", '%s=%s' % (key, value)] cmd += [src, '-'] cmdline = cmd[0] for param in cmd[1:]: try: key, value = param.split('=') if any(char in value for char in [' ', ';']): value = '"%s"' % value cmdline += " %s=%s" % (key, value) except ValueError: cmdline += " " + param LOGGER.info("RUN: %s", ' '.join(cmd)) return subprocess.check_output(cmd), None

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9,483

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false

0.012821

0.102564

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0.012821

0

null

0

null

0

1

0

56b2fc9930c872a6e85f2a12f4ba1b8f96b7e270

1,484

py

Python

python/practices/docset.py

gloomyline/ML

3764ac7dd64e3a92de1b34d6a92a809e02f7c038

[ "MIT" ]

null

python/practices/docset.py

gloomyline/ML

3764ac7dd64e3a92de1b34d6a92a809e02f7c038

[ "MIT" ]

null

python/practices/docset.py

gloomyline/ML

3764ac7dd64e3a92de1b34d6a92a809e02f7c038

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Author: Administrator # @Date: 2018-05-17 11:09:22 # @Last Modified by: Administrator # @Last Modified time: 2018-05-17 11:23:24 class Dict(dict): ''' Simple dict but also support access as x.y style. >>> d1 = Dict() >>> d1['x'] = 100 >>> d1.x 100 >>> d1.y = 200 >>> d1['y'] 200 >>> d2 = Dict(a=1, b=2, c='3') >>> d2.c '3' >>> d2['empty'] Traceback (most recent call last): ... KeyError: 'empty' >>> d2.empty Traceback (most recent call last): ... AttributeError: 'Dict' object has no attribute 'empty' ''' def __init__(self, **kw): super(Dict, self).__init__(**kw) def __getattr__(self, key): try: return self[key] except KeyError: raise AttributeError(r"'Dict' object has no attribute '%s'" % key) def __setattr__(self, key, value): self[key] = value def fact(n): ''' Calculate 1*2*3...(n-1)*n >>> fact(1) 1 >>> fact(10) 3628800 >>> fact(-1) Traceback (most recent call last): File "D:\\programTools\\python\\lib\\doctest.py", line 1330, in __run compileflags, 1), test.globs) File "<doctest __main__.fact[2]>", line 1, in <module> fact(-1) File "E:\\localRepositories\\ML\\python\\practices\\docset.py", line 53, in fact raise ValueError() ValueError ''' if n < 1: raise ValueError() if n == 1: return 1 else: return n*fact(n-1) if __name__=='__main__': import doctest doctest.testmod()

21.823529

84

0.584232

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3.985714

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0.082437

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0

0.074009

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1,484

68

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21.823529

0.663436

0.607143

0

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0

1

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false

0

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0

0.45

0

null

0

null

0

1

0

56b37ef771a3d6c20dd29b8a7bb0b23c17d962e6

185

py

Python

OpenCV/task_1.2.py

Riyaagrawal2001/Autumn-of-Automation

5e15d1a9943e41ac548fc0862db2eddea1752c02

[ "MIT" ]

null

OpenCV/task_1.2.py

Riyaagrawal2001/Autumn-of-Automation

5e15d1a9943e41ac548fc0862db2eddea1752c02

[ "MIT" ]

null

OpenCV/task_1.2.py

Riyaagrawal2001/Autumn-of-Automation

5e15d1a9943e41ac548fc0862db2eddea1752c02

[ "MIT" ]

null

import cv2 cap = cv2.VideoCapture(0) while(True): ret,frame = cap.read() cv2.imshow('frame',frame) if cv2.waitKey(1)&0xFF==ord('q'): break cap.release() cv2.destroyAllWindows()

14.230769

34

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28

185

4.535714

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0

0.049383

0.124324

185

13

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14.230769

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0

0.032258

0

0.021505

0

1

0

false

0

0.111111

0

0.111111

0

1

0

null

0

null

0

1

56b5106f514ff350c7e6209c3bde64f4fb351f79

231

py

Python

genno.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

null

genno.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

4

2020-10-04T07:49:30.000Z

2021-10-02T05:24:40.000Z

genno.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

7

2020-10-04T07:46:55.000Z

2021-11-05T14:30:00.000Z

def gen(a,j): if j>=len(a): print a return i=j while i<9: a[j]=i+1 gen(a,j+1) j-=1 def main(): a=['0','0'] #print len(a) gen(a,0) if __name__=='__main__': main();

14.4375

24

0.406926

41

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0.365854

0.139535

0.116279

0

0.049645

0.38961

231

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0

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0

null

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null

0.071429

0

null

0

null

0

1

0

1

56b748953a338c9c796774f938f8407392ae2efe

1,498

py

Python

docs/latex/src/plots/FOvsAsy2.py

vbertone/apfelxx

7a37b982083b2a1cded2f5d6ab3aae267877f3c4

[ "MIT" ]

5

2019-10-07T14:01:59.000Z

2021-04-13T19:54:47.000Z

docs/latex/src/plots/FOvsAsy2.py

vbertone/apfelxx

7a37b982083b2a1cded2f5d6ab3aae267877f3c4

[ "MIT" ]

3

2017-05-30T10:43:40.000Z

2018-09-11T14:29:53.000Z

docs/latex/src/plots/FOvsAsy2.py

vbertone/apfelxx

7a37b982083b2a1cded2f5d6ab3aae267877f3c4

[ "MIT" ]

4

2019-06-23T08:42:00.000Z

2022-03-18T15:25:46.000Z

import ruamel.yaml as yaml import numpy as np import matplotlib.pyplot as plt import MatplotlibSettings from scipy.interpolate import make_interp_spline, BSpline # Loada data data = np.loadtxt("FOvsAsy2.dat") f, (ax1, ax2) = plt.subplots(2, 1, sharex = "all", gridspec_kw = dict(width_ratios = [1], height_ratios = [4, 1])) plt.subplots_adjust(wspace = 0, hspace = 0) ax1.set_title(r"\textbf{SIDIS at $\mathcal{O}(\alpha_s)$, $\sqrt{s}=10.5$ GeV}") ax1.text(0.0002, 0.2, r"\textbf{$Q^2 = 2$ GeV$^2$}", fontsize = 16) ax1.text(0.0002, 0.1, r"\textbf{$x = 0.1$}", fontsize = 16) ax1.text(0.0002, 0.05, r"\textbf{$z = 0.2$}", fontsize = 16) ax1.set(ylabel = r"$\displaystyle\left|\frac{d\sigma}{dy dz dQ dq_T}\right|$") ax1.set_xscale("log") ax1.set_yscale("log") ax1.set_xlim([0.0001, 1]) ax1.set_ylim([0.0001, 10]) ax1.plot(data[:, 0], np.absolute(data[:, 1]), color = "red", label = r"\textbf{Fixed order}") ax1.plot(data[:, 0], np.absolute(data[:, 2]), color = "blue", label = r"\textbf{Asymptotic}") ax1.plot(data[:, 0], np.absolute(data[:, 1] - data[:, 2]), color = "orange", label = r"\textbf{Difference}") ax1.legend(fontsize = 20) ax2.set_xlabel(r"\textbf{$q_T$ [GeV]}") ax2.set_ylabel(r"\textbf{Ratio}", fontsize = 16) ax2.set_ylim([0.55, 1.45]) ax2.plot(data[:, 0], np.absolute(data[:, 1] / data[:, 2]), color = "green") ax2.plot(data[:, 0], np.absolute(data[:, 1] / data[:, 1]), color = "black", ls = "--", lw = 1.5) ax2.set_xlim([0.0001, 1]) plt.savefig("FOvsAsy2.pdf") plt.close()

41.611111

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1,498

3.643678

0.398467

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0.057834

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0.214511

0.138801

0.107256

0.071504

0

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1,498

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115

42.8

0.63273

0.006676

0

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0.222746

0.04105

0

1

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false

0

0.172414

0

0.172414

0

null

0

null

0

1

0

56b8192795c518e7928ca09a5572608668256f99

3,578

py

Python

OptimizedMovingAveragesUpdated.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

3

2019-04-26T11:13:14.000Z

2020-01-10T05:58:16.000Z

OptimizedMovingAveragesUpdated.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

null

OptimizedMovingAveragesUpdated.py

adamrvfisher/TechnicalAnalysisLibrary

38a22b2b2b5052623f81edb11b3c5460fc254e45

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ @author: Adam Reinhold Von Fisher - https://www.linkedin.com/in/adamrvfisher/ """ #This is a strategy tester with a brute force optimizer #Pandas_datareader is deprecated, use YahooGrabber #Import modules import numpy as np from pandas_datareader import data import random as rand import pandas as pd import time as t #Number of iterations iterations = range(0,40000) #Empty data structures empty = [] asone = pd.DataFrame() #Start timer start = t.time() #Request data s = data.DataReader('^GSPC', 'yahoo', start='1/1/1900', end='01/01/2050') #Calculate log returns s['LogRet'] = np.log(s['Adj Close']/s['Adj Close'].shift(1)) s['LogRet'] = s['LogRet'].fillna(0) #For number of iterations for i in iterations: #Generate random params a = rand.randint(1,60) b = rand.randint(2,504) #Constraint if a > b: continue #Generate random params c = (rand.random())/10 e = (rand.random())/4 #Constraint if c > e: continue #Generate random params d = (rand.random())/10 f = (rand.random())/4 #Constraint if d > f: continue #Calculate SMA s['a'] = s['Adj Close'].rolling(window=a, center=False).mean() s['b'] = s['Adj Close'].rolling(window=b, center=False).mean() #SMA spread s['a-b'] = s['a'] - s['b'] #SMA spread in % s['Trend']= s['a-b']/s['Adj Close'] s['Trend'] = s['Trend'].fillna(0) #Directional methodology s['Touch'] = np.where(s['Trend'] > c, 1, 0) s['Touch'] = np.where(s['Trend'] < -d, -1, s['Touch']) s['Sustain'] = np.where(s['Touch'].shift(1) == 1, 1, 0) s['Sustain'] = np.where(s['Sustain'].shift(1) == 1, 1, s['Sustain']) s['Sustain'] = np.where(s['Touch'].shift(1) == -1, -1, 0) s['Sustain'] = np.where(s['Sustain'].shift(1) == -1, -1, s['Sustain']) s['Sustain'] = np.where(s['Trend'] > e, 0, s['Sustain']) s['Sustain'] = np.where(s['Trend'] < -f , 0, s['Sustain']) s['Regime'] = s['Touch'] + s['Sustain'] #Apply postition to returns s['Strategy'] = (s['Regime']).shift(1)*s['LogRet'] s['Strategy'] = s['Strategy'].fillna(0) #Ones endgains = 1 endreturns = 1 #Compound returns for m in s['LogRet']: slate = endreturns * (1+m) endreturns = slate for n in s['Strategy']: otherslate = endgains * (1+n) endgains = otherslate #Constraint if endreturns * 1.2 > endgains: continue #Save params and metrics to list empty.append(a) empty.append(b) empty.append(c) empty.append(d) empty.append(e) empty.append(f) empty.append(endreturns) empty.append(endgains) #List to series emptyseries pd.Series(empty) #Series to dataframe asone[i] = emptyseries.values #Clear list empty[:] = [] #End timer end = t.time() #Metric of choice z = asone.iloc[7] #Threshold w = np.percentile(z, 99) v = [] #this variable stores the Nth percentile of top params u = pd.DataFrame() #this variable stores your params #For all metrics for h in z: #If greater than threshold if h > w: #Add to list v.append(h) #For top metrics for j in v: #Get column ID of metric r = asone.columns[(asone == j).iloc[7]] #Add param set to dataframe u = pd.concat([u,asone[r]], axis = 1) #Top metrics y = max(z) #Column ID of top param set x = asone.columns[(asone == y).iloc[7]] #Top param set print(asone[x]) #Timer stats print(end-start)

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3,578

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null

0

1

0

1

56b9ba77444e2cd8a93d2c91b41f8c6f997f8056

2,006

py

Python

generation/process_datasets/process-NYT.py

Pratik-11/roft

29c54c9712832051170c47909a5d38790ff5350b

[ "MIT" ]

10

2020-05-31T19:19:42.000Z

2022-01-15T01:44:33.000Z

generation/process_datasets/process-NYT.py

kirubarajan/trick

04ef53c1d9646e0d7e7ec0eb47cc94d423682421

[ "MIT" ]

121

2020-06-05T20:29:24.000Z

2021-09-24T21:33:33.000Z

generation/process_datasets/process-NYT.py

kirubarajan/trick

04ef53c1d9646e0d7e7ec0eb47cc94d423682421

[ "MIT" ]

2

2020-06-05T20:10:29.000Z

2020-09-30T14:55:48.000Z

''' Script to parse out the raw text of articles from the NYT Articles Corpus This script will look for a directory named raw and find any .ta.xml files inside, parse out the "text" field in the file, strip all newlines and carriage returns from the file and then write the text out, one article per line to two files in an 80/20 split named "nyt-articles-test.txt" and "nyt-articles-train.txt" ''' import os, json, random import xml.etree.ElementTree as xml corpus_location = './raw' pretraining_output_file_path = './processed/nyt-articles-train.txt' dev_output_file_path = './processed/nyt-articles-dev.txt' sampling_output_file_path = './processed/nyt-articles-test.txt' def clean(text): return text.replace('\n', ' ').replace('\r', '') + '\n' def get_outfile(filename): rng = random.random() if rng < 0.90: return pretraining_output_file_path elif rng < 0.95: return dev_output_file_path else: return sampling_output_file_path def makedirs(filename): ''' https://stackoverflow.com/a/12517490 ''' if not os.path.exists(os.path.dirname(filename)): try: os.makedirs(os.path.dirname(filename)) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise return filename if __name__ == '__main__': if os.path.exists(corpus_location) and os.path.isdir(corpus_location): total = len(os.listdir(corpus_location)) for index, filename in enumerate(os.listdir(corpus_location)): if filename.endswith('.ta.xml'): path = os.path.join(corpus_location, filename) outfile = get_outfile(path) with open(path, 'r+') as f: with open(makedirs(outfile), 'a+') as out_f: data = json.load(f) out_f.write(clean(data['text'])) print('Read in file {0}/{1}: {2}'.format(index, total, path))

37.849057

84

0.638584

277

2,006

4.494585

0.422383

0.053012

0.06747

0.055422

0.081928

0

0.013917

0.247757

2,006

52

85

38.576923

0.811133

0.228814

0

0.105368

0.065606

0

1

0.083333

false

0

0.055556

0.027778

0.277778

0.027778

0

null

0

null

0

1

0

56baa2531aac4a1b2d5cf0f754d7c2d4f1573f35

853

py

Python

DataMGT/consumers.py

BerryBC/SpyDataWebAppAndAPI

6dd42a186e6955575fb747f7ff69c5b5a060ca19

[ "MIT" ]

null

DataMGT/consumers.py

BerryBC/SpyDataWebAppAndAPI

6dd42a186e6955575fb747f7ff69c5b5a060ca19

[ "MIT" ]

null

DataMGT/consumers.py

BerryBC/SpyDataWebAppAndAPI

6dd42a186e6955575fb747f7ff69c5b5a060ca19

[ "MIT" ]

null

''' @Descripttion: @Author: BerryBC @Date: 2020-02-24 23:40:18 @LastEditors: BerryBC @LastEditTime: 2020-04-29 22:28:49 ''' import json import Lib.LLearn as LLearn from channels.generic.websocket import WebsocketConsumer class wsCreatSklearnModel(WebsocketConsumer): def funFB2C(self,strMsg, intCode): self.send(text_data=json.dumps({ 'msg': strMsg, 'code': intCode })) def connect(self): self.accept() self.funFB2C('OK', 1) print(' Client Start Sklearn Learn Websocket.') def disconnect(self, close_code): print(' Learn Websocket disconnected') def receive(self, text_data): objRevData = json.loads(text_data) intCode = objRevData['doCode'] if intCode == 0: LLearn.funGoLearn(self.funFB2C) self.funFB2C('Done', 3)

23.054054

56

0.638921

98

853

5.520408

0.622449

0.044362

0

0.054096

0.241501

853

37

57

23.054054

0.782071

0.135991

0

0.117808

0

1

0.2

false

0

0.15

0

0.4

0.1

0

null

0

null

0

1

0

56bb48fc93cbdd9d51e108045eb0d3f0918f92f4

821

py

Python

main/test/test_image.py

kittenh2o/mosaic

19dc7cb3300b00a055fad874a097aa7a011ca56f

[ "MIT" ]

null

main/test/test_image.py

kittenh2o/mosaic

19dc7cb3300b00a055fad874a097aa7a011ca56f

[ "MIT" ]

null

main/test/test_image.py

kittenh2o/mosaic

19dc7cb3300b00a055fad874a097aa7a011ca56f

[ "MIT" ]

null

import unittest from main.core.process_pic import Image class TestImage(unittest.TestCase): def test_read(self): uris = [ "https://res.cloudinary.com/dwf6x1ohn/image/upload/v1534347950/bgnppredgmslafb5pkpw.jpg", "https://res.cloudinary.com/dwf6x1ohn/image/upload/v1534347979/wptzfdqidfnlyhgt3kti.jpg" ] sizes = [ (540, 547), (259, 194) ] for (uri, size) in zip(uris, sizes): image = Image(uri) self.assertEqual(size[0], image.width()) self.assertEqual(size[1], image.height()) self.assertEqual(size[0] * size[1], image.size()) if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(TestImage) unittest.TextTestRunner(verbosity=2).run(suite)

28.310345

101

0.618758

87

821

5.724138

0.586207

0.090361

0.114458

0.084337

0.164659

0

0.069805

0.249695

821

28

102

29.321429

0.738636

0

0.219245

0

0.15

1

0.05

false

0

0.1

0

0.2

0

null

0

null

0

1

0

56bc157b8432a7e32a0436c1af87a1c616b37163

1,670

py

Python

flockos/models/error.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

14

2017-02-14T07:02:59.000Z

2022-03-30T13:59:59.000Z

flockos/models/error.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

10

2016-10-22T20:52:00.000Z

2021-05-10T10:40:30.000Z

flockos/models/error.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

8

2017-03-03T13:16:34.000Z

2020-07-23T17:59:54.000Z

# coding: utf-8 from pprint import pformat from ..utils import to_dict class Error(object): def __init__(self, error=None, description=None, parameter=None, disabled_by=None): self._error = error self._description = description self._parameter = parameter self._disabled_by = disabled_by @property def error(self): return self._error @error.setter def error(self, error): self._error = error @property def description(self): return self._description @description.setter def description(self, description): self._description = description @property def parameter(self): return self._parameter @parameter.setter def parameter(self, parameter): self._parameter = parameter @property def disabled_by(self): return self._disabled_by @disabled_by.setter def disabled_by(self, disabled_by): self._disabled_by = disabled_by def to_dict(self): """ Returns the model properties as a dict """ return to_dict(self.__dict__) def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

20.120482

87

0.602395

187

1,670

5.080214

0.256684

0.105263

0.058947

0.069474

0.176842

0.075789

0

0.000867

0.309581

1,670

82

88

20.365854

0.82307

0.123952

0

0.285714

0

1

0.333333

false

0

0.047619

0.095238

0.619048

0.02381

0

null

0

null

0

1

0

1

56bc4761d12edf3388b485bea197b4fd8ed7b433

4,390

py

Python

tf2onnx/tflite_handlers/tfl_nn.py

BobLiu20/tensorflow-onnx

bec7c1fd610c27d7cb22271c9fdf45d9f4ecee44

[ "Apache-2.0" ]

1

2021-04-30T15:26:06.000Z

tf2onnx/tflite_handlers/tfl_nn.py

BobLiu20/tensorflow-onnx

bec7c1fd610c27d7cb22271c9fdf45d9f4ecee44

[ "Apache-2.0" ]

null

tf2onnx/tflite_handlers/tfl_nn.py

BobLiu20/tensorflow-onnx

bec7c1fd610c27d7cb22271c9fdf45d9f4ecee44

[ "Apache-2.0" ]

1

2021-05-22T02:24:21.000Z

# SPDX-License-Identifier: Apache-2.0 """ tfl_nn """ from tf2onnx.handler import tfl_op from tf2onnx.tflite_handlers.tfl_math import separate_fused_activation_function # pylint: disable=unused-argument,missing-docstring,unused-variable,pointless-string-statement,invalid-name @tfl_op(["TFL_TRANSPOSE_CONV"], tf_op="Conv2DBackpropInput") class TflTransposeConv: @classmethod def to_tf(cls, ctx, node, **kwargs): # No need to change 'padding' attribute stride_h = node.get_attr_int("stride_h") stride_w = node.get_attr_int("stride_w") node.set_attr("strides", [1, stride_h, stride_w, 1]) del node.attr["stride_h"] del node.attr["stride_w"] transpose_node = ctx.insert_new_node_on_input(node, "Transpose", node.input[1], name=None, perm=[1, 2, 0, 3]) transpose_node.skip_conversion = True node.set_attr("data_format", "NHWC") @tfl_op(["TFL_CONV_2D"], tf_op="Conv2D") class TflConv2D: @classmethod def to_tf(cls, ctx, node, **kwargs): separate_fused_activation_function(ctx, node) # No need to change 'padding' attribute stride_h = node.get_attr_int("stride_h") stride_w = node.get_attr_int("stride_w") dilation_w_factor = node.get_attr_int("dilation_w_factor") dilation_h_factor = node.get_attr_int("dilation_h_factor") node.set_attr("strides", [1, stride_h, stride_w, 1]) node.set_attr("dilations", [1, dilation_h_factor, dilation_w_factor, 1]) del node.attr["stride_h"] del node.attr["stride_w"] del node.attr["dilation_h_factor"] del node.attr["dilation_w_factor"] transpose_node = ctx.insert_new_node_on_input(node, "Transpose", node.input[1], name=None, perm=[1, 2, 3, 0]) transpose_node.skip_conversion = True node.set_attr("data_format", "NHWC") @tfl_op(["TFL_AVERAGE_POOL_2D"], tf_op="AvgPool") @tfl_op(["TFL_MAX_POOL_2D"], tf_op="MaxPool") class TflAveragePool: @classmethod def to_tf(cls, ctx, node, **kwargs): separate_fused_activation_function(ctx, node) # No need to change 'padding' attribute stride_h = node.get_attr_int("stride_h") stride_w = node.get_attr_int("stride_w") filter_height = node.get_attr_int("filter_height") filter_width = node.get_attr_int("filter_width") node.set_attr("strides", [1, stride_h, stride_w, 1]) node.set_attr("ksize", [1, filter_height, filter_width, 1]) del node.attr["stride_h"] del node.attr["stride_w"] del node.attr["filter_height"] del node.attr["filter_width"] node.set_attr("data_format", "NHWC") @tfl_op(["TFL_DEPTHWISE_CONV_2D"], tf_op="DepthwiseConv2dNative") class TflDepthwiseConv2D: @classmethod def to_tf(cls, ctx, node, **kwargs): separate_fused_activation_function(ctx, node) # No need to change 'padding' or 'depth_multiplier' attributes stride_h = node.get_attr_int("stride_h") stride_w = node.get_attr_int("stride_w") dilation_w_factor = node.get_attr_int("dilation_w_factor") dilation_h_factor = node.get_attr_int("dilation_h_factor") node.set_attr("strides", [1, stride_h, stride_w, 1]) node.set_attr("dilations", [1, dilation_h_factor, dilation_w_factor, 1]) del node.attr["stride_h"] del node.attr["stride_w"] del node.attr["dilation_h_factor"] del node.attr["dilation_w_factor"] transpose_node = ctx.insert_new_node_on_input(node, "Transpose", node.input[1], name=None, perm=[1, 2, 3, 0]) transpose_node.skip_conversion = True node.set_attr("data_format", "NHWC") @tfl_op(["TFL_BATCH_TO_SPACE_ND"], tf_op="BatchToSpaceND") class TflSlice: @classmethod def to_tf(cls, ctx, node, **kwargs): pass @tfl_op(["TFL_SPACE_TO_BATCH_ND"], tf_op="SpaceToBatchND") class TFlSpaceToBatchNDOp: @classmethod def to_tf(cls, ctx, node, **kwargs): pass @tfl_op(["TFL_SPACE_TO_DEPTH"], tf_op="SpaceToDepth") class TFlSpaceToDepthOp: @classmethod def to_tf(cls, ctx, node, **kwargs): node.set_attr("data_format", "NHWC") @tfl_op(["TFL_NON_MAX_SUPPRESSION_V4"], tf_op="NonMaxSuppressionV4") class TflNonMaxSuppressionV4Op: @classmethod def to_tf(cls, ctx, node, **kwargs): node.set_attr("pad_to_max_output_size", 1)

39.54955

117

0.682916

629

4,390

4.426073

0.18124

0.04023

0.055316

0.070402

0.716236

0.696121

0.683908

0.664152

0

0.012882

0.18656

4,390

110

118

39.909091

0.766732

0.073804

0

0.701149

0

0.191957

0.032568

0

1

0.091954

false

0.022989

0

0.206897

0

null

0

1

0

1

0

null

0

2

56bf87b62349b915ce3f570672341fdac70f6f1f

58

py

Python

physballs/physballs.py

Dhhoyt/Physballs

2225f5d88c7e16ac2b9aa59eb6e312eb62750955

[ "MIT" ]

null

physballs/physballs.py

Dhhoyt/Physballs

2225f5d88c7e16ac2b9aa59eb6e312eb62750955

[ "MIT" ]

null

physballs/physballs.py

Dhhoyt/Physballs

2225f5d88c7e16ac2b9aa59eb6e312eb62750955

[ "MIT" ]

null

from graphics.render import open_window open_window()

14.5

40

0.793103

8

58

5.5

0.75

0.454545

0

0.155172

58

3

41

19.333333

0.897959

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

56bfc33a7af917c5815e0bac91e9b07e0bede4f2

247

py

Python

ex016-Descubra a Hipotenusa.py

Mathelzu/PythonExercicios

9bb3f4ce97818fd4f0cb296c262818d7b1c76adb

[ "Apache-2.0" ]

null

ex016-Descubra a Hipotenusa.py

Mathelzu/PythonExercicios

9bb3f4ce97818fd4f0cb296c262818d7b1c76adb

[ "Apache-2.0" ]

null

ex016-Descubra a Hipotenusa.py

Mathelzu/PythonExercicios

9bb3f4ce97818fd4f0cb296c262818d7b1c76adb

[ "Apache-2.0" ]

null

import math catOp = float(input('Valor Cateto Oposto: ')) catAd = float (input('Valor Cateto adjacente: ')) hip = (catOp**2) + (catAd**2) # ou hip = math.hypot (catOp , catAd) hip2 = math.sqrt(hip) print (f'O valor da hipotenusa é de {hip2:.2f}')

30.875

49

0.663968

39

247

4.205128

0.615385

0.121951

0.182927

0.256098

0

0.024038

0.157895

247

7

50

35.285714

0.764423

0.1417

0

0.390476

0

1

0

false

0

0.166667

0

0.166667

0

null

0

1

0

1

0

null

0

3

56c02adeb142ee8a2831146de93928ab4c1be844

60

py

Python

compute/dbconn/dbconn/models/incident.py

djfurman/well-managed-deployments

b61c9adb7212bb2f2a03f007568760ec5a36af72

[ "BSD-3-Clause" ]

1

2020-05-18T00:28:12.000Z

compute/dbconn/dbconn/models/incident.py

djfurman/well-managed-deployments

b61c9adb7212bb2f2a03f007568760ec5a36af72

[ "BSD-3-Clause" ]

10

2018-04-02T23:09:50.000Z

2018-04-22T15:58:08.000Z

compute/dbconn/dbconn/models/incident.py

djfurman/well-managed-deployments

b61c9adb7212bb2f2a03f007568760ec5a36af72

[ "BSD-3-Clause" ]

null

from orator import Model class Incident(Model): pass

8.571429

24

0.716667

8

60

5.375

0.875

0

0.233333

60

6

25

10

0.934783

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

56c06ffe14aeeb05cf11a1e9b700fb840312480b

2,137

py

Python

services/traction/bdd-tests/features/steps/holder.py

bcgov/traction

90cec4f1aebccd68eb986cb89dfae5819a07a2ee

[ "Apache-2.0" ]

12

2022-01-29T20:30:03.000Z

2022-03-29T11:46:14.000Z

services/traction/bdd-tests/features/steps/holder.py

bcgov/traction

90cec4f1aebccd68eb986cb89dfae5819a07a2ee

[ "Apache-2.0" ]

38

2021-11-22T17:52:50.000Z

2022-03-31T17:52:00.000Z

services/traction/bdd-tests/features/steps/holder.py

bcgov/traction

90cec4f1aebccd68eb986cb89dfae5819a07a2ee

[ "Apache-2.0" ]

9

2021-11-22T18:05:48.000Z

2022-03-29T11:25:08.000Z

import json import requests import pprint import time from behave import * from starlette import status @when('"{holder}" will have a credential_offer from "{issuer}"') @then('"{holder}" will have a credential_offer from "{issuer}"') def step_impl(context, holder: str, issuer: str): response = requests.get( context.config.userdata.get("traction_host") + "/tenant/v0/credentials/holder/offer", headers=context.config.userdata[holder]["auth_headers"], ) assert response.status_code == status.HTTP_200_OK, response.__dict__ resp_json = json.loads(response.content) assert len(resp_json) == 1, resp_json contact_id = context.config.userdata[holder]["connections"][issuer]["contact_id"] assert resp_json[0]["credential"]["contact_id"] == contact_id assert resp_json[0]["credential"]["issue_state"] == "offer_received" context.config.userdata[holder]["cred_offers"] = [ a["credential"] for a in resp_json ] @when('"{holder}" will accept credential_offer from "{issuer}"') @then('"{holder}" will accept credential_offer from "{issuer}"') def step_impl(context, holder, issuer): cred_issue_id = context.config.userdata[holder]["cred_offers"][0]["id"] response = requests.post( context.config.userdata.get("traction_host") + "/tenant/v0/credentials/holder/accept_offer" + "?cred_issue_id=" + cred_issue_id, headers=context.config.userdata[holder]["auth_headers"], ) assert response.status_code == status.HTTP_200_OK, response.__dict__ resp_json = json.loads(response.content) assert resp_json["credential"]["issue_state"] == "request_sent", resp_json time.sleep(2) @then('"{holder}" will have a credential') def step_impl(context, holder): response = requests.get( context.config.userdata.get("traction_host") + "/tenant/v0/credentials/holder/", headers=context.config.userdata[holder]["auth_headers"], ) assert response.status_code == status.HTTP_200_OK, response.__dict__ resp_json = json.loads(response.content) assert len(resp_json) == 1, resp_json

33.920635

88

0.697707

270

2,137

5.288889

0.22963

0.067227

0.132353

0.113445

0.77521

0.736695

0.664566

0.571429

0.532913

0.464286

0

0.010028

0.160037

2,137

62

89

34.467742

0.785515

0

0.319149

0

0.277492

0.05007

0

0.170213

1

0.06383

false

0

0.12766

0

0.191489

0.021277

0

null

0

1

0

null

0

1

56c2cfe7d870ad1404c84801425ee3e0576bff7d

99

py

Python

Exercise6.py

alxsklv/homework

dd629a9b6bb5e6d79ad84de6f69f26c80d50bb22

[ "MIT" ]

null

Exercise6.py

alxsklv/homework

dd629a9b6bb5e6d79ad84de6f69f26c80d50bb22

[ "MIT" ]

null

Exercise6.py

alxsklv/homework

dd629a9b6bb5e6d79ad84de6f69f26c80d50bb22

[ "MIT" ]

null

a = int(input('enter side: \n')) b = '* ' i = 0 while i < a: print(b) b += '* ' i += 1

12.375

32

0.383838

17

99

2.235294

0.705882

0.105263

0

0.031746

0.363636

99

8

33

12.375

0.571429

0

0.18

0

1

0

false

0

0.142857

1

0

null

0

1

0

null

0

2

56c443957d86bdbc9785b7c592e15dd086bbef8a

527

py

Python

Chapter__8/Examples/running_time_measuring/main.py

nil1729/python__noob

d82d951dc511eafa9f4315e1fdfdc749f484abf1

[ "MIT" ]

null

Chapter__8/Examples/running_time_measuring/main.py

nil1729/python__noob

d82d951dc511eafa9f4315e1fdfdc749f484abf1

[ "MIT" ]

null

Chapter__8/Examples/running_time_measuring/main.py

nil1729/python__noob

d82d951dc511eafa9f4315e1fdfdc749f484abf1

[ "MIT" ]

null

class my_timer(object): def __init__(self, original_function): self.original_function = original_function def __call__(self, *args, **kwargs): import time t1 = time.time() result = self.original_function(*args, **kwargs) t2 = time.time() print(f"{self.original_function.__name__}() function ran in: {t2 - t1} sec") return result @my_timer def my_loop(): sum = 0 for i in range(10000000): sum += i*i print(sum) my_loop()

25.095238

85

0.586338

66

527

4.363636

0.469697

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0

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527

20

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false

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0.117647

0

null

1

0

null

0

2

56c4a181e46ff75702be3d6706e5216784d4e18d

12,870

py

Python

umpleonline/chatbot/processresponse.py

YounesB-McGill/Comp550-Project

bbc9cf91e295a26fd1e8f2ba8371f737a449a47a

[ "MIT" ]

null

umpleonline/chatbot/processresponse.py

YounesB-McGill/Comp550-Project

bbc9cf91e295a26fd1e8f2ba8371f737a449a47a

[ "MIT" ]

6

2020-07-19T01:29:06.000Z

2021-05-10T21:21:27.000Z

umpleonline/chatbot/processresponse.py

YounesB-McGill/Comp550-Project

bbc9cf91e295a26fd1e8f2ba8371f737a449a47a

[ "MIT" ]

null

#!/usr/bin/python3 import re from typing import List import numpy as np from keras.models import load_model from action import (add_class_json, add_attribute, create_association, create_inheritance, create_composition, return_error_to_user) from data import ADD_WORDS, CONTAINS_WORDS, HAVE_WORDS, ISA_WORDS from model import predict, getIntent, keyIntent from npparser import get_chunks, get_NP_subtrees, get_num_nonnested_NP_subtrees, get_noun_from_np from utils import (first_letter_lowercase, first_letter_uppercase, contains_one_of, get_DT_for_word, is_attribute, get_detected_keywords, strip_punctuation) classes_created = [] # Must keep track of this to avoid errors def process_response_model(user_input: str) -> str: message_text = strip_punctuation(user_input.lower()) intent = get_intent(predict(user_input)) if intent == "add_class": return add_class_action(message_text) elif intent == "add_attribute": return add_attribute_action(message_text) elif intent == "create_composition": return make_composition(message_text) elif intent == "create_association": return make_association(message_text) elif intent == "create_inheritance": return make_inheritance(message_text) else: return process_response_baseline(user_input) # The following three functions call into the same NP parser as the baseline, once the intent is determined. def add_class_action(message_text): return handle_add_kw(message_text) def make_composition(message_text): return handle_contain_kw(message_text) def make_inheritance(message_text): return handle_isa_kw(message_text) # Since handle_have_kw tries to guess whether it needs to add an attribute (A student has a name) or an association # (A student has an address), the logic for the following two functions needs to be specified separately. def add_attribute_action(message_text): chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st == 0: return return_error_to_user("I really don't understand what you meant. Please rephrase.") elif n_st == 1: class_name = get_noun_from_np(nps[0]) if class_name in classes_created: return return_error_to_user(f"What do want to specify about {class_name}?") else: dt = get_DT_for_word(class_name) return return_error_to_user(f"Are trying to add a class? Try saying 'Create {dt} {class_name}.'") else: class_name = get_noun_from_np(nps[0]) attribute_name = first_letter_lowercase(get_noun_from_np(nps[1])) if class_name in classes_created: classes_created.append(class_name) return add_attribute(class_name, attribute_name) def make_association(message_text): chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st == 0: return return_error_to_user("I really don't understand what you meant. Please rephrase.") elif n_st == 1: class_name = get_noun_from_np(nps[0]) if class_name in classes_created: return return_error_to_user(f"What do want to specify about {class_name}?") else: dt = get_DT_for_word(class_name) return return_error_to_user(f"Are trying to add a class? Try saying 'Create {dt} {class_name}.'") else: class1 = get_noun_from_np(nps[0]) class2 = get_noun_from_np(nps[1]) if class1 in classes_created: classes_created.append(class1) if class2 not in classes_created: classes_created.append(class2) return create_association(class1, class2) def process_response_baseline(user_input: str) -> str: """ Function used to reply with a baseline response based on the Socio model. This function assumes valid input. """ print("Processing message in baseline mode.") message_text = strip_punctuation(user_input.lower()) detected_keywords = get_detected_keywords(message_text) nk = len(detected_keywords) if nk == 0: return handle_no_kw(message_text) elif nk == 1: kw = list(detected_keywords.keys())[0] if kw == "ADD": return handle_add_kw(message_text) elif kw == "CONTAIN": return handle_contain_kw(message_text) elif kw == "HAVE": return handle_have_kw(message_text) elif kw == "ISA": return handle_isa_kw(message_text) elif nk == 2: if "CONTAIN" in detected_keywords.keys() and "ISA" in detected_keywords.keys(): # "can consist of" return handle_contain_kw(message_text) else: print("nk = 2", detected_keywords) return process_response_fallback(message_text) else: # TODO Handle more complex multiple keyword scenarios print("nk =", nk, detected_keywords) return process_response_fallback(message_text) def handle_add_kw(message_text: str) -> str: chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st == 0: kw = get_detected_keywords(message_text).get("ADD", "add") return return_error_to_user(f"Please specify what you want to {kw}.") elif n_st == 1: class_name = get_noun_from_np(nps[0]) return add_class(class_name) elif n_st == 2: class_name = get_noun_from_np(nps[1]) attribute_name = first_letter_lowercase(get_noun_from_np(nps[0])) return add_attribute(class_name, attribute_name) else: return process_response_fallback(message_text) def handle_contain_kw(message_text: str) -> str: chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st < 2: return return_error_to_user( "I don't get what you meant. If you want to make a composition, specify the two classes.") elif n_st == 2: first_noun = get_noun_from_np(nps[0]) second_noun = get_noun_from_np(nps[1]) if first_noun not in classes_created: classes_created.append(first_noun) if is_attribute(get_noun_from_np(nps[1])): return add_attribute(first_noun, first_letter_lowercase(second_noun)) else: whole = first_noun part = second_noun if part not in classes_created: classes_created.append(part) return create_composition(whole, part) else: return process_response_fallback(message_text) def handle_have_kw(message_text: str) -> str: chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st == 0: return return_error_to_user("I really don't understand what you meant. Please rephrase.") elif n_st == 1: class_name = get_noun_from_np(nps[0]) if class_name in classes_created: return return_error_to_user(f"What do want to specify about {class_name}?") else: dt = get_DT_for_word(class_name) return return_error_to_user(f"Are trying to add a class? Try saying 'Create {dt} {class_name}.'") else: # TODO In the future, also allow multiple attributes ("Student has a name and email"). # This requires updating the website. class_name = get_noun_from_np(nps[0]) second_noun = get_noun_from_np(nps[1]) if class_name in classes_created: classes_created.append(class_name) if is_attribute(second_noun): return add_attribute(class_name, first_letter_lowercase(second_noun)) else: if second_noun not in classes_created: classes_created.append(second_noun) return create_association(class_name, second_noun) return process_response_fallback(message_text) def handle_isa_kw(message_text: str) -> str: chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st < 2: return return_error_to_user("If you're trying to create an inheritance, clearly specify both classes.") else: if ((" serve" in message_text and " as " in message_text) or (" play" in message_text and " role" in message_text)): child = get_noun_from_np(nps[1]) parent = get_noun_from_np(nps[0]) else: child = get_noun_from_np(nps[0]) parent = get_noun_from_np(nps[1]) if child not in classes_created: classes_created.append(child) if parent not in classes_created: classes_created.append(parent) return create_inheritance(child, parent) return process_response_fallback(message_text) def handle_no_kw(message_text: str) -> str: """ Add an association if possible, otherwise create a class. """ chunks = get_chunks(message_text) nps = get_NP_subtrees(chunks) n_st = get_num_nonnested_NP_subtrees(chunks) if n_st == 0: return return_error_to_user("I really don't understand what you meant. Please rephrase.") elif n_st == 1: class_name = get_noun_from_np(nps[0]) return add_class(class_name) elif n_st == 2: class1 = get_noun_from_np(nps[0]) class2 = get_noun_from_np(nps[1]) if class1 not in classes_created: classes_created.append(class1) if class2 not in classes_created: classes_created.append(class2) return create_association(class1, class2) return process_response_fallback(message_text) def process_response_fallback(user_input: str) -> str: """ Fallback method from Younes' undergrad project, to be used for the cases not handled by Socio's logic. """ print("Processing request in fallback mode") message_text = user_input.lower() words = message_text.split(' ') # This logic is not always correct, eg "Add attribute in class." if contains_one_of(message_text, ADD_WORDS): for i in range(len(words) - 2): if words[i] in ADD_WORDS: # strip punctuation class_name = first_letter_uppercase(strip_punctuation(words[i + 2])) return add_class(class_name) if "has a" in message_text: for i in range(len(words) - 2): if words[i] == 'has': class_name = first_letter_uppercase(words[i - 1]) attribute_name = strip_punctuation(words[i + 2]) return add_attribute(class_name, attribute_name) if "is composed of" in message_text: for i in range(len(words) - 2): if words[i] == "is": whole_class_name = first_letter_uppercase(words[i - 1]) part_class_name = first_letter_uppercase(strip_punctuation(words[i + 3])) # assume the plural when part_class_name ends with s if part_class_name[-1] == "s": part_class_name = part_class_name[:-1] return create_composition(whole_class_name, part_class_name) # not very useful, but good for testing if "is associated with" in message_text: for i in range(len(words) - 3): if words[i] == "is": class_name1 = first_letter_uppercase(words[i - 1]) if words[i + 3] in ["a", "an"]: class_name2 = words[i + 4] else: class_name2 = words[i + 3] class_name2 = first_letter_uppercase(strip_punctuation(class_name2)) return create_association(class_name1, class_name2) if "is a" in message_text: for i in range(len(words) - 2): if words[i] == "is": child = first_letter_uppercase(words[i - 1]) parent = first_letter_uppercase(strip_punctuation(words[i + 2])) return create_inheritance(child, parent) return return_error_to_user("Sorry, I could not process your request :(") def get_intent(predicts): prediction = predicts[0] intents = np.array(keyIntent) ids = np.argsort(-prediction) intents = intents[ids] predictions = -np.sort(-prediction) return intents[np.argmax(predictions)] # These functions are kept here since they modify the global state def add_class(class_name: str) -> str: global classes_created if class_name in classes_created: return return_error_to_user(f"{class_name} is already created, so let's not make it again.") return add_class_json(class_name) def reset_classes_created(): global classes_created classes_created = []

37.304348

115

0.671484

1,788

12,870

4.530201

0.134228

0.071975

0.031235

0.036914

0.611852

0.539012

0.475062

0.422469

0.37037

0.333827

0

0.008782

0.247941

12,870

344

116

37.412791

0.828081

0.083683

0

0.505882

0

0.003922

0.095396

0

0.002907

0

1

0.062745

false

0

0.035294

0.011765

0.313725

0.015686

0

null

0

null

0

1

0

56c81124f1aaadd1eefd7577b6f9a4ae2b4cf780

2,890

py

Python

quantum_machine_learning/qml_100_GeneratingFourierState_solution.py

zemarchezi/QHack2022

e388a546027168c3f1d48ad2e7ecf831425bb2dc

[ "CC0-1.0" ]

null

quantum_machine_learning/qml_100_GeneratingFourierState_solution.py

zemarchezi/QHack2022

e388a546027168c3f1d48ad2e7ecf831425bb2dc

[ "CC0-1.0" ]

null

quantum_machine_learning/qml_100_GeneratingFourierState_solution.py

zemarchezi/QHack2022

e388a546027168c3f1d48ad2e7ecf831425bb2dc

[ "CC0-1.0" ]

5

2022-03-16T00:02:24.000Z

2022-03-23T20:12:23.000Z

#! /usr/bin/python3 import sys from pennylane import numpy as np import pennylane as qml def generating_fourier_state(n_qubits, m): """Function which, given the number of qubits and an integer m, returns the circuit and the angles that generate the state QFT|m> following the above template. Args: - n_qubits (int): number of qubits in the circuit. - m (int): basis state that we generate. For example, for 'm = 3' and 'n_qubits = 4' we would generate the state QFT|0011> (3 in binary is 11). Returns: - (qml.QNode): circuit used to generate the state. - (list[float]): angles that generate the state QFT|m>. """ dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def circuit(angles): """This is the quantum circuit that we will use.""" # QHACK # # Add the template of the statement with the angles passed as an argument. for w in range(n_qubits): qml.Hadamard(wires=w) qml.RZ(angles[w],wires=w) # QHACK # # We apply QFT^-1 to return to the computational basis. # This will help us to see how well we have done. qml.adjoint(qml.QFT)(wires=range(n_qubits)) # We return the probabilities of seeing each basis state. return qml.probs(wires=range(n_qubits)) def error(angles): """This function will determine, given a set of angles, how well it approximates the desired state. Here it will be necessary to call the circuit to work with these results. """ probs = circuit(angles) # QHACK # # The return error should be smaller when the state m is more likely to be obtained. target=np.zeros(2**n_qubits) target[m]=1 loss=np.sum((target-probs)**2) return loss # QHACK # # This subroutine will find the angles that minimize the error function. # Do not modify anything from here. opt = qml.AdamOptimizer(stepsize=0.8) epochs = 5000 angles = np.zeros(n_qubits, requires_grad=True) for epoch in range(epochs): angles = opt.step(error, angles) angles = np.clip(opt.step(error, angles), -2 * np.pi, 2 * np.pi) return circuit, angles if __name__ == "__main__": # DO NOT MODIFY anything in this code block inputs = sys.stdin.read().split(",") n_qubits = int(inputs[0]) m = int(inputs[1]) output = generating_fourier_state(n_qubits, m) output[0](output[1]) dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def check_with_arbitrary_state(): for i in range(n_qubits): qml.RY(i, wires=i) for op in output[0].qtape.operations: qml.apply(op) return qml.state() print(",".join([f"{p.real.round(5)},{p.imag.round(5)}" for p in check_with_arbitrary_state()]))

32.111111

126

0.63045

428

2,890

4.182243

0.376168

0.050838

0.035754

0.031844

0.141899

0.122905

0.089385

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0

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0.264014

2,890

89

127

32.47191

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0.075

0

0.275

0.025

0

null

0

null

0

1

0

56cb8fcd45f5672fe2b1eb0a6363664189af573d

2,004

py

Python

Class08.py

Kiran1178/Python201901

8f387c9ed451a8b0cf5c20e9d8f6ae53cafaf0df

[ "MIT" ]

null

Class08.py

Kiran1178/Python201901

8f387c9ed451a8b0cf5c20e9d8f6ae53cafaf0df

[ "MIT" ]

null

Class08.py

Kiran1178/Python201901

8f387c9ed451a8b0cf5c20e9d8f6ae53cafaf0df

[ "MIT" ]

null

# # # #### # import_os.path as os_path from os import path, makedirs # ######################### # 1) Python Absoulute path # ######################## # # current absolute path # file_path = r"c:\repos\Library" # current_file_path = path.abspath(__file__) # print(current_file_path) # print(path.dirname(current_file_path)) # print(path.basename(current_file_path)) # Get current directory current_directory = path.dirname(path.abspath(__file__)) # print(current_directory) # Concat file path jason_file_path = path.join( current_directory, 'test_demo', 'jason_file', 'parse_jason_dat.jason' ) # if path.exists(jason_file_path): # print("hello JSON") # xml_file_path = path.join( current_directory, 'test_demo', 'xml_file', 'parse_xml_data.xml' ) # # if path.exists(xml_file_path): # print("hello XML") # text_file_path = path.join( current_directory, 'test_demo', 'xml_file', 'parse_xml_data.xml' ) # print("hello text") # CSV_file_path = path.join( current_directory, 'test_demo', 'xml_file', 'parse_xml_data.xml' ) # print("hello csv") # # # class_09 = path.join( # current_directory, 'test_demo', 'class_09', 'test_dr', 'whynot dr' # ) # print(class_09) # if not path.exists(path.dirname(text_file_path)): makedirs(path.dirname(text_file_path)) file_data = "This is my classo9 file, which is created for test purpose," with open(text_file_path, 'w+') as text_file: text_file.writelines(file_data) from pprint import pprint # with open(text_file_path, 'r+') as text_file_read: # data = text_file_read.readlines() # pprint(data, width=120/ # if path.exists(text_file_path): # print("exists") # with open(text_file_path, 'r+') as text_file_read: # for line in text_file_read: # print(line.replace("\n", '')) def generator_parse_file(file_path): with open(file_path, 'r+') as text_file: for line in text_file: yield line.replace("\n", '') for i in generator_parse_file(text_file_path): print(i) #

21.094737

73

0.683633

290

2,004

4.403448

0.231034

0.131558

0.075176

0.09397

0.440094

0.287392

0.247455

0.216132

0

0.006501

0.155689

2,004

94

74

21.319149

0.748227

0.428643

0

0.115385

0

0.201149

0.020115

0

1

0.038462

false

0

0.076923

0

0.115385

0.076923

0

null

0

null

0

1

0

56ccc4b8eb0e2fdef41c04fd52d0622b0ed55475

542

py

Python

zoo/migrations/0002_animal_size.py

5akusei/test-project-django

c8a7108a5872dc9e396d48a59541c39dd8246f5c

[ "MIT" ]

null

zoo/migrations/0002_animal_size.py

5akusei/test-project-django

c8a7108a5872dc9e396d48a59541c39dd8246f5c

[ "MIT" ]

null

zoo/migrations/0002_animal_size.py

5akusei/test-project-django

c8a7108a5872dc9e396d48a59541c39dd8246f5c

[ "MIT" ]

null

# Generated by Django 4.0.3 on 2022-03-21 00:10 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('animal_size', '0001_initial'), ('zoo', '0001_initial'), ] operations = [ migrations.AddField( model_name='animal', name='size', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='animal_size.animalsize', to_field='name'), ), ]

25.809524

151

0.632841

65

542

5.153846

0.6

0.071642

0.083582

0.131343

0

0.055422

0.234317

542

20

152

27.1

0.751807

0.083026

0

1

0

0.149495

0.044444

0

1

0

false

0

0.142857

0

0.357143

0

null

0

1

0

null

0

1

56ccf9b464e87b0ee37675d5598960af66f6aaee

996

py

Python

explicalib/calibration/evaluation/diagrams/binary/binary_calibration_error_curve.py

euranova/estimating_eces

9bfa81dd7a39ebe069c5b11b8e7a9bf9017e9350

[ "MIT" ]

2

2021-11-30T18:44:11.000Z

2021-11-30T18:44:19.000Z

explicalib/calibration/evaluation/diagrams/binary/binary_calibration_error_curve.py

euranova/estimating_eces

9bfa81dd7a39ebe069c5b11b8e7a9bf9017e9350

[ "MIT" ]

null

explicalib/calibration/evaluation/diagrams/binary/binary_calibration_error_curve.py

euranova/estimating_eces

9bfa81dd7a39ebe069c5b11b8e7a9bf9017e9350

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @author: nicolas.posocco """ from .binary_reliability_curve import binary_reliability_curve def binary_calibration_error_curve(model=None, X=None, Y=None, kernel=None, bandwidth=None, positive_scores=None, positive_scores_for_positive_gt=None, positive_class_probability=None): reliability_curve = binary_reliability_curve(model=model, X=X, Y=Y, kernel=kernel, bandwidth=bandwidth, positive_scores=positive_scores, positive_scores_for_positive_gt=positive_scores_for_positive_gt, positive_class_probability=positive_class_probability) result = {"scores": reliability_curve["scores"], } return result

39.84

113

0.5251

84

996

5.845238

0.333333

0.171079

0.13442

0.152749

0.181263

0.126273

0

0.001698

0.408635

996

24

114

41.5

0.831919

0.047189

0

0.012752

0

1

0.071429

false

0

0.071429

0

0.214286

0

null

0

null

0

1

0

56cdb1a4bb76205dcc32cb83ce84f25a331f0228

217

py

Python

coast/timeseries.py

British-Oceanographic-Data-Centre/NEMO-ENTRUST

41ed278e56428404ab8ec41d74a9a3a761e308ae

[ "MIT" ]

null

coast/timeseries.py

British-Oceanographic-Data-Centre/NEMO-ENTRUST

41ed278e56428404ab8ec41d74a9a3a761e308ae

[ "MIT" ]

null

coast/timeseries.py

British-Oceanographic-Data-Centre/NEMO-ENTRUST

41ed278e56428404ab8ec41d74a9a3a761e308ae

[ "MIT" ]

null

"""Timeseries Class""" from .index import Indexed from . import general_utils class Timeseries(Indexed): """Parent class for Tidegauge and other timeseries type datasets Common methods ... """ pass

18.083333

68

0.700461

25

217

6.04

0.72

0

0.207373

217

11

69

19.727273

0.877907

0.447005

0

1

0

true

0.25

0.5

0

0.75

0

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null

0

null

0

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0

1

0

5

56cefc1f7836af65100b50a748c1af6718286e94

8,714

py

Python

bayesmark/np_util.py

goncaloperes/bayesmark

8c420e935718f0d6867153b781e58943ecaf2338

[ "Apache-2.0" ]

102

2019-09-27T02:38:52.000Z

2022-03-12T13:31:11.000Z

bayesmark/np_util.py

goncaloperes/bayesmark

8c420e935718f0d6867153b781e58943ecaf2338

[ "Apache-2.0" ]

17

2019-10-07T18:20:21.000Z

2022-01-03T08:19:16.000Z

bayesmark/np_util.py

goncaloperes/bayesmark

8c420e935718f0d6867153b781e58943ecaf2338

[ "Apache-2.0" ]

34

2019-09-27T02:38:31.000Z

2022-02-09T21:32:25.000Z

# Copyright (c) 2019 Uber Technologies, 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. """Utilities to that could be included in `numpy` but aren't. """ import numpy as np # np seed must be in [0, 2**32 - 1] = [0, uint32 max] SEED_MAX_INCL = np.iinfo(np.uint32).max # Access default numpy rng in way that is short and sphinx friendly random = np.random.random.__self__ def random_seed(random=random): """Draw a random seed compatible with :class:`numpy:numpy.random.RandomState`. Parameters ---------- random : :class:`numpy:numpy.random.RandomState` Random stream to use to draw the random seed. Returns ------- seed : int Seed for a new random stream in ``[0, 2**32-1)``. """ # np randint is exclusive on the high value, py randint is inclusive. We # must use inclusive limit here to work with both. We are missing one # possibility here (2**32-1), but I don't think that matters. seed = random.randint(0, SEED_MAX_INCL) return seed def shuffle_2d(X, random=random): """Generalization of :func:`numpy:numpy.random.shuffle` of 2D array. Performs in-place shuffling of `X`. So, it has no return value. Parameters ---------- X : :class:`numpy:numpy.ndarray` of shape (n, m) Array-like 2D data to shuffle in place. Shuffles order of rows and order of elements within a row. random : :class:`numpy:numpy.random.RandomState` Random stream to use to draw the random seed. """ random.shuffle(X) for rr in X: random.shuffle(rr) def strat_split(X, n_splits, inplace=False, random=random): """Make a stratified random split of items. Parameters ---------- X : :class:`numpy:numpy.ndarray` of shape (n, m) Data we would like to split randomly into groups. We should get the same number +/-1 of elements from each row in each group. n_splits : int How many groups we want to split into. inplace : bool If true, this function will cause in place modifications to `X`. random : :class:`numpy:numpy.random.RandomState` Random stream to use for reproducibility. Returns ------- Y : list(:class:`numpy:numpy.ndarray`) Stratified split of `X` where each row of `Y` contains the same number +/-1 of elements from each row of `X`. Must be a list of arrays since each row may have a different length. """ # Arguably, this function could go in stats assert np.ndim(X) == 2 assert n_splits > 0 if not inplace: X = np.array(X, copy=True) shuffle_2d(X, random=random) # Note this is like X.T.ravel() Y = np.array_split(np.ravel(X, order="F"), n_splits) # Just for good measure make sure this is shuffled too, prob not needed. shuffle_2d(Y, random=random) return Y def isclose_lte(x, y): """Check that less than or equal to (lte, ``x <= y``) is approximately true between all elements of `x` and `y`. This is similar to :func:`numpy:numpy.allclose` for equality. Shapes of all input variables must be broadcast compatible. Parameters ---------- x : :class:`numpy:numpy.ndarray` Lower limit in ``<=`` check. y : :class:`numpy:numpy.ndarray` Upper limit in ``<=`` check. Returns ------- lte : bool True if ``x <= y`` is approximately true element-wise. """ # Use np.less_equal to ensure always np type consistently lte = np.less_equal(x, y) | np.isclose(x, y) return lte def clip_chk(x, lb, ub, allow_nan=False): """Clip all element of `x` to be between `lb` and `ub` like :func:`numpy:numpy.clip`, but also check :func:`numpy:numpy.isclose`. Shapes of all input variables must be broadcast compatible. Parameters ---------- x : :class:`numpy:numpy.ndarray` Array containing elements to clip. lb : :class:`numpy:numpy.ndarray` Lower limit in clip. ub : :class:`numpy:numpy.ndarray` Upper limit in clip. allow_nan : bool If true, we allow ``nan`` to be present in `x` without out raising an error. Returns ------- x : :class:`numpy:numpy.ndarray` An array with the elements of `x`, but where values < `lb` are replaced with `lb`, and those > `ub` with `ub`. """ assert np.all(lb <= ub) # np.clip does not do this check x = np.asarray(x) # These are asserts not exceptions since clip_chk most used internally. if allow_nan: assert np.all(isclose_lte(lb, x) | np.isnan(x)) assert np.all(isclose_lte(x, ub) | np.isnan(x)) else: assert np.all(isclose_lte(lb, x)) assert np.all(isclose_lte(x, ub)) x = np.clip(x, lb, ub) return x def snap_to(x, fixed_val=None): """Snap input `x` to the `fixed_val` unless `fixed_val` is `None`, where `x` is returned. Parameters ---------- x : :class:`numpy:numpy.ndarray` Array containing elements to snap. fixed_val : :class:`numpy:numpy.ndarray` or None Values to be returned if `x` is close, otherwise an error is raised. If `fixed_val` is `None`, `x` is returned. Returns ------- fixed_val : :class:`numpy:numpy.ndarray` Snapped to value of `x`. """ if fixed_val is None: return x # Include == for discrete types where allclose doesn't work if not (np.all(x == fixed_val) or np.allclose(x, fixed_val)): raise ValueError("Expected fixed value %s, got %s." % (repr(fixed_val), repr(x))) assert np.all(x == fixed_val) or np.allclose(x, fixed_val) fixed_val = np.broadcast_to(fixed_val, np.shape(x)) return fixed_val def linear_rescale(X, lb0, ub0, lb1, ub1, enforce_bounds=True): """Linearly transform all elements of `X`, bounded between `lb0` and `ub0`, to be between `lb1` and `ub1`. Shapes of all input variables must be broadcast compatible. Parameters ---------- X : :class:`numpy:numpy.ndarray` Array containing elements to rescale. lb0 : :class:`numpy:numpy.ndarray` Current lower bound of `X`. ub0 : :class:`numpy:numpy.ndarray` Current upper bound of `X`. lb1 : :class:`numpy:numpy.ndarray` Desired lower bound of `X`. ub1 : :class:`numpy:numpy.ndarray` Desired upper bound of `X`. enforce_bounds : bool If True, perform input bounds check (and clipping if slight violation) on the input `X` and again on the output. This argument is not meant to be vectorized like the other input variables. Returns ------- X : :class:`numpy:numpy.ndarray` Elements of input `X` after linear rescaling. """ assert np.all(np.isfinite(lb0)) assert np.all(np.isfinite(lb1)) assert np.all(np.isfinite(ub0)) assert np.all(np.isfinite(ub1)) assert np.all(lb0 < ub0) assert np.all(lb1 <= ub1) m = np.true_divide(ub1 - lb1, ub0 - lb0) assert np.all(m >= 0) if enforce_bounds: X = clip_chk(X, lb0, ub0) # This will flag any non-finite X input. X = clip_chk(m * (X - lb0) + lb1, lb1, ub1) else: X = m * (X - lb0) + lb1 return X def argmin_2d(X): """Take the arg minimum of a 2D array.""" assert X.size > 0, "argmin of empty array not defined" ii, jj = np.unravel_index(X.argmin(), X.shape) return ii, jj def cummin(x_val, x_key): """Get the cumulative minimum of `x_val` when ranked according to `x_key`. Parameters ---------- x_val : :class:`numpy:numpy.ndarray` of shape (n, d) The array to get the cumulative minimum of along axis 0. x_key : :class:`numpy:numpy.ndarray` of shape (n, d) The array for ranking elements as to what is the minimum. Returns ------- c_min : :class:`numpy:numpy.ndarray` of shape (n, d) The cumulative minimum array. """ assert x_val.shape == x_key.shape assert x_val.ndim == 2 assert not np.any(np.isnan(x_key)), "cummin not defined for nan key" n, _ = x_val.shape xm = np.minimum.accumulate(x_key, axis=0) idx = np.maximum.accumulate((x_key <= xm) * np.arange(n)[:, None]) c_min = np.take_along_axis(x_val, idx, axis=0) return c_min

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null

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1

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56cf21dd91586a112ff72573b3ee25e290d3bbe0

5,141

py

Python

kra/analytics/container.py

smpio/kube-resource-analyzer

d214b7c32bc5e404ac951f66cf0914fcae1f580f

[ "MIT" ]

null

kra/analytics/container.py

smpio/kube-resource-analyzer

d214b7c32bc5e404ac951f66cf0914fcae1f580f

[ "MIT" ]

50

2021-05-26T14:15:09.000Z

2021-07-24T12:08:14.000Z

kra/analytics/container.py

smpio/kube-resource-analyzer

d214b7c32bc5e404ac951f66cf0914fcae1f580f

[ "MIT" ]

null

from kra import models def get_containers_summary(container_ids=None): if container_ids is not None: container_ids_str = ','.join(str(cid) for cid in container_ids) container_filter = f'AND id IN ({container_ids_str})' else: container_filter = '' return models.Container.objects.raw(r""" SELECT * FROM %(container_tblname)s AS c LEFT JOIN LATERAL ( SELECT * FROM ( SELECT since, till, total_seconds, total_cpu_m_seconds, max_memory_mi, max_cpu_m, total_memory_mi_seconds, (total_memory_mi_seconds / total_seconds) AS avg_memory_mi, (total_cpu_m_seconds / total_seconds) AS avg_cpu_m FROM ( SELECT *, extract(epoch FROM (till - since)) AS total_seconds FROM ( SELECT c.started_at AS since, max(measured_at) AS till, max(cpu_m) AS max_cpu_m, max(cpu_m_seconds) AS total_cpu_m_seconds, max(memory_mi) AS max_memory_mi, sum(delta_memory_mi_seconds) AS total_memory_mi_seconds FROM ( SELECT *, memory_mi * delta_seconds AS delta_memory_mi_seconds, delta_cpu_m_seconds / delta_seconds AS cpu_m FROM ( SELECT measured_at, cpu_m_seconds, memory_mi, ( CASE WHEN lag(measured_at) OVER w IS NOT NULL THEN extract(epoch FROM (measured_at - lag(measured_at) OVER w)) ELSE extract(epoch FROM (measured_at - c.started_at)) END ) AS delta_seconds, ( CASE WHEN lag(cpu_m_seconds) OVER w IS NOT NULL THEN cpu_m_seconds - lag(cpu_m_seconds) OVER w ELSE cpu_m_seconds END ) AS delta_cpu_m_seconds FROM %(ru_tblname)s WHERE container_id = c.id WINDOW w AS (ORDER BY measured_at) ) AS pass1q0 ) AS pass1q1 ) AS pass1q2 ) AS pass1q3 ) AS pass1 LEFT JOIN LATERAL ( SELECT sqrt(total_stddev_memory_mi2_seconds / total_seconds) AS stddev_memory_mi, sqrt(total_stddev_cpu_m2_seconds / total_seconds) AS stddev_cpu_m FROM ( SELECT sum(stddev_memory_mi2_seconds) AS total_stddev_memory_mi2_seconds, sum(stddev_cpu_m2_seconds) AS total_stddev_cpu_m2_seconds FROM ( SELECT ((memory_mi - avg_memory_mi)^2 * delta_seconds) AS stddev_memory_mi2_seconds, ((delta_cpu_m_seconds / delta_seconds - avg_cpu_m)^2 * delta_seconds) AS stddev_cpu_m2_seconds FROM ( SELECT cpu_m_seconds, memory_mi, ( CASE WHEN lag(measured_at) OVER w IS NOT NULL THEN extract(epoch FROM (measured_at - lag(measured_at) OVER w)) ELSE extract(epoch FROM (measured_at - c.started_at)) END ) AS delta_seconds, ( CASE WHEN lag(cpu_m_seconds) OVER w IS NOT NULL THEN cpu_m_seconds - lag(cpu_m_seconds) OVER w ELSE cpu_m_seconds END ) AS delta_cpu_m_seconds FROM %(ru_tblname)s WHERE container_id = c.id WINDOW w AS (ORDER BY measured_at) ) AS pass2q0 ) AS pass2q1 ) AS pass2q2 ) AS pass2 ON TRUE ) AS summary ON TRUE WHERE total_seconds IS NOT NULL %(container_filter)s """ % { 'container_tblname': models.Container._meta.db_table, 'ru_tblname': models.ResourceUsage._meta.db_table, 'container_filter': container_filter, })

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null

0

1

0

3

56d09b11ed25ec6017ed1280c06b4a542229e329

9,712

py

Python

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

null

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

null

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

2

2021-08-25T08:14:52.000Z

2021-11-29T12:56:17.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 3 15:47:19 2018 @author: Yann Roussel and Tuan Bui Editted by: Emine Topcu on Oct 2021 """ from random import gauss from Beat_and_glide import Beat_and_glide_base from Izhikevich_class import Izhikevich_9P, Leaky_Integrator class Beat_and_glide_with_sigmas(Beat_and_glide_base): sigmaD = 0 sigmaL = 0 sigmaP = 0 sigmaW = 0 def __init__ (self, stim0 = 2.89, sigma = 0, sigma_LR = 0.1, sigmaD = 0, sigmaL = 0, sigmaP = 0, sigmaW = 0, E_glu = 0, E_gly = -70, cv = 0.80, nMN = 15, ndI6 = 15, nV0v = 15, nV2a = 15, nV1 = 15, nMuscle = 15, R_str = 1.0): super().__init__(stim0, sigma, sigma_LR, E_glu, E_gly, cv, nMN, ndI6, nV0v, nV2a, nV1, nMuscle, R_str) self.sigmaD = sigmaD self.sigmaL = sigmaL self.sigmaP = sigmaP self.sigmaW = sigmaW def initNeurons(self): ## Declare Neuron Types self.L_MN = [ Izhikevich_9P(a = 0.5*gauss(1, self.sigmaP), b = 0.01*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 100*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -65*gauss(1, self.sigmaP), vt = -58*gauss(1, self.sigmaP), k = 0.5*gauss(1, self.sigmaP), Cm = 20*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.0+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nMN)] self.R_MN = [ Izhikevich_9P(a = 0.5*gauss(1, self.sigmaP), b = 0.01*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 100*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -65*gauss(1, self.sigmaP), vt = -58*gauss(1, self.sigmaP), k = 0.5*gauss(1, self.sigmaP), Cm = 20*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.0+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nMN)] self.L_dI6 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.ndI6)] self.R_dI6 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.ndI6)] self.L_V0v = [ Izhikevich_9P(a = 0.01*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 2*gauss(1, self.sigmaP), vmax = 8*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV0v)] self.R_V0v = [ Izhikevich_9P(a = 0.01*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 2*gauss(1, self.sigmaP), vmax = 8*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV0v)] self.L_V2a = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV2a)] self.R_V2a = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV2a)] self.L_V1 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 7.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV1)] self.R_V1 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 7.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV1)] self.L_Muscle = [ Leaky_Integrator(1.0, 3.0, self.getdt(), 5.0+1.6*i,-1) for i in range(self.nMuscle)] self.R_Muscle = [ Leaky_Integrator(1.0, 3.0, self.getdt(), 5.0+1.6*i, 1) for i in range(self.nMuscle)] def getStimulus(self, t): if t > 2000: # Let the initial conditions dissipate for the first 200 ms return self.stim0 * gauss(1, self.sigmaD) return 0 def rangeNoiseMultiplier(self): return gauss(1, self.sigmaL) def weightNoiseMultiplier(self): return gauss(1, self.sigmaW)

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8

56d2471cad9e5b70a2a31907d09626de3051974a

183

py

Python

__init__.py

jobscry/vz-blog

de968541a0412d5ce8f09c1ba638261a9f9151f1

[ "MIT" ]

3

2016-01-29T09:31:15.000Z

2016-05-08T19:33:23.000Z

__init__.py

jobscry/vz-blog

de968541a0412d5ce8f09c1ba638261a9f9151f1

[ "MIT" ]

null

__init__.py

jobscry/vz-blog

de968541a0412d5ce8f09c1ba638261a9f9151f1

[ "MIT" ]

null

# -*- mode: python; coding: utf-8; -*- VERSION = (1, 3, 3) __version__ = '.'.join(map(str, VERSION)) __author__ = 'Joe Vasquez' __email__ = 'joe.vasquez@gmail.com' __license__ = 'MIT'

30.5

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1

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null

0

2

56d2493a0437ee12a9fdd6e4cf50891d7f181b49

35,632

py

Python

mysite/patterns/35.py

BioinfoNet/prepub

e19c48cabf8bd22736dcef9308a5e196cfd8119a

[ "MIT" ]

19

2016-06-17T23:36:27.000Z

2020-01-13T16:41:55.000Z

mysite/patterns/35.py

BioinfoNet/prepub

e19c48cabf8bd22736dcef9308a5e196cfd8119a

[ "MIT" ]

13

2016-06-06T12:57:05.000Z

2019-02-05T02:21:00.000Z

patterns/35.py

OmnesRes/GRIMMER

173c99ebdb6a9edb1242d24a791d0c5d778ff643

[ "MIT" ]

7

2017-03-28T18:12:22.000Z

2021-06-16T09:32:59.000Z

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56d37c682b8fa36e0cc92147b67a5132d916883c

1,332

py

Python

Leetcode/74_search-a-2d-matrix.py

diekaltesonne/Contexts

064f61e84896852d6579675e2423537ee5bf8331

[ "MIT" ]

null

Leetcode/74_search-a-2d-matrix.py

diekaltesonne/Contexts

064f61e84896852d6579675e2423537ee5bf8331

[ "MIT" ]

null

Leetcode/74_search-a-2d-matrix.py

diekaltesonne/Contexts

064f61e84896852d6579675e2423537ee5bf8331

[ "MIT" ]

null

class Solution: def _search(self,l,r,x): if r >= l: mid = l + (r - l) // 2 # If element is present at the middle itself if self.nums[mid][-1] >= x and self.nums[mid][0] <=x: return self._search_small(0,len(self.nums[mid])-1,x,mid) elif self.nums[mid][-1] > x: return self._search(l, mid-1, x) # Else the element can only be present # in right subarray else: return self._search(mid + 1, r, x) else: return False def _search_small(self,l,r,x,a): if len(self.nums[a]) == 0 and x == 0: return False if r >= l: mid = l + (r - l) // 2 # If element is present at the middle itself if self.nums[a][mid] == x: return True elif self.nums[a][mid] > x: return self._search_small(l, mid-1, x,a) # Else the element can only be present # in right subarray else: return self._search_small(mid + 1, r, x,a) else: return False def searchMatrix(self, matrix: List[List[int]], target: int) -> bool: self.nums = matrix return self._search(0,len(self.nums)-1,target)

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56d420fdf0723b65174b088f7049275c4a6b46ff

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py

Python

src/SimulationControl/SpheralGnuPlotUtilities.py

jmikeowen/Spheral

3e1082a7aefd6b328bd3ae24ca1a477108cfc3c4

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

22

2018-07-31T21:38:22.000Z

2020-06-29T08:58:33.000Z

src/SimulationControl/SpheralGnuPlotUtilities.py

jmikeowen/Spheral

3e1082a7aefd6b328bd3ae24ca1a477108cfc3c4

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

41

2020-09-28T23:14:27.000Z

2022-03-28T17:01:33.000Z

src/SimulationControl/SpheralGnuPlotUtilities.py

jmikeowen/Spheral

3e1082a7aefd6b328bd3ae24ca1a477108cfc3c4

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

7

2019-12-01T07:00:06.000Z

2020-09-15T21:12:39.000Z

import Gnuplot import mpi from Spheral import * from math import * import numpy import os from SpheralTestUtilities import multiSort SpheralGnuPlotCache = [] from spheralDimensions import spheralDimensions dims = spheralDimensions() #------------------------------------------------------------------------------- # Define a dummy Gnuplot class, so that non-master processes can silently # and harmlessly accept Gnuplot commands. #------------------------------------------------------------------------------- class fakeGnuplot: def __init__(self): return def __call__(self, *arghs, **keyw): return def plot(self, *arghs, **keyw): return def replot(self, *arghs, **keyw): return def refresh(self, *arghs, **keyw): return def xlabel(self, *arghs, **keyw): return def ylabel(self, *arghs, **keyw): return def title(self, *arghs, **keyw): return def hardcopy(self, *arghs, **keyw): return def generateNewGnuPlot(persist = False): if mpi.rank == 0: result = Gnuplot.Gnuplot(persist = persist) if "GNUTERM" in os.environ.keys(): result("set term %s" % os.environ["GNUTERM"]) return result else: return fakeGnuplot() #------------------------------------------------------------------------------- # Since the default Gnuplot.py doesn't support png output, I'll add it here # myself. #------------------------------------------------------------------------------- def pngFile(plot, filename, color = 1, fontSize = "medium"): setLine = "set terminal png " + fontSize if color: setLine += " color" if filename[-4:] != ".png": filename += ".png" plot(setLine) plot.set_string("output", filename) plot.refresh() plot("set terminal x11") plot.set_string("output") return #------------------------------------------------------------------------------- # Calculate the radial velocity component, given a FieldList of positions # and a FieldList of velocities. #------------------------------------------------------------------------------- def radialVelocityFieldList(positions, velocities): dim = type(positions).__name__[-2:] radialVelocity = None fieldConstructor = None if dim == "1d": radialVelocity = ScalarFieldList1d() fieldConstructor = ScalarField1d elif dim == "2d": radialVelocity = ScalarFieldList2d() fieldConstructor = ScalarField2d elif dim == "3d": radialVelocity = ScalarFieldList3d() fieldConstructor = ScalarField3d radialVelocity.copyFields() for field in positions: radialVelocity.appendField(fieldConstructor("radial velocity", field.nodeList())) assert positions.numFields == velocities.numFields == radialVelocity.numFields for fieldID in xrange(positions.numFields): rfield = positions[fieldID] vfield = velocities[fieldID] vrfield = radialVelocity[fieldID] assert rfield.numElements == vfield.numElements == vrfield.numElements for nodeID in xrange(rfield.numElements): r = rfield[nodeID] v = vfield[nodeID] runit = r.unitVector() vrfield[nodeID] = v.dot(runit) return radialVelocity #------------------------------------------------------------------------------- # Calculate the azimuthal velocity component, given a FieldList of positions # and a FieldList of velocities. #------------------------------------------------------------------------------- def azimuthalVelocityFieldList(positions, velocities): dim = type(positions).__name__[-2:] azimuthalVelocity = None fieldConstructor = None if dim == "1d": azimuthalVelocity = ScalarFieldList1d() fieldConstructor = ScalarField1d elif dim == "2d": azimuthalVelocity = ScalarFieldList2d() fieldConstructor = ScalarField2d elif dim == "3d": azimuthalVelocity = ScalarFieldList3d() fieldConstructor = ScalarField3d azimuthalVelocity.copyFields() for field in positions: azimuthalVelocity.appendField(fieldConstructor("azimuthal velocity", field.nodeList())) assert positions.numFields == velocities.numFields == azimuthalVelocity.numFields for fieldID in xrange(positions.numFields): rfield = positions[fieldID] vfield = velocities[fieldID] vafield = azimuthalVelocity[fieldID] assert rfield.numElements == vfield.numElements == vafield.numElements for nodeID in xrange(rfield.numElements): r = rfield[nodeID] v = vfield[nodeID] raz = r.unitVector() x = raz.x y = raz.y raz.x = -y raz.y = x vafield[nodeID] = v.dot(raz) return azimuthalVelocity #------------------------------------------------------------------------------- # Helper method to determine the angular momentum per node. #------------------------------------------------------------------------------- def angularMomentum(mass, position, velocity): assert mass.numFields == position.numFields == velocity.numFields result = [] for massField, positionField, velocityField in zip(mass, position, velocity): assert (massField.nodeList().numInternalNodes == positionField.nodeList().numInternalNodes == velocityField.nodeList().numInternalNodes) for j in xrange(massField.nodeList().numInternalNodes): result.append((positionField[j].cross(velocityField[j]))*massField[j]) return result #------------------------------------------------------------------------------- # Plot a FieldList #------------------------------------------------------------------------------- def plotFieldList(fieldList, xFunction = "%s.x", yFunction = "%s", plotGhosts = False, colorNodeLists = False, plot = None, userXRange = [None, None], userYRange = [None, None], plotStyle = "lines", lineStyle = "linetype -1 linewidth 1 pointtype 4 pointsize 1.0", winTitle = None, lineTitle = "", xlabel = None, ylabel = None, filterFunc = None): if plot is None: plot = generateNewGnuPlot() SpheralGnuPlotCache.append(plot) def nullFilter(pos): return True if filterFunc is None: filterFunc = nullFilter # Gather the fieldList info across all processors to process 0. globalNumNodes = [] globalX = [] globalY = [] for field in fieldList: if plotGhosts: xvals = field.nodeList().positions().allValues() yvals = field.allValues() else: xvals = field.nodeList().positions().internalValues() yvals = field.internalValues() localX = [] localY = [] for x, y in zip(xvals, yvals): if filterFunc(x): localX.append(eval(xFunction % "x")) localY.append(eval(yFunction % "y")) n = len(localX) if mpi: globalNumNodes.append(mpi.allreduce(n, mpi.SUM)) globalX.extend(mpi.allreduce(localX, mpi.SUM)) globalY.extend(mpi.allreduce(localY, mpi.SUM)) else: globalNumNodes.append(n) globalX.extend(localX) globalY.extend(localY) if mpi.rank == 0: # Find the total number of nodes. totalNumNodes = sum(globalNumNodes) assert(len(globalNumNodes) == fieldList.numFields) assert(len(globalX) == totalNumNodes) assert(len(globalY) == totalNumNodes) # Copy the input ranges, since for some reason these seem to have been # preserved between calls? xRange = userXRange[:] yRange = userYRange[:] # Set the line style ## plot("set linestyle 1 " + lineStyle) # Set the labels. if winTitle: plot.title(winTitle) if xlabel: plot.xlabel(xlabel) if ylabel: plot.ylabel(ylabel) # Set the ranges. xmin = 1e30 xmax = -1e30 ymin = 1e30 ymax = -1e30 for x in globalX: xmin = min(xmin, x) xmax = max(xmax, x) for y in globalY: ymin = min(ymin, y) ymax = max(ymax, y) if xmin == xmax: xmin = xmin - 0.5 xmax = xmax + 0.5 if ymin == ymax: ymin = ymin - 0.5 ymax = ymax + 0.5 if xRange[0] == None: xRange[0] = xmin if xRange[1] == None: xRange[1] = xmax if yRange[0] == None: yRange[0] = ymin - 0.05*max(1e-5, ymax - ymin) if yRange[1] == None: yRange[1] = ymax + 0.05*max(1e-5, ymax - ymin) plot("set xrange [%f:%f]" % tuple(xRange)) plot("set yrange [%f:%f]" % tuple(yRange)) # Finally, loop over the fields and do the deed. assert(len(globalX) == len(globalY)) if colorNodeLists: legendNodeList = {} for i in xrange(fieldList.numFields): legendNodeList[i] = lineTitle + ": " + fieldList[i].nodeList().name cumulativeNumNodes = 0 for fieldID in xrange(len(globalNumNodes)): n = globalNumNodes[fieldID] iNodeList = fieldID % fieldList.numFields x = numpy.array(globalX[cumulativeNumNodes: cumulativeNumNodes + n]) y = numpy.array(globalY[cumulativeNumNodes: cumulativeNumNodes + n]) if n: ## plot("set linestyle %i lt %i pt %i" % (iNodeList + 1, ## iNodeList + 1, ## iNodeList + 1)) legend = legendNodeList[iNodeList] legendNodeList[iNodeList] = None data = Gnuplot.Data(x, y, with_ = plotStyle + " lt %i" % iNodeList, title = legend, inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) cumulativeNumNodes += n else: x = numpy.array(globalX) y = numpy.array(globalY) data = Gnuplot.Data(x, y, with_ = plotStyle + " lt -1 pt 3", title = lineTitle, inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) lineTitle = None # That's it, return the Gnuplot object. mpi.barrier() return plot #------------------------------------------------------------------------------- # Plot the mass density, velocity, pressure, and smoothing scale for the fluid # node lists in the given data base. Implicitly assuming 1-D. #------------------------------------------------------------------------------- def plotState(thingus, plotGhosts = False, colorNodeLists = False, plotStyle = "points", xFunction = "%s.x", vecyFunction = "%s.x", tenyFunction = "%s.xx ** -1", lineTitle = "Simulation", filterFunc = None): dim = type(thingus).__name__[-2:] if isinstance(thingus, eval("State%s" % dim)): rho = thingus.scalarFields(HydroFieldNames.massDensity) vel = thingus.vectorFields(HydroFieldNames.velocity) eps = thingus.scalarFields(HydroFieldNames.specificThermalEnergy) P = thingus.scalarFields(HydroFieldNames.pressure) H = thingus.symTensorFields(HydroFieldNames.H) else: assert isinstance(thingus, eval("DataBase%s" % dim)) rho = thingus.fluidMassDensity vel = thingus.fluidVelocity eps = thingus.fluidSpecificThermalEnergy P = thingus.newFluidScalarFieldList(0.0, "pressure") thingus.fluidPressure(P) H = thingus.fluidHfield rhoPlot = plotFieldList(rho, xFunction = xFunction, plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = plotStyle, winTitle = "Mass Density", lineTitle = lineTitle, xlabel="x", filterFunc = filterFunc) velPlot = plotFieldList(vel, xFunction = xFunction, yFunction = vecyFunction, plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = plotStyle, winTitle = "Velocity", lineTitle = lineTitle, xlabel="x", filterFunc = filterFunc) epsPlot = plotFieldList(eps, xFunction = xFunction, plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = plotStyle, winTitle = "Specific Thermal Energy", lineTitle = lineTitle, xlabel="x", filterFunc = filterFunc) PPlot = plotFieldList(P, xFunction = xFunction, plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = plotStyle, winTitle = "Pressure", lineTitle = lineTitle, xlabel="x", filterFunc = filterFunc) HPlot = plotFieldList(H, xFunction = xFunction, yFunction = tenyFunction, plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = plotStyle, winTitle = "Smoothing scale", lineTitle = lineTitle, xlabel="x", filterFunc = filterFunc) return rhoPlot, velPlot, epsPlot, PPlot, HPlot #------------------------------------------------------------------------------- # Plot the state vs. radius #------------------------------------------------------------------------------- def plotRadialState(dataBase, plotGhosts = False, colorNodeLists = False, lineTitle = "Simulation", filterFunc = None): rhoPlot = plotFieldList(dataBase.fluidMassDensity, xFunction = "%s.magnitude()", plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = "points", winTitle = "Mass density", lineTitle = lineTitle, xlabel = "r", filterFunc = filterFunc) radialVelocity = radialVelocityFieldList(dataBase.fluidPosition, dataBase.fluidVelocity) velPlot = plotFieldList(radialVelocity, xFunction = "%s.magnitude()", plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = "points", winTitle = " Radial Velocity", lineTitle = lineTitle, xlabel = "r", filterFunc = filterFunc) epsPlot = plotFieldList(dataBase.fluidSpecificThermalEnergy, xFunction = "%s.magnitude()", plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = "points", winTitle = "Specific Thermal Energy", lineTitle = lineTitle, xlabel = "r", filterFunc = filterFunc) fluidPressure = dataBase.newFluidScalarFieldList(0.0, "pressure") dataBase.fluidPressure(fluidPressure) PPlot = plotFieldList(fluidPressure, xFunction = "%s.magnitude()", plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = "points", winTitle = "Pressure", lineTitle = lineTitle, xlabel = "r", filterFunc = filterFunc) HPlot = plotFieldList(dataBase.fluidHfield, xFunction = "%s.magnitude()", yFunction = "%s.xx**-1", plotGhosts = plotGhosts, colorNodeLists = colorNodeLists, plotStyle = "points", winTitle = "Smoothing scale", lineTitle = lineTitle, xlabel = "r", filterFunc = filterFunc) return rhoPlot, velPlot, epsPlot, PPlot, HPlot #------------------------------------------------------------------------------- # Overplot the answer on results from plotState. #------------------------------------------------------------------------------- def plotAnswer(answerObject, time, rhoPlot = None, velPlot = None, epsPlot = None, PPlot = None, APlot = None, HPlot = None, x = None): try: x, v, u, rho, P, h = answerObject.solution(time, x) A = None except: try: x, v, u, rho, P, A, h = answerObject.solution(time, x) except: x, v, u, rho, P = answerObject.solution(time, x) A = None h = None if rhoPlot is not None: data = Gnuplot.Data(x, rho, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) rhoPlot.replot(data) if velPlot is not None: data = Gnuplot.Data(x, v, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) velPlot.replot(data) if epsPlot is not None: data = Gnuplot.Data(x, u, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) epsPlot.replot(data) if PPlot is not None: data = Gnuplot.Data(x, P, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) PPlot.replot(data) if APlot is not None and A: data = Gnuplot.Data(x, A, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) APlot.replot(data) if HPlot is not None: data = Gnuplot.Data(x, h, with_="lines lt 7 lw 2", title="Solution", inline = True) SpheralGnuPlotCache.append(data) HPlot.replot(data) return #------------------------------------------------------------------------------- # Plot the node positions #------------------------------------------------------------------------------- def plotNodePositions2d(thingy, xFunction = "%s.x", yFunction = "%s.y", plotGhosts = False, colorNodeLists = True, colorDomains = False, title = "", style = "points", persist = None): assert colorNodeLists + colorDomains <= 1 if isinstance(thingy, DataBase2d): nodeLists = thingy.nodeLists() else: nodeLists = thingy # Gather the node positions across all domains. # Loop over all the NodeLists. xNodes = [] yNodes = [] for nodeList in nodeLists: if plotGhosts: pos = nodeList.positions().allValues() else: pos = nodeList.positions().internalValues() xNodes.append([eval(xFunction % "x") for x in pos]) yNodes.append([eval(yFunction % "x") for x in pos]) assert len(xNodes) == len(nodeLists) assert len(xNodes) == len(yNodes) globalXNodes = mpi.gather(xNodes) globalYNodes = mpi.gather(yNodes) if mpi.rank == 0: assert len(globalXNodes) == mpi.procs assert len(globalYNodes) == mpi.procs xlist, ylist = [], [] if colorDomains: for xDomain, yDomain in zip(globalXNodes, globalYNodes): assert len(xDomain) == len(nodeLists) assert len(yDomain) == len(nodeLists) xlist.append([]) ylist.append([]) for xx in xDomain: xlist[-1].extend(xx) for yy in yDomain: ylist[-1].extend(yy) assert len(xlist) == mpi.procs assert len(ylist) == mpi.procs elif colorNodeLists: for i in xrange(len(nodeLists)): xlist.append([]) ylist.append([]) for xDomain, yDomain in zip(globalXNodes, globalYNodes): assert len(xDomain) == len(nodeLists) assert len(yDomain) == len(nodeLists) for i in xrange(len(nodeLists)): xlist[i].extend(xDomain[i]) ylist[i].extend(yDomain[i]) assert len(xlist) == len(nodeLists) assert len(ylist) == len(nodeLists) else: xlist, ylist = [[]], [[]] for xDomain, yDomain in zip(globalXNodes, globalYNodes): print len(xDomain), len(nodeLists) assert len(xDomain) == len(nodeLists) assert len(yDomain) == len(nodeLists) for i in xrange(len(nodeLists)): xlist[0].extend(xDomain[i]) ylist[0].extend(yDomain[i]) plot = generateNewGnuPlot(persist = persist) plot("set size square") plot.title = title assert len(xlist) == len(ylist) for x, y in zip(xlist, ylist): data = Gnuplot.Data(x, y, with_ = style, inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) return plot else: return fakeGnuplot() #------------------------------------------------------------------------------- # Plot all the nodes in the given data base, and then color the control/ghost # nodes of the given boundary condition independently. #------------------------------------------------------------------------------- def plotBoundaryNodes(dataBase, boundary): # First build one set of position pairs for all of the nodes in the # data base. positions = [] for nodeList in dataBase.nodeLists(): for r in list(nodeList.positions())[:nodeList.numInternalNodes]: positions.append((r.x, r.y)) # Now build a list of the control node positions from the boundary # condition. controlPositions = [] for nodeList in dataBase.nodeLists(): controlNodes = boundary.controlNodes(nodeList) for nodeID in controlNodes: r = nodeList.positions()[nodeID] controlPositions.append((r.x, r.y)) # Now build a list of the ghost node positions from the boundary # condition. ghostPositions = [] for nodeList in dataBase.nodeLists(): ghostNodes = boundary.ghostNodes(nodeList) for nodeID in ghostNodes: r = nodeList.positions()[nodeID] ghostPositions.append((r.x, r.y)) # Finally we can plot these various sets of nodes. plot = plotXYTuples([positions, controlPositions, ghostPositions]) return plot #------------------------------------------------------------------------------- # Plot the given sequences of (x,y) pairs, each with a distinct color. # [ [(x0,y0), (x1,y1), ...], # [(x0,y0), (x1,y1), ...], # . # . # . # [(x0,y0), (x1,y1), ...] ] #------------------------------------------------------------------------------- def plotXYTuples(listOfXYTuples): # Find the (min,max) of X and Y for all sets. xmin, ymin, xmax, ymax = findPairMinMax(listOfXYTuples[0]) for seq in listOfXYTuples[1:]: xmin0, ymin0, xmax0, ymax0 = findPairMinMax(seq) xmin = min(xmin, xmin0) ymin = min(ymin, ymin0) xmax = max(xmax, xmax0) ymax = max(ymax, ymax0) # Create our plot result. plot = generateNewGnuPlot() plot("set size square") # Loop over the list of sequences of positions. icolor = 0 for seq in listOfXYTuples: icolor += 1 # Build the local arrays of x and y. x = numpy.array([0.0]*len(seq)) y = numpy.array([0.0]*len(seq)) for i in xrange(len(seq)): x[i] = seq[i][0] y[i] = seq[i][1] # Build the gnuplot data. data = Gnuplot.Data(x, y, with_ = "points", inline = True) SpheralGnuPlotCache.append(data) # Plot this set of data. ## plot("set linestyle %i lt %i pt 1" % (icolor, icolor)) plot.replot(data) # That"s it, return the plot. return plot #------------------------------------------------------------------------------- # Find the (min, max) of a set of pairs. #------------------------------------------------------------------------------- def findPairMinMax(listOfPairs): minX, minY = 1e90, 1e90 maxX, maxY = -1e90, -1e90 for pair in listOfPairs: minX = min(minX, pair[0]) minY = min(minY, pair[1]) maxX = max(maxX, pair[0]) maxY = max(maxY, pair[1]) return minX, minY, maxX, maxY #------------------------------------------------------------------------------- # Plot the velocity field as a set of arrows. # This is maintained here for backward compatibility, as a specialization of # plotVectorField2d. #------------------------------------------------------------------------------- def plotVelocityField2d(dataBase, plotGhosts = False, velMultiplier = 1.0, colorNodeLists = False, colorDomains = False, title = ""): return plotVectorField2d(dataBase, dataBase.globalVelocity, plotGhosts, velMultiplier, colorNodeLists, colorDomains, title) #------------------------------------------------------------------------------- # Plot the node spacing in 1D. #------------------------------------------------------------------------------- def plotNodeSpacing1d(dataBase): pos = dataBase.globalPosition xvals = [] for ifield in xrange(len(pos)): xvals += [pos[ifield][i].x for i in xrange(pos[ifield].numInternalElements)] xvals = mpi.allreduce(xvals, mpi.SUM) xvals.sort() deltas = [xvals[i+1] - xvals[i] for i in xrange(len(xvals) - 1)] + [xvals[-1] - xvals[-2]] plot = generateNewGnuPlot() d = Gnuplot.Data(xvals, deltas, with_="lines") plot.plot(d) return plot #------------------------------------------------------------------------------- # Plot an arbitrary vector field as a set of arrows. #------------------------------------------------------------------------------- def plotVectorField2d(dataBase, fieldList, plotGhosts = False, vectorMultiplier = 1.0, colorNodeLists = False, colorDomains = False, title = ""): assert colorNodeLists + colorDomains <= 1 # Gather the node positions and vectors across all domains. # Loop over all the NodeLists. localNumNodes = [] xNodes = [] yNodes = [] vxNodes = [] vyNodes = [] for i in xrange(dataBase.numNodeLists): nodeList = dataBase.nodeLists()[i] assert i < fieldList.numFields vectorField = fieldList[i] if plotGhosts: n = nodeList.numNodes else: n = nodeList.numInternalNodes localNumNodes.append(n) xNodes += numpy.array(map(lambda x: x.x, list(nodeList.positions())[:n])) yNodes += numpy.array(map(lambda x: x.y, list(nodeList.positions())[:n])) vxNodes += numpy.array(map(lambda x: x.x, list(vectorField)[:n]))*vectorMultiplier vyNodes += numpy.array(map(lambda x: x.y, list(vectorField)[:n]))*vectorMultiplier assert len(xNodes) == len(yNodes) == len(vxNodes) == len(vyNodes) numDomainNodes = [len(xNodes)] numNodesPerDomain = mpi.gather(numDomainNodes) globalNumNodes = mpi.gather(localNumNodes) globalXNodes = mpi.gather(xNodes) globalYNodes = mpi.gather(yNodes) globalVxNodes = mpi.gather(vxNodes) globalVyNodes = mpi.gather(vyNodes) if mpi.rank == 0: plot = generateNewGnuPlot() plot("set size square") plot.title = title if colorDomains: cumulativeN = 0 for domain in xrange(len(numNodesPerDomain)): n = numNodesPerDomain[domain] x = numpy.array(globalXNodes[cumulativeN:cumulativeN + n]) y = numpy.array(globalYNodes[cumulativeN:cumulativeN + n]) vx = numpy.array(globalVxNodes[cumulativeN:cumulativeN + n]) vy = numpy.array(globalVyNodes[cumulativeN:cumulativeN + n]) cumulativeN += n ## plot("set linestyle %i lt %i pt %i" % (domain + 1, ## domain + 1, ## domain + 1)) data = Gnuplot.Data(x, y, vx, vy, with_ = "vector ls %i" % (domain + 1), inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) elif colorNodeLists: cumulativeN = 0 for i in xrange(len(globalNumNodes)): n = globalNumNodes[i] if n > 0: iNodeList = i % dataBase.numNodeLists x = numpy.array(globalXNodes[cumulativeN:cumulativeN + n]) y = numpy.array(globalYNodes[cumulativeN:cumulativeN + n]) vx = numpy.array(globalVxNodes[cumulativeN:cumulativeN + n]) vy = numpy.array(globalVyNodes[cumulativeN:cumulativeN + n]) cumulativeN += n ## plot("set linestyle %i lt %i pt %i" % (iNodeList + 1, ## iNodeList + 1, ## iNodeList + 1)) data = Gnuplot.Data(x, y, vx, vy, with_ = "vector ls %i" % (iNodeList + 1), inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) else: x = numpy.array(globalXNodes) y = numpy.array(globalYNodes) vx = numpy.array(globalVxNodes) vy = numpy.array(globalVyNodes) data = Gnuplot.Data(x, y, vx, vy, with_ = "vector", inline = True) plot.replot(data) SpheralGnuPlotCache.append(data) return plot else: SpheralGnuPlotCache.append(data) #------------------------------------------------------------------------------- # Generate a regularly spaced sampling of the given FieldList # The answer is returned in a 2-D numpy array. #------------------------------------------------------------------------------- def gridSample(fieldList, zFunction = "%s", nx = 100, ny = 100, xmin = None, xmax = None, ymin = None, ymax = None): assert nx > 0 and ny > 0 # Set up our return value array. xValues = numpy.array([[0.0]*nx]*ny) yValues = numpy.array([[0.0]*nx]*ny) zValues = numpy.array([[0.0]*nx]*ny) # Gather the fieldList info across all processors to process 0. localNumNodes = [] localX = [] localY = [] for ifield in xrange(fieldList.numFields): field = fieldList[ifield] n = field.nodeList().numNodes localNumNodes.append(n) for r in field.nodeList().positions(): localX.append(r.x) localY.append(r.y) globalNumNodes = mpi.gather(localNumNodes) globalX = mpi.gather(localX) globalY = mpi.gather(localY) # If the user did not specify the sampling volume, then find the min and # max node positions. if xmin == None: xmin = min(localX) if ymin == None: ymin = min(localY) if xmax == None: xmax = max(localX) if ymax == None: ymax = max(localY) xmin = mpi.allreduce(xmin, mpi.MIN) ymin = mpi.allreduce(ymin, mpi.MIN) xmax = mpi.allreduce(xmax, mpi.MAX) ymax = mpi.allreduce(ymax, mpi.MAX) assert xmax > xmin assert ymax > ymin # Figure out the sizes of the bins we're going to be sampling in dx = (xmax - xmin)/nx dy = (ymax - ymin)/ny # Loop over all the grid sampling positions, and figure out this processors # contribution. for iy in xrange(ny): for ix in xrange(nx): xValues[iy][ix] = xmin + (ix + 0.5)*dx yValues[iy][ix] = ymin + (iy + 0.5)*dy r = Vector2d(xValues[iy][ix], yValues[iy][ix]) z = fieldList.sample(r) localZ = eval(zFunction % "z") globalZ = mpi.reduce(localZ, mpi.SUM) if mpi.rank == 0: print "%i %i %i %s %g %g" % (mpi.rank, ix, iy, r, z, localZ) print "%i %g" % (mpi.rank, globalZ) zValues[iy][ix] = globalZ return xValues, yValues, zValues #------------------------------------------------------------------------------- # Plot the energy history of the given conservation object. #------------------------------------------------------------------------------- def plotEHistory(conserve): if mpi.rank == 0: t = conserve.timeHistory E = conserve.EHistory KE = conserve.KEHistory TE = conserve.TEHistory UE = conserve.EEHistory Edata = Gnuplot.Data(t, E, with_ = "lines", title = "Total Energy", inline = True) KEdata = Gnuplot.Data(t, KE, with_ = "lines", title = "Kinetic Energy", inline = True) TEdata = Gnuplot.Data(t, TE, with_ = "lines", title = "Thermal Energy", inline = True) UEdata = Gnuplot.Data(t, UE, with_ = "lines", title = "Potential Energy", inline = True) plot = generateNewGnuPlot() plot.replot(Edata) plot.replot(KEdata) plot.replot(TEdata) plot.replot(UEdata) plot.replot() SpheralGnuPlotCache.extend([Edata, KEdata, TEdata, UEdata]) return plot else: return fakeGnuplot() #------------------------------------------------------------------------------- # Plot the linear momentum history of the given conservation object. #------------------------------------------------------------------------------- def plotpmomHistory(conserve): if mpi.rank == 0: t = conserve.timeHistory p = conserve.pmomHistory px = [x.x for x in p] py = [x.y for x in p] pz = [x.z for x in p] pmag = [x.magnitude() for x in p] pxdata = Gnuplot.Data(t, px, with_ = "lines", title = "x momentum", inline = True) pydata = Gnuplot.Data(t, py, with_ = "lines", title = "y momentum ", inline = True) pzdata = Gnuplot.Data(t, pz, with_ = "lines", title = "z momentum", inline = True) pmagdata = Gnuplot.Data(t, pmag, with_ = "lines", title = "total momentum", inline = True) plot = generateNewGnuPlot() plot.replot(pxdata) plot.replot(pydata) plot.replot(pzdata) plot.replot(pmagdata) plot.replot() SpheralGnuPlotCache.extend([pxdata, pydata, pzdata, pmagdata]) return plot else: return fakeGnuplot() #------------------------------------------------------------------------------- # Plot a polygon. #------------------------------------------------------------------------------- def plotPolygon(polygon, plotVertices = True, plotFacets = True, plotNormals = False, plotCentroid = False, plot = None, persist = False, plotLabels = True): px = [] py = [] for v in polygon.vertices: px.append(v.x) py.append(v.y) fx = [] fy = [] fdx = [] fdy = [] nx = [] ny = [] ndx = [] ndy = [] for f in polygon.facets: dr = f.point2 - f.point1 hdr = dr/2.0 fx.append(f.point1.x) fy.append(f.point1.y) fdx.append(dr.x) fdy.append(dr.y) nx.append(fx[-1] + hdr.x) ny.append(fy[-1] + hdr.y) ndx.append(f.normal.x) ndy.append(f.normal.y) if plot is None: plot = generateNewGnuPlot(persist) if plotLabels: vlabel, flabel, nlabel = "Vertices", "Facets", "Normals" else: vlabel, flabel, nlabel = None, None, None dataPoints = Gnuplot.Data(px, py, with_ = "points pt 1 ps 2", title = vlabel, inline = True) dataFacets = Gnuplot.Data(fx, fy, fdx, fdy, with_ = "vectors", title = flabel, inline = True) dataNormals = Gnuplot.Data(nx, ny, ndx, ndy, with_ = "vectors", title = nlabel, inline = True) if plotVertices: plot.replot(dataPoints) if plotFacets: plot.replot(dataFacets) if plotNormals: plot.replot(dataNormals) if plotCentroid: c = polygon.centroid dataCentroid = Gnuplot.Data([c.x], [c.y], with_ = "points pt 2 ps 2", title = "Centroid", inline = True) plot.replot(dataCentroid) SpheralGnuPlotCache.extend([dataPoints, dataFacets, dataNormals, plot]) return plot #------------------------------------------------------------------------------- # Plot a PolygonalMesh #------------------------------------------------------------------------------- def plotPolygonalMesh(mesh, persist = False): polylocal = [] for izone in xrange(mesh.numZones): zone = mesh.zone(izone) polylocal.append([mesh.node(i).position() for i in zone.nodeIDs]) polylocal[-1].append(polylocal[-1][0]) assert len(polylocal) == mesh.numZones p = generateNewGnuPlot(persist) for sendProc in xrange(mpi.procs): polys = mpi.bcast(polylocal, root=sendProc) for poly in polys: p.replot(Gnuplot.Data([x.x for x in poly], [x.y for x in poly], with_ = "lines lt %i lw 2" % 1, title = None, inline = True)) return p ## edges0 = [(mesh.node(mesh.edge(i).node1ID).position(), mesh.node(mesh.edge(i).node2ID).position()) ## for i in xrange(mesh.numEdges)] ## p = generateNewGnuPlot() ## datas = [] ## for sendProc in xrange(mpi.procs): ## edges = mpi.bcast(edges0, root=sendProc) ## for edge in edges: ## datas.append(Gnuplot.Data([edge[0].x, edge[1].x], [edge[0].y, edge[1].y], ## with_ = "lines %s" % linetype, ## title = None, ## inline = True)) ## p.replot(datas[-1]) ## p.datas = datas ## return p

37.872776

105

0.467688

3,654

42,569

5.434866

0.147783

0.016063

0.00846

0.010474

0.349867

0.308626

0.22977

0.183896

0.143512

0.122614

0

0.008482

0.365759

42,569

1,123

106

37.9065

0.727064

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0.003468

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0

null

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1

0

1

56d64070737123e2a1fe8ad2cdb328ef5f1d6afc

77

py

Python

CodeUp/1152_10보다작은수.py

woorimlee/cpp_CTCI_6E_APSS

ff1d42e871ba853ac3de726df0c609885ba07573

[ "MIT" ]

2

2020-12-30T03:35:51.000Z

2021-02-28T20:39:09.000Z

CodeUp/1152_10보다작은수.py

woorimlee/cpp_CTCI_6E_APSS

ff1d42e871ba853ac3de726df0c609885ba07573

[ "MIT" ]

1

2020-12-08T08:48:40.000Z

2021-04-09T04:58:57.000Z

CodeUp/1152_10보다작은수.py

woorimlee/Algorithm-Repository

ff1d42e871ba853ac3de726df0c609885ba07573

[ "MIT" ]

null

a = int(input()) if a < 10 : print("small") else : print("big")

11

18

0.467532

11

77

3.272727

0.818182

0

0.038462

0.324675

77

6

19

12.833333

0.653846

0

0.103896

0

1

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false

0

0.4

1

0

null

0

1

0

null

0

2

56d7e1352e0a41bda99357c7991be824ba742bcd

6,484

py

Python

fingerprint/client/util.py

ghoshishan/comp-sec

f1bec8fc68814bc421337069e58a67447baf2a89

[ "MIT" ]

null

fingerprint/client/util.py

ghoshishan/comp-sec

f1bec8fc68814bc421337069e58a67447baf2a89

[ "MIT" ]

null

fingerprint/client/util.py

ghoshishan/comp-sec

f1bec8fc68814bc421337069e58a67447baf2a89

[ "MIT" ]

null

import json import base64 import random import logging from Crypto.Cipher import AES from Crypto.Protocol.KDF import PBKDF2 from phe import paillier, EncryptedNumber, PaillierPublicKey import client.dbhandler as dbhandler from client.exceptions import WrongPin, UnknownUser logger = logging.getLogger('client') # for salting pins of users SALT = b'=sNmXf\xd6\xefe\xf8\xd0\x10\xe5\xb2\xf3o\x01|\xf3\x99\xbf\xd6\x88\x0c\xb6\x9b\x08\xb3\xac\xf0\xb9g' def generate_verification_code(): """ Generates a list of random numbers which is used to transform the fingerprint vector to protect against malicious users who have access to the fingerprint data of the user they want to impersonate. :return: user verification code """ user_vcode = random.sample(range(1, 255), 4) return user_vcode def generate_shuffle_code(): """ Returns a random shuffle code. :return: shuffle code """ user_shuffle_code = random.randint(1000, 9999) return user_shuffle_code def enrollment_transform(user_fingerprint, user_vcode, user_shuffle_code): """ Performs fingerprint transform during enrollment :param user_fingerprint: fingerprint feature vector :param user_vcode: verification code of the user :return: transformed fingerprint vector """ transformed_fingerprint = user_fingerprint + user_vcode sumOfXiSquare = sum(x*x for x in user_fingerprint) sumOfViSquare = sum(v*v for v in user_vcode) transformed_fingerprint.extend([1, 1, sumOfXiSquare, sumOfViSquare]) random.Random(user_shuffle_code).shuffle(transformed_fingerprint) return transformed_fingerprint def string_encrypt(pin, plaintext): """ Performs AES encryption based on a pin. Used for storing paillier key pair and verification code of a user. :param pin: 4 digit integer string :param plaintext: JSON dumps of reaquired data to be encrypted :return: ciphertext and initialization vector """ key = PBKDF2(pin, SALT, dkLen=32) data = plaintext.encode('utf-8') # CFB basically doesn't require padding to maintain block size cipher_encrypt = AES.new(key, AES.MODE_CFB) ciphered_bytes = cipher_encrypt.encrypt(data) iv = cipher_encrypt.iv return ciphered_bytes, iv def string_decrypt(pin, iv, ciphertext): """ Performs AES decryption on a ciphertext given a pin and iv. :param pin: 4 digit integer string :param iv: Initialization vector returned during encryption :param ciphertext: encrypted cipher text :return: decrypted string data """ key = PBKDF2(pin, SALT, dkLen=32) cipher_decrypt = AES.new(key, AES.MODE_CFB, iv) deciphered_bytes = cipher_decrypt.decrypt(ciphertext) try: decrypted_data = deciphered_bytes.decode('utf-8') except UnicodeDecodeError as e: logger.info(f'Incorrect pin') return None return decrypted_data def paillier_encrypt_vector(pub_key, transformed_fingerprint): """ Performs encryption on the transformmed fingerprint using the paillier cryptosystem. :param pub_key: public key of the user :param transformed_fingerprint: a fingerprint feature vector :return: encrypted feature vector """ encrypted_fingerprint = [pub_key.encrypt( feature) for feature in transformed_fingerprint] serialized_fingerprint = [] # readable form of the ciphertext for entry in encrypted_fingerprint: serialized_fingerprint.append(entry._EncryptedNumber__ciphertext) logger.debug(json.dumps(serialized_fingerprint, indent=2)) return encrypted_fingerprint def store_credentials(user_roll_no, user_pin, user_tid, user_pub_key, user_priv_key, user_vcode, user_shuffle_code): """ Store credentials of the user in an encrypted format. :param user_roll_no: user roll no :param user_pin: user 4 digit integer pin :param user_tid: user fingerprint id stored on the server :param user_pub_key: user paillier public key :param user_priv_key: user paillier private key :param user_vcode: user verification code """ data = dbhandler.read_data('userdata.json') user_data = { 'tid': user_tid, 'vcode': user_vcode, 'scode': user_shuffle_code, 'n': user_pub_key.n, 'p': user_priv_key.p, 'q': user_priv_key.q } user_data_string = json.dumps(user_data) ciphertext, iv = string_encrypt(user_pin, user_data_string) store_data = { 'roll_no': user_roll_no, 'ciphertext': base64.b64encode(ciphertext).decode('utf-8'), 'iv': base64.b64encode(iv).decode('utf-8') } data.append(store_data) dbhandler.write_data(data, 'userdata.json') logger.info(f'User data stored: {user_roll_no}') def retrieve_credentials(user_roll_no, user_pin): """ Fetch and decrypt encrypted user data stored in the database :param user_roll_no: user roll number :param user_pin: user pin :return: decrypted data """ data = dbhandler.read_data('userdata.json') ciphertext = None iv = None flag = 0 for user in data: if user['roll_no'] == user_roll_no: ciphertext = base64.b64decode(user['ciphertext'].encode('utf-8')) iv = base64.b64decode(user['iv'].encode('utf-8')) flag = 1 break if flag == 0: print(f'Unknown user: {user_roll_no}') raise UnknownUser return None user_data = string_decrypt(user_pin, iv, ciphertext) if not user_data: print(f'Incorrect pin: {user_roll_no}') raise WrongPin return None user_data = json.loads(user_data) return user_data def verification_transform(user_fingerprint, user_vcode, user_shuffle_code): """ Performs transformation on the fingerprint feature vector required during verification. :param user_fingerprint: fingerprint feature vector :param user_vcode: verification code of the user :return: transformed fingerprint """ # is not this same as enrollment_transform transformed_fingerprint = user_fingerprint + user_vcode transformed_fingerprint = [-2*n for n in transformed_fingerprint] sumOfYiSquare = sum(y*y for y in user_fingerprint) sumOfViSquare = sum(v*v for v in user_vcode) transformed_fingerprint.extend([sumOfYiSquare, sumOfViSquare, 1, 1]) random.Random(user_shuffle_code).shuffle(transformed_fingerprint) return transformed_fingerprint

33.947644

116

0.715299

849

6,484

5.287397

0.249706

0.073513

0.024504

0.015594

0.258409

0.253063

0.175095

0.146581

0.121631

0

0.015516

0.204812

6,484

190

117

34.126316

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0

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0

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0.07867

0.023434

0

1

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false

0

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0.292929

0.232323

0

null

0

null

0

1

0

56dac085576643fac64d1abfca3b7fade3bb0fb0

50

py

Python

arbory/subcommands/__init__.py

n8jhj/arbory

702917acecace85eb4a1597dd86c553148db1432

[ "BSD-2-Clause" ]

null

arbory/subcommands/__init__.py

n8jhj/arbory

702917acecace85eb4a1597dd86c553148db1432

[ "BSD-2-Clause" ]

null

arbory/subcommands/__init__.py

n8jhj/arbory

702917acecace85eb4a1597dd86c553148db1432

[ "BSD-2-Clause" ]

null

from .config import config from .tree import tree

16.666667

26

0.8

8

50

5

0.5

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0.16

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0.952381

0

1

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true

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1

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1

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null

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null

0

1

0

1

0

1

0

5

56dc55fee9c5b749a9b50ac4f9d5e574bceb9dda

6,622

py

Python

kemeny.py

cai-michael/kemenyapprox

802e22c58f8649dcc8ddf888603f8c19ae32621c

[ "Apache-2.0" ]

null

kemeny.py

cai-michael/kemenyapprox

802e22c58f8649dcc8ddf888603f8c19ae32621c

[ "Apache-2.0" ]

null

kemeny.py

cai-michael/kemenyapprox

802e22c58f8649dcc8ddf888603f8c19ae32621c

[ "Apache-2.0" ]

null

""" Implements the Kemeny Rule and various heuristics """ import time import datetime from itertools import combinations, permutations from multiprocessing import Pool import functools from collections import defaultdict from matrix import generate_zeros_matrix, matrix_multiplication NUM_WORKERS = 2 STATIONARY_DISTRIBUTION_ITERATIONS = 1000 def kendall_tau_distance(ranking_a, ranking_b): """ Determines the Kendell Tau Distance between two orderings """ distance = 0 num_candidates = len(ranking_a) pairs = combinations(range(1, num_candidates + 1), 2) for alt_x, alt_y in pairs: a_order = ranking_a.index(alt_x) - ranking_a.index(alt_y) b_order = ranking_b.index(alt_x) - ranking_b.index(alt_y) if a_order * b_order < 0: distance += 1 return distance def calculate_ranking_score(ranking, profile): """ Calculates the ranking score for a particular strict ordering """ ranking_score = 0 for profile_ranking in profile: ranking_score += kendall_tau_distance(ranking, profile_ranking) return ranking_score def kemeny_rule(profile, num_workers=1): """ Implements the kemeny rule by calculating all Kendell-Tau distances """ print('\nApplying the Kemeny Rule to the Profile...') # Start timer time_start = time.perf_counter() num_candidates = len(profile[0]) ranking_scores = [] rank_permutations = list(permutations(range(1, num_candidates + 1))) calculate_scores = functools.partial(calculate_ranking_score, profile=profile) with Pool(num_workers) as worker_pool: ranking_scores = worker_pool.map(calculate_scores, rank_permutations) min_ranking_score = min(ranking_scores) win_idx = [index for index, score in enumerate(ranking_scores) if score == min_ranking_score] print("The winning ranking(s) are as follows: ") for index in win_idx: winning_ranking = rank_permutations[index] winning_ranking_stringified = [str(i) for i in winning_ranking] print(", ".join(winning_ranking_stringified)) # Calculate time required to finish time_finish = time.perf_counter() time_elapsed = datetime.timedelta(seconds = (time_finish - time_start)) print(f"Applying the Kemeny Rule took {time_elapsed}") def determine_pairwise_victories(profile): """ Determines the pairwise victories for candidates Returns a dictionary indexed by tuples of candidates """ pairwise_victories = defaultdict(int) num_candidates = len(profile[0]) candidiate_pairs = list(permutations(range(1, num_candidates + 1), 2)) for pair in candidiate_pairs: for vote in profile: if vote.index(pair[0]) < vote.index(pair[1]): pairwise_victories[pair] += 1 return pairwise_victories def create_transition_matrix(pairwise_victories, num_candidates, num_votes, mc_type): """ Generates a transition matrix based on the MC heuristic type Type 1: The transition probability of a to b is: 1 / # Candidates if b is preferred to a at some point 0 otherwise The transition probability from a to a is 1 - Sum of all other transitions Type 2: The transition probability of a to b is: 1 / # Candidates if the majority of ballots prefer b to a 0 otherwise The transition probability from a to a is 1 - Sum of all other transitions Type 3: The transition probability of a to b is: Summation of all orderings where sum(orderings where b is preferred to a) / Orderings * candidates The transition probability from a to a is 1 - Sum of all other transitions """ # Put 0's on transition matrix transition_matrix = generate_zeros_matrix(num_candidates, num_candidates) # Populate transition probabilities in the matrix candidiate_pairs = list(permutations(range(1, num_candidates + 1), 2)) # Based on preferences of a and b assign probability of a -> b if mc_type == 1: for first, second in candidiate_pairs: if pairwise_victories[(second, first)] > 0: probability = 1 / num_candidates else: probability = 0 transition_matrix[first - 1][second - 1] = probability elif mc_type == 2: for first, second in candidiate_pairs: if pairwise_victories[(second, first)] > (num_votes // 2): probability = 1 / num_candidates else: probability = 0 transition_matrix[first - 1][second - 1] = probability elif mc_type == 3: for first, second in candidiate_pairs: probability = pairwise_victories[(second, first)] / (num_votes * num_candidates) transition_matrix[first - 1][second - 1] = probability # Determine the probability of a self-transition for candidate in range(1, num_candidates + 1): self_transition_probability = 1 - sum(transition_matrix[candidate - 1]) transition_matrix[candidate - 1][candidate - 1] = self_transition_probability return transition_matrix def markov_heuristic(profile, mc_type): """ Applies the Markov Chain Heuristic to a Profile using a transition function of mc_type """ print(f'\nApplying the MC{mc_type} Markov Heuristic to the Profile...') # Start timer time_start = time.perf_counter() num_candidates = len(profile[0]) num_votes = len(profile) # Determine pairwise victories for each pair of candidates pairwise_wins = determine_pairwise_victories(profile) transition_matrix = create_transition_matrix(pairwise_wins, num_candidates, num_votes, mc_type) # Put the probability matrix to a high power to find the stationary distribution stationary_distribution = transition_matrix.copy() for _ in range(STATIONARY_DISTRIBUTION_ITERATIONS): stationary_distribution = matrix_multiplication(stationary_distribution, transition_matrix) final_probabilities = stationary_distribution[0] prob_tuples = [(idx + 1, prob) for idx, prob in enumerate(final_probabilities)] prob_tuples.sort(key=lambda x: x[1], reverse=True) final_ranking = [pair[0] for pair in prob_tuples] print("The winning ranking is as follows: ") winning_ranking_stringified = [str(i) for i in final_ranking] print(", ".join(winning_ranking_stringified)) # Calculate time required to finish time_finish = time.perf_counter() time_elapsed = datetime.timedelta(seconds = (time_finish - time_start)) print(f"Applying the MC{mc_type} Markov Model took {time_elapsed}")

36.384615

99

0.706131

864

6,622

5.21875

0.196759

0.04613

0.021734

0.021069

0.357951

0.334664

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0

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0.219873

6,622

181

100

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false

0

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0

0.175258

0.082474

0

null

0

null

0

1

0

56dc67205242f7ff839dde303a1973e4737ed5cb

1,331

py

Python

utilities/write_spatial_file.py

markfoleyie/gisp_2021

6077b0980d775fefeb46983e70a1f03faa1220ec

[ "MIT" ]

1

2022-01-28T13:39:42.000Z

utilities/write_spatial_file.py

markfoleyie/gisp_2021

6077b0980d775fefeb46983e70a1f03faa1220ec

[ "MIT" ]

null

utilities/write_spatial_file.py

markfoleyie/gisp_2021

6077b0980d775fefeb46983e70a1f03faa1220ec

[ "MIT" ]

null

try: import fiona from fiona.crs import from_epsg import utilities.fiona_supported_drivers as fsd import os except Exception as e: print(f"{e}") quit(1) def write_spatial(file=None, directory=None, data=None, **meta): try: if not data: raise ValueError(f"No data to write.") if not os.path.exists(directory): raise ValueError(f"Target directory doesn't exist.") if "driver" not in meta: raise ValueError(f"Missing driver.") if "crs" not in meta: raise ValueError(f"Missing CRS.") if "schema" not in meta: raise ValueError(f"Missing schema.") if meta["driver"] not in fsd.file_extensions: raise ValueError(f"Invalid driver.") target = os.path.join(directory, f"{file}.{fsd.file_extensions[meta['driver']]}") meta["crs"] = from_epsg(meta["crs"]) for k, v in meta["schema"]["properties"].items(): if v == "string": meta["schema"]["properties"][k] = "str" elif v == "double": meta["schema"]["properties"][k] = "float" with fiona.open(target, "w", **meta) as fh: for feature in data: fh.write(feature) except Exception as e: print(f"{e}") quit(1)

32.463415

89

0.561232

171

1,331

4.327485

0.356725

0.121622

0.12973

0.056757

0.210811

0.081081

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0.304282

1,331

40

90

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0.796976

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0

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0.028571

false

0

0.114286

0

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0.057143

0

null

0

null

0

1

0

56dfe03b101cc2f8e7b14651f15e361abb52dfc4

3,536

py

Python

src/pytest_qatouch/plugin.py

MohamedRaslan/pytest-qatouch

8d6ddd414d2ee836da1ebb9bee64a7672ed7e04f

[ "MIT" ]

null

src/pytest_qatouch/plugin.py

MohamedRaslan/pytest-qatouch

8d6ddd414d2ee836da1ebb9bee64a7672ed7e04f

[ "MIT" ]

6

2021-06-26T20:11:10.000Z

2022-02-21T19:41:50.000Z

src/pytest_qatouch/plugin.py

MohamedRaslan/pytest-qatouch

8d6ddd414d2ee836da1ebb9bee64a7672ed7e04f

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest from .utils import QATOUCH_MARK, MissingQatouchData, ExpectedIntegerValue from .qatouch import QatouchTestResult __QATOUCH_TEST_RSESULT = None ___Enable_PLUGIN = None def pytest_addoption(parser): group = parser.getgroup("QaTouch") def add_option(option, dest, help, default=None, type=None, **kwargs): group.addoption(option, dest=dest, default=default, **kwargs) parser.addini(dest, default=default, type=type, help=help) add_option( option="--qatouch", action="store", dest="qatouch", default="False", help="Enable the qatouch plugin (Set ['True', 'False'])", ) add_option( option="--qatouch-subdomain", action="store", dest="qatouch-subdomain", help="Your qatouch submodule name (i.e <your_subdomain>.qatouch.com)", ) add_option( "--qatouch-api-token", action="store", dest="qatouch-api-token", help="Your qatouch API token", ) add_option( "--qatouch-project-key", action="store", dest="qatouch-project-key", help="The qatouch project key", ) add_option( "--qatouch-testrun-key", action="store", dest="qatouch-testrun-key", help="The testrun key in qatouch project", ) def pytest_configure(config): config.addinivalue_line("markers", f"{QATOUCH_MARK}(TR): Mark test") global ___Enable_PLUGIN ___Enable_PLUGIN = ( str(config.getoption("--qatouch")).lower() == "true" or str(config.getini("qatouch")).lower() == "true" ) if ___Enable_PLUGIN: def get_option(option: str): value = config.getoption("--" + option) or config.getini(option) if value is None: raise MissingQatouchData( f"The option ['--'{option}] or the ini option[{option}] not set" ) return value global __QATOUCH_TEST_RSESULT __QATOUCH_TEST_RSESULT = QatouchTestResult( domain=get_option("qatouch-subdomain"), api_token=get_option("qatouch-api-token"), project_key=get_option("qatouch-project-key"), testrun_key=get_option("qatouch-testrun-key"), ) @pytest.hookimpl(hookwrapper=True) def pytest_runtest_makereport(item, call): outcome = yield test_result = outcome.get_result() qa_marker = item.get_closest_marker(QATOUCH_MARK) if __QATOUCH_TEST_RSESULT and qa_marker: if test_result.when == "call": __add_test(qa_marker, test_result) elif test_result.when in ("setup", "teardown") and test_result.outcome != "passed": __add_test(qa_marker, test_result) def pytest_sessionfinish(): global __QATOUCH_TEST_RSESULT if ___Enable_PLUGIN and __QATOUCH_TEST_RSESULT: __QATOUCH_TEST_RSESULT.push_results_to_qatouch() __QATOUCH_TEST_RSESULT = None def __add_test(qa_marker, test_result): if "TR" in qa_marker.kwargs: tr_value = qa_marker.kwargs["TR"] if not isinstance(tr_value, int): raise ExpectedIntegerValue( f"Expected the TR value to be a valid integer value bug insted got {tr_value} of type {type(tr_value)}" ) else: raise MissingQatouchData(f"Expected to have a TR and its value, but not found") __QATOUCH_TEST_RSESULT.push_testcase_to_results( testcase_id=tr_value, testcase_status=test_result.outcome )

30.747826

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0.63914

414

3,536

5.169082

0.270531

0.046262

0.075701

0.051402

0.092056

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0.000376

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3,536

114

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0

0.122222

0

null

0

null

0

1

0

56e0b3bab19585c01401fc4f7a552420d5771661

4,066

py

Python

tsai/models/ROCKET.py

radi-cho/tsai

32f24d55ee58df1a14d1e68618f230097a266c77

[ "Apache-2.0" ]

1

2022-01-02T18:21:27.000Z

tsai/models/ROCKET.py

radi-cho/tsai

32f24d55ee58df1a14d1e68618f230097a266c77

[ "Apache-2.0" ]

31

2021-12-01T23:08:51.000Z

2021-12-29T02:59:49.000Z

tsai/models/ROCKET.py

radi-cho/tsai

32f24d55ee58df1a14d1e68618f230097a266c77

[ "Apache-2.0" ]

1

2022-03-13T16:47:04.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/111_models.ROCKET.ipynb (unless otherwise specified). __all__ = ['RocketClassifier', 'load_rocket', 'RocketRegressor'] # Cell import sklearn from sklearn.linear_model import RidgeClassifierCV, RidgeCV from sklearn.metrics import make_scorer from ..imports import * from ..data.external import * from .layers import * warnings.filterwarnings("ignore", category=FutureWarning) # Cell class RocketClassifier(sklearn.pipeline.Pipeline): """Time series classification using ROCKET features and a linear classifier""" def __init__(self, num_kernels=10_000, normalize_input=True, random_state=None, alphas=np.logspace(-3, 3, 7), normalize_features=True, memory=None, verbose=False, scoring=None, class_weight=None, **kwargs): """ RocketClassifier is recommended for up to 10k time series. For a larger dataset, you can use ROCKET (in Pytorch). scoring = None --> defaults to accuracy. Rocket args: num_kernels : int, number of random convolutional kernels (default 10,000) normalize_input : boolean, whether or not to normalise the input time series per instance (default True) random_state : Optional random seed (default None) """ try: import sktime from sktime.transformations.panel.rocket import Rocket except ImportError: print("You need to install sktime to be able to use RocketClassifier") self.steps = [('rocket', Rocket(num_kernels=num_kernels, normalise=normalize_input, random_state=random_state)), ('ridgeclassifiercv', RidgeClassifierCV(alphas=alphas, normalize=normalize_features, scoring=scoring, class_weight=class_weight, **kwargs))] store_attr() self._validate_steps() def __repr__(self): return f'Pipeline(steps={self.steps.copy()})' def save(self, fname='Rocket', path='./models'): path = Path(path) filename = path/fname with open(f'{filename}.pkl', 'wb') as output: pickle.dump(self, output, pickle.HIGHEST_PROTOCOL) # Cell def load_rocket(fname='Rocket', path='./models'): path = Path(path) filename = path/fname with open(f'{filename}.pkl', 'rb') as input: output = pickle.load(input) return output # Cell class RocketRegressor(sklearn.pipeline.Pipeline): """Time series regression using ROCKET features and a linear regressor""" def __init__(self, num_kernels=10_000, normalize_input=True, random_state=None, alphas=np.logspace(-3, 3, 7), normalize_features=True, memory=None, verbose=False, scoring=None, **kwargs): """ RocketRegressor is recommended for up to 10k time series. For a larger dataset, you can use ROCKET (in Pytorch). scoring = None --> defaults to r2. Args: num_kernels : int, number of random convolutional kernels (default 10,000) normalize_input : boolean, whether or not to normalise the input time series per instance (default True) random_state : Optional random seed (default None) """ try: import sktime from sktime.transformations.panel.rocket import Rocket except ImportError: print("You need to install sktime to be able to use RocketRegressor") self.steps = [('rocket', Rocket(num_kernels=num_kernels, normalise=normalize_input, random_state=random_state)), ('ridgecv', RidgeCV(alphas=alphas, normalize=normalize_features, scoring=scoring, **kwargs))] store_attr() self._validate_steps() def __repr__(self): return f'Pipeline(steps={self.steps.copy()})' def save(self, fname='Rocket', path='./models'): path = Path(path) filename = path/fname with open(f'{filename}.pkl', 'wb') as output: pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)

42.8

143

0.657649

479

4,066

5.448852

0.2881

0.030651

0.021456

0.029119

0.748659

0.723372

0.701149

0.661303

0

0.011039

0.242499

4,066

95

144

42.8

0.836364

0.266847

0

0.603774

1

0

0.129249

0.024788

0

1

0.132075

false

0

0.226415

0.037736

0.45283

0.037736

0

null

0

1

0

1

0

1

0

null

0

4

56e136e9f8cd4fb32fc3b35b6dbfa5fc8c91cf9e

6,596

py

Python

sheetmaker/language_strings.py

cosme12/cheet-sheet-maker

7bbb4f4dd310127d9ca57a9365dc2bfb6bce91da

[ "MIT" ]

112

2017-02-08T20:42:14.000Z

2022-03-04T01:50:32.000Z

sheetmaker/language_strings.py

cosme12/cheet-sheet-maker

7bbb4f4dd310127d9ca57a9365dc2bfb6bce91da

[ "MIT" ]

20

2017-02-09T11:22:08.000Z

2018-06-22T19:04:23.000Z

sheetmaker/language_strings.py

cosme12/cheet-sheet-maker

7bbb4f4dd310127d9ca57a9365dc2bfb6bce91da

[ "MIT" ]

30

2017-02-09T13:05:52.000Z

2022-01-30T05:59:09.000Z

"""Language selector handler Todo: * Use internacionalization * Add more languages """ english = { "INTRO_MESSAGE" : "Welcome to CheatSheet Maker", "MAIN_MENU_OPTIONS" : { 1: "Create sheet", 2: "Export (NOT CODED YET)", 3: "Help (NOT CODED YET)", 4: "Exit", }, "MENU_MESSAGE" : "Type the number to choose your option.", "CONFIG_SHEET_MESSAGE1" : "Building the basic layout... answer the next questions.", "CONFIG_SHEET_MESSAGE2" : "How many columns your sheet will have?", "CONFIG_SHEET_MESSAGE3" : "Which color style do you prefer?", "CONFIG_SHEET_OPTIONS1" : { 1: "What is your sheet title? ('CheatSheet' is added automatically)" }, "CONFIG_SHEET_OPTIONS2" : { 1: "1 main column", 2: "2 main columns", 3: "3 main columns" }, "CONFIG_SHEET_OPTIONS3" : { 1: "Orange", 2: "Black and white", 3: "Red", 4: "Yellow", 5: "Green", 6: "Blue", }, "HEADER_MESSAGE" : "Building the header... answer the next questions.", "HEADER_OPTIONS" : { 1: "What is the author name?" }, "FOOTER_MESSAGE" : "Building the footer... answer the next questions.", "FOOTER_OPTIONS1" : { 1: "What is the author picture url?" }, "FOOTER_OPTIONS2" : { 1: "What is the author website url? (use http://)" }, "FOOTER_OPTIONS3" : { 1: "What is the sponsor name?" }, "FOOTER_OPTIONS4" : { 1: "What is the sponsor webite url? (use http://)" }, "BLOCK_MESSAGE" : "Building the blocks... answer the next questions.", "BLOCK_OPTIONS" : { 1: "Create text block", 2: "Create block with rows", 0: "Done" }, "BLOCK_ROWS_MESSAGE1" : "Building block with rows... answer the next questions.", "BLOCK_ROWS_MESSAGE2" : "In what main column do you want to build it?", "BLOCK_ROWS_OPTIONS1" : { 1: "What is the title of the block?" }, "BLOCK_ROWS_OPTIONS2" : { 1: "How many rows does it have?" }, "BLOCK_ROWS_OPTIONS3" : { 1: "What is the text of each row? (text row1. # text row2. # text row3)" }, "TEXT_BLOCK_MESSAGE" : "Building text block... answer the next questions.", "TEXT_BLOCK_EXTRA" : "main column", "TEXT_BLOCK_OPTIONS1" : { 1: "What is the title of the block?" }, "TEXT_BLOCK_OPTIONS2" : { 1: "What is the text for the block (use <br> for new line or any html tag for formatting)" }, "END_MESSAGE" : "Thanks for using CheatSheet Maker. Feel free to share your ideas at http://github.com/cosme12/cheasheet-maker", "EXIT_MESSAGE" : "Press any key to exit", "INVALID_INPUT_MESSAGE" : "Invalid input. Try again.", } espanol = { "INTRO_MESSAGE" : "Bienvenido a CheatSheet Maker", "MAIN_MENU_OPTIONS" : { 1: "Crear hoja", 2: "Exportar (NOT CODED YET)", 3: "Ayuda (NOT CODED YET)", 4: "Salir", }, "MENU_MESSAGE" : "Escribe el numero para elegir tu opcion", "CONFIG_SHEET_MESSAGE1" : "Cosntruyendo la estructura basica... responde las siguientes preguntas.", "CONFIG_SHEET_MESSAGE2" : "Cuantas columnas tiene tu hoja?", "CONFIG_SHEET_MESSAGE3" : "Que color de estilo prefieres?", "CONFIG_SHEET_OPTIONS1" : { 1: "Cual es el titulo de tu hoja? ('CheatSheet' se agrega automaticamente)" }, "CONFIG_SHEET_OPTIONS2" : { 1: "1 columna principal", 2: "2 columnas principales", 3: "3 columnas principales" }, "CONFIG_SHEET_OPTIONS3" : { 1: "Naranja", 2: "Negro y Blanco", 3: "Rojo", 4: "Amarillo", 5: "Verde", 6: "Azul", }, "HEADER_MESSAGE" : "Cosntruyendo el encabezado... contesta las siguientes preguntas.", "HEADER_OPTIONS" : { 1: "Cual es el nombre del autor?" }, "FOOTER_MESSAGE" : "Construyendo el pie de pagina... contesta las siguientes preguntas.", "FOOTER_OPTIONS1" : { 1: "Cual es la url de la imagen del autor?" }, "FOOTER_OPTIONS2" : { 1: "Cual es la url del sitio web del autor? (use http://)" }, "FOOTER_OPTIONS3" : { 1: "Cual es el nombre del sponsor?" }, "FOOTER_OPTIONS4" : { 1: "Cual es la url del sitio web del sponsor? (use http://)" }, "BLOCK_MESSAGE" : "Construyendo los bloques... contesta las siguientes preguntas.", "BLOCK_OPTIONS" : { 1: "Crear bloque de texto", 2: "Crear bloque con filas", 0: "Fin" }, "BLOCK_ROWS_MESSAGE1" : "Construyendo bloque con filas... contesta las siguientes preguntas.", "BLOCK_ROWS_MESSAGE2" : "En que columna principal quieres construilo?", "BLOCK_ROWS_OPTIONS1" : { 1: "Cual es el titulo del bloque?" }, "BLOCK_ROWS_OPTIONS2" : { 1: "Cuantas filas tiene?" }, "BLOCK_ROWS_OPTIONS3" : { 1: "Cual es el texto de cada fila? (texto fila1. # texto fila2. # texto fila3.)" }, "TEXT_BLOCK_MESSAGE" : "Construyendo bloque de texto... contesta las siguientes preguntas.", "TEXT_BLOCK_EXTRA" : "columna principal", "TEXT_BLOCK_OPTIONS1" : { 1: "Cual es el titulo del bloque?" }, "TEXT_BLOCK_OPTIONS2" : { 1: "Cual es el texto para el bloque? (usa <br> para nueva linea o cualquier html tag para dar formato)" }, "END_MESSAGE" : "Gracias por utilizar CheatSheet Maker. Comparte tus ideas en http://github.com/cosme12/cheasheet-maker", "EXIT_MESSAGE" : "Presiona cualquier tecla para salir", "INVALID_INPUT_MESSAGE" : "Entrada invalida. Pruba otra vez.", }

52.349206

133

0.516828

693

6,596

4.78355

0.326118

0.039819

0.021116

0.027149

0.238612

0.118552

0.082051

0.035596

0

0.0252

0.374318

6,596

126

134

52.349206

0.778047

0.013341

0

0.033898

0.586371

0.045224

0

0.007937

0

1

0

false

0

null

0

null

0

1

0

56e3647b49151976eed2cee6aff44b3b5d0d4f87

1,007

py

Python

corecat/tests/_utils/conftest.py

DanceCats/dancecat-core

877c475413237205526cca02372f378b6f39dbb3

[ "MIT" ]

1

2017-03-25T14:30:30.000Z

corecat/tests/_utils/conftest.py

DanceCats/dancecat-core

877c475413237205526cca02372f378b6f39dbb3

[ "MIT" ]

3

2017-03-23T11:05:02.000Z

2017-04-03T08:38:40.000Z

corecat/tests/_utils/conftest.py

DanceCats/dancecat-core

877c475413237205526cca02372f378b6f39dbb3

[ "MIT" ]

1

2017-03-18T07:21:59.000Z

import pytest import datetime @pytest.fixture def freeze_datetime(monkeypatch): """Patch datetime.now function to return fixed timestamp.""" original_datetime = datetime.datetime class FrozenDateTimeMeta(type): """Meta class for FrozenDateTime class.""" def __instancecheck__(self, instance): return isinstance(instance, (original_datetime, FrozenDateTime)) class FrozenDateTime(datetime.datetime): """Use freeze method to control result of datetime.datetime.now().""" __metaclass__ = FrozenDateTimeMeta @classmethod def freeze(cls, freezing_timestamp): """Freeze time at freezing_timestamp.""" cls.frozen_time = freezing_timestamp @classmethod def now(cls, tz=None): """Return the frozen time.""" return cls.frozen_time monkeypatch.setattr(datetime, 'datetime', FrozenDateTime) FrozenDateTime.freeze(original_datetime.now()) return FrozenDateTime

31.46875

77

0.680238

98

1,007

6.816327

0.418367

0.11976

0.038922

0

0.228401

1,007

31

78

32.483871

0.859717

0.212512

0

0.105263

0

0.01043

0

1

0.210526

false

0

0.105263

0.052632

0.631579

0

null

0

null

0

1

0

1

56e43e574c097624d7cc4d755ce6b382a96ac7b6

219

py

Python

app01.py

YA-androidapp/Book-FlaskApp-03-AddStaicFilesAndTemplates

2ccbda0b0707a240c3824f5d31457c293f8aa95b

[ "Apache-2.0" ]

null

app01.py

YA-androidapp/Book-FlaskApp-03-AddStaicFilesAndTemplates

2ccbda0b0707a240c3824f5d31457c293f8aa95b

[ "Apache-2.0" ]

null

app01.py

YA-androidapp/Book-FlaskApp-03-AddStaicFilesAndTemplates

2ccbda0b0707a240c3824f5d31457c293f8aa95b

[ "Apache-2.0" ]

null

from flask import Flask, redirect, url_for from markupsafe import escape app = Flask(__name__) @app.route('/') def index(): print(url_for('static', filename='icon.png')) return app.send_static_file('icon.png')

24.333333

49

0.721461

32

219

4.6875

0.65625

0.08

0

0.13242

219

9

50

24.333333

0.789474

0

0.104545

0

1

0.142857

false

0

0.285714

0

0.571429

0.142857

0

null

0

1

0

null

0

1

0

2

56e45b9b2bac49923c6b840cd9115a922b1a9b93

815

py

Python

preprocess/BDD_Driving_Model/test.py

ksuvislab/geovisuals-bdd

82a2b835db59f3d0431cd0cc7f218c410abb1446

[ "Apache-2.0" ]

null

preprocess/BDD_Driving_Model/test.py

ksuvislab/geovisuals-bdd

82a2b835db59f3d0431cd0cc7f218c410abb1446

[ "Apache-2.0" ]

null

preprocess/BDD_Driving_Model/test.py

ksuvislab/geovisuals-bdd

82a2b835db59f3d0431cd0cc7f218c410abb1446

[ "Apache-2.0" ]

null

import wrapper import tensorflow as tf from tensorflow.core.example import example_pb2 from StringIO import StringIO from PIL import Image from matplotlib.pyplot import imshow, show import numpy as np a = wrapper.Wrapper('discrete_tcnn1','./data/pretrained_models/discrete_tcnn1/model.ckpt-126001.bestmodel', 20) example = example_pb2.Example() in_file = './data/tfrecord_release/tfrecords/b1c9c847-3bda4659.tfrecords' count = 0 for example_serialized in tf.python_io.tf_record_iterator(in_file): example.ParseFromString(example_serialized) feature_map = example.features.feature encoded = feature_map['image/encoded'].bytes_list.value print(count) count += 1 file_jpgdata = StringIO(encoded[0]) dt = Image.open(file_jpgdata) imshow(np.asarray(dt)) print(a.observe_a_frame(np.asarray(dt)))

30.185185

111

0.791411

116

815

5.387931

0.517241

0.032

0.0352

0

0.034388

0.107975

815

26

112

31.346154

0.825309

0

0.190418

0.157248

0

1

0

false

0

0.333333

0

0.333333

0.095238

0

null

0

null

0

1

0

1

56e46bb7818acd0c03702e88afa0e940878c4a01

2,989

py

Python

Hourglass_network/train.py

Ali-Sahili/Background-Subtraction-Unsupervised-Learning

445b2cf8736a4a28cff2b074a32afe8fe6986d53

[ "MIT" ]

5

2021-05-17T06:52:28.000Z

2022-02-20T15:35:51.000Z

Hourglass_network/train.py

WN1695173791/Background-Subtraction-Unsupervised-Learning

445b2cf8736a4a28cff2b074a32afe8fe6986d53

[ "MIT" ]

null

Hourglass_network/train.py

WN1695173791/Background-Subtraction-Unsupervised-Learning

445b2cf8736a4a28cff2b074a32afe8fe6986d53

[ "MIT" ]

1

2021-05-17T06:52:33.000Z

import torch from torch import nn import torchvision.utils as vutils import numpy as np from focal_loss import FocalLoss from Param import * from utils import weights_init from net import PoseNet from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True def fit(data, mask, Net, optimizer, criterion, max_norm=0): img = data[0].to(device) heat_maps, output = Net(img) loss = 0 for i in range(output.shape[1]): loss += criterion(output[:,i], mask[0].to(device)) optimizer.zero_grad() loss.backward() optimizer.step() loss.detach_() if max_norm > 0: torch.nn.utils.clip_grad_norm_(Encoder.parameters(), max_norm) torch.nn.utils.clip_grad_norm_(Decoder.parameters(), max_norm) return loss def train(dataloader, dataloader_mask, print_epoch=batch_size, verbose=False): assert image_size == 256 model = PoseNet(nstack, image_size, oup_dim, bn, increase).to(device) #if initialize_weights: # model.apply(weights_init) #criterion = nn.MSELoss() criterion = FocalLoss(gamma=2) optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=1e-5) n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print('number of params ', n_parameters) print("Starting Training Loop...") losses = [] img_list = [] heat_maps_list = [] # For each epoch for epoch in range(num_epochs): torch.cuda.empty_cache() model.train() # For each batch in the dataloader for i, (data, mask) in enumerate(zip(dataloader, dataloader_mask), 0): if verbose: print(data[0].shape) if verbose: print(data[1].shape) recons_loss = fit(data, mask, model, optimizer, criterion) # Output training stats if i % print_epoch == 0: print('[%d/%d][%d/%d]\tLoss: %.4f' % (epoch+1, num_epochs, i, len(dataloader), recons_loss.item())) # Save Losses for plotting later losses.append(recons_loss.item()) # Check how the generator is doing by saving G's output on fixed_noise if (i % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)): with torch.no_grad(): heat_maps, img_out = model(data[0].to(device)) img_out = img_out.detach().cpu() heat_maps = heat_maps.detach().cpu() img_list.append(vutils.make_grid(img_out[0:10,0], nrow=5, normalize=True)) if epoch == (num_epochs-1): for qq in range(heat_maps.shape[2]): heat_maps_list.append(vutils.make_grid(heat_maps[0:5,nstack-1,qq].unsqueeze(1), nrow=5, normalize=True, padding=5, pad_value=1).permute(1,2,0)) heat_map_out = np.vstack(heat_maps_list) return losses, img_list, heat_map_out, model

27.675926

159

0.613249

408

2,989

4.330882

0.367647

0.040747

0.01528

0.014714

0.054329

0.027165

0

0.01924

0.269655

2,989

107

160

27.934579

0.790197

0.082302

0

0.024908

0.007692

0

0.017544

1

0.035088

false

0

0.157895

0

0.22807

0.122807

0

null

0

null

0

1

0

56e51968e0b294a8b19d2f549c0b644ea69e8277

6,308

py

Python

main.py

abditag2/DCGAN-tensorflow

432b0d91bd8252c48869c205b86701993eb37618

[ "MIT" ]

4

2019-04-30T08:46:13.000Z

2020-09-08T07:18:23.000Z

main.py

abditag2/DCGAN-tensorflow

432b0d91bd8252c48869c205b86701993eb37618

[ "MIT" ]

null

main.py

abditag2/DCGAN-tensorflow

432b0d91bd8252c48869c205b86701993eb37618

[ "MIT" ]

1

2019-10-24T12:24:23.000Z

import io import os import os.path from os import listdir from os.path import isfile, join import numpy as np import tensorflow as tf from PIL import Image import horovod.tensorflow as hvd from model import DCGAN from utils import pp, visualize, show_all_variables flags = tf.app.flags flags.DEFINE_integer("epoch", 25, "Epoch to train [25]") flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]") flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]") flags.DEFINE_float("train_size", np.inf, "The size of train images [np.inf]") flags.DEFINE_integer("batch_size", None, "The size of batch images [64]") flags.DEFINE_integer("grid_height", 8, "Grid Height") flags.DEFINE_integer("grid_width", 8, "Grid Width") flags.DEFINE_integer("input_height", None, "The size of image to use (will be center cropped). [108]") flags.DEFINE_integer("input_width", None, "The size of image to use (will be center cropped). If None, same value as input_height [None]") flags.DEFINE_integer("output_height", None, "The size of the output images to produce [64]") flags.DEFINE_integer("output_width", None, "The size of the output images to produce. If None, same value as output_height [None]") flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, mnist, lsun]") flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]") flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]") flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]") flags.DEFINE_integer("sample_rate", None, "If == 5, it will take a sample image every 5 iterations") flags.DEFINE_boolean("train", False, "True for training, False for testing [False]") flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]") flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]") flags.DEFINE_integer("generate_test_images", 100, "Number of images to generate during test. [100]") flags.DEFINE_integer("nbr_of_layers_d", 5, "Number of layers in Discriminator") flags.DEFINE_integer("nbr_of_layers_g", 5, "Number of layers in Generator") flags.DEFINE_boolean("use_checkpoints", True, "Save and load checkpoints") FLAGS = flags.FLAGS # default batch_size if FLAGS.batch_size is None and FLAGS.grid_height is not None and FLAGS.grid_width is not None: batch_size = FLAGS.grid_height * FLAGS.grid_width elif FLAGS.batch_size is not None: batch_size = FLAGS.batch_size else: raise Exception('grid_height/grid_width or batch_size must be provided') # default size parameters input_width = FLAGS.input_width input_height = FLAGS.input_height output_width = FLAGS.output_width output_height = FLAGS.output_height if (input_height is None and input_width is None) or (output_height is None and output_width is None): data_path = 'data/' + FLAGS.dataset first_image = [f for f in listdir(data_path) if isfile(join(data_path, f))][0] image_data = open(data_path + '/' + first_image, "rb").read() image = Image.open(io.BytesIO(image_data)) rgb_im = image.convert('RGB') input_width = rgb_im.size[0] output_width = rgb_im.size[0] input_height = rgb_im.size[1] output_height = rgb_im.size[1] def main(_): pp.pprint(flags.FLAGS.__flags) hvd.init() if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height if FLAGS.use_checkpoints and not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) sample_dir = FLAGS.sample_dir + "_g" + str(FLAGS.nbr_of_layers_g) + "_d" + str(FLAGS.nbr_of_layers_d) if not os.path.exists(sample_dir): os.makedirs(sample_dir) #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) run_config = tf.ConfigProto() run_config.gpu_options.allow_growth=True run_config.gpu_options.visible_device_list = str(hvd.local_rank()) with tf.Session(config=run_config) as sess: if FLAGS.dataset == 'mnist': dcgan = DCGAN( sess, input_width=input_width, input_height=input_height, output_width=output_width, output_height=output_height, grid_height=FLAGS.grid_height, grid_width=FLAGS.grid_width, batch_size=batch_size, sample_num=batch_size, y_dim=10, z_dim=FLAGS.generate_test_images, dataset_name=FLAGS.dataset, input_fname_pattern=FLAGS.input_fname_pattern, crop=FLAGS.crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=sample_dir, nbr_of_layers_d=FLAGS.nbr_of_layers_d, nbr_of_layers_g=FLAGS.nbr_of_layers_g, use_checkpoints=FLAGS.use_checkpoints) else: dcgan = DCGAN( sess, input_width=input_width, input_height=input_height, output_width=output_width, output_height=output_height, grid_height=FLAGS.grid_height, grid_width=FLAGS.grid_width, batch_size=batch_size, sample_num=batch_size, z_dim=FLAGS.generate_test_images, dataset_name=FLAGS.dataset, input_fname_pattern=FLAGS.input_fname_pattern, crop=FLAGS.crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=sample_dir, sample_rate=FLAGS.sample_rate, nbr_of_layers_d=FLAGS.nbr_of_layers_d, nbr_of_layers_g=FLAGS.nbr_of_layers_g, use_checkpoints=FLAGS.use_checkpoints) show_all_variables() if FLAGS.train: dcgan.train(FLAGS) else: if not dcgan.load(FLAGS.checkpoint_dir)[0]: raise Exception("[!] Train a model first, then run test mode") # to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0], # [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1], # [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2], # [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3], # [dcgan.h4_w, dcgan.h4_b, None]) # Below is codes for visualization OPTION = 1 visualize(sess, dcgan, FLAGS, batch_size, OPTION) if __name__ == '__main__': tf.app.run()

40.696774

138

0.708307

939

6,308

4.497338

0.200213

0.05991

0.051148

0.017049

0.354487

0.292209

0.267582

0.250059

0

0.012668

0.186588

6,308

154

139

40.961039

0.810368

0.067058

0

0.330645

0

0.008065

0.231489

0.003745

0

1

0.008065

false

0

0.08871

0

0.096774

0.008065

0

null

0

null

0

1

0

56e55db3074073781e32a309aaad46301011098d

2,768

py

Python

Packs/Okta/Integrations/OktaEventCollector/OktaEventCollector_test.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

null

Packs/Okta/Integrations/OktaEventCollector/OktaEventCollector_test.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

40

2022-03-03T07:34:00.000Z

2022-03-31T07:38:35.000Z

Packs/Okta/Integrations/OktaEventCollector/OktaEventCollector_test.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

null

from OktaEventCollector import ReqParams, Client, Request, GetEvents, Method import pytest req_params = ReqParams(since='', sortOrder='ASCENDING', limit='5') request = Request(method=Method.GET, url='https://testurl.com', headers={}, params=req_params) client = Client(request) get_events = GetEvents(client) id1 = {'uuid': 'a5b57ec5febb'} id2 = {'uuid': 'a5b57ec5fecc'} id3 = {'uuid': 'a12f3c5d77f3'} id4 = {'uuid': 'a12f3c5dxxxx'} class MockResponse: def __init__(self, data): self.data = data def json(self): return self.data @pytest.mark.parametrize("events,ids,result", [ ([id1, id2, id3], ['a12f3c5d77f3'], [id1, id2]), ([id1, id2, id3], ['a12f3c5dxxxx'], [id1, id2, id3]), ([], ['a12f3c5d77f3'], []), ([{'uuid': 0}, {'uuid': 1}, {'uuid': 2}, {'uuid': 3}, {'uuid': 4}, {'uuid': 5}, {'uuid': 6}, {'uuid': 7}, {'uuid': 8}, {'uuid': 9}], [0, 4, 7, 9], [{'uuid': 1}, {'uuid': 2}, {'uuid': 3}, {'uuid': 5}, {'uuid': 6}, {'uuid': 8}])]) def test_remove_duplicates(events, ids, result): assert get_events.remove_duplicates(events, ids) == result @pytest.mark.parametrize("events,result", [ ([{'published': '2022-04-17T12:31:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5faaa'}, {'published': '2022-04-17T12:32:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5fbbb'}, {'published': '2022-04-17T12:33:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5fccc'}], {'after': '2022-04-17T12:33:36.667000', 'ids': ['1d0844b6-3148-11ec-9027-a5b57ec5fccc']}), ([{'published': '2022-04-17T12:31:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5faaa'}, {'published': '2022-04-17T12:32:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5fbbb'}, {'published': '2022-04-17T12:32:36.667', 'uuid': '1d0844b6-3148-11ec-9027-a5b57ec5fccc'}], {'after': '2022-04-17T12:32:36.667000', 'ids': ['1d0844b6-3148-11ec-9027-a5b57ec5fccc', '1d0844b6-3148-11ec-9027-a5b57ec5fbbb']})]) def test_get_last_run(events, result): assert get_events.get_last_run(events) == result @pytest.mark.parametrize("time", ['2022-04-17T12:32:36.667)']) def test_set_since_value(time): req_params.set_since_value(time) assert req_params.since == time def test_make_api_call(mocker): mock_res = MockResponse([{1}, {1}, {1}, {1}, {1}]) mocker.patch.object(client, 'call', return_value=mock_res) assert get_events.make_api_call() == [{1}, {1}, {1}, {1}, {1}] mock_res.data = [{1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}] assert get_events.make_api_call() == [{1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}]

42.584615

109

0.58526

355

2,768

4.459155

0.247887

0.032849

0.041693

0.045483

0.50537

0.396083

0.384713

0.360708

0.30638

0

0.19627

0.186416

2,768

64

110

43.25

0.506661

0

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0

0.311777

0.194364

0

0.096154

1

0.115385

false

0

0.038462

0.019231

0.192308

0

null

0

null

0

1

0

56e5c536074d74d31f4d24ac8e326a346c1ae65e

2,563

py

Python

test/models/test_deepset.py

NetKet/netket

96758e814fc3128e6821564d6cc2852bac40ecf2

[ "Apache-2.0" ]

null

test/models/test_deepset.py

NetKet/netket

96758e814fc3128e6821564d6cc2852bac40ecf2

[ "Apache-2.0" ]

null

test/models/test_deepset.py

NetKet/netket

96758e814fc3128e6821564d6cc2852bac40ecf2

[ "Apache-2.0" ]

null

# Copyright 2021 The NetKet 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. import pytest import jax import jax.numpy as jnp import netket as nk @pytest.mark.parametrize( "cusp_exponent", [pytest.param(None, id="cusp=None"), pytest.param(5, id="cusp=5")] ) @pytest.mark.parametrize( "L", [ pytest.param(1.0, id="1D"), pytest.param((1.0, 1.0), id="2D-Square"), pytest.param((1.0, 0.5), id="2D-Rectangle"), ], ) def test_deepsets(cusp_exponent, L): hilb = nk.hilbert.Particle(N=2, L=L, pbc=True) sdim = len(hilb.extent) x = jnp.hstack([jnp.ones(4), -jnp.ones(4)]).reshape(1, -1) xp = jnp.roll(x, sdim) ds = nk.models.DeepSetRelDistance( hilbert=hilb, cusp_exponent=cusp_exponent, layers_phi=2, layers_rho=2, features_phi=(10, 10), features_rho=(10, 1), ) p = ds.init(jax.random.PRNGKey(42), x) assert jnp.allclose(ds.apply(p, x), ds.apply(p, xp)) def test_deepsets_error(): hilb = nk.hilbert.Particle(N=2, L=1.0, pbc=True) sdim = len(hilb.extent) x = jnp.hstack([jnp.ones(4), -jnp.ones(4)]).reshape(1, -1) xp = jnp.roll(x, sdim) ds = nk.models.DeepSetRelDistance( hilbert=hilb, layers_phi=3, layers_rho=3, features_phi=(10, 10), features_rho=(10, 1), ) with pytest.raises(ValueError): p = ds.init(jax.random.PRNGKey(42), x) with pytest.raises(AssertionError): ds = nk.models.DeepSetRelDistance( hilbert=hilb, layers_phi=2, layers_rho=2, features_phi=(10, 10), features_rho=(10, 2), ) p = ds.init(jax.random.PRNGKey(42), x) with pytest.raises(ValueError): ds = nk.models.DeepSetRelDistance( hilbert=nk.hilbert.Particle(N=2, L=1.0, pbc=False), layers_phi=2, layers_rho=2, features_phi=(10, 10), features_rho=(10, 2), ) p = ds.init(jax.random.PRNGKey(42), x)

29.125

87

0.616075

373

2,563

4.171582

0.340483

0.03856

0.020566

0.071979

0.446015

0.423522

0.405527

0.338689

0.308483

0

0.042465

0.246586

2,563

87

88

29.45977

0.763335

0.225127

0

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0

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0

0.032258

1

0.032258

false

0

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0

0.096774

0

null

0

null

0

1

0

56e652ef605ac7b4aafdbb77c6ebeaccf9aada5f

158

py

Python

newGui.py

Mishin870/meat_bonch_hackaton_2017

cf8637c295981a728d36576eb2cfe8ab93202a9f

[ "MIT" ]

1

2017-10-20T21:56:41.000Z

newGui.py

Mishin870/meat_bonch_hackaton_2017

cf8637c295981a728d36576eb2cfe8ab93202a9f

[ "MIT" ]

null

newGui.py

Mishin870/meat_bonch_hackaton_2017

cf8637c295981a728d36576eb2cfe8ab93202a9f

[ "MIT" ]

null

from tkinter import * root = Tk() b = Button(root) b['text'] = 'test' def test(event): import test b.bind('<Button-1>', test) b.pack() root.mainloop()

12.153846

26

0.620253

25

158

3.92

0.6

0.102041

0

0.007692

0.177215

158

13

27

12.153846

0.746154

0

0.113208

0

1

0.111111

false

0

0.222222

0

0.333333

0

1

0

null

0

null

0

1

56e712fc9d5f888dd3b32ea02057e2f0440b72f3

803

py

Python

2020/day_15/day_15.py

viddrobnic/adventofcode

8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82

[ "MIT" ]

null

2020/day_15/day_15.py

viddrobnic/adventofcode

8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82

[ "MIT" ]

null

2020/day_15/day_15.py

viddrobnic/adventofcode

8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82

[ "MIT" ]

1

2020-12-01T16:49:12.000Z

from collections import defaultdict starting_numbers = [16, 12, 1, 0, 15, 7, 11] def solver(rounds): last_spoken = dict() number_spoken = defaultdict(int) for i, n in enumerate(starting_numbers): last_spoken[n] = i + 1 number_spoken[n] += 1 most_recent = starting_numbers[-1] turn = len(starting_numbers) while turn != rounds: turn += 1 prev_most_recent = most_recent if number_spoken[most_recent] <= 1: most_recent = 0 else: most_recent = turn - 1 - last_spoken[most_recent] number_spoken[most_recent] += 1 last_spoken[prev_most_recent] = turn - 1 return most_recent if __name__ == '__main__': print(f'Part One: {solver(2020)}') print(f'Part Two: {solver(30000000)}')

23.617647

61

0.620174

106

803

4.396226

0.415094

0.214592

0.103004

0.094421

0.098712

0

0.054795

0.272727

803

33

62

24.333333

0.743151

0

0.07472

0

1

0.043478

false

0

0.043478

0

0.130435

0.086957

0

null

1

0

null

0

1

56e77e033d14f603000e73fa84271bc6b5607ec9

3,987

py

Python

exp/hou_ximg.py

schaban/crosscore_dev

1eae118a485cb1de1d54d2da01ff0b32966205ef

[ "MIT" ]

5

2022-01-26T03:25:00.000Z

2022-03-06T03:27:13.000Z

exp/hou_ximg.py

schaban/crosscore_dev

1eae118a485cb1de1d54d2da01ff0b32966205ef

[ "MIT" ]

null

exp/hou_ximg.py

schaban/crosscore_dev

1eae118a485cb1de1d54d2da01ff0b32966205ef

[ "MIT" ]

null

# Author: Sergey Chaban <sergey.chaban@gmail.com> import sys import hou import os import imp import re import inspect from math import * from array import array import xcore import xhou try: xrange except: xrange = range def writeBits(bw, bits, nbits): nbytes = xcore.ceilDiv(nbits, 8) wk = bits for i in xrange(nbytes): bw.writeU8(wk & 0xFF) wk >>= 8 class ImgPlane: def __init__(self, ximg, name, rawFlg = not True): self.ximg = ximg self.name = name self.nameId = ximg.strLst.add(name) if name == "a": self.data = ximg.cop.allPixels("A") else: self.data = ximg.cop.allPixels("C", xhou.getRGBComponentName(ximg.cop, name)) ref = self.data[0] self.constFlg = True for val in self.data: if val != ref: self.constFlg = False break self.compress(rawFlg) def compress(self, rawFlg): self.minVal = min(self.data) self.maxVal = max(self.data) self.valOffs = self.minVal if self.valOffs > 0: self.valOffs = 0 self.bitCnt = 0 self.bits = 0 self.minTZ = 32 if self.constFlg: self.format = 0 return if rawFlg: self.format = -1 return self.format = 1 for fval in self.data: fval -= self.valOffs ival = xcore.getBitsF32(fval) & ((1<<31)-1) self.minTZ = min(self.minTZ, xcore.ctz32(ival)) tblSize = 1 << 8 tbl = [0 for i in xrange(tblSize)] pred = 0 hash = 0 nlenBits = 5 w = self.ximg.w h = self.ximg.h for y in xrange(h): for x in xrange(w): idx = (h-1-y)*w + x fval = self.data[idx] - self.valOffs ival = xcore.getBitsF32(fval) & ((1<<31)-1) ival >>= self.minTZ xor = ival ^ pred tbl[hash] = ival hash = ival >> 21 hash &= tblSize - 1 pred = tbl[hash] xlen = 0 if xor: xlen = xcore.bitLen32(xor) dat = xlen if xlen: dat |= (xor & ((1<<xlen)-1)) << nlenBits self.bits |= dat << self.bitCnt self.bitCnt += nlenBits + xlen def writeInfo(self, bw): bw.writeU32(0) # +00 -> data self.ximg.writeStrId16(bw, self.nameId) # +04 bw.writeU8(self.minTZ) # +06 bw.writeI8(self.format) # +07 bw.writeF32(self.minVal) # +08 bw.writeF32(self.maxVal) # +0C bw.writeF32(self.valOffs) # +10 bw.writeU32(self.bitCnt) # +14 bw.writeU32(0) # +18 reserved0 bw.writeU32(0) # +1C reserved1 def writeData(self, bw): if self.format == 0: bw.writeF32(self.data[0]) elif self.format == 1: writeBits(bw, self.bits, self.bitCnt) else: w = self.ximg.w h = self.ximg.h for y in xrange(h): for x in xrange(w): idx = (h-1-y)*w + x bw.writeF32(self.data[idx]) class ImgExporter(xcore.BaseExporter): def __init__(self): xcore.BaseExporter.__init__(self) self.sig = "XIMG" def build(self, copPath, rawFlg = True): self.copPath = copPath self.nameId, self.pathId = self.strLst.addNameAndPath(copPath) self.cop = hou.node(copPath) self.w = self.cop.xRes() self.h = self.cop.yRes() self.planes = {} self.addPlane("r", rawFlg) self.addPlane("g", rawFlg) self.addPlane("b", rawFlg) self.addPlane("a", rawFlg) def addPlane(self, name, rawFlg = True): self.planes[name] = ImgPlane(self, name, rawFlg) def writeHead(self, bw, top): npln = len(self.planes) bw.writeU32(self.w) # +20 bw.writeU32(self.h) # +24 bw.writeU32(npln) # +28 self.patchPos = bw.getPos() bw.writeI32(0) # +2C -> info def writeData(self, bw, top): plnLst = [] for plnName in self.planes: plnLst.append(self.planes[plnName]) npln = len(plnLst) bw.align(0x10) infoTop = bw.getPos() bw.patch(self.patchPos, bw.getPos() - top) # -> info for i in xrange(npln): plnLst[i].writeInfo(bw) for i, pln in enumerate(plnLst): bw.align(4) bw.patch(infoTop + (i*0x20), bw.getPos() - top) xcore.dbgmsg("Saving plane " + pln.name) pln.writeData(bw) def save(self, outPath): xcore.BaseExporter.save(self, outPath)

25.234177

81

0.616002

589

3,987

4.149406

0.264856

0.032733

0.028642

0.01473

0.094926

0.075286

0.04419

0

0.037841

0.237773

3,987

157

82

25.394904

0.766371

0.037121

0

0.135714

0

0.006552

0

0.003276

0

1

0.078571

false

0

0.071429

0

0.178571

0

null

0

null

0

1

0

56e98dd7ca93ca487d380c97603b7cfdaf2404c3

4,000

py

Python

nsd1802/python/ansible_project/myansible/webansi/views.py

MrWangwf/nsd1806

069e993b0bb64cb21adc2a25aa56f6da674453bc

[ "Apache-2.0" ]

null

nsd1802/python/ansible_project/myansible/webansi/views.py

MrWangwf/nsd1806

069e993b0bb64cb21adc2a25aa56f6da674453bc

[ "Apache-2.0" ]

null

nsd1802/python/ansible_project/myansible/webansi/views.py

MrWangwf/nsd1806

069e993b0bb64cb21adc2a25aa56f6da674453bc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import shutil from collections import namedtuple from ansible.parsing.dataloader import DataLoader from ansible.vars.manager import VariableManager from ansible.inventory.manager import InventoryManager from ansible.playbook.play import Play from ansible.executor.task_queue_manager import TaskQueueManager import ansible.constants as C from django.shortcuts import render, HttpResponse from .models import Host, Group, Module, Args def mainpage(request): return render(request, 'webansi/mainpage.html') def index(request): return render(request, 'webansi/hostinfo.html') def addhosts(request): if request.method == 'POST': g = request.POST.get('group') ip = request.POST.get('ipaddr') h = request.POST.get('hostname') gobj = Group.objects.get_or_create(group=g)[0] # host = Host(hostname=h, ipaddr=ip, group=gobj) # host.save() Host.objects.get_or_create(hostname=h, ipaddr=ip, group=gobj) host_info = {} # {'webservers': ['node1', 'node2'], 'dbservers': 'hosts'} groups = Group.objects.all() # qset for g in groups: hosts = [] for host in g.host_set.all(): hosts.append(host.hostname) host_info[g] = hosts return render(request, 'webansi/addhosts.html', {'host_info': host_info}) def addmodules(request): if request.method == 'POST': m = request.POST.get('module') a = request.POST.get('args') mobj = Module.objects.get_or_create(mod_name=m)[0] # args = Args(mod_args=a, mod=mobj) # args.save() Args.objects.get_or_create(mod_args=a, mod=mobj) mod_info = {} mods = Module.objects.all() for m in mods: argss = [] for args in m.args_set.all(): argss.append(args.mod_args) mod_info[m] = argss return render(request, 'webansi/addmodules.html', {'mod_info': mod_info}) def exec_task(dest, mod, args): Options = namedtuple('Options', ['connection', 'module_path', 'forks', 'become', 'become_method', 'become_user', 'check', 'diff']) options = Options(connection='smart', module_path=['/to/mymodules'], forks=10, become=None, become_method=None, become_user=None, check=False, diff=False) loader = DataLoader() passwords = dict() inventory = InventoryManager(loader=loader, sources=['ansicfg/dhosts.py']) variable_manager = VariableManager(loader=loader, inventory=inventory) play_source = dict( name="Ansible Play", hosts=dest, gather_facts='no', tasks=[ dict(action=dict(module=mod, args=args), register='shell_out'), ] ) play = Play().load(play_source, variable_manager=variable_manager, loader=loader) tqm = None try: tqm = TaskQueueManager( inventory=inventory, variable_manager=variable_manager, loader=loader, options=options, passwords=passwords, ) result = tqm.run(play) finally: if tqm is not None: tqm.cleanup() shutil.rmtree(C.DEFAULT_LOCAL_TMP, True) def tasks(request): if request.method == 'POST': ip = request.POST.get('ipaddr') group = request.POST.get('group') mod = request.POST.get('module') args = request.POST.get('args') print ip, group, mod, args if ip: dest = ip else: dest = group exec_task(dest, mod, args) hosts = list(Host.objects.all()) groups = list(Group.objects.all()) mod_info = {} mods = Module.objects.all() for m in mods: argss = [] for args in m.args_set.all(): argss.append(args.mod_args) mod_info[m] = argss result = {'hosts': hosts, 'groups': groups, 'mods': mods, 'args': args, 'mod_info': mod_info} return render(request, 'webansi/tasks.html', result)

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56e9fd79e108a7ca6eae3fd77971936796edbc9e

9,698

py

Python

macadam/conf/constant_params.py

yongzhuo/Macadam

794a29c760ce25264388c3a85a6b118733afb023

[ "MIT" ]

290

2020-06-04T17:01:30.000Z

2022-03-29T13:10:18.000Z

macadam/conf/constant_params.py

furtherthanfar/Macadam

794a29c760ce25264388c3a85a6b118733afb023

[ "MIT" ]

7

2020-06-05T02:30:51.000Z

2022-03-17T01:05:42.000Z

macadam/conf/constant_params.py

furtherthanfar/Macadam

794a29c760ce25264388c3a85a6b118733afb023

[ "MIT" ]

35

2020-06-11T07:32:17.000Z

2022-03-09T06:08:03.000Z

# !/usr/bin/python # -*- coding: utf-8 -*- # @time : 2020/4/26 20:25 # @author : Mo # @function: constant of token-symbol and hyper-parameters-default from macadam.conf.path_config import path_model_dir from typing import Dict import os EMBEDDING_TYPE = ["ROBERTA","ELECTRA","RANDOM","ALBERT", "XLNET","NEZHA","GPT2","WORD","BERT", "MIX"] # symbol of common token MASK = "[MASK]" CLS = "[CLS]" SEP = "[SEP]" PAD = "[PAD]" UNK = "[UNK]" BOS = "[BOS]" EOS = "[EOS]" WC = "[WC]" # task of macadam SL = "SL" # sequence-labeling(ner, pos, tag) TC = "TC" # text-classification RE = "RE" # relation-extraction # hyper_parameters of deep-learning, include sharing, embed, graph, train, save and data hyper_parameters_default = { "sharing": {"length_max": None, # 句子最大长度, 不配置则会选择前95%数据的最大长度, 配置了则会强制选择, 固定推荐20-50, bert越长会越慢, 占用空间也会变大, 小心OOM "embed_size": 768, # 字/词向量维度, bert取768, word取300, char可以更小些 "vocab_size": None, # 字典/词典大小, 可根据具体语料更新, 可不配置 "trainable": True, # embedding是静态的还是动态的, 即控制可不可以微调 "task": None, # 任务类型, "SL"(sequence-labeling), "TC"(text-classification),"RE"(relation-extraction) "token_type": "CHAR", # 级别, 最小单元, 字/词, 填 "CHAR" or "WORD", "NGRAM", 注意:word2vec模式下训练语料要首先切好 "embed_type": "BERT", # 级别, 嵌入类型, 还可以填"WORD"、"RANDOM"、 "BERT"、 "ALBERT"、"ROBERTA"、"NEZHA"、"XLNET"、"ELECTRA"、"GPT2" "gpu_memory_fraction": 0.6, # gpu使用率, 0-1 }, "embed": {"layer_idx": [-2], # 取bert的layer层输出, -1~-12, 0-11等, eg. 0, 1, 11, -1, -2, -12等 "path_embed": None, # 外部embed模型地址, 如word2vec, bert "merge_type": "concat", # bert的layer层输出融合方式, 包括 "concat", "add", "pool-max", "pool-avg", "multi" "application": "encode", # bert4keras下游任务, "encode", "lm", "unilm"等 "length_first": None, # 第一句最大长度, 大则截断-小则padding "length_second": None, # 第二句最大长度, 大则截断-小则padding "xlnet_embed": {"attention_type": "bi", "memory_len": 0, "target_len": 5}, # xlnet的参数, 使用的是keras-xlnet }, "graph": {"filters_size": [3, 4, 5], # 卷积核尺寸, 1-10 "filters_num": 300, # 卷积个数 text-cnn:300-600 "rnn_type": None, # 循环神经网络, select "LSTM", "GRU", "Bidirectional-GRU" "rnn_unit": 256, # RNN隐藏层, 8的倍数, 一般取64, 128, 256, 512, 768等 "dropout": 0.5, # 随机失活, 概率， 0-1 "activate_mid": "tanh", # 中间激活函数, 非线性变幻, 提升逼近能力, 选择"relu","tanh"或"sigmoid" "activate_end": "softmax", # 结束激活函数, 即最后一层的激活函数, 如cls激活函数, ner激活函数 "use_onehot": True, # label是否使用独热编码 "use_crf": False, # 是否使用CRF(条件随机场), task="sl"(序列标注任务)任务 "loss": None, # 损失函数, 真实值与实际预测的差值损失, 最优化的方向, "categorical_crossentropy" "metrics": "accuracy", # 评估指标, 保存更好模型的评价标准, 一般选择loss, acc或f1等 "optimizer": "Adam", # 优化器, 可选["Adam", "Radam", "RAdam,Lookahead"] "optimizer_extend":[ "gradient_accumulation", "piecewise_linear_lr", "layer_adaptation", "lazy_optimization", "]weight_decay", "lookahead"], # 优化器拓展, ["gradient_accumulation", "piecewise_linear_lr", "layer_adaptation", # "lazy_optimization","weight_decay", "lookahead"] }, "train": {"learning_rate": 1e-3, # 学习率, 必调参数, 对训练影响较大, word2vec一般设置1e-3, bert设置5e-5或2e-5 "decay_rate": 0.999, # 学习率衰减系数, 即乘法, lr = lr * rate "decay_step": 1000, # 学习率每step步衰减, 每N个step衰减一次 "batch_size": 32, # 批处理尺寸, 设置过小会造成收敛困难、陷入局部最小值或震荡, 设置过大会造成泛化能力降低 "early_stop": 6, # 早停, N个轮次(epcoh)评估指标(metrics)不增长就停止训练 "epochs": 20, # 训练最大轮次, 即最多训练N轮 "label": None, # 类别数, auto无需定义, 如果定义则是强制指定 "is_training": True, # 是否训练, 用以区分训练train或预测predict, 用它判断后确定加不加载优化器optimizer }, "save": { # "path_hyper_parameters": None, # 超参数文件地址 "path_model_dir": None, # 模型目录, loss降低则保存的依据, save_best_only=True, save_weights_only=True "path_model_info": None, # 模型所有超参数, 保存在model_info.json "path_fineture": None, # 微调后embedding文件地址, 例如字向量、词向量、bert向量等 }, "data": {"train_data": None, # 训练数据 "val_data": None # 验证数据 }, } class Config: def __init__(self, hyper_parameters: Dict={}): """ Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "train", "save" and "data". Returns: None """ # 各种超参数, 设置默认超参数 self.hyper_parameters = self.get_hyper_parameters_default() # 只更新传入的key-value for k in hyper_parameters.keys(): self.hyper_parameters[k].update(hyper_parameters.get(k, {})) self.params_sharing = self.hyper_parameters.get("sharing", {}) self.params_embed = self.hyper_parameters.get("embed", {}) self.params_graph = self.hyper_parameters.get("graph", {}) self.params_train = self.hyper_parameters.get("train", {}) self.params_save = self.hyper_parameters.get("save", {}) self.params_data = self.hyper_parameters.get("data", {}) # params of sharing self.gpu_memory_fraction = self.params_sharing.get("gpu_memory_fraction", 0.60) self.embed_type = self.params_sharing.get("embed_type", "RANDOM") self.token_type = self.params_sharing.get("token_type", "CHAR") self.task = self.params_sharing.get("task", None) self.length_max = self.params_sharing.get("length_max", None) self.vocab_size = self.params_sharing.get("vocab_size", None) self.embed_size = self.params_sharing.get("embed_size", None) self.trainable = self.params_sharing.get("trainable", True) # params of embed self.layer_idx = self.params_embed.get("layer_idx", []) self.path_embed = self.params_embed.get("path_embed", None) self.merge_type = self.params_embed.get("merge_type", "concat") self.length_first = self.params_embed.get("length_first", None) self.length_second = self.params_embed.get("length_second", None) self.xlnet_embed = self.params_embed.get("xlnet_embed", {}) self.attention_type = self.params_embed.get("attention_type", "bi") self.memory_len = self.params_embed.get("memory_len", 128) self.target_len = self.params_embed.get("target_len", 128) # params of graph self.filters_size = self.params_graph.get("filters_size", [3, 4, 5]) self.filters_num = self.params_graph.get("filters_num", 300) self.rnn_type = self.params_graph.get("rnn_type", None) self.rnn_unit = self.params_graph.get("rnn_unit", 256) self.dropout = self.params_graph.get("dropout", 0.5) self.activate_mid = self.params_graph.get("activate_mid", "tanh") self.activate_end = self.params_graph.get("activate_end", "softmax") self.use_onehot = self.params_graph.get("use_onehot", True) self.use_crf = self.params_graph.get("use_crf", False) self.loss = self.params_graph.get("loss", "categorical_crossentropy" if self.use_onehot else "sparse_categorical_crossentropy") self.metrics = self.params_graph.get("metrics", "accuracy") self.optimizer = self.params_graph.get("optimizer", "Adam").upper() self.optimizer_extend = self.params_graph.get("optimizer_extend", []) # params of train self.learning_rate = self.params_train.get("learning_rate", 5e-5) self.decay_rate = self.params_train.get("decay_rate", 0.999) self.decay_step = self.params_train.get("decay_step", 32000) self.early_stop = self.params_train.get("early_stop", 6) self.batch_size = self.params_train.get("batch_size", 32) self.epochs = self.params_train.get("epochs", 20) self.label = self.params_train.get("label", None) self.is_training = self.params_train.get("is_training", True) # params of save self.path_model_dir = self.params_save.get("path_model_dir", path_model_dir) # self.path_model_info = self.params_save.get("path_model_info", None) self.path_fineture = self.params_save.get("path_fineture", None) # params of data self.train_data = self.params_data.get("train_data", None) self.val_data = self.params_data.get("val_data", None) # 特殊符号 self.token_dict = {PAD: 0, UNK: 1, CLS: 2, SEP: 3, BOS: 4, EOS: 5, MASK: 6, WC: 7 } # 递归创建模型保存目录 if not self.path_model_dir: self.path_model_dir = path_model_dir if not os.path.exists(self.path_model_dir): os.makedirs(self.path_model_dir) def get_hyper_parameters_default(self) -> Dict: """ Get hyper_parameters of default. Args: None Returns: Dict """ return hyper_parameters_default

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null

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null

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56ea4043e94445a1fa0825bf267b5e1fb99e0df2

786

py

Python

tests/test_model/test_backbone/test_mobilenetv3_backbone.py

ZJCV/PyCls

1ef59301646b6134f2ffcc009b4fd76550fa4089

[ "Apache-2.0" ]

110

2021-02-04T14:32:57.000Z

2022-03-30T01:51:56.000Z

tests/test_model/test_backbone/test_mobilenetv3_backbone.py

likyoo/ZCls

568621aca3a8b090c93345f0858d52c5757f2f0e

[ "Apache-2.0" ]

8

2021-04-11T02:46:57.000Z

2021-12-14T19:30:58.000Z

tests/test_model/test_backbone/test_mobilenetv3_backbone.py

likyoo/ZCls

568621aca3a8b090c93345f0858d52c5757f2f0e

[ "Apache-2.0" ]

20

2021-02-07T14:17:07.000Z

2022-03-22T05:20:40.000Z

# -*- coding: utf-8 -*- """ @date: 2020/12/30 下午9:36 @file: test_mobilenetv3_backbone.py @author: zj @description: """ import torch from zcls.model.backbones.mobilenet.mobilenetv3_backbone import MobileNetV3Backbone def test_mobilenet_v3_backbone(): data = torch.randn(1, 3, 224, 224) model = MobileNetV3Backbone( in_channels=3, base_channels=16, out_channels=960, width_multiplier=1., round_nearest=8, reduction=4, attention_type='SqueezeAndExcitationBlock2D', conv_layer=None, norm_layer=None, act_layer=None, ) print(model) outputs = model(data) print(outputs.shape) assert outputs.shape == (1, 960, 7, 7) if __name__ == '__main__': test_mobilenet_v3_backbone()

20.684211

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null

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null

0

1

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56eb32a92cc867cd71aa0914a66e1907fb58aeae

4,348

py

Python

analog_sim/spice/ngspice.py

yrrapt/spice_interface

2a66bd2672b5154920457676bbaaef8ddd694640

[ "Apache-2.0" ]

5

2021-04-29T21:38:17.000Z

2021-07-07T04:03:45.000Z

analog_sim/spice/ngspice.py

yrrapt/spice_interface

2a66bd2672b5154920457676bbaaef8ddd694640

[ "Apache-2.0" ]

null

analog_sim/spice/ngspice.py

yrrapt/spice_interface

2a66bd2672b5154920457676bbaaef8ddd694640

[ "Apache-2.0" ]

1

2021-11-30T01:12:21.000Z

import os, re, subprocess import numpy as np from spyci import spyci from PySpice.Spice.NgSpice.Shared import NgSpiceShared from analog_sim.spice.generic import GenericSpiceInterface class NgSpiceInterface(GenericSpiceInterface): ''' ''' def __init__(self, verbose=True, netlist_path=None, pdk_path=None): ''' Instantiate the object ''' self.config = {} self.config['simulator'] = {'executable' : 'ngspice', # 'shared' : True, 'shared' : False, 'silent' : False} self.config['verbose'] = verbose # create an ngspice shared object self.ngspice = NgSpiceShared.new_instance() def run_simulation(self, new_instance=True, outputs=None): ''' Run simulation ''' # pre-create the file locations netlist_path = self.run_dir + '/' + self.temp_netlist raw_path = self.run_dir + '/' + self.temp_result log_path = self.run_dir + '/' + self.temp_log # run ngspice if self.config['simulator']['shared']: # destroy previous run data self.ngspice.destroy() # self.ngspice.exec_command("reset") # self.ngspice.reset() # load the netlist into the if new_instance: self.ngspice.source(netlist_path) # run the simulation if self.config['simulator']['silent']: with suppress_stdout_stderr(): self.ngspice.run() else: self.ngspice.run() # save the outputs self.ngspice.exec_command("set filetype=ascii") self.ngspice.exec_command("write %s" % raw_path) else: # set the output format to ascii required by spyci os.environ["SPICE_ASCIIRAWFILE"] = "1" self.result_type = 'ascii' # run the simulation through command line bash_command = "ngspice -b -r %s -o %s %s" % (raw_path, log_path, netlist_path) process = subprocess.Popen(bash_command.split(), stdout=subprocess.PIPE) output, error = process.communicate() # check if error occured with open(log_path) as f: sim_log = f.read() if 'fatal' in sim_log or 'aborted' in sim_log: print('\033[91m') print('-'*150) print('ERROR IN SIMULATION:') print(sim_log) print('-'*150) print('\033[0m') # read in the results of the simulation if outputs: self.simulation_data = {} for output in outputs: self.read_results("rundir/spiceinterface_temp_"+output+".raw", output) else: self.read_results(raw_path) def netlist_voltage_pwl(self, name, voltage, negative='0', dc=0): ''' Write a netlist line for a DC PWL source ''' return 'V' + name + ' ' + name + ' ' + negative + ' dc %f ' % dc + 'pwl ( ' + voltage + ' )' def netlist_temperature(self, temperature): ''' Set the temperature ''' # form the include line line = '.option TEMP=%s' % temperature return line def netlist_control_block(self, control_block): ''' Set a control block ''' # form the include line line = '.control\n' line += control_block + '\n' line += '.endc' return line def netlist_sim_tran(self, final_time, initial_step=-1, use_intitial_conditions=False): ''' Define a transient simulation TRAN <initial step value> <final time value> ''' # if the rise and fall is not set then default to 1/50 of the period if initial_step < 0: initial_step = final_time/1000 # form the transient instruction line = '.tran %s %s' % (self.unit_format(initial_step), self.unit_format(final_time)) if use_intitial_conditions: line += ' uic' return line

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56ee5b13733521aa2c6d7583b5c0eff94fcf5da5

728

py

Python

producer/kwebmon_producer/json_schemas.py

nicolalamacchia/kwebmon

13d8720314e9faff99b34dd5cb6c10d1cf45d786

[ "MIT" ]

null

producer/kwebmon_producer/json_schemas.py

nicolalamacchia/kwebmon

13d8720314e9faff99b34dd5cb6c10d1cf45d786

[ "MIT" ]

4

2021-04-28T03:19:37.000Z

2021-04-28T13:10:27.000Z

producer/kwebmon_producer/json_schemas.py

nicolalamacchia/kwebmon

13d8720314e9faff99b34dd5cb6c10d1cf45d786

[ "MIT" ]

null

SITES_JSON_SCHEMA = { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "sites": {"type": "array"}, "items": {"$ref": "#/$defs/site"} }, "$defs": { "site": { "type": "object", "required": ["url"], "properties": { "url": { "type": "string", "description": "Website URL" }, "pattern": { "type": "string", "description": ("Python-compatible RegEx pattern to be " "used to validate website content") } } } } }

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null

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56ee82e7574951e2d9ede6d10b02540ba09cf793

68

py

Python

src/traits/__init__.py

5monkeys/django-traits

206c91f2738974fe5df0a12a7e94f1ba1dd28f39

[ "BSD-3-Clause" ]

null

src/traits/__init__.py

5monkeys/django-traits

206c91f2738974fe5df0a12a7e94f1ba1dd28f39

[ "BSD-3-Clause" ]

2

2021-10-07T18:14:13.000Z

2021-10-07T20:30:49.000Z

src/traits/__init__.py

5monkeys/django-traits

206c91f2738974fe5df0a12a7e94f1ba1dd28f39

[ "BSD-3-Clause" ]

null

from .base import Trait __version__ = "0.0.1" __all__ = ("Trait",)

13.6

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56ee8b1c1d8d6917b939b39a1094ae81309532e0

4,404

py

Python

willie/modules/whois.py

ezoSresyeK/willie

5782628d15996d0cc901bb4ee27d89e9c7ad94ae

[ "EFL-2.0" ]

null

willie/modules/whois.py

ezoSresyeK/willie

5782628d15996d0cc901bb4ee27d89e9c7ad94ae

[ "EFL-2.0" ]

null

willie/modules/whois.py

ezoSresyeK/willie

5782628d15996d0cc901bb4ee27d89e9c7ad94ae

[ "EFL-2.0" ]

null

""" whois.py - Willie Whois module Copyright 2014, Ellis Percival (Flyte) willie@failcode.co.uk Licensed under the Eiffel Forum License 2. http://willie.dftba.net A module to enable Willie to perform WHOIS lookups on nicknames. This can either be to have Willie perform lookups on behalf of other people, or can be imported and used by other modules. """ from willie.module import commands, event, rule from time import sleep from datetime import datetime, timedelta AGE_THRESHOLD = timedelta(days=1) class Whois(object): def __init__(self, data): to, self.nick, self.ident, self.host, star, self.name = data self.datetime = datetime.now() def __repr__(self): return '%s(nick=%r, ident=%r, host=%r, name=%r, datetime=%r)' % ( self.__class__.__name__, self.nick, self.ident, self.host, self.name, self.datetime ) def __str__(self): return '%s!%s@%s * %s' % ( self.nick, self.ident, self.host, self.name) def set_chans(self, trigger): self.chans = trigger class WhoisFailed(Exception): pass def setup(bot): bot.memory['whois'] = {} def check_setup(bot): if 'whois' not in bot.memory: bot.memory['whois'] = {} def _clear_old_entries(bot): """ Removes entries from the bot's memory which are older than AGE_THRESHOLD. """ to_del = [] for nick, whois in bot.memory['whois'].items(): if whois.datetime < datetime.now() - AGE_THRESHOLD: to_del.append(nick) for nick in to_del: try: del bot.memory['whois'][nick] except KeyError: pass def send_whois(bot, nick): """ Sends the WHOIS command to the server for the specified nick. """ bot.write(['WHOIS', nick]) def get_whois(bot, nick): """ Waits for the response to be put into the bot's memory by the receiving thread. """ check_setup(bot) i = 0 while nick.lower() not in bot.memory['whois'] and i < 10: i += 1 sleep(2) if nick.lower() not in bot.memory['whois']: return #raise WhoisFailed('No reply from server') elif bot.memory['whois'][nick.lower()] is None: try: del bot.memory['whois'][nick.lower()] except KeyError: pass #raise WhoisFailed('No such nickname') # A little housekeeping _clear_old_entries(bot) try: return bot.memory['whois'][nick.lower()] except KeyError: return None def whois(bot, nick): """ Sends the WHOIS command to the server then waits for the response to be put into the bot's memory by the receiving thread. """ # Remove entry first so that we get the latest check_setup(bot) try: del bot.memory['whois'][nick] except KeyError: pass send_whois(bot, nick) return get_whois(bot, nick) @event('311') @rule(r'.*') def whois_found_reply(bot, trigger): """ Listens for successful WHOIS responses and saves them to the bot's memory. """ check_setup(bot) nick = trigger.args[1] bot.memory['whois'][nick.lower()] = Whois(trigger.args) @event('319') @rule(r'.*') def whois_chan_list(bot, trigger): nick = trigger.args[1] if nick not in bot.memory['whois']: sleep(3) bot.memory['whois'][nick.lower()].set_chans(trigger) @event('401') @rule(r'.*') def whois_not_found_reply(bot, trigger): """ Listens for unsuccessful WHOIS responses and saves None to the bot's memory so that the initial whois function is aware that the lookup failed. """ check_setup(bot) nick = trigger.args[1] bot.memory['whois'][nick] = None print("Encountered 401") # Give the initiating whois function time to see # that the lookup has failed, then remove the None. sleep(5) try: del bot.memory['whois'][nick] except KeyError: pass @commands('whois') def display_whois(bot, trigger): """PM's you the chans the nick is in.""" nick = trigger.group().split()[1] try: w = whois(bot, nick) sleep(3) bot.msg(trigger.nick, '%s is on the following chans: %s' % (w.nick, w.chans)) except: bot.msg(trigger.nick, '%s could not be found' % (nick))

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0.60604

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0.285008

0.055004

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0.061879

0.326585

0.274637

0.239878

0.190222

0.165011

0.116883

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0.273842

4,404

180

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0.809256

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0

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0

0.009615

0.077273

0

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0.134615

false

0.048077

0.028846

0.019231

0.240385

0.009615

0

null

0

null

0

1

0

56ef8a75099969f876b3cdd3157b7f50324c1ed5

1,188

py

Python

setup.py

satyrius/cmsplugin-scripts

bffcaefa36377b0baeedc6a0006b2c3ce5a50a98

[ "MIT" ]

null

setup.py

satyrius/cmsplugin-scripts

bffcaefa36377b0baeedc6a0006b2c3ce5a50a98

[ "MIT" ]

null

setup.py

satyrius/cmsplugin-scripts

bffcaefa36377b0baeedc6a0006b2c3ce5a50a98

[ "MIT" ]

null

from setuptools import setup, find_packages from cmsplugin_scripts import __version__ CLASSIFIERS = [ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Communications', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content :: Message Boards', 'Topic :: Internet :: WWW/HTTP :: Site Management', 'Programming Language :: Python :: 2.7', ] setup( name='cmsplugin-scripts', version=__version__, description='Django CMS plugin for script tag injection', author='Anton Egorov', author_email='anton.egoroff@gmail.com', url='https://github.com/satyrius/cmsplugin-scripts', license='MIT', long_description=open('README.rst').read(), classifiers=CLASSIFIERS, platforms=['OS Independent'], packages=find_packages(), include_package_data=True, install_requires=[ 'django-cms', ], tests_require=['tox>=1.8'], zip_safe=False, )

29.7

73

0.661616

125

1,188

6.144

0.672

0.0625

0.078125

0.088542

0

0.005236

0.196128

1,188

39

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0

1

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false

0

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0

0.057143

0

null

0

null

0

1

0

56efd06e8d45906c55ba8332a46d55aa2ff8358b

698

py

Python

test/fix_name.py

xhuang98/Dtect

929d01945fd2768032dbb84d8ba1f62069132172

[ "MIT" ]

1

2021-12-25T01:43:43.000Z

test/fix_name.py

xhuang98/Dtect

929d01945fd2768032dbb84d8ba1f62069132172

[ "MIT" ]

null

test/fix_name.py

xhuang98/Dtect

929d01945fd2768032dbb84d8ba1f62069132172

[ "MIT" ]

1

2021-09-02T15:30:04.000Z

import os import json if __name__ == '__main__': f = open('coverage/codeclimate.json', 'r') summary = json.load(f) f.close() for i in range(len(summary['source_files'])): if summary['source_files'][i]['name'][0] == '/': path = summary['source_files'][i]['name'].replace('//', '/') fields = path.split(os.sep) local = list(fields) for field in fields: if field == 'Dtect': break local.remove(field) new_name = os.path.join(*local) summary['source_files'][i]['name'] = new_name f = open('coverage/codeclimate.json', 'w') json.dump(summary, f)

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698

4.240964

0.445783

0.147727

0.204545

0.161932

0.355114

0

0.002062

0.305158

698

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