xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

6f67dc1568ee757508905abb02a33c1edb9248fc

170

py

Python

hackerrank/forcia2019/yfp_a.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

1

2018-11-12T15:18:55.000Z

hackerrank/forcia2019/yfp_a.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

hackerrank/forcia2019/yfp_a.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

s = int(input().replace('b', '0').replace('B', '1'), 2) t = int(input().replace('b', '0').replace('B', '1'), 2) print(bin(s + t)[2:].replace('0', 'b').replace('1', 'B'))

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0.376471

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null

0

5

6f73ff53c69c0f29f3b30629ae060d750fe57093

190

py

Python

NewsPaper/NewsPaper/NewsPaper/news/admin.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

[ "MIT" ]

null

NewsPaper/NewsPaper/NewsPaper/news/admin.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

[ "MIT" ]

null

NewsPaper/NewsPaper/NewsPaper/news/admin.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

[ "MIT" ]

null

from django.contrib import admin from .models import Category, Post, Author admin.site.register(Category) admin.site.register(Post) admin.site.register(Author) # Register your models here.

23.75

42

0.805263

27

190

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190

7

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48a024988481367c58396d630a4731b23dcd4d6d

141

py

Python

src/flotilla.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

5

2016-01-01T15:50:21.000Z

2018-11-27T17:38:15.000Z

src/flotilla.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

27

2015-12-17T07:49:56.000Z

2018-07-13T15:06:33.000Z

src/flotilla.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

7

2015-12-01T22:04:24.000Z

2021-11-28T13:21:35.000Z

#!/usr/bin/env python from main import setup_logging from flotilla.cli import cli if __name__ == '__main__': setup_logging() cli()

15.666667

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4.5

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141

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null

0

1

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1

0

5

48a46b486d3501db93dc65960f51bb0bec0ea51a

154

py

Python

api/source/testing/cases/splash_case.py

1pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

1

2019-12-17T10:31:48.000Z

api/source/testing/cases/splash_case.py

c-pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

null

api/source/testing/cases/splash_case.py

c-pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

1

2019-04-29T08:19:36.000Z

from .base_case import BaseCase from actions import Splash class SplashCase(BaseCase): def test_splash_result(self): return NotImplemented

17.111111

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0.766234

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154

6.052632

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0

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154

8

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false

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null

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null

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5

48f24012698359067f29497d265827d68ccdd405

34

py

Python

torchswarm/utils/parameters.py

rohanmohapatra/torchswarm

0b0c1aee787ff97bee4bce469fe8481734e14ee5

[ "MIT" ]

15

2020-12-26T15:22:53.000Z

2022-02-01T23:21:59.000Z

torchswarm/utils/parameters.py

rohanmohapatra/torchswarm

0b0c1aee787ff97bee4bce469fe8481734e14ee5

[ "MIT" ]

null

torchswarm/utils/parameters.py

rohanmohapatra/torchswarm

0b0c1aee787ff97bee4bce469fe8481734e14ee5

[ "MIT" ]

3

2021-08-06T09:30:01.000Z

2022-02-11T05:38:10.000Z

class SwarmParameters: pass

11.333333

23

0.705882

3

34

8

1

0

0.264706

34

2

24

17

0.96

0

1

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true

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null

0

1

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null

0

1

0

5

48fb5e3262b09fb64a9f14dd6c7c92b507fa0a4c

51

py

Python

modules/MMM-Testpython/sample.py

ENTITYSmartMirror/BeautyMirrorTest1

d19ccbddc03fe87ae48485863cb84b2f33cd6796

[ "MIT" ]

null

modules/MMM-Testpython/sample.py

ENTITYSmartMirror/BeautyMirrorTest1

d19ccbddc03fe87ae48485863cb84b2f33cd6796

[ "MIT" ]

3

2021-12-01T09:31:40.000Z

2022-03-25T18:41:33.000Z

modules/MMM-Testpython/sample.py

ENTITYSmartMirror/BeautyMirrorTest1

d19ccbddc03fe87ae48485863cb84b2f33cd6796

[ "MIT" ]

null

print ("sample.py ! I'm Python. Nice to meet you!")

51

0.666667

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3.4

1

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51

1

51

0.790698

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null

0

1

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1

0

5

5b16290da097104e6ec9565b0904c99f18758a9f

32

py

Python

AER/fairseq_c/fairseq/version.py

formiel/NeurIPS2021

a2a29a1904779c3ffd1a002fd057bf90b31a234d

[ "MIT" ]

5

2021-10-11T13:44:05.000Z

2022-02-22T12:55:25.000Z

AER/fairseq_c/fairseq/version.py

formiel/NeurIPS2021

a2a29a1904779c3ffd1a002fd057bf90b31a234d

[ "MIT" ]

null

AER/fairseq_c/fairseq/version.py

formiel/NeurIPS2021

a2a29a1904779c3ffd1a002fd057bf90b31a234d

[ "MIT" ]

8

2021-09-09T14:49:14.000Z

2022-03-03T15:53:09.000Z

__version__ = "1.0.0a0+59b0556"

16

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1

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null

0

5

d28d7c797ff8aa2243b0b71fd13154658327df06

57

py

Python

datasets/__init__.py

ZhengPeng7/GLCNet

5ec7d4eb0ddece3f789df0b25f414bc4b5ac1d9e

[ "MIT" ]

5

2021-12-07T03:11:02.000Z

2022-01-22T15:52:19.000Z

datasets/__init__.py

ZhengPeng7/GLCNet

5ec7d4eb0ddece3f789df0b25f414bc4b5ac1d9e

[ "MIT" ]

2

2021-12-18T07:24:25.000Z

2022-03-31T08:43:48.000Z

datasets/__init__.py

ZhengPeng7/GLCNet

5ec7d4eb0ddece3f789df0b25f414bc4b5ac1d9e

[ "MIT" ]

null

from .build import build_test_loader, build_train_loader

28.5

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1

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5

d2b017f8da1c58e536a2cbf80e635bb4db171a28

30,551

py

Python

old_codes/GEETools.py

ollinevalainen/satellitetools

c36cb686bb6d87d5268890706d71f2144144b4c0

[ "MIT" ]

6

2021-02-26T09:17:15.000Z

2022-01-10T17:10:04.000Z

old_codes/GEETools.py

ollinevalainen/satellitetools

c36cb686bb6d87d5268890706d71f2144144b4c0

[ "MIT" ]

2

2020-06-09T09:55:45.000Z

2022-02-23T12:36:01.000Z

old_codes/GEETools.py

ollinevalainen/satellitetools

c36cb686bb6d87d5268890706d71f2144144b4c0

[ "MIT" ]

1

2021-06-08T01:09:22.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Dec 10 10:43:18 2019 @author: nevalaio """ import ee import time import datetime import satelliteTools as st import pandas as pd from geetools import batch, tools import numpy as np ee.Initialize() #----------------- Sentinel-2 ------------------------------------- def S2_getBandData_within_bbox_single_feature(S2_timseries_dataframe, aoi_shp, AOI_id_property,AOI_id, bufferdist, datestart, dateend): bands= ['B3', 'B4','B5','B6','B7','B8A','B11','B12'] #] # properties = ['cos(View_Zenith)', 'cos(Sun_Zenith)', 'cos(Rel_Azimuth)'] start = time.time() image_list = {} crs_list = {} key = AOI_id full_assetids = "COPERNICUS/S2_SR/" + S2_timseries_dataframe[key]['assetid'] image_list[key] = [ee.Image(a) for a in full_assetids] crs_list[key] = [crs for crs in S2_timseries_dataframe[key]['crs']][0] attributes = st.getShapeAtrrtibutesWithIdentifier(aoi_shp, AOI_id_property) feature = ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(attributes[key]['geometry'])),\ {'name':key, 'image_list':image_list[key], 'crs':crs_list[key]}) if bufferdist: bbox = ee.Feature(feature.geometry().buffer(bufferdist).bounds(0.1,feature.get("crs"))) else: bbox = ee.Feature(feature.geometry().bounds(0.1,feature.get("crs"))) imageCollection = ee.ImageCollection.fromImages(feature.get("image_list"))\ .filterBounds(bbox.geometry())\ .filterDate(datestart,dateend)\ .select(bands) # imageCollection = imageCollection.map(S2_addNDVI) #lisää ja laske indeksejä tässä? def S2_getBandData_image_single_feature(img): img = img.clip(bbox.geometry()) productid = img.get('PRODUCT_ID') assetid = img.get('assetid') tileid = img.get('MGRS_TILE') system_index = img.get('system:index') sun_azimuth = img.get('MEAN_SOLAR_AZIMUTH_ANGLE') sun_zenith = img.get('MEAN_SOLAR_ZENITH_ANGLE') view_azimuth = ee.Array([img.get('MEAN_INCIDENCE_AZIMUTH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]).get([0]) view_zenith = ee.Array([img.get('MEAN_INCIDENCE_ZENITH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]).get([0]) img = img.resample('bilinear').reproject(crs=feature.get("crs"), scale=10) # get the lat lon and add the ndvi image_grid = ee.Image.pixelCoordinates(ee.Projection(feature.get("crs")))\ .addBands([img.select(b) for b in bands]) # apply reducer to list image_grid = image_grid.reduceRegion( reducer=ee.Reducer.toList(), geometry=bbox.geometry(), maxPixels=1e8, scale=10) # get data into arrays x_coords = ee.Array(image_grid.get("x")) y_coords = ee.Array(image_grid.get("y")) # band_data = [] # [band_data.extend(b,ee.Array(image_grid.get("%s"%b))) for b in bands[:-1]] band_data = {b:ee.Array(image_grid.get("%s"%b)) for b in bands} # NDVI_array = ee.Array(image_grid.get("NDVI")) # B6_array = ee.Array(image_grid.get("B6")) # perform LAI et al. computation possibly here! tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('productid', productid)\ .set('system_index',system_index)\ .set('assetid', assetid)\ .set('tileid', tileid)\ .set('crs', feature.get("crs"))\ .set('sun_zenith',sun_zenith)\ .set('sun_azimuth',sun_azimuth)\ .set('view_zenith',view_zenith)\ .set('view_azimuth',view_azimuth)\ .set('x_coords', x_coords)\ .set('y_coords', y_coords)\ .set(band_data) return tmpfeature S2_single_feature_data = imageCollection.map(S2_getBandData_image_single_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return S2_single_feature_data def S2_getBandData_within_aoi_single_feature(S2_timseries_dataframe, aoi_shp, AOI_id_property,AOI_id, datestart, dateend): bands= ['B3', 'B4','B5','B6','B7','B8A','B11','B12'] #] # properties = ['cos(View_Zenith)', 'cos(Sun_Zenith)', 'cos(Rel_Azimuth)'] start = time.time() image_list = {} crs_list = {} key = AOI_id full_assetids = "COPERNICUS/S2_SR/" + S2_timseries_dataframe[key]['assetid'] image_list[key] = [ee.Image(a) for a in full_assetids] crs_list[key] = [crs for crs in S2_timseries_dataframe[key]['crs']][0] attributes = st.getShapeAtrrtibutesWithIdentifier(aoi_shp, AOI_id_property) feature = ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(attributes[key]['geometry'])),\ {'name':key, 'image_list':image_list[key], 'crs':crs_list[key]}) geom = feature.geometry(0.1,feature.get("crs")) imageCollection = ee.ImageCollection.fromImages(feature.get("image_list"))\ .filterBounds(geom)\ .filterDate(datestart,dateend)\ .select(bands) # imageCollection = imageCollection.map(S2_addNDVI) #lisää ja laske indeksejä tässä? def S2_getBandData_image_single_feature(img): img = img.clip(geom) productid = img.get('PRODUCT_ID') assetid = img.get('assetid') tileid = img.get('MGRS_TILE') system_index = img.get('system:index') sun_azimuth = img.get('MEAN_SOLAR_AZIMUTH_ANGLE') sun_zenith = img.get('MEAN_SOLAR_ZENITH_ANGLE') view_azimuth = ee.Array([img.get('MEAN_INCIDENCE_AZIMUTH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]).get([0]) view_zenith = ee.Array([img.get('MEAN_INCIDENCE_ZENITH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]).get([0]) img = img.resample('bilinear').reproject(crs=feature.get("crs"), scale=10) # get the lat lon and add the ndvi image_grid = ee.Image.pixelCoordinates(ee.Projection(feature.get("crs")))\ .addBands([img.select(b) for b in bands]) # apply reducer to list image_grid = image_grid.reduceRegion( reducer=ee.Reducer.toList(), geometry=geom, maxPixels=1e8, scale=10) # get data into arrays x_coords = ee.Array(image_grid.get("x")) y_coords = ee.Array(image_grid.get("y")) # band_data = [] # [band_data.extend(b,ee.Array(image_grid.get("%s"%b))) for b in bands[:-1]] band_data = {b:ee.Array(image_grid.get("%s"%b)) for b in bands} # NDVI_array = ee.Array(image_grid.get("NDVI")) # B6_array = ee.Array(image_grid.get("B6")) # perform LAI et al. computation possibly here! tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('productid', productid)\ .set('system_index',system_index)\ .set('assetid', assetid)\ .set('tileid', tileid)\ .set('crs', feature.get("crs"))\ .set('sun_zenith',sun_zenith)\ .set('sun_azimuth',sun_azimuth)\ .set('view_zenith',view_zenith)\ .set('view_azimuth',view_azimuth)\ .set('x_coords', x_coords)\ .set('y_coords', y_coords)\ .set(band_data) return tmpfeature S2_single_feature_data = imageCollection.map(S2_getBandData_image_single_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return S2_single_feature_data def S2_getBandData_single_feature_to_dict(featureDict): featureCollection_dict = {} for farm, featureCollection in featureDict.items(): featureCollection_dict[farm]= {'Date': []} featureCollection_dict[farm].update({prop:[] for prop in featureCollection['features'][0]['properties'].keys()}) for featnum in range(len(featureCollection['features'])): productid = featureCollection['features'][featnum]['properties']['productid'] date = st.sentinelTitle2Datetime(productid) featureCollection_dict[farm]['Date'].append(date) for prop in featureCollection['features'][featnum]['properties'].keys(): if prop is not 'Date': featureCollection_dict[farm][prop].append(featureCollection['features'][featnum]['properties'][prop]) return featureCollection_dict def featureCollection_dict_to_dataframes(featureCollection_dict,props): dataframes = {} for key, item in featureCollection_dict.items(): # dataframes[key] = pd.DataFrame({'Date': item['Date'], # 'lai': list(np.mean(np.array(item['lai']), axis = 1)) , # 'lai_std': list(np.std(np.array(item['lai']), axis = 1)) }) dataframes[key] = pd.DataFrame({'Date': item['Date']}) for prop in props: dataframes[key][prop] = list(np.mean(np.array(item[prop]), axis = 1)) dataframes[key][prop+'_std'] = list(np.std(np.array(item['lai']), axis = 1)) return dataframes def S2_getBandData(S2_timseries_dataframe, aoi_shp, AOI_id_property, bufferdist, datestart, dateend): bands= ['B3', 'B4','B5','B6','B7','B8A','B11','B12'] #] # properties = ['cos(View_Zenith)', 'cos(Sun_Zenith)', 'cos(Rel_Azimuth)'] start = time.time() image_list = {} crs_list = {} for key, item in S2_timseries_dataframe.items(): full_assetids = "COPERNICUS/S2_SR/" + item['assetid'] image_list[key] = [ee.Image(a) for a in full_assetids] crs_list[key] = [crs for crs in item['crs']][0] attributes = st.getShapeAtrrtibutesWithIdentifier(aoi_shp, AOI_id_property) features = [ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(attributes[key]['geometry'])),\ {'name':key, 'image_list':image_list[key], 'crs':crs_list[key]}) for key,item in S2_timseries_dataframe.items()] featureCollection = ee.FeatureCollection(features) def S2_getBandData_feature(feature): if bufferdist: bbox = ee.Feature(feature.geometry().buffer(bufferdist).bounds(0.1,feature.get("crs"))) else: bbox = ee.Feature(feature.geometry().bounds(0.1,feature.get("crs"))) imageCollection = ee.ImageCollection.fromImages(feature.get("image_list"))\ .filterBounds(bbox.geometry())\ .filterDate(datestart,dateend)\ .select(bands) # imageCollection = imageCollection.map(S2_addNDVI) #lisää ja laske indeksejä tässä? def S2_getBandData_image(img): img = img.clip(bbox.geometry()) productid = img.get('PRODUCT_ID') assetid = img.get('assetid') tileid = img.get('MGRS_TILE') system_index = img.get('system:index') sun_azimuth = img.get('MEAN_SOLAR_AZIMUTH_ANGLE') sun_zenith = img.get('MEAN_SOLAR_ZENITH_ANGLE') view_azimuth = ee.Array([img.get('MEAN_INCIDENCE_AZIMUTH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]) view_zenith = ee.Array([img.get('MEAN_INCIDENCE_ZENITH_ANGLE_%s'%b) for b in bands]).reduce(ee.Reducer.mean(), [0]) img = img.resample('bilinear').reproject(crs=feature.get("crs"), scale=10) # get the lat lon and add the ndvi image_grid = ee.Image.pixelCoordinates(ee.Projection(feature.get("crs")))\ .addBands([img.select(b) for b in bands]) # apply reducer to list image_grid = image_grid.reduceRegion( reducer=ee.Reducer.toList(), geometry=bbox.geometry(), maxPixels=1e8, scale=10) # get data into arrays x_coords = ee.Array(image_grid.get("x")) y_coords = ee.Array(image_grid.get("y")) # band_data = [] # [band_data.extend(b,ee.Array(image_grid.get("%s"%b))) for b in bands[:-1]] band_data = {b:ee.Array(image_grid.get("%s"%b)) for b in bands} # NDVI_array = ee.Array(image_grid.get("NDVI")) # B6_array = ee.Array(image_grid.get("B6")) # perform LAI et al. computation possibly here! tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('productid', productid)\ .set('system_index',system_index)\ .set('assetid', assetid)\ .set('tileid', tileid)\ .set('crs', feature.get("crs"))\ .set('sun_zenith',sun_zenith)\ .set('sun_azimuth',sun_azimuth)\ .set('view_zenith',view_zenith)\ .set('view_azimuth',view_azimuth)\ .set('x_coords', x_coords)\ .set('y_coords', y_coords)\ .set(band_data) return tmpfeature S2_image_data = imageCollection.map(S2_getBandData_image) return feature.set('productid',S2_image_data.aggregate_array('productid'))\ .set('system_index', S2_image_data.aggregate_array('system_index'))\ .set('assetid',S2_image_data.aggregate_array('assetid'))\ .set('tileid',S2_image_data.aggregate_array('tileid'))\ .set('crs',S2_image_data.aggregate_array('crs'))\ .set('x_coords',S2_image_data.aggregate_array('x_coords'))\ .set('y_coords',S2_image_data.aggregate_array('y_coords'))\ .set('sun_zenith',S2_image_data.aggregate_array('sun_zenith'))\ .set('sun_azimuth',S2_image_data.aggregate_array('sun_azimuth'))\ .set('view_zenith',S2_image_data.aggregate_array('view_zenith'))\ .set('view_azimuth',S2_image_data.aggregate_array('view_azimuth'))\ .set({b:S2_image_data.aggregate_array(b) for b in bands}) featureCollection = featureCollection.map(S2_getBandData_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return featureCollection def S2_addNDVI(image): ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI') return image.addBands(ndvi) def S2_computeNDVItimeseries(AOI_shp,AOI_id_property,datestart, dateend): start = time.time() #aoi_shp = "/home/nevalaio/Dropbox/Työura/FMI/CARBO/analysis/ruukki_blocks_wgs84.shp" attributes = st.getShapeAtrrtibutesWithIdentifier(AOI_shp,AOI_id_property) features = [ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(item['geometry'])), {'name':key}) for key,item in attributes.items()] featureCollection = ee.FeatureCollection(features) def S2_computeNDVItimeseries_feature(feature): area = feature.geometry() collection = ee.ImageCollection("COPERNICUS/S2_SR").filterBounds(area)\ .filterDate(datestart,dateend)\ .select(['B8','B4','SCL']) collection = collection.map(S2_addNDVI) def S2_computeNDVItimeseries_image(img): # ndvi = ee.Image(img.select(['NDVI'])) # scl = ee.Image(img.select(['SCL'])) productid = img.get('PRODUCT_ID') assetid = img.id() tileid = img.get('MGRS_TILE') system_index = img.get('system:index') proj = img.select("B8").projection() # get the lat lon and add the ndvi # latlon = ee.Image.pixelLonLat().addBands([scl,ndvi]) # apply reducer to list img = img.reduceRegion( reducer=ee.Reducer.toList(), geometry=area, maxPixels=1e8, scale=10) # get data into arrays classdata = ee.Array(ee.Algorithms.If(img.get("SCL"),ee.Array(img.get("SCL")),ee.Array([0]))) ndvidata = ee.Array(ee.Algorithms.If(img.get("NDVI"),ee.Array(img.get("NDVI")),ee.Array([-9999]))) classmask = classdata.eq(0).add(classdata.eq(1).add(classdata.eq(3).add(classdata.eq(7)\ .add(classdata.eq(8).add(classdata.eq(9)\ .add(classdata.eq(10).add(classdata.eq(11)\ ))))))) badcount = classmask.reduce(ee.Reducer.sum(),[0]) totalcount = classmask.length() goodcount = totalcount.get([0]) # ndvidata_masked = ndvidata.mask(classmask.Not()) mean = ndvidata.reduce(ee.Reducer.mean(),[0]).get([0]) std = ndvidata.reduce(ee.Reducer.stdDev(),[0]).get([0]) qualityUncertainty = badcount.divide(totalcount).get([0]) tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('productid', productid)\ .set('system_index',system_index)\ .set('assetid', assetid)\ .set('tileid', tileid)\ .set('projection', proj)\ .set('sample_n', goodcount)\ .set('ndvi_mean',mean)\ .set('ndvi_std',std)\ .set('quality_uncertainty',qualityUncertainty) return tmpfeature ndvi_timeseries = collection.map(S2_computeNDVItimeseries_image) return feature.set('productid',ndvi_timeseries.aggregate_array('productid'))\ .set('system_index', ndvi_timeseries.aggregate_array('system_index'))\ .set('assetid',ndvi_timeseries.aggregate_array('assetid'))\ .set('tileid',ndvi_timeseries.aggregate_array('tileid'))\ .set('projection',ndvi_timeseries.aggregate_array('projection'))\ .set('sample_n',ndvi_timeseries.aggregate_array('sample_n'))\ .set('ndvi_mean',ndvi_timeseries.aggregate_array('ndvi_mean'))\ .set('ndvi_std',ndvi_timeseries.aggregate_array('ndvi_std'))\ .set('quality_uncertainty',ndvi_timeseries.aggregate_array('quality_uncertainty')) featureCollection = featureCollection.map(S2_computeNDVItimeseries_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return featureCollection def S2_getTimeseriesQualityInformation(AOI_shp,AOI_id_property,datestart, dateend): start = time.time() attributes = st.getShapeAtrrtibutesWithIdentifier(AOI_shp,AOI_id_property) features = [ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(item['geometry'])), {'name':key}) for key,item in attributes.items()] featureCollection = ee.FeatureCollection(features) def S2_getTimeseriesQualityInformation_feature(feature): area = feature.geometry() collection = ee.ImageCollection("COPERNICUS/S2_SR").filterBounds(area)\ .filterDate(datestart,dateend)\ .select(['SCL']) def S2_getTimeseriesQualityInformation_image(img): productid = img.get('PRODUCT_ID') assetid = img.id() tileid = img.get('MGRS_TILE') system_index = img.get('system:index') proj = img.select("SCL").projection() # apply reducer to list img = img.reduceRegion( reducer=ee.Reducer.toList(), geometry=area, maxPixels=1e8, scale=10) # get data into arrays classdata = ee.Array(ee.Algorithms.If(img.get("SCL"),ee.Array(img.get("SCL")),ee.Array([0]))) classmask = classdata.eq(0).add(classdata.eq(1).add(classdata.eq(3).add(classdata.eq(7)\ .add(classdata.eq(8).add(classdata.eq(9)\ .add(classdata.eq(10).add(classdata.eq(11)\ ))))))) badcount = classmask.reduce(ee.Reducer.sum(),[0]) totalcount = classmask.length() goodcount = totalcount.get([0]) qualityUncertainty = badcount.divide(totalcount).get([0]) tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('productid', productid)\ .set('system_index',system_index)\ .set('assetid', assetid)\ .set('tileid', tileid)\ .set('projection', proj)\ .set('sample_n', goodcount)\ .set('quality_uncertainty',qualityUncertainty) return tmpfeature QI_timeseries = collection.map(S2_getTimeseriesQualityInformation_image) return feature.set('productid',QI_timeseries.aggregate_array('productid'))\ .set('system_index', QI_timeseries.aggregate_array('system_index'))\ .set('assetid',QI_timeseries.aggregate_array('assetid'))\ .set('tileid',QI_timeseries.aggregate_array('tileid'))\ .set('projection',QI_timeseries.aggregate_array('projection'))\ .set('sample_n',QI_timeseries.aggregate_array('sample_n'))\ .set('quality_uncertainty',QI_timeseries.aggregate_array('quality_uncertainty')) featureCollection = featureCollection.map(S2_getTimeseriesQualityInformation_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return featureCollection def S2_featureCollection2Dataframe(featureCollection): dataframes = {} for featnum in range(len(featureCollection['features'])): featureCollection_dict = {} key = featureCollection['features'][featnum]['properties']['name'] productid = featureCollection['features'][featnum]['properties']['productid'] projections = featureCollection['features'][featnum]['properties']['projection'] crs = [d['crs'] for d in projections] dates = [st.sentinelTitle2Datetime(d) for d in productid] featureCollection_dict.update({'Date': dates, 'crs': crs}) for prop, data in featureCollection['features'][featnum]['properties'].items(): if prop not in ['Date','crs','projection']: featureCollection_dict.update({prop: data}) dataframes[key] = pd.DataFrame(featureCollection_dict) return dataframes def S2_NDVIfeatureCollection2Dataframe(featureCollection): dataframes = {} for featnum in range(len(featureCollection['features'])): key = featureCollection['features'][featnum]['properties']['name'] productid = featureCollection['features'][featnum]['properties']['productid'] # dates = [datetime.datetime.strptime(d.split('_')[1], '%Y%m%dT%H%M%S') for d in dataid] projections = featureCollection['features'][featnum]['properties']['projection'] crs = [d['crs'] for d in projections] dates = [st.sentinelTitle2Datetime(d) for d in productid] featureCollection_dict= {'Date': dates, 'productid': productid, 'system_index': featureCollection['features'][featnum]['properties']['system_index'], 'assetid': featureCollection['features'][featnum]['properties']['assetid'], 'tileid': featureCollection['features'][featnum]['properties']['tileid'], 'crs': crs, 'sample_n': featureCollection['features'][featnum]['properties']['sample_n'], 'ndvi_mean': featureCollection['features'][featnum]['properties']['ndvi_mean'], 'ndvi_std': featureCollection['features'][featnum]['properties']['ndvi_std'], 'quality_uncertainty': featureCollection['features'][featnum]['properties']['quality_uncertainty'] } dataframes[key] = pd.DataFrame(featureCollection_dict, columns= ['Date','productid','system_index','assetid','tileid','crs','sample_n','ndvi_mean','ndvi_std','quality_uncertainty']) return dataframes def S2_exportImageCollection(assetIDs, aoi): assetIDs = ["COPERNICUS/S2_SR/" + a for a in assetIDs] images = [ee.Image(assetid) for assetid in assetIDs] imageCollection = ee.ImageCollection(images) aoi = ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(aoi))) batch.imagecollection.toDrive(imageCollection, 'FOLDER', region=tools.geometry.getRegion(aoi), scale=10, verbose=True) #----------------- LANDSAT-8 ------------------------------------- def L8_addNDVI(image): ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI') return image.addBands(ndvi) def L8_computeNDVItimeseries(AOI_shp,AOI_id_property,datestart, dateend): start = time.time() attributes = st.getShapeAtrrtibutesWithIdentifier(AOI_shp,AOI_id_property) features = [ee.Feature(ee.Geometry.Polygon(st.wkt2coordinates(item['geometry'])), {'name':key}) for key,item in attributes.items()] featureCollection = ee.FeatureCollection(features) def L8_comuteNDVItimeseries_feature(feature): area = feature.geometry() collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(area)\ .filterDate(datestart,dateend)\ .select(['B5','B4','pixel_qa']) collection = collection.map(L8_addNDVI) def L8_computeNDVItimeseries_image(img): # ndvi = ee.Image(img.select(['NDVI'])) dataid = img.get('LANDSAT_ID') sensingtime = img.get('SENSING_TIME') # qa = ee.Image(img.select(['pixel_qa'])) # get the lat lon and add the ndvi # latlon = ee.Image.pixelLonLat().addBands([qa, ndvi]) # apply reducer to list img = img.reduceRegion( reducer=ee.Reducer.toList(), geometry=area, maxPixels=1e8, scale=30); # get data into arrays classdata = ee.Array(ee.Algorithms.If(img.get("pixel_qa"),ee.Array(img.get("pixel_qa")),ee.Array([0]))) ndvidata = ee.Array(ee.Algorithms.If(img.get("NDVI"),ee.Array(img.get("NDVI")),ee.Array([-9999]))) # classdata = ee.Array(latlon.get("pixel_qa")) # ndvidata = ee.Array(latlon.get("NDVI")) mean = ndvidata.reduce(ee.Reducer.mean(),[0]).get([0]) std = ndvidata.reduce(ee.Reducer.stdDev(),[0]).get([0]) classmask = classdata.eq(322).Or(classdata.eq(386)).Not() badcount = classmask.reduce(ee.Reducer.sum(),[0]) totalcount = classmask.length() qualityUncertainty = badcount.divide(totalcount).get([0]) tmpfeature = ee.Feature(ee.Geometry.Point([0,0]))\ .set('dataid',dataid)\ .set('sensing_time', sensingtime)\ .set('ndvi_mean',mean)\ .set('ndvi_std',std)\ .set('quality_uncertainty',qualityUncertainty) return tmpfeature ndvi_timeseries = collection.map(L8_computeNDVItimeseries_image) return feature.set('dataid',ndvi_timeseries.aggregate_array('dataid'))\ .set('sensing_time',ndvi_timeseries.aggregate_array('sensing_time'))\ .set('ndvi_mean',ndvi_timeseries.aggregate_array('ndvi_mean'))\ .set('ndvi_std',ndvi_timeseries.aggregate_array('ndvi_std'))\ .set('quality_uncertainty',ndvi_timeseries.aggregate_array('quality_uncertainty')) featureCollection = featureCollection.map(L8_comuteNDVItimeseries_feature).getInfo() end = time.time() total_time = end - start print ("Processsing time in seconds %s"%total_time) return featureCollection def L8_featureCollection2Dataframe(L8_featureCollection): dataframes = {} for featnum in range(len(L8_featureCollection['features'])): key = L8_featureCollection['features'][featnum]['properties']['name'] dataid = L8_featureCollection['features'][featnum]['properties']['dataid'] dates = [datetime.datetime.strptime(d.split('.')[0], '%Y-%m-%dT%H:%M:%S') for d in L8_featureCollection['features'][featnum]['properties']['sensing_time']] featureCollection_dict= {'Date': dates, 'dataid':dataid, 'ndvi_mean':L8_featureCollection['features'][featnum]['properties']['ndvi_mean'], 'ndvi_std':L8_featureCollection['features'][featnum]['properties']['ndvi_std'], 'quality_uncertainty':L8_featureCollection['features'][featnum]['properties']['quality_uncertainty'] } dataframes[key] = pd.DataFrame(featureCollection_dict, columns= ['Date','dataid','ndvi_mean','ndvi_std','quality_uncertainty']) return dataframes

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d2c80b3ecf64360017b06703d1d976586b238850

290

py

Python

src/waldur_mastermind/marketplace_rancher/extension.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

[ "MIT" ]

26

2017-10-18T13:49:58.000Z

2021-09-19T04:44:09.000Z

src/waldur_mastermind/marketplace_rancher/extension.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

[ "MIT" ]

14

2018-12-10T14:14:51.000Z

2021-06-07T10:33:39.000Z

src/waldur_mastermind/marketplace_rancher/extension.py

geant-multicloud/MCMS-mastermind

81333180f5e56a0bc88d7dad448505448e01f24e

[ "MIT" ]

32

2017-09-24T03:10:45.000Z

2021-10-16T16:41:09.000Z

from waldur_core.core import WaldurExtension class MarketplaceRancherExtension(WaldurExtension): class Settings: pass @staticmethod def django_app(): return 'waldur_mastermind.marketplace_rancher' @staticmethod def is_assembly(): return True

19.333333

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14

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null

0

null

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1

0

5

8252f69eebf8614888e5121c2d0ad3c23abd6c65

43

py

Python

lib/python/treadmill/syscall/__init__.py

vrautela/treadmill

05e47fa8acdf8bad7af78e737efb26ea6488de82

[ "Apache-2.0" ]

133

2016-09-15T13:36:12.000Z

2021-01-18T06:29:13.000Z

lib/python/treadmill/syscall/__init__.py

bretttegart/treadmill

812109e31c503a6eddaee2d3f2e1faf2833b6aaf

[ "Apache-2.0" ]

108

2016-12-28T23:41:27.000Z

2020-03-05T21:20:37.000Z

lib/python/treadmill/syscall/__init__.py

bretttegart/treadmill

812109e31c503a6eddaee2d3f2e1faf2833b6aaf

[ "Apache-2.0" ]

69

2016-09-23T20:38:58.000Z

2020-11-11T02:31:21.000Z

"""Linux direct system call interface. """

14.333333

38

0.697674

5

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43

2

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21.5

0.810811

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null

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null

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null

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null

true

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null

0

1

0

1

0

null

0

1

0

5

825fad92ce86d06a272eb8c33f8214580f9e35db

164

py

Python

segmentation/model/decoder/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

506

2020-06-12T01:07:56.000Z

2022-03-26T00:56:52.000Z

segmentation/model/decoder/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

85

2020-06-12T04:51:31.000Z

2022-03-23T16:19:44.000Z

segmentation/model/decoder/__init__.py

RajasekharChowdary9/panoptic-deeplab

7645bc1cf51e3ebc85153666f26f8630a407b52b

[ "Apache-2.0" ]

102

2020-06-12T06:45:44.000Z

2022-03-22T14:03:04.000Z

from .aspp import ASPP from .deeplabv3 import DeepLabV3Decoder from .deeplabv3plus import DeepLabV3PlusDecoder from .panoptic_deeplab import PanopticDeepLabDecoder

32.8

52

0.878049

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164

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0

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0.097561

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4

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1

0

1

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5

82653c88172a502e67527d557f7c2247f700acf5

883

py

Python

xilinx/python/bba.py

ilesser/nextpnr-xilinx

2425dde6a3cc65d990e0bb06bbe8a40e661b0b45

[ "0BSD" ]

79

2019-09-26T12:46:28.000Z

2021-04-22T16:10:12.000Z

xilinx/python/bba.py

ilesser/nextpnr-xilinx

2425dde6a3cc65d990e0bb06bbe8a40e661b0b45

[ "0BSD" ]

15

2019-12-24T11:25:28.000Z

2021-02-07T21:11:58.000Z

xilinx/python/bba.py

ilesser/nextpnr-xilinx

2425dde6a3cc65d990e0bb06bbe8a40e661b0b45

[ "0BSD" ]

12

2019-12-27T16:22:47.000Z

2021-04-21T09:19:58.000Z

class BBAWriter: def __init__(self, f): self.f = f def pre(self, s): print("pre {}".format(s), file=self.f) def post(self, s): print("post {}".format(s), file=self.f) def push(self, s): print("push {}".format(s), file=self.f) def offset32(self): print("offset32", file=self.f) def ref(self, r, comment=""): print("ref {} {}".format(r, comment), file=self.f) def str(self, s, comment=""): print("str |{}| {}".format(s, comment), file=self.f) def align(self): print("align", file=self.f) def label(self, s): print("label {}".format(s), file=self.f) def u8(self, n, comment=""): print("u8 {} {}".format(int(n), comment), file=self.f) def u16(self, n, comment=""): print("u16 {} {}".format(int(n), comment), file=self.f) def u32(self, n, comment=""): print("u32 {} {}".format(int(n), comment), file=self.f) def pop(self): print("pop", file=self.f)

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883

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883

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false

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null

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1

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1

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5

8281cd488d2aba5f3800592490a1d60bba7f815f

326

py

Python

test.py

jakobbaek/link_collector

18fa1083a876b653e345f35075402a415aa71b6d

[ "MIT" ]

null

test.py

jakobbaek/link_collector

18fa1083a876b653e345f35075402a415aa71b6d

[ "MIT" ]

null

test.py

jakobbaek/link_collector

18fa1083a876b653e345f35075402a415aa71b6d

[ "MIT" ]

null

from referals.referals import Collector import pandas as pd #inputfile = "/home/jakob/antivax/inputfiles/DATA AntiVax Norden_updated August.xlsx" #col = Collector(inputfile,title="test2") #col.add_services(services=["crowdtangle"]) #col.add_services(services=["twitter"]) #col.get_referals(running_export=False,update=False)

36.222222

85

0.800613

43

326

5.953488

0.674419

0.046875

0.109375

0.171875

0

0.003279

0.064417

326

8

86

40.75

0.836066

0.782209

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

8283b0201fb1fdfb6f1565d6ebbd985fe57065ce

117

py

Python

IntroProPython/aula9-Arquivos/listagem09-20.py

SweydAbdul/estudos-python

b052708d0566a0afb9a1c04d035467d45f820879

[ "MIT" ]

null

IntroProPython/aula9-Arquivos/listagem09-20.py

SweydAbdul/estudos-python

b052708d0566a0afb9a1c04d035467d45f820879

[ "MIT" ]

null

IntroProPython/aula9-Arquivos/listagem09-20.py

SweydAbdul/estudos-python

b052708d0566a0afb9a1c04d035467d45f820879

[ "MIT" ]

null

import os.path if os.path.exists('z'): print('O diretorio z existe.') else: print('O diretorio z nao existe')

23.4

37

0.666667

20

117

3.9

0.6

0.153846

0.384615

0.410256

0

0.179487

117

5

37

23.4

0.8125

0

0.389831

0

1

0

true

0

0.2

0

0.2

0.4

1

0

null

0

1

0

1

0

null

0

1

0

5

8286140ff338dc9e50fabdd39ebee99b12c86433

4,646

py

Python

functions.py

StanislavPetrovV/3D-Fractal-Mandelbulb-Ray-marching-

e7528c01e953159f957535c7549dbfdb63e19909

[ "MIT" ]

16

2020-05-14T04:29:10.000Z

2021-09-11T11:46:51.000Z

functions.py

StanislavPetrovV/3D-Fractal-Mandelbulb-Ray-marching-

e7528c01e953159f957535c7549dbfdb63e19909

[ "MIT" ]

1

2020-08-31T18:50:31.000Z

functions.py

StanislavPetrovV/3D-Fractal-Mandelbulb-Ray-marching-

e7528c01e953159f957535c7549dbfdb63e19909

[ "MIT" ]

5

2020-05-16T10:45:45.000Z

2021-09-01T07:41:37.000Z

import math # from settings import * from numba import njit @njit(fastmath=True)#, cache=True) def mod_vec3_n(vec, n): return (vec[0] % n, vec[1] % n, vec[2] % n) @njit(fastmath=True)#, cache=True) def length_vec3(vec): return math.sqrt(vec[0] ** 2 + vec[1] ** 2 + vec[2] ** 2) @njit(fastmath=True)#, cache=True) def length_vec2(vec): return math.sqrt(vec[0] ** 2 + vec[1] ** 2) @njit(fastmath=True)#, cache=True) def sub_vecs3(v1, v2): return (v1[0] - v2[0], v1[1] - v2[1], v1[2] - v2[2]) @njit(fastmath=True)#, cache=True) def sub_vecs2(v1, v2): return (v1[0] - v2[0], v1[1] - v2[1]) @njit(fastmath=True)#, cache=True) def sub_vec3_n(v1, n): return (v1[0] - n, v1[1] - n, v1[2] - n) @njit(fastmath=True)#, cache=True) def sub_n_vec3(n, v1): return (-v1[0] + n, -v1[1] + n, -v1[2] + n) @njit(fastmath=True)#, cache=True) def sub_vec2_n(v1, n): return (v1[0] - n, v1[1] - n) @njit(fastmath=True)#, cache=True) def sum_vecs3(v1, v2): return (v1[0] + v2[0], v1[1] + v2[1], v1[2] + v2[2]) @njit(fastmath=True)#, cache=True) def sum_vec2_n(v1, n): return (v1[0] + n, v1[1] + n) @njit(fastmath=True)#, cache=True) def mul_vec3_n(v1, n): return (v1[0] * n, v1[1] * n, v1[2] * n) @njit(fastmath=True)#, cache=True) def mul_vec2_n(v1, n): return (v1[0] * n, v1[1] * n) @njit(fastmath=True)#, cache=True) def div_vec3_n(v1, n): n = 1 / n return (v1[0] * n, v1[1] * n, v1[2] * n) @njit(fastmath=True)#, cache=True) def div_vecs2(v1, v2): v2 = 1 / v2 return (v1[0] * v2[0], v1[1] * v2[1]) @njit(fastmath=True)#, cache=True) def dot_vecs3(v1, v2): return (v1[0] * v2[0] + v1[1] * v2[1] + v1[2] * v2[2]) @njit(fastmath=True)#, cache=True) def abs_vec3(vec): return (abs(vec[0]), abs(vec[1]), abs(vec[2])) @njit(fastmath=True)#, cache=True) def normalize_vec3(vec): len_vec = 1 / math.sqrt(vec[0] ** 2 + vec[1] ** 2 + vec[2] ** 2) return (vec[0] * len_vec, vec[1] * len_vec, vec[2] * len_vec) @njit(fastmath=True)#, cache=True) def cross_vecs3(v1, v2): return (v1[1] * v2[2] - v1[2] * v2[1], v1[2] * v2[0] - v1[0] * v2[2], v1[0] * v2[1] - v1[1] * v2[0]) @njit(fastmath=True)#, cache=True) def view_matrix(): cam_pos = (0.0, 2.5, 9.0) center = (0.0, 0.0, 0.0) up = (0.0, 1.0, 0.0) f = normalize_vec3(sub_vecs3(center, cam_pos)) s = normalize_vec3(cross_vecs3(f, up)) u = cross_vecs3(s, f) s = (s[0], s[1], s[2], 0.0) u = (u[0], u[1], u[2], 0.0) f = (f[0], f[1], f[2], 0.0) return (s, u, f) @njit(fastmath=True)#, cache=True) def rotate_y_matrix(ray_dir, angle): s = math.sin(angle) c = math.cos(angle) a0 = (c, 0, -s, 0) a1 = (0, 1, 0, 0) a2 = (s, 0, c, 0) a = (a0, a1, a2) return mul_matrix_vec3(a, ray_dir) @njit(fastmath=True)#, cache=True) def rotate_x_matrix(ray_dir, angle): s = math.sin(angle) c = math.cos(angle) a0 = (1, 0, 0, 0) a1 = (0, c, -s, 0) a2 = (0, s, c, 0) a = (a0, a1, a2) return mul_matrix_vec3(a, ray_dir) @njit(fastmath=True)#, cache=True) def rotate_z_matrix(ray_dir, angle): s = math.sin(angle) c = math.cos(angle) a0 = (c, -s, 0, 0) a1 = (s, c, 0, 0) a2 = (0, 0, 1, 0) a = (a0, a1, a2) return mul_matrix_vec3(a, ray_dir) @njit(fastmath=True)#, cache=True) def translation_matrix(ray_dir, vec): a0 = (1, 0, 0, vec[0]) a1 = (0, 1, 0, vec[1]) a2 = (0, 0, 1, vec[2]) a = (a0, a1, a2) return mul_matrix_vec3(a, ray_dir) @njit(fastmath=True)#, cache=True) def scale_matrix(ray_dir, n): a0 = (n, 0, 0, 0) a1 = (0, n, 0, 0) a2 = (0, 0, n, 0) a = (a0, a1, a2) return mul_matrix_vec3(a, ray_dir) @njit(fastmath=True) def mul_matrix_vec3(a, b): c0 = (a[0][0] * b[0] + a[0][1] * b[1] + a[0][2] * b[2] + a[0][3] * 1) c1 = (a[1][0] * b[0] + a[1][1] * b[1] + a[1][2] * b[2] + a[1][3] * 1) c2 = (a[2][0] * b[0] + a[2][1] * b[1] + a[2][2] * b[2] + a[2][3] * 1) return (c0, c1, c2) @njit(fastmath=True)#, cache=True) def rotate_y(vec3, angle): s = math.sin(angle) c = math.cos(angle) return (vec3[0] * s, vec3[1], vec3[2] * c) @njit(fastmath=True)#, cache=True) def rotate_x(vec3, angle): s = math.sin(angle) c = math.cos(angle) return (vec3[0], vec3[1] * s, vec3[2] * c) @njit(fastmath=True)#, cache=True) def rotate_z(vec3, angle): s = math.sin(angle) c = math.cos(angle) return (vec3[0] * s, vec3[1] * s, vec3[2])

24.978495

105

0.525613

860

4,646

2.756977

0.076744

0.141712

0.18895

0.23914

0.754534

0.721215

0.685787

0.652889

0.589625

0

0.103945

0.252475

4,646

186

106

24.978495

0.57875

0.080284

0

0.390977

0

1

0.210526

false

0

0.015038

0.112782

0.43609

0

null

0

1

0

1

0

null

0

1

0

1

0

5

8296d3ae67e4ec09bed29e92f953636bc870a350

67

py

Python

b3q/__init__.py

nthparty/b3q

d39d5b943ccb665796dddbdf0a1dc02329ea5775

[ "MIT" ]

null

b3q/__init__.py

nthparty/b3q

d39d5b943ccb665796dddbdf0a1dc02329ea5775

[ "MIT" ]

null

b3q/__init__.py

nthparty/b3q

d39d5b943ccb665796dddbdf0a1dc02329ea5775

[ "MIT" ]

null

"""Gives users direct access to method.""" from b3q.b3q import get

22.333333

42

0.731343

11

67

4.454545

0.909091

0

0.035088

0.149254

67

2

43

33.5

0.824561

0.537313

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

82d14d4fc6a3bc267ab0b84ae227a48360e485db

116

py

Python

autodse/__main__.py

falcon-computing/Merlin_DSE

f8d22fa11c4664b782f060f0d79b87b5e3fbf205

[ "Apache-2.0" ]

6

2021-11-16T16:22:19.000Z

2022-03-05T14:27:23.000Z

autodse/__main__.py

falcon-computing/Merlin_DSE

f8d22fa11c4664b782f060f0d79b87b5e3fbf205

[ "Apache-2.0" ]

2

2021-12-01T07:59:49.000Z

2022-01-10T08:20:38.000Z

autodse/__main__.py

falcon-computing/Merlin_DSE

f8d22fa11c4664b782f060f0d79b87b5e3fbf205

[ "Apache-2.0" ]

5

2021-11-04T19:59:39.000Z

2022-02-27T22:32:52.000Z

""" The package and console entry. """ from autodse.main import Main if __name__ == '__main__': Main().main()

12.888889

30

0.655172

15

116

4.533333

0.733333

0.235294

0

0.189655

116

8

31

14.5

0.723404

0.258621

0

0.102564

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

82e3f9f4f9815f2b42378f3276b083b566880e9b

129

py

Python

python-sdk/nuscenes/eval/common/config.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/common/config.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/common/config.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

version https://git-lfs.github.com/spec/v1 oid sha256:650ce3dc1d1180763afe12a5c75a47f401008772752ccf3bc73809b03a4c9d39 size 1503

32.25

75

0.883721

13

129

8.769231

1

0

0.414634

0.046512

129

3

76

43

0.512195

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

7d81edb8204eddff88d1a22cbb08dad7fa815c19

5,505

py

Python

tests/unit/dataactvalidator/test_fabs41_detached_award_financial_assistance_3.py

dael-victoria-reyes/data-act-broker-backend

f83c7cad29cac24d95f45a262710dc1564de7dc1

[ "CC0-1.0" ]

1

2019-06-22T21:53:16.000Z

tests/unit/dataactvalidator/test_fabs41_detached_award_financial_assistance_3.py

dael-victoria-reyes/data-act-broker-backend

f83c7cad29cac24d95f45a262710dc1564de7dc1

[ "CC0-1.0" ]

null

tests/unit/dataactvalidator/test_fabs41_detached_award_financial_assistance_3.py

dael-victoria-reyes/data-act-broker-backend

f83c7cad29cac24d95f45a262710dc1564de7dc1

[ "CC0-1.0" ]

null

from tests.unit.dataactcore.factories.staging import DetachedAwardFinancialAssistanceFactory from tests.unit.dataactvalidator.utils import number_of_errors, query_columns from dataactcore.models.domainModels import Zips _FILE = 'fabs41_detached_award_financial_assistance_3' def test_column_headers(database): expected_subset = {"row_number", "place_of_performance_code", "place_of_performance_zip4a"} actual = set(query_columns(_FILE, database)) assert expected_subset == actual def test_success(database): """ When provided, PrimaryPlaceofPerformanceZIP+4 must be in the state specified by PrimaryPlaceOfPerformanceCode. In this specific submission row, the ZIP5 (and by extension the full ZIP+4) is not a valid ZIP code in the state in question.""" zips = Zips(zip5="12345", zip_last4="6789", state_abbreviation="NY") # ignored because no zip4 det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="NY*****", place_of_performance_zip4a="") det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="Ny**123", place_of_performance_zip4a=None) det_award_3 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="Ny**123", place_of_performance_zip4a="city-wide") # valid 5 digit zip det_award_4 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="Ny**123", place_of_performance_zip4a="12345") det_award_5 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="NY98765", place_of_performance_zip4a="12345") # valid 9 digit zip det_award_6 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="NY98765", place_of_performance_zip4a="123456789") det_award_7 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny98765", place_of_performance_zip4a="123456789") det_award_8 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny98765", place_of_performance_zip4a="12345-6789") # invalid 9 digit zip but this should pass for this rule, it will be handled for d_41_5 det_award_9 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny98765", place_of_performance_zip4a="12345-6788") errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, det_award_3, det_award_4, det_award_5, det_award_6, det_award_7, det_award_8, det_award_9, zips]) assert errors == 0 # random wrong length zips and zips with '-' in the wrong place, formatting is checked in another rule det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="12345678") det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="1234567898") det_award_3 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="12345678-9") det_award_4 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="123-456789") errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, det_award_3, det_award_4, zips]) assert errors == 0 def test_failure(database): """ Test failure for when provided, PrimaryPlaceofPerformanceZIP+4 must be in the state specified by PrimaryPlaceOfPerformanceCode. In this specific submission row, the ZIP5 (and by extension the full ZIP+4) is not a valid ZIP code in the state in question.""" zips = Zips(zip5="12345", zip_last4="6789", state_abbreviation="NY") # invalid 5 digit zip det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="12346") det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="NA*****", place_of_performance_zip4a='12345') errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, zips]) assert errors == 2 # invalid 9 digit zip - first five fail (see d41_5 for the last four to fail) det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="ny10986", place_of_performance_zip4a="123466789") det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code="NY*****", place_of_performance_zip4a='12346-6789') errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, zips]) assert errors == 2

68.8125

119

0.651408

573

5,505

5.886562

0.219895

0.074711

0.192114

0.117403

0.745034

0.726949

0.690187

0.68485

0

0.069114

0.28247

5,505

79

120

69.683544

0.78481

0.151499

0

0.272727

0

0.080087

0.020563

0

0.090909

1

0.054545

false

0

0.054545

0

0.109091

0

null

0

1

0

1

0

1

0

null

0

5

7d8da3897e90f29b0860f30a43ce22a4f7a383ab

105

py

Python

slack_g_cal/wit/__init__.py

wfa207/slack_g_cal

2acfb19d156e3b6f14c4ad0b079b9c3a5f79c9b0

[ "Apache-2.0" ]

1

2018-11-01T15:53:18.000Z

slack_g_cal/wit/__init__.py

wfa207/slack_g_cal

2acfb19d156e3b6f14c4ad0b079b9c3a5f79c9b0

[ "Apache-2.0" ]

null

slack_g_cal/wit/__init__.py

wfa207/slack_g_cal

2acfb19d156e3b6f14c4ad0b079b9c3a5f79c9b0

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- from __future__ import unicode_literals from client import wit_client # NOQA

21

39

0.742857

14

105

5.142857

0.785714

0

0.011364

0.161905

105

4

40

26.25

0.806818

0.266667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7da74abf170d937e7dc8e8d6028eaba12c819cf6

225

py

Python

Python-Advanced/comprehension_exercise/capitals.py

Xamaneone/SoftUni-Intro

985fe3249cd2adf021c2003372e840219811d989

[ "MIT" ]

null

Python-Advanced/comprehension_exercise/capitals.py

Xamaneone/SoftUni-Intro

985fe3249cd2adf021c2003372e840219811d989

[ "MIT" ]

null

Python-Advanced/comprehension_exercise/capitals.py

Xamaneone/SoftUni-Intro

985fe3249cd2adf021c2003372e840219811d989

[ "MIT" ]

null

countries = input().split(", ") capitals = input().split(", ") my_dict = {country: capital for country, capital in tuple(zip(countries, capitals))} [print (f"{country} -> {capital}") for country, capital in my_dict.items()]

37.5

84

0.68

29

225

5.206897

0.517241

0.370861

0.225166

0.317881

0.437086

0

0.124444

225

6

85

37.5

0.766497

0

0.115044

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

5

7db3383c72f24f87c415c7549f7af75f82d03253

77

py

Python

001-unittest/matrix.py

jake-bladt/pysandbox

02056ef22fa61f2ab1460167e0681fef28e57730

[ "Apache-2.0" ]

null

001-unittest/matrix.py

jake-bladt/pysandbox

02056ef22fa61f2ab1460167e0681fef28e57730

[ "Apache-2.0" ]

null

001-unittest/matrix.py

jake-bladt/pysandbox

02056ef22fa61f2ab1460167e0681fef28e57730

[ "Apache-2.0" ]

null

def get_matrix(): return [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]

11

17

0.363636

13

77

2.076923

1

0

0.191489

0.38961

77

6

18

12.833333

0.382979

0

1

0.166667

true

0

0.166667

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7dbfb410cc6962a84eb253cb613a328a20aefd8d

76

py

Python

execute_multi_updater.py

LUXROBO/-modi2-firmware-updater-

44fb7835f561d8629fe80d8e90b6f07d560c3e5d

[ "MIT" ]

1

2022-02-04T01:22:00.000Z

execute_multi_updater.py

LUXROBO/-modi2-firmware-updater-

44fb7835f561d8629fe80d8e90b6f07d560c3e5d

[ "MIT" ]

null

execute_multi_updater.py

LUXROBO/-modi2-firmware-updater-

44fb7835f561d8629fe80d8e90b6f07d560c3e5d

[ "MIT" ]

null

from main import run_gui if __name__ == "__main__": run_gui(multi=True)

19

26

0.723684

12

76

3.75

0.75

0.266667

0

0.171053

76

4

27

19

0.714286

0

0.103896

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

7ded7dde5b93fea2799ac74e1db3649ed19974f6

49

py

Python

med/encoder/__init__.py

jjeamin/sleep

c38ee3ef51405ae7ebd49b833c4cec9c6132f320

[ "MIT" ]

1

2020-10-12T02:57:25.000Z

med/encoder/__init__.py

jjeamin/sleep

c38ee3ef51405ae7ebd49b833c4cec9c6132f320

[ "MIT" ]

null

med/encoder/__init__.py

jjeamin/sleep

c38ee3ef51405ae7ebd49b833c4cec9c6132f320

[ "MIT" ]

1

2021-01-17T11:48:40.000Z

from .segnet import SegNetv2, SegNet, get_encoder

49

0.836735

7

49

5.714286

0.857143

0

0.022727

0.102041

49

1

49

0.886364

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

8156737158dc8beee799bc8e08ad0942f21ed62e

186

py

Python

abstractions/recognition/feature_extractor.py

Trapov/osu-recognition

703822f6700896f6d3e36b9fa005609f113fef73

[ "MIT" ]

null

abstractions/recognition/feature_extractor.py

Trapov/osu-recognition

703822f6700896f6d3e36b9fa005609f113fef73

[ "MIT" ]

2

2020-09-24T19:01:18.000Z

2020-09-24T19:31:55.000Z

abstractions/recognition/feature_extractor.py

Trapov/osu-recognition

703822f6700896f6d3e36b9fa005609f113fef73

[ "MIT" ]

1

2020-03-28T16:04:00.000Z

from abc import ABC, abstractmethod from numpy import ndarray class FeatureExtractor(ABC): @abstractmethod def extract(self, numpy_array: ndarray, faces: []) -> []: ...

23.25

61

0.682796

20

186

6.3

0.65

0.269841

0

0.204301

186

7

62

26.571429

0.851351

0

1

0.166667

false

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

8181b856f7d56f12e1b9e253568829fd79c621df

294

py

Python

website/addons/zotero/tests/test_utils.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

[ "Apache-2.0" ]

null

website/addons/zotero/tests/test_utils.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

[ "Apache-2.0" ]

1

2019-08-16T13:45:12.000Z

website/addons/zotero/tests/test_utils.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

[ "Apache-2.0" ]

null

import unittest from nose.tools import * from website.addons.citations.utils import serialize_account class TestSerializeAccount(unittest.TestCase): # TODO: Move to website/addons/citations/tests def test_serialize_account_none(self): assert_is_none(serialize_account(None))

26.727273

60

0.795918

37

294

6.135135

0.648649

0.211454

0.193833

0

0.132653

294

11

61

26.727273

0.890196

0.14966

0

0.090909

0.166667

1

0.166667

false

0

0.5

0

0.833333

0

null

1

0

null

0

1

0

1

0

1

0

5

8182dff3f3f0426bbe469eae5d776e478de1df19

204

py

Python

UniCoin/web/views.py

WckdAwe/UniCoin

955eadcf34e8b5829ef294a497f532ae31ae963b

[ "MIT" ]

null

UniCoin/web/views.py

WckdAwe/UniCoin

955eadcf34e8b5829ef294a497f532ae31ae963b

[ "MIT" ]

null

UniCoin/web/views.py

WckdAwe/UniCoin

955eadcf34e8b5829ef294a497f532ae31ae963b

[ "MIT" ]

null

# from flask import render_template, redirect, request # from UniCoin import app # # # @app.route('/') # def index(): # return render_template('index.html', # page_title='• Index')

22.666667

55

0.637255

24

204

5.291667

0.708333

0.220472

0

0.210784

204

8

56

25.5

0.78882

0.877451

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

5

81936e8e7cec44ee0e4c85293f0e47e631c1b8b3

147

py

Python

GameLogic/Utils.py

Insineer/OSS-13

6ddceea4fdd4b869ad438190237bbbc2610ba4bc

[ "MIT" ]

null

GameLogic/Utils.py

Insineer/OSS-13

6ddceea4fdd4b869ad438190237bbbc2610ba4bc

[ "MIT" ]

null

GameLogic/Utils.py

Insineer/OSS-13

6ddceea4fdd4b869ad438190237bbbc2610ba4bc

[ "MIT" ]

null

import contextlib from datetime import timedelta from Engine import GGame def spawn(delay, activity): GGame.AddDelayedActivity(delay, activity)

18.375

42

0.823129

18

147

6.722222

0.666667

0.214876

0

0.122449

147

7

43

21

0.937985

0

1

0.2

false

0

0.6

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

81b8506858e82ded4fc55adcbe347e2532d7d46b

123

py

Python

intro_to_pytorch/data.py

emlozin/intro_to_pytorch

5dc06c5c340f07f34582f6f7a539215a899f58a1

[ "MIT" ]

1

2021-01-15T16:18:23.000Z

intro_to_pytorch/data.py

emlozin/intro_to_pytorch

5dc06c5c340f07f34582f6f7a539215a899f58a1

[ "MIT" ]

null

intro_to_pytorch/data.py

emlozin/intro_to_pytorch

5dc06c5c340f07f34582f6f7a539215a899f58a1

[ "MIT" ]

null

from pathlib import Path PROJECT_ROOT = Path(__file__).parent.parent DATA_PATH = PROJECT_ROOT / "intro_to_pytorch/data"

17.571429

50

0.796748

18

123

4.944444

0.666667

0.247191

0.337079

0

0.121951

123

6

51

20.5

0.824074

0

0.172131

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

81d4eba54bcdebd22b12ccdf7a0bbee711e5c21f

89

py

Python

ce_expansion/atomgraph/__init__.py

loevlie/ce_expansion

17417b9467914dd91ee8e0325cfdc3bd19ad7f1e

[ "MIT" ]

1

2020-11-04T08:01:22.000Z

ce_expansion/atomgraph/__init__.py

loevlie/ce_expansion

17417b9467914dd91ee8e0325cfdc3bd19ad7f1e

[ "MIT" ]

2

2021-04-19T23:45:54.000Z

2022-02-21T17:40:41.000Z

ce_expansion/atomgraph/__init__.py

loevlie/ce_expansion

17417b9467914dd91ee8e0325cfdc3bd19ad7f1e

[ "MIT" ]

3

2021-05-10T14:25:28.000Z

2022-02-18T01:09:05.000Z

from ce_expansion.atomgraph import adjacency, atomgraph AtomGraph = atomgraph.AtomGraph

22.25

55

0.853933

10

89

7.5

0.6

0.72

0

0.101124

89

3

56

29.666667

0.9375

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

81e0639b73160c34af27c22681a83a5bd28be44b

54

py

Python

helloworld/hello_world.py

LinuxClient/python-learning

a8fee0f244e1b0b1666ba4cf47f3d496fb11801f

[ "Apache-2.0" ]

null

helloworld/hello_world.py

LinuxClient/python-learning

a8fee0f244e1b0b1666ba4cf47f3d496fb11801f

[ "Apache-2.0" ]

null

helloworld/hello_world.py

LinuxClient/python-learning

a8fee0f244e1b0b1666ba4cf47f3d496fb11801f

[ "Apache-2.0" ]

null

print("Hello World!") print("Greetings from Python!")

18

31

0.722222

7

54

5.571429

0.857143

0

0.092593

54

2

32

27

0.795918

0

0.62963

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c48e7a083661ef1fb67cf8588314e1ed3c4e57d0

121

py

Python

backend/app/models/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

backend/app/models/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

backend/app/models/components/__init__.py

griviala/garpix_page

55f1d9bc6d1de29d18e15369bebcbef18811b5a4

[ "MIT" ]

null

from .text_component import TextComponent # noqa from .text_description_component import TextDescriptionComponent # noqa

40.333333

71

0.867769

13

121

7.846154

0.615385

0.156863

0

0.099174

121

2

72

60.5

0.93578

0.07438

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c4c1a38ff9f80af8b58e88dd8ce8cf7a996bf869

38

py

Python

cntopic/__init__.py

thunderhit/cntopic

a74e15879c79e7e55b6f92a50cf6f7c56dc589e3

[ "MIT" ]

13

2020-06-22T11:40:58.000Z

2021-05-10T04:08:28.000Z

cntopic/__init__.py

XIAOHUOCHAI123/cntopic

a74e15879c79e7e55b6f92a50cf6f7c56dc589e3

[ "MIT" ]

null

cntopic/__init__.py

XIAOHUOCHAI123/cntopic

a74e15879c79e7e55b6f92a50cf6f7c56dc589e3

[ "MIT" ]

6

2020-07-31T14:15:04.000Z

2021-05-10T04:08:34.000Z

from cntopic.chinesetopic import Topic

38

0.894737

5

38

6.8

1

0

0.078947

38

1

38

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c4d46a91a3ae74e0c4074a72c55a6ca1e2f9b839

135

py

Python

src/wepy/boundary_conditions/unbinding.py

edeustua/wepy

f1a2ef5c8cc368d5602c9d683983b3af69a48ce2

[ "MIT" ]

35

2017-08-22T15:39:06.000Z

2022-03-20T15:17:52.000Z

src/wepy/boundary_conditions/unbinding.py

edeustua/wepy

f1a2ef5c8cc368d5602c9d683983b3af69a48ce2

[ "MIT" ]

33

2017-10-02T22:04:45.000Z

2022-03-02T22:19:08.000Z

src/wepy/boundary_conditions/unbinding.py

stxinsite/wepy

352d4c1316b20e839aae8824eedd66f0f2d0b456

[ "MIT" ]

17

2018-07-14T15:33:30.000Z

2022-01-18T16:30:55.000Z

""" Alias for the new receptor.py module for old pickles etc.. Will be deprecated.""" from wepy.boundary_conditions.receptor import *

33.75

85

0.762963

20

135

5.1

0.9

0

0.140741

135

3

86

45

0.87931

0.577778

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c4de07554b1193ef510b12a8b58c4c0b75fa0bc1

186

py

Python

dentexchange/apps/libs/context_processors.py

hellhound/dentexchange

58ae303e842404fc9e1860f294ec8044a332bef3

[ "BSD-3-Clause" ]

1

2017-11-09T23:09:51.000Z

dentexchange/apps/libs/context_processors.py

hellhound/dentexchange

58ae303e842404fc9e1860f294ec8044a332bef3

[ "BSD-3-Clause" ]

null

dentexchange/apps/libs/context_processors.py

hellhound/dentexchange

58ae303e842404fc9e1860f294ec8044a332bef3

[ "BSD-3-Clause" ]

3

2015-08-11T16:58:47.000Z

2021-01-04T08:23:51.000Z

# -*- coding:utf-8 -*- from django.conf import settings def conf(request): return getattr(settings, 'CONTEXT_CONF', {}) def debug(request): return dict(DEBUG=settings.DEBUG)

16.909091

48

0.688172

24

186

5.291667

0.625

0.204724

0

0.00641

0.16129

186

10

49

18.6

0.807692

0.107527

0

0.073171

0

1

0.4

false

0

0.2

0.4

1

0

1

0

null

1

0

null

0

1

0

1

0

5

c4e2710d9a9d509ed7d88f8fe26df0dda4eb1e80

63

py

Python

models/py_utils/__init__.py

xuduo35/CenterUnet

2a97376ee4df93ea945ab55c30bfcd9af9b5923d

[ "MIT" ]

27

2020-04-08T14:35:39.000Z

2022-02-03T09:03:15.000Z

models/py_utils/__init__.py

xuduo35/CenterUnet

2a97376ee4df93ea945ab55c30bfcd9af9b5923d

[ "MIT" ]

1

2021-06-18T12:35:53.000Z

2021-11-15T04:59:11.000Z

models/py_utils/__init__.py

xuduo35/CenterUnet

2a97376ee4df93ea945ab55c30bfcd9af9b5923d

[ "MIT" ]

15

2020-05-28T10:10:50.000Z

2022-02-21T11:12:26.000Z

from ._cpools import TopPool, BottomPool, LeftPool, RightPool

21

61

0.809524

7

63

7.142857

1

0

0.126984

63

2

62

31.5

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c4fd10e08ac71cb74150557b1aa45d11d4876046

2,258

py

Python

data/colormaps/xx.py

TACC/GravIT

0a79dc74036c11669075198e01b30a92a8150693

[ "BSD-3-Clause" ]

24

2015-08-13T20:16:11.000Z

2020-03-02T17:03:17.000Z

data/colormaps/xx.py

TACC/GravIT

0a79dc74036c11669075198e01b30a92a8150693

[ "BSD-3-Clause" ]

16

2015-10-16T03:42:37.000Z

2019-08-07T21:54:47.000Z

data/colormaps/xx.py

TACC/GravIT

0a79dc74036c11669075198e01b30a92a8150693

[ "BSD-3-Clause" ]

8

2015-08-25T15:07:35.000Z

2019-03-10T11:00:32.000Z

import os def xyzzy(name, colors): print name f = open(name, "w") f.write("%d\n" % len(colors)) for i,c in enumerate(colors): f.write("%f %f %f %f\n" % tuple([i / (len(colors)-1.0)] + c)) f.close() colors = [] colors.append([0 , 0 , 0.562493 ]) colors.append([0 , 0 , 1 ]) colors.append([0 , 1 , 1 ]) colors.append([0.500008 , 1 , 0.500008 ]) colors.append([1 , 1 , 0 ]) colors.append([1 , 0 , 0 ]) colors.append([0.500008 , 0 , 0 ]) xyzzy(os.environ["HOME"] + "/colormaps/Jet.cmap", colors) colors = [] colors.append([0 , 0 , 0 ]) colors.append([0 , 0.120394 , 0.302678 ]) colors.append([0 , 0.216587 , 0.524575 ]) colors.append([0.0552529 , 0.345022 , 0.659495 ]) colors.append([0.128054 , 0.492592 , 0.720287 ]) colors.append([0.188952 , 0.641306 , 0.792096 ]) colors.append([0.327672 , 0.784939 , 0.873426 ]) colors.append([0.60824 , 0.892164 , 0.935546 ]) colors.append([0.881376 , 0.912184 , 0.818097 ]) colors.append([0.9514 , 0.835615 , 0.449271 ]) colors.append([0.904479 , 0.690486 , 0 ]) colors.append([0.854063 , 0.510857 , 0 ]) colors.append([0.777096 , 0.330175 , 0.000885023 ]) colors.append([0.672862 , 0.139086 , 0.00270085 ]) colors.append([0.508812 , 0 , 0 ]) colors.append([0.299413 , 0.000366217 , 0.000549325 ]) colors.append([0.0157473 , 0.00332647 , 0 ]) xyzzy(os.environ["HOME"] + "/colormaps/IceFire.cmap", colors) colors = [] colors.append([0.231373 , 0.298039 , 0.752941 ]) colors.append([0.865003 , 0.865003 , 0.865003 ]) colors.append([0.705882 , 0.0156863 , 0.14902 ]) xyzzy(os.environ["HOME"] + "/colormaps/CoolWarm.cmap", colors) colors = [] colors.append([0 , 0 , 1 ]) colors.append([1 , 0 , 0 ]) xyzzy(os.environ["HOME"] + "/colormaps/Rainbow.cmap", colors) colors = [] colors.append([0., 0., 0.]) colors.append([1., 1., 1.]) xyzzy(os.environ["HOME"] + "/colormaps/Grayscale.cmap", colors)

37.016393

63

0.517715

294

2,258

3.97619

0.282313

0.318221

0.300257

0.083832

0.334474

0.249786

0.183062

0.073567

0

0.272612

0.299823

2,258

60

64

37.633333

0.466793

0

0.183673

0

0.067316

0.042073

0

null

0

0.020408

null

0.020408

0

null

1

0

1

0

1

0

null

0

1

0

5

f217a1025ecb620b4274de333a7c75b033ffc2f9

41

py

Python

tests/assets/successpackage/successpackage/two/alpha.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

63

2018-06-21T10:39:54.000Z

2021-06-04T14:28:44.000Z

tests/assets/successpackage/successpackage/two/alpha.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

86

2018-06-20T13:30:30.000Z

2019-06-04T12:47:28.000Z

tests/assets/successpackage/successpackage/two/alpha.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

4

2021-01-16T04:16:22.000Z

2021-12-23T02:50:04.000Z

from ..one import alpha BAR = alpha.BAR

10.25

23

0.707317

7

41

4.142857

0.714286

0.551724

0

0.195122

41

3

24

13.666667

0.878788

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

f21d3e75d9071b8bf64807c3829ca1bec21feb1e

159

py

Python

server/apps/system/tasks.py

wanghaiqing2015/django-vue-admin

c7f05d3ce749e5d4deb594fd332a3696ae2b3093

[ "MIT" ]

null

server/apps/system/tasks.py

wanghaiqing2015/django-vue-admin

c7f05d3ce749e5d4deb594fd332a3696ae2b3093

[ "MIT" ]

null

server/apps/system/tasks.py

wanghaiqing2015/django-vue-admin

c7f05d3ce749e5d4deb594fd332a3696ae2b3093

[ "MIT" ]

null

# Create your tasks here from __future__ import absolute_import, unicode_literals from celery import shared_task @shared_task def show(): print('ok!!!')

17.666667

56

0.767296

22

159

5.181818

0.772727

0.175439

0

0.150943

159

9

57

17.666667

0.844444

0.138365

0

0.036765

0

1

0.2

true

0

0.4

0

0.6

0.2

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1eed4938b21b19a61d0f25a6a9d9b302405cd0ab

400

py

Python

lrose_solopy/__init__.py

ncareol/lrose-soloPy

fb438d537aadd6703a059ad4dc9fe0db5645356e

[ "CNRI-Python" ]

5

2015-02-11T02:04:28.000Z

2019-06-01T07:49:12.000Z

lrose_solopy/__init__.py

ncareol/lrose-soloPy

fb438d537aadd6703a059ad4dc9fe0db5645356e

[ "CNRI-Python" ]

1

2017-08-12T10:39:20.000Z

lrose_solopy/__init__.py

ncareol/lrose-soloPy

fb438d537aadd6703a059ad4dc9fe0db5645356e

[ "CNRI-Python" ]

5

2015-01-27T15:34:57.000Z

2022-02-07T02:51:58.000Z

# *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* # ** Copyright UCAR (c) 1992 - 2014 # ** University Corporation for Atmospheric Research(UCAR) # ** National Center for Atmospheric Research(NCAR) # ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA # ** See LICENSE.TXT for license details # *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

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5

1ef585599a36a2986760cf418ae9e982eed5a6c0

2,454

py

Python

Roadside Data(Final)- SVM/new Redundant data/SVM_source_Code.py

ZadaK-bit/Real-Time-AQI-Pridiction

a0707ea190dddd6477a306644c733fc58745d022

[ "MIT" ]

null

Roadside Data(Final)- SVM/new Redundant data/SVM_source_Code.py

ZadaK-bit/Real-Time-AQI-Pridiction

a0707ea190dddd6477a306644c733fc58745d022

[ "MIT" ]

null

Roadside Data(Final)- SVM/new Redundant data/SVM_source_Code.py

ZadaK-bit/Real-Time-AQI-Pridiction

a0707ea190dddd6477a306644c733fc58745d022

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri May 8 09:10:44 2020 @author: ZAdak """ import pandas as pd dataset = pd.read_csv('ReduceData_Roadside.csv') dataset.shape import warnings warnings.filterwarnings("ignore") X = dataset.drop(['AQI'],axis=1) X.head() y = dataset['AQI'] y.head() from sklearn.model_selection import train_test_split X_train , X_test,y_train, y_test = train_test_split(X,y,test_size = 0.2,random_state=20) print(' X is ', X_train.shape) print(' X is ', X_test.shape) print(' Y is ', y_train.shape) print(' Y is ', y_test.shape) from sklearn.svm import SVC sv = SVC(kernel='linear') sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = SVC(kernel='rbf') sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = SVC(kernel='polynomial') sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = SVC(kernel='sigmoid') sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = SVC(kernel='precomputed') sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = SVR() sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) from sklearn.svm import SVC sv = LinearSVC() sv.fit(X_train,y_train) y_pred = sv.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred))

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2,454

92

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null

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null

0

5

1efa551a90df3c1daf921701a735be2e05b38b7c

549

py

Python

pytest/test-discovery/test_file.py

imsardine/learning

925841ddd93d60c740a62e12d9f57ef15b6e0a20

[ "MIT" ]

null

pytest/test-discovery/test_file.py

imsardine/learning

925841ddd93d60c740a62e12d9f57ef15b6e0a20

[ "MIT" ]

null

pytest/test-discovery/test_file.py

imsardine/learning

925841ddd93d60c740a62e12d9f57ef15b6e0a20

[ "MIT" ]

null

import unittest def test_method(): assert True def testmethod(): assert False class TestClass: def test_method(self): assert True def testmethod(self): assert False class TestRun: # regular class @property def test_reports(self): assert False class ClassTest: def test_method(self): assert False def testmethod(self): assert False class MyTestSuite(unittest.TestCase): def test_method(self): assert True def testmethod(self): assert False

14.076923

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549

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0.301587

0.201149

0.215517

0.198276

0.491379

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0.316092

0

0.291439

549

38

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0.023679

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false

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0.041667

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0.583333

0

null

1

0

null

0

1

0

1

0

5

480a7bcae2d272611ac226800ef63eeac172f91c

787

py

Python

awsdiscovery/codecommit.py

giovannicuriel/aws-discovery

f5b35bec086ee8f6d1d92edc0f8757db7e28e001

[ "BSD-3-Clause" ]

null

awsdiscovery/codecommit.py

giovannicuriel/aws-discovery

f5b35bec086ee8f6d1d92edc0f8757db7e28e001

[ "BSD-3-Clause" ]

null

awsdiscovery/codecommit.py

giovannicuriel/aws-discovery

f5b35bec086ee8f6d1d92edc0f8757db7e28e001

[ "BSD-3-Clause" ]

null

import boto3 import json import sys client = boto3.client('codecommit', region_name=sys.argv[1]) results = client.list_repositories() for item in results['repositories']: branch_results = client.list_branches(repositoryName=item['repositoryName']) print(f"{item['repositoryName']}: {len(branch_results['branches'])} branch(es)") next_token = results['nextToken'] if 'nextToken' in results else None while next_token != None: results = client.list_repositories(nextToken=next_token) for item in results['repositories']: branch_results = client.list_branches(repositoryName=item['repositoryName']) print(f"{item['repositoryName']}: {len(branch_results['branches'])} branch(es)") next_token = results['nextToken'] if 'nextToken' in results else None

41.421053

88

0.739517

96

787

5.927083

0.3125

0.091388

0.119508

0.101933

0.702988

0

0.004354

0.124524

787

18

89

43.722222

0.82148

0

0.533333

0

0.302799

0.147583

0

1

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false

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0.133333

0

null

0

1

0

null

0

5

486331d86566cea325561d253a9129aef371d159

138

py

Python

hello_world/main.py

philiWeitz/google-cloud-terraform-sandbox

43e4d9f231ec6f312be4c44fe07d6306a0d80f71

[ "Apache-2.0" ]

null

hello_world/main.py

philiWeitz/google-cloud-terraform-sandbox

43e4d9f231ec6f312be4c44fe07d6306a0d80f71

[ "Apache-2.0" ]

null

hello_world/main.py

philiWeitz/google-cloud-terraform-sandbox

43e4d9f231ec6f312be4c44fe07d6306a0d80f71

[ "Apache-2.0" ]

null

def hello_world(event): return f"Hello, World!" def hello_bucket(event, context): return f"A new file was uploaded to the bucket"

27.6

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23

138

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0

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138

5

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0

1

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0

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1

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1

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null

0

1

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null

0

1

0

1

0

5

6fa253bf8e0b8bdde441b65c366eb478145dc211

20,679

py

Python

pyCEvNS/oscillation.py

Ikaroshu/pyCEvNS

c5bf63da4a487014d4cb40550113d2faad5f8448

[ "MIT" ]

5

2018-04-05T22:55:33.000Z

2019-04-30T23:33:14.000Z

pyCEvNS/oscillation.py

Ikaroshu/pyCEvNS

c5bf63da4a487014d4cb40550113d2faad5f8448

[ "MIT" ]

null

pyCEvNS/oscillation.py

Ikaroshu/pyCEvNS

c5bf63da4a487014d4cb40550113d2faad5f8448

[ "MIT" ]

4

2018-04-06T15:27:13.000Z

2019-07-23T15:29:32.000Z

""" neutrino oscillation related funtions """ from .parameters import * # solar number density at r=0.05 solar radius, unit is MeV^3 (natural unit) __ne_solar = 4.163053492437814e-07 __nu_solar = 1.0053941490424488e-06 __nd_solar = 7.618722503535536e-07 def survival_solar(ev, epsi=NSIparameters(), op=oscillation_parameters(), nui='e', nuf='e'): """ calculating survival/transitional probability of solar neutrino :param ev: neutrino energy in MeV :param epsi: nsi parameters :param nui: intial state :param nuf: final state, 0: electron neutrino, 1: muon neutrino, 2: tau neutrino :param op: oscillation parameters :return: survival/transitional probability """ op = op.copy() dic = {'e': 0, 'mu': 1, 'tau': 2} fi = dic[nui] ff = dic[nuf] o23 = np.array([[1, 0, 0], [0, np.cos(op['t23']), np.sin(op['t23'])], [0, -np.sin(op['t23']), np.cos(op['t23'])]]) u13 = np.array([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], [0, 1, 0], [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) o12 = np.array([[np.cos(op['t12']), np.sin(op['t12']), 0], [-np.sin(op['t12']), np.cos(op['t12']), 0], [0, 0, 1]]) umix = o23 @ u13 @ o12 m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) v = np.sqrt(2) * gf * (__ne_solar * (epsi.ee() + np.diag(np.array([1, 0, 0]))) + __nu_solar * epsi.eu() + __nd_solar * epsi.ed()) hvac = umix @ m @ umix.conj().T def sorteig(w, vec): """ sort the eigenstates to make the resultant eigenvalue continuous """ minindex = 0 maxindex = 0 for j in range(3): if w[minindex] > w[j]: minindex = j for j in range(3): if w[maxindex] < w[j]: maxindex = j midindex = 3 - minindex - maxindex avec = np.array(vec) return np.array([avec[:, minindex], avec[:, midindex], avec[:, maxindex]]).T wr, vecr = np.linalg.eigh(hvac + v) utr = sorteig(wr, vecr) ws, vecs = np.linalg.eigh(hvac) uts = sorteig(ws, vecs) res = 0 for i in range(3): res += np.conj(utr[0, i]) * utr[0, i] * np.conj(uts[ff, i]) * uts[ff, i] return np.real(res) def survival_solar_amp(ev, epsi=NSIparameters(), op=oscillation_parameters(), nui='e', nuf='e', **kwargs): """ calculating survival/transitional amplitude of solar neutrino, this is just hack, not real amplitude! :param ev: neutrino energy in MeV :param epsi: nsi parameters :param nui: intial state :param nuf: final state, 0: electron neutrino, 1: muon neutrino, 2: tau neutrino :param op: oscillation parameters :return: survival/transitional probability """ op = op.copy() dic = {'e': 0, 'mu': 1, 'tau': 2} fi = dic[nui] ff = dic[nuf] o23 = np.array([[1, 0, 0], [0, np.cos(op['t23']), np.sin(op['t23'])], [0, -np.sin(op['t23']), np.cos(op['t23'])]]) u13 = np.array([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], [0, 1, 0], [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) o12 = np.array([[np.cos(op['t12']), np.sin(op['t12']), 0], [-np.sin(op['t12']), np.cos(op['t12']), 0], [0, 0, 1]]) umix = o23 @ u13 @ o12 m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) v = np.sqrt(2) * gf * (__ne_solar * (epsi.ee() + np.diag(np.array([1, 0, 0]))) + __nu_solar * epsi.eu() + __nd_solar * epsi.ed()) hvac = umix @ m @ umix.conj().T def sorteig(w, vec): """ sort the eigenstates to make the resultant eigenvalue continuous """ minindex = 0 maxindex = 0 for j in range(3): if w[minindex] > w[j]: minindex = j for j in range(3): if w[maxindex] < w[j]: maxindex = j midindex = 3 - minindex - maxindex avec = np.array(vec) return np.array([avec[:, minindex], avec[:, midindex], avec[:, maxindex]]).T wr, vecr = np.linalg.eigh(hvac + v) utr = sorteig(wr, vecr) ws, vecs = np.linalg.eigh(hvac) uts = sorteig(ws, vecs) res = 0 for i in range(3): res += np.conj(utr[fi, i]) * utr[fi, i] * np.conj(uts[ff, i]) * uts[ff, i] return np.sqrt(np.real(res)) # using Caylay-Hamilton theorem to calculate survival probability, it has probems at transitsion probabilities # # def survival_probability(ev, length, epsi=NSIparameters(), nui=0, nuf=0, # op=ocsillation_parameters(), ne=2.2*6.02e23*(100*meter_by_mev)**3): # o23 = np.matrix([[1, 0, 0], # [0, np.cos(op['t23']), np.sin(op['t23'])], # [0, -np.sin(op['t23']), np.cos(op['t23'])]]) # u13 = np.matrix([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], # [0, 1, 0], # [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) # o12 = np.matrix([[np.cos(op['t12']), np.sin(op['t12']), 0], # [-np.sin(op['t12']), np.cos(op['t12']), 0], # [0, 0, 1]]) # umix = o23 * u13 * o12 # m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) # vf = np.sqrt(2) * gf * ne * (epsi.ee() + 3 * epsi.eu() + 3 * epsi.ed()) # hf = umix * m * umix.H + vf # w, v = np.linalg.eigh(hf) # # print(w) # b = e**(-1j*w*length) # # print(b) # a = np.array([[1, 1, 1], -1j * length * w, -length**2 * w**2]).T # # print(a) # x = np.linalg.solve(a, b) # tnp.matrix = x[0] + -1j * length * x[1] * hf - length**2 * x[2] * hf.dot(hf) # # print(tnp.matrix) # return abs(tnp.matrix[nui, nuf])**2 def survival_const(ev, length=0.0, epsi=NSIparameters(), op=oscillation_parameters(), ne=2.2 * 6.02e23 * (100 * meter_by_mev) ** 3, nui='e', nuf='e'): """ survival/transitional probability with constant matter density :param ev: nuetrino energy in MeV :param length: oscillation length in meters :param epsi: epsilons :param nui: initail flavor :param nuf: final flavor :param op: oscillation parameters :param ne: electron number density in MeV^3 :return: survival/transitional probability """ op = op.copy() dic = {'e': 0, 'mu': 1, 'tau': 2, 'ebar': 0, 'mubar': 1, 'taubar': 2} fi = dic[nui] ff = dic[nuf] length = length / meter_by_mev if nuf[-1] == 'r': op['delta'] = -op['delta'] o23 = np.array([[1, 0, 0], [0, np.cos(op['t23']), np.sin(op['t23'])], [0, -np.sin(op['t23']), np.cos(op['t23'])]]) u13 = np.array([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], [0, 1, 0], [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) o12 = np.array([[np.cos(op['t12']), np.sin(op['t12']), 0], [-np.sin(op['t12']), np.cos(op['t12']), 0], [0, 0, 1]]) umix = o23 @ u13 @ o12 m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) vf = np.sqrt(2) * gf * ne * ((epsi.ee() + np.diag(np.array([1, 0, 0]))) + 3 * epsi.eu() + 3 * epsi.ed()) if nuf[-1] == 'r': hf = umix @ m @ umix.conj().T - np.conj(vf) else: hf = umix @ m @ umix.conj().T + vf w, v = np.linalg.eigh(hf) res = 0.0 for i in range(3): for j in range(3): theta = (w[i]-w[j]) * length res += v[ff, i] * np.conj(v[fi, i]) * np.conj(v[ff, j]) * v[fi, j] * (np.cos(theta) - 1j * np.sin(theta)) return np.real(res) def survival_const_amp(ev, length=0.0, epsi=NSIparameters(), op=oscillation_parameters(), ne=2.2 * 6.02e23 * (100 * meter_by_mev) ** 3, nui='e', nuf='e'): """ survival/transitional amplitude with constant matter density :param ev: nuetrino energy in MeV :param length: oscillation length in meters :param epsi: epsilons :param nui: initail flavor :param nuf: final flavor :param op: oscillation parameters :param ne: electron number density in MeV^3 :return: survival/transitional probability """ op = op.copy() dic = {'e': 0, 'mu': 1, 'tau': 2, 'ebar': 0, 'mubar': 1, 'taubar': 2} fi = dic[nui] ff = dic[nuf] length = length / meter_by_mev if nuf[-1] == 'r': op['delta'] = -op['delta'] o23 = np.array([[1, 0, 0], [0, np.cos(op['t23']), np.sin(op['t23'])], [0, -np.sin(op['t23']), np.cos(op['t23'])]]) u13 = np.array([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], [0, 1, 0], [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) o12 = np.array([[np.cos(op['t12']), np.sin(op['t12']), 0], [-np.sin(op['t12']), np.cos(op['t12']), 0], [0, 0, 1]]) umix = o23 @ u13 @ o12 m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) vf = np.sqrt(2) * gf * ne * (epsi.ee() + np.diag(np.array([1, 0, 0])) + 3 * epsi.eu() + 3 * epsi.ed()) if nuf[-1] == 'r': hf = umix @ m @ umix.conj().T - np.conj(vf) else: hf = umix @ m @ umix.conj().T + vf w, v = np.linalg.eigh(hf) res = 0.0 for i in range(3): # for j in range(3): theta = (w[i]) * length res += v[ff, i] * np.conj(v[fi, i]) * (np.cos(theta) - 1j * np.sin(theta)) return res def survival_average(ev, epsi=NSIparameters(), op=oscillation_parameters(), ne=2.2 * 6.02e23 * (100 * meter_by_mev) ** 3, nui='e', nuf='e'): dic = {'e': 0, 'mu': 1, 'tau': 2, 'ebar': 0, 'mubar': 1, 'taubar': 2} op = op.copy() fi = dic[nui] ff = dic[nuf] if nuf[-1] == 'r': op['delta'] = -op['delta'] o23 = np.array([[1, 0, 0], [0, np.cos(op['t23']), np.sin(op['t23'])], [0, -np.sin(op['t23']), np.cos(op['t23'])]]) u13 = np.array([[np.cos(op['t13']), 0, np.sin(op['t13']) * (np.exp(- op['delta'] * 1j))], [0, 1, 0], [-np.sin(op['t13'] * (np.exp(op['delta'] * 1j))), 0, np.cos(op['t13'])]]) o12 = np.array([[np.cos(op['t12']), np.sin(op['t12']), 0], [-np.sin(op['t12']), np.cos(op['t12']), 0], [0, 0, 1]]) umix = o23 @ u13 @ o12 m = np.diag(np.array([0, op['d21'] / (2 * ev), op['d31'] / (2 * ev)])) vf = np.sqrt(2) * gf * ne * ((epsi.ee() + np.diag(np.array([1, 0, 0]))) + 3 * epsi.eu() + 3 * epsi.ed()) if nuf[-1] == 'r': hf = umix @ m @ umix.conj().T - np.conj(vf) else: hf = umix @ m @ umix.conj().T + vf w, v = np.linalg.eigh(hf) res = 0.0 for i in range(3): res += v[ff, i] * np.conj(v[fi, i]) * np.conj(v[ff, i]) * v[fi, i] return np.real(res) def survial_atmos(ev, zenith=1.0, epsi=NSIparameters(), op=oscillation_parameters(), nui='e', nuf='e'): """ survival probability of atmospherical neutrino, assuming 2 layers of the earth, and eath is perfect sphere, it depends on zenith angle :param ev: nuetrino energy in MeV :param zenith: cosine of zenith angle respect to the detector, upward is positive :param epsi: NSI parameters :param nui: initial flavor :param nuf: final flavor :param op: oscillation parameters :return: survival probability in this direction """ op = op.copy() n_core = 11850.56/1.672621898e-27/2*(meter_by_mev**3) n_mantle = 4656.61/1.672621898e-27/2*(meter_by_mev**3) r_core = 3480000 r_mantle = 6368000 cos_th = -np.sqrt(r_mantle**2 - r_core**2) / r_mantle if zenith >= 0: return 1 if nui == nuf else 0 elif zenith >= cos_th: length = -r_mantle * zenith * 2 return survival_const(ev, length, epsi=epsi, nui=nui, nuf=nuf, op=op, ne=n_mantle) else: vert = r_mantle * np.sqrt(1 - zenith**2) l_core = 2 * np.sqrt(r_core**2 - vert**2) l_mantle_half = -r_mantle * zenith - l_core / 2 res = 0 if nuf[-1] == 'r': f_list = ['ebar', 'mubar', 'taubar'] else: f_list = ['e', 'mu', 'tau'] for i in f_list: for j in f_list: res += survival_const_amp(ev, l_mantle_half, epsi=epsi, nui=nui, nuf=i, op=op, ne=n_mantle) * \ survival_const_amp(ev, l_core, epsi=epsi, nui=i, nuf=j, ne=n_core) * \ survival_const_amp(ev, l_mantle_half, epsi=epsi, nui=j, nuf=nuf, ne=n_mantle) return np.real(res * np.conj(res)) def survial_atmos_amp(ev, zenith=1.0, epsi=NSIparameters(), op=oscillation_parameters(), nui='e', nuf='e'): """ survival amplitude of atmospherical neutrino assuming 2 layers of the earth, and eath is perfect sphere, it depends on zenith angle :param ev: nuetrino energy in MeV :param zenith: cosine of zenith angle respect to the detector, upward is positive :param epsi: NSI parameters :param nui: initial flavor :param nuf: final flavor :param op: oscillation parameters :return: survival probability in this direction """ op = op.copy() n_core = 11850.56/1.672621898e-27/2*(meter_by_mev**3) n_mantle = 4656.61/1.672621898e-27/2*(meter_by_mev**3) r_core = 3480000 r_mantle = 6368000 cos_th = -np.sqrt(r_mantle**2 - r_core**2) / r_mantle if zenith >= 0: return 1 if nui == nuf else 0 elif zenith >= cos_th: length = -r_mantle * zenith * 2 return survival_const(ev, length, epsi=epsi, nui=nui, nuf=nuf, op=op, ne=n_mantle) else: vert = r_mantle * np.sqrt(1 - zenith**2) l_core = 2 * np.sqrt(r_core**2 - vert**2) l_mantle_half = -r_mantle * zenith - l_core / 2 res = 0 if nuf[-1] == 'r': f_list = ['ebar', 'mubar', 'taubar'] else: f_list = ['e', 'mu', 'tau'] for i in f_list: for j in f_list: res += survival_const_amp(ev, l_mantle_half, epsi=epsi, nui=nui, nuf=i, op=op, ne=n_mantle) * \ survival_const_amp(ev, l_core, epsi=epsi, nui=i, nuf=j, ne=n_core) * \ survival_const_amp(ev, l_mantle_half, epsi=epsi, nui=j, nuf=nuf, ne=n_mantle) return res class Oscillator: def __init__(self, layers, nsi_parameter: NSIparameters, oscillation_parameter: OSCparameters, **kwargs): """ init :param layers: :param nsi_parameter: :param oscillation_parameter: :param kwargs: the parameters that goes into each layer """ self.layers = layers self.nsi_parameter = nsi_parameter self.oscillation_paramter = oscillation_parameter self.kwargs = kwargs def _dfs(self, ev, amplist, inter, cur_layer, cur_value, nui, nuf): if cur_layer == len(self.layers)-1: cur_value *= self.layers[cur_layer](ev, nui=nui, nuf=nuf, epsi=self.nsi_parameter, op=self.oscillation_paramter, **self.kwargs) amplist.append(cur_value) return for internu in inter: cv = cur_value * self.layers[cur_layer](ev, nui=nui, nuf=internu, epsi=self.nsi_parameter, op=self.oscillation_paramter, **self.kwargs) self._dfs(ev, amplist, inter, cur_layer+1, cv, internu, nuf) def transition_probability(self, ev, nui, nuf): if (nui[-1] == 'r' and nuf[-1] != 'r') or (nui[-1] != 'r' and nuf[-1] == 'r'): return 0 inter = ['e', 'mu', 'tau'] if nui[-1] == 'r': inter = ['ebar', 'mubar', 'taubar'] amplist = [] self._dfs(ev, amplist, inter, 0, 1, nui, nuf) amp = sum(amplist) return np.real(amp * np.conj(amp)) def transform(self, flux): if flux.nu is None: nu = None else: nu = {'ev': flux.ev} for flavor in ['e', 'mu', 'tau']: if flux.nu[flavor] is not None: if 'e' not in nu: nu['e'] = np.zeros_like(flux.ev) nu['mu'] = np.zeros_like(flux.ev) nu['tau'] = np.zeros_like(flux.ev) for i in range(flux.ev.shape[0]): nu['e'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'e') nu['mu'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'mu') nu['tau'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'tau') for flavor in ['ebar', 'mubar', 'taubar']: if flux.nu[flavor] is not None: if 'ebar' not in nu: nu['ebar'] = np.zeros_like(flux.ev) nu['mubar'] = np.zeros_like(flux.ev) nu['taubar'] = np.zeros_like(flux.ev) for i in range(flux.ev.shape[0]): nu['ebar'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'ebar') nu['mubar'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'mubar') nu['taubar'][i] += flux.nu[flavor][i] * self.transition_probability(flux.ev[i], flavor, 'taubar') if flux.delta_nu is None: dnu = None else: dnu = {} for flavor in ['e', 'mu', 'tau']: if flux.delta_nu[flavor] is not None: if 'e' not in dnu: dnu['e'] = [] dnu['mu'] = [] dnu['tau'] = [] for d in flux.delta_nu[flavor]: dnu['e'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'e'))) dnu['mu'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'mu'))) dnu['tau'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'tau'))) for flavor in ['ebar', 'mubar', 'taubar']: if flux.delta_nu[flavor] is not None: if 'ebar' not in dnu: dnu['ebar'] = [] dnu['mubar'] = [] dnu['taubar'] = [] for d in flux.delta_nu[flavor]: dnu['ebar'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'ebar'))) dnu['mubar'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'mubar'))) dnu['taubar'].append((d[0], d[1]*self.transition_probability(d[0], flavor, 'taubar'))) from .flux import NeutrinoFlux return NeutrinoFlux(continuous_fluxes=nu, delta_fluxes=dnu, norm=flux.norm/((100 * meter_by_mev) ** 2)) def change_parameters(self, **kwargs): for k, v in kwargs.items(): self.kwargs[k] = v class OscillatorFactory: def __init__(self): self.oscillator_list = ['solar', 'atmospheric', 'beam'] def print_available(self): print(self.oscillator_list) def get(self, oscillator_name, **kwargs): if oscillator_name not in self.oscillator_list: raise Exception('such oscillator not in factory yet, consider build your own.') if oscillator_name == 'solar': return Oscillator([survival_solar_amp], **kwargs) if oscillator_name == 'beam': if 'length' not in kwargs: raise Exception('Please specify the oscillation length in meters.') return Oscillator([survival_const_amp], **kwargs) if oscillator_name == 'atmospheric': if 'zenith' not in kwargs: raise Exception('please specify zenith angle') return Oscillator([survial_atmos_amp], **kwargs) def survival_sterile(ev, dm41=0, ua4=(0,0,0), epsi=NSIparameters(), op=oscillation_parameters(), nui='e', nuf='e', lenth=19.3/meter_by_mev): idx = {'e': 0, 'mu': 1, 'tau': 2, 'ebar': 0, 'mubar': 1, 'taubar': 2} ni = idx[nui] nf = idx[nuf] if ni == nf: u = ua4[ni] return 1 - 4 * u * (1 - u) * np.sin(dm41 * lenth / 4 / ev) else: ua = ua4[ni] ub = ua4[nf] return 4 * ua * ub * np.sin(dm41 * lenth / 4 / ev)

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null

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6fad098b58ad51e0b44065461007ede30af9bc91

19

py

Python

rosreestr2coord/version.py

sergeybarkov/rosreestr2coord

0f55e4af259d6e5ae03a899de91a3fc43eec6932

[ "MIT" ]

null

rosreestr2coord/version.py

sergeybarkov/rosreestr2coord

0f55e4af259d6e5ae03a899de91a3fc43eec6932

[ "MIT" ]

null

rosreestr2coord/version.py

sergeybarkov/rosreestr2coord

0f55e4af259d6e5ae03a899de91a3fc43eec6932

[ "MIT" ]

null

VERSION = "4.0.13"

9.5

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null

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5

6fcfdd5f30c6e19adfcc18a3d0e9aae92969ed88

105

py

Python

lopy4_bme680_ttnuplink/bme680_ttnuplink/boot.py

AidanTek/Fab-Cre8_IoT

3d358a484aea2e2a50d6dbef443e9a2757ef9ab8

[ "MIT" ]

null

lopy4_bme680_ttnuplink/bme680_ttnuplink/boot.py

AidanTek/Fab-Cre8_IoT

3d358a484aea2e2a50d6dbef443e9a2757ef9ab8

[ "MIT" ]

null

lopy4_bme680_ttnuplink/bme680_ttnuplink/boot.py

AidanTek/Fab-Cre8_IoT

3d358a484aea2e2a50d6dbef443e9a2757ef9ab8

[ "MIT" ]

null

# lopy4_bme680_ttnuplink/bme680_ttnuplink/boot.py import pycom pycom.heartbeat(False) # Disable WiFi?

13.125

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6ff6fa2b9c19a0a5d467151516df1a9b64593368

37

py

Python

admin_tools_zinnia/modules/__init__.py

django-blog-zinnia/admin-tools-zinnia

f370e6b05dfc62eab0a9739ebf5b17fd13ebf7dc

[ "BSD-3-Clause" ]

12

2015-04-13T22:16:20.000Z

2019-12-12T08:52:23.000Z

admin_tools_zinnia/modules/__init__.py

SusannaGr/admin-tools-zinnia

f370e6b05dfc62eab0a9739ebf5b17fd13ebf7dc

[ "BSD-3-Clause" ]

1

2015-04-14T10:03:14.000Z

admin_tools_zinnia/modules/__init__.py

SusannaGr/admin-tools-zinnia

f370e6b05dfc62eab0a9739ebf5b17fd13ebf7dc

[ "BSD-3-Clause" ]

3

2015-04-13T21:46:08.000Z

2019-11-12T10:36:18.000Z

"""Modules for admin_tools_zinnia"""

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1

0

null

0

1

0

5

b50a74df4d0578c9d7c7345de5c4cc9252637169

129

py

Python

chat/admin.py

rukbotto/financial-chat-django

3d7f6782471df7c58026aecce6ac374fa7d68bcc

[ "MIT" ]

null

chat/admin.py

rukbotto/financial-chat-django

3d7f6782471df7c58026aecce6ac374fa7d68bcc

[ "MIT" ]

null

chat/admin.py

rukbotto/financial-chat-django

3d7f6782471df7c58026aecce6ac374fa7d68bcc

[ "MIT" ]

null

from django.contrib import admin from chat.models import Profile, Room admin.site.register(Profile) admin.site.register(Room)

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null

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5

82f6e035910a55e7cec63a946334ee8a2aae3eaf

74

py

Python

hermes_fix/message_lib/FIX_4_3/__init__.py

yabov/hermes_fix

0a5e89fd15903a7ee0929e82b39879362e2e1008

[ "Apache-2.0" ]

2

2020-02-20T15:00:35.000Z

2020-02-21T19:27:53.000Z

hermes_fix/message_lib/FIX_5_0SP2/__init__.py

yabov/hermes_fix

0a5e89fd15903a7ee0929e82b39879362e2e1008

[ "Apache-2.0" ]

3

2020-02-21T03:25:35.000Z

2020-02-21T18:37:42.000Z

hermes_fix/message_lib/FIX_5_0SP2/__init__.py

yabov/hermes_fix

0a5e89fd15903a7ee0929e82b39879362e2e1008

[ "Apache-2.0" ]

null

from . import field_types from . import fields from . import fix_messages

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null

0

1

0

1

0

5

82f7b51205cec92b9609690acbbf1a78c613595c

306

py

Python

hls4ml/templates/__init__.py

marcinswiniarski20/hls4ml

dff4d1d733ae2ed139bb4fc1175ec46e8ec17ba4

[ "Apache-2.0" ]

1

2020-09-10T13:18:55.000Z

hls4ml/templates/__init__.py

mswiniars/hls4ml

dff4d1d733ae2ed139bb4fc1175ec46e8ec17ba4

[ "Apache-2.0" ]

null

hls4ml/templates/__init__.py

mswiniars/hls4ml

dff4d1d733ae2ed139bb4fc1175ec46e8ec17ba4

[ "Apache-2.0" ]

null

from __future__ import absolute_import from hls4ml.templates.templates import Backend, register_backend, get_backend from hls4ml.templates.vivado_template import VivadoBackend from hls4ml.templates.oneapi_template import OneAPI register_backend('Vivado', VivadoBackend) register_backend('oneAPI', OneAPI)

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0

1

0

1

0

5

d201a6f267a1faf1c220dddaf0879e58042cf7bb

28

py

Python

configs/__init__.py

xumingze0308/eLR-WACV2018

ac2ef36f6272f02c44fb862c9f6d63140349eb6b

[ "MIT" ]

11

2019-05-04T18:37:05.000Z

2019-12-09T13:32:31.000Z

configs/__init__.py

xumingze0308/eLR-WACV2018

ac2ef36f6272f02c44fb862c9f6d63140349eb6b

[ "MIT" ]

null

configs/__init__.py

xumingze0308/eLR-WACV2018

ac2ef36f6272f02c44fb862c9f6d63140349eb6b

[ "MIT" ]

5

2019-05-05T01:16:07.000Z

2020-02-17T19:39:19.000Z

from .base_configs import *

14

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0

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5

d20fec7f322daa1aca97af6599ecbbfb8780d14e

975

py

Python

onepay_new/forms.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

onepay_new/forms.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

onepay_new/forms.py

shaoren0110/onepay_flask

c736971113763ab5e1a67c85d5599137f3a373fc

[ "MIT" ]

null

# -*- coding: utf-8 -*- from flask_wtf import FlaskForm from wtforms.validators import DataRequired, Length from wtforms import StringField, SubmitField, SelectField, TextAreaField, ValidationError, HiddenField, \ BooleanField, PasswordField class LoginForm(FlaskForm): username = StringField('用户名', validators=[DataRequired(), Length(1, 20)]) password = PasswordField('密码', validators=[DataRequired(), Length(1, 20)]) remember = BooleanField('记住密码') submit = SubmitField('登陆') class RegisterForm(FlaskForm): username = StringField('用户名(邮箱地址)', validators=[DataRequired(), Length(1, 20)]) truename = StringField('真实姓名', validators=[DataRequired(), Length(1, 20)]) password = PasswordField('密码', validators=[DataRequired(), Length(1, 20)]) password_again = PasswordField('重复密码', validators=[DataRequired(), Length(1, 20)]) phone_number = PasswordField('手机号', validators=[DataRequired(), Length(1, 20)]) submit = SubmitField('注册')

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0

1

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1

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5

d21b218ffaad2e7c3dd3097053d6dd60fd0453cc

37

py

Python

django_replicated/__init__.py

svetlyak40wt/django_replicated

464f1a76684df944ab75401870b1228c66243aca

[ "BSD-3-Clause" ]

null

django_replicated/__init__.py

svetlyak40wt/django_replicated

464f1a76684df944ab75401870b1228c66243aca

[ "BSD-3-Clause" ]

null

django_replicated/__init__.py

svetlyak40wt/django_replicated

464f1a76684df944ab75401870b1228c66243aca

[ "BSD-3-Clause" ]

1

2019-06-10T16:48:39.000Z

from router import ReplicationRouter

18.5

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1

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1

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5

d259fb38f228bd602da2156aa66ab1e46be9d625

7,860

py

Python

api/network/base/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

97

2016-11-15T14:44:23.000Z

2022-03-13T18:09:15.000Z

api/network/base/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

334

2016-11-17T19:56:57.000Z

2022-03-18T10:45:53.000Z

api/network/base/views.py

klebed/esdc-ce

2c9e4591f344247d345a83880ba86777bb794460

[ "Apache-2.0" ]

33

2017-01-02T16:04:13.000Z

2022-02-07T19:20:24.000Z

from api.decorators import api_view, request_data_defaultdc from api.permissions import IsAnyDcNetworkAdmin from api.network.base.api_views import NetworkView __all__ = ('net_list', 'net_manage') @api_view(('GET',)) @request_data_defaultdc(permissions=(IsAnyDcNetworkAdmin,)) def net_list(request, data=None): """ List (:http:get:`GET </network>`) all networks. .. http:get:: /network :DC-bound?: * |dc-no| :Permissions: * |NetworkAdmin| :Asynchronous?: * |async-no| :arg data.full: Return list of objects with all network details (default: false) :type data.full: boolean :arg data.extended: Return list of objects with extended network details (default: false) :type data.extended: boolean :arg data.order_by: :ref:`Available fields for sorting <order_by>`: ``name``, ``created`` (default: ``name``) :type data.order_by: string :status 200: SUCCESS :status 403: Forbidden """ return NetworkView(request, None, data).get(many=True) @api_view(('GET', 'POST', 'PUT', 'DELETE')) @request_data_defaultdc(permissions=(IsAnyDcNetworkAdmin,)) def net_manage(request, name, data=None): """ Show (:http:get:`GET </network/(name)>`), create (:http:post:`POST </network/(name)>`, update (:http:put:`PUT </network/(name)>`) or delete (:http:delete:`DELETE </network/(name)>`) a virtual network. .. http:get:: /network/(name) :DC-bound?: * |dc-yes| - ``dc_bound=true`` * |dc-no| - ``dc_bound=false`` :Permissions: * |NetworkAdmin| - ``dc_bound=true`` * |SuperAdmin| - ``dc_bound=false`` :Asynchronous?: * |async-no| :arg name: **required** - Network name :type name: string :arg data.extended: Display extended network details (default: false) :type data.extended: boolean :status 200: SUCCESS :status 403: Forbidden :status 404: Network not found .. http:post:: /network/(name) :DC-bound?: * |dc-yes| - ``dc_bound=true`` * |dc-no| - ``dc_bound=false`` :Permissions: * |NetworkAdmin| - ``dc_bound=true`` * |SuperAdmin| - ``dc_bound=false`` :Asynchronous?: * |async-no| :arg name: **required** - Network name :type name: string :arg data.alias: Short network name (default: ``name``) :type data.alias: string :arg data.access: Access type (1 - Public, 3 - Private, 4 - Deleted) (default: 3) :type data.access: integer :arg data.owner: User that owns the network (default: logged in user) :type data.owner: string :arg data.desc: Network description :type data.desc: string :arg data.network: **required** - IPv4 network prefix in quad-dotted format :type data.network: string :arg data.netmask: **required** - IPv4 subnet mask in quad-dotted format :type data.netmask: string :arg data.gateway: **required** - IPv4 gateway in quad-dotted format :type data.gateway: string :arg data.nic_tag: **required** - NIC tag or device name on compute node :type data.nic_tag: string :arg data.vlan_id: **required** - 802.1Q virtual LAN ID (0 - 4096; 0 = none) :type data.vlan_id: integer :arg data.vxlan_id: VXLAN ID required for overlay NIC tags (1 - 16777215, default: null) :type data.vxlan_id: integer :arg data.mtu: MTU for the network vNIC (576 - 9000) :type data.mtu: integer :arg data.resolvers: List of IPv4 addresses that can be used as resolvers :type data.resolvers: array :arg data.dns_domain: Existing domain name used for creating A records for VMs :type data.dns_domain: string :arg data.ptr_domain: Existing in-addr.arpa domain used for creating PTR associations with VMs :type data.ptr_domain: string :arg data.dhcp_passthrough: When true, IP addresses for this network are managed by an external service \ (default: false) :type data.dhcp_passthrough: boolean :arg data.dc_bound: Whether the network is bound to a datacenter (requires |SuperAdmin| permission) \ (default: true) :type data.dc_bound: boolean :arg data.dc: Name of the datacenter the network will be attached to (**required** if DC-bound) :type data.dc: string :status 201: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: Datacenter not found :status 406: Network already exists .. http:put:: /network/(name) :DC-bound?: * |dc-yes| - ``dc_bound=true`` * |dc-no| - ``dc_bound=false`` :Permissions: * |NetworkAdmin| - ``dc_bound=true`` * |SuperAdmin| - ``dc_bound=false`` :Asynchronous?: * |async-no| :arg name: **required** - Network name :type name: string :arg data.alias: Short network name :type data.alias: string :arg data.access: Access type (1 - Public, 3 - Private, 4 - Deleted) :type data.access: integer :arg data.owner: User that owns the network :type data.owner: string :arg data.desc: Network description :type data.desc: string :arg data.network: IPv4 network prefix in quad-dotted format :type data.network: string :arg data.netmask: IPv4 subnet mask in quad-dotted format :type data.netmask: string :arg data.gateway: IPv4 gateway in quad-dotted format :type data.gateway: string :arg data.nic_tag: NIC tag or device name on compute node :type data.nic_tag: string :arg data.vlan_id: 802.1Q virtual LAN ID (0 - 4096; 0 = none) :type data.vlan_id: integer :arg data.vxlan_id: VXLAN ID required for overlay NIC tags (1 - 16777215) :type data.vxlan_id: integer :arg data.mtu: MTU for the network vNIC (576 - 9000) :type data.mtu: integer :arg data.resolvers: List of IPv4 addresses that can be used as resolvers :type data.resolvers: array :arg data.dns_domain: Existing domain name used for creating A records for VMs :type data.dns_domain: string :arg data.ptr_domain: Existing in-addr.arpa domain used for creating PTR associations with VMs :type data.ptr_domain: string :arg data.dhcp_passthrough: When true, IP addresses for this network are managed by an external service :type data.dhcp_passthrough: boolean :arg data.dc_bound: Whether the network is bound to a datacenter (requires |SuperAdmin| permission) :type data.dc_bound: boolean :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: Network not found .. http:delete:: /network/(name) .. note:: A virtual network cannot be deleted when it is used by even one virtual server. In order to \ disable further use of such a virtual network, the network can be marked as deleted by \ :http:put:`changing its access property to deleted (4) </network/(name)>`. :DC-bound?: * |dc-yes| - ``dc_bound=true`` * |dc-no| - ``dc_bound=false`` :Permissions: * |NetworkAdmin| - ``dc_bound=true`` * |SuperAdmin| - ``dc_bound=false`` :Asynchronous?: * |async-no| :arg name: **required** - Network name :type name: string :status 200: SUCCESS :status 400: FAILURE :status 403: Forbidden :status 404: Network not found :status 428: Network is used by some VMs """ return NetworkView(request, name, data).response()

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d25f6384f553f3738111bb8eddbdbf37ca406585

62

py

Python

01_Language/01_Functions/python/atanh.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

3

2020-06-28T07:42:51.000Z

2021-01-15T10:32:11.000Z

01_Language/01_Functions/python/atanh.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

9

2021-03-10T22:45:40.000Z

2022-02-27T06:53:20.000Z

01_Language/01_Functions/python/atanh.py

cliff363825/TwentyFour

09df59bd5d275e66463e343647f46027397d1233

[ "MIT" ]

1

2021-01-15T10:51:24.000Z

# coding: utf-8 import math print(math.atanh(math.tanh(2)))

10.333333

31

0.693548

11

62

3.909091

0.818182

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0.037037

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62

5

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null

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1

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null

0

1

0

1

0

1

0

5

9638fc0418e62e5263356d3162d3e7556a3387a8

336

py

Python

naive_tokenizers/__init__.py

naivenlp/naive-tokenizers

811e1eb7452e85a42e4646204a02f28e626c73f6

[ "Apache-2.0" ]

null

naive_tokenizers/__init__.py

naivenlp/naive-tokenizers

811e1eb7452e85a42e4646204a02f28e626c73f6

[ "Apache-2.0" ]

null

naive_tokenizers/__init__.py

naivenlp/naive-tokenizers

811e1eb7452e85a42e4646204a02f28e626c73f6

[ "Apache-2.0" ]

null

from .abstract_tokenizer import AbstractIntervener, DefaultIntervener from .abstract_tokenizer import AbstractTokenizer, VocabBasedTokenizer from .bert_tokenizer import BertTokenizer from .jieba_tokenizer import JiebaTokenizer from .transformer_tokenizer import TransformerTokenizer __name__ = 'naive_tokenizers' __version__ = '0.0.1'

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null

0

1

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1

0

5

963adb3c68190b58e24c0769a2077abc842bd6ea

19

py

Python

yfinance/version.py

victorymr/yfinance

608849efc91dcbca0c66f9d46e0816ff70fed7a8

[ "Apache-2.0" ]

null

yfinance/version.py

victorymr/yfinance

608849efc91dcbca0c66f9d46e0816ff70fed7a8

[ "Apache-2.0" ]

null

yfinance/version.py

victorymr/yfinance

608849efc91dcbca0c66f9d46e0816ff70fed7a8

[ "Apache-2.0" ]

1

2021-02-21T12:31:04.000Z

version = "0.1.66"

9.5

18

0.578947

4

19

2.75

1

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0.25

0.157895

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1

19

0.4375

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null

0

1

0

1

0

1

0

null

0

5

9650a01d1c339a5eea475706c98f20261aaa6a17

168

py

Python

rvpvp/isa/rvf/fmsub.py

ultrafive/riscv-pvp

843e38422c3d545352b955764927d5e7847e5453

[ "Unlicense" ]

5

2021-05-10T09:57:00.000Z

2021-10-05T14:39:20.000Z

rvpvp/isa/rvf/fmsub.py

ultrafive/riscv-pvp

843e38422c3d545352b955764927d5e7847e5453

[ "Unlicense" ]

null

rvpvp/isa/rvf/fmsub.py

ultrafive/riscv-pvp

843e38422c3d545352b955764927d5e7847e5453

[ "Unlicense" ]

1

2021-05-14T20:24:11.000Z

from ...isa.inst import * import numpy as np class Fmsub(Inst): name = 'fmsub.s' def golden(self): return self['rs1']*self['rs2']-self['rs3']

16.8

50

0.571429

24

168

4

0.75

0

0.024

0.255952

168

9

51

18.666667

0.744

0

0.095238

0

1

0.166667

false

0

0.333333

0.166667

1

0

1

0

null

0

1

0

null

0

1

0

5

9654748971907ce35d000326b491070bab34398a

61

py

Python

DependencyInjection/Processor.py

mschepers188/Programming2

9dc481758435671a72608dbe0d4546f92b5b6c8d

[ "MIT" ]

null

DependencyInjection/Processor.py

mschepers188/Programming2

9dc481758435671a72608dbe0d4546f92b5b6c8d

[ "MIT" ]

null

DependencyInjection/Processor.py

mschepers188/Programming2

9dc481758435671a72608dbe0d4546f92b5b6c8d

[ "MIT" ]

null

class Processor: def __init__(self, select): pass

20.333333

31

0.639344

7

61

5

1

0

0.278689

61

3

32

20.333333

0.795455

0

1

0.333333

false

0.333333

0

0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

966f993c4767697f783419bf9b09a31ee870c67d

109

py

Python

models/__init__.py

tolgabirdal/PHDimGeneralization

e5c0a7f7a7fc1afab6d1843093e4e03954f4c017

[ "MIT" ]

10

2021-11-18T13:57:05.000Z

2022-03-27T04:00:55.000Z

models/__init__.py

tolgabirdal/PHDimGeneralization

e5c0a7f7a7fc1afab6d1843093e4e03954f4c017

[ "MIT" ]

null

models/__init__.py

tolgabirdal/PHDimGeneralization

e5c0a7f7a7fc1afab6d1843093e4e03954f4c017

[ "MIT" ]

2

2021-12-03T05:34:08.000Z

2021-12-09T09:44:39.000Z

from .fc import fc_mnist, fc_cifar from .alexnet import alexnet from .vgg import vgg from .lenet import lenet

27.25

34

0.807339

19

109

4.526316

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0.146789

109

4

35

27.25

0.924731

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true

0

1

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1

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null

0

1

0

1

0

1

0

5

967d3235069ab44753ceae5d20f958849183fc4e

190

py

Python

Clase 8/app/matematica.py

FreddyGJ/Portafolio-de-prog-3

ea16b7f8951d02b62291b8e7b73833f25b56578e

[ "MIT" ]

null

Clase 8/app/matematica.py

FreddyGJ/Portafolio-de-prog-3

ea16b7f8951d02b62291b8e7b73833f25b56578e

[ "MIT" ]

null

Clase 8/app/matematica.py

FreddyGJ/Portafolio-de-prog-3

ea16b7f8951d02b62291b8e7b73833f25b56578e

[ "MIT" ]

null

def sumar(a, b): return a + b def restar(a, b): return a - b def multiplicador(a, b): return a * b def dividir(numerador, denominador): return float(numerador)/denominador

17.272727

39

0.652632

29

190

4.275862

0.37931

0.096774

0.193548

0.217742

0.314516

0

0.231579

190

11

39

17.272727

0.849315

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

969b48fea47d93b601d1865364ea17bb9905c493

162

py

Python

bedrock/main.py

ronbeltran/webapp2-bedrock

42909fd6eb99ffe19ff941f9c66c9c84548139c6

[ "MIT" ]

1

2019-01-09T10:14:38.000Z

bedrock/main.py

ronbeltran/webapp2-bedrock

42909fd6eb99ffe19ff941f9c66c9c84548139c6

[ "MIT" ]

null

bedrock/main.py

ronbeltran/webapp2-bedrock

42909fd6eb99ffe19ff941f9c66c9c84548139c6

[ "MIT" ]

null

import webapp2 import config import app.handlers.home ROUTES = [] ROUTES += app.handlers.home.ROUTES app = webapp2.WSGIApplication(ROUTES, debug=config.DEBUG)

16.2

57

0.777778

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162

6

0.428571

0.174603

0.238095

0.333333

0

0.013986

0.117284

162

9

58

18

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0

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false

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null

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1

0

1

0

null

0

1

0

5

96b7b68388d4454689bc31a17e2fe37f75a9c4d0

5,985

py

Python

release/stubs.min/Autodesk/Revit/DB/Electrical.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

182

2017-06-27T02:26:15.000Z

2022-03-30T18:53:43.000Z

release/stubs.min/Autodesk/Revit/DB/Electrical.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

28

2017-06-27T13:38:23.000Z

2022-03-15T11:19:44.000Z

release/stubs.min/Autodesk/Revit/DB/Electrical.py

htlcnn/ironpython-stubs

780d829e2104b2789d5f4d6f32b0ec9f2930ca03

[ "MIT" ]

67

2017-06-28T09:43:59.000Z

2022-03-20T21:17:10.000Z

# encoding: utf-8 # module Autodesk.Revit.DB.Electrical calls itself Electrical # from RevitAPI,Version=17.0.0.0,Culture=neutral,PublicKeyToken=null # by generator 1.145 # no doc # no imports # no functions # classes from Electrical_parts.CableTrayConduitBase import CableTrayConduitBase from Electrical_parts.CableTray import CableTray from Electrical_parts.CableTrayConduitRunBase import CableTrayConduitRunBase from Electrical_parts.CableTrayRun import CableTrayRun from Electrical_parts.CableTraySettings import CableTraySettings from Electrical_parts.CableTrayShape import CableTrayShape from Electrical_parts.CableTraySizeIterator import CableTraySizeIterator from Electrical_parts.CableTraySizes import CableTraySizes from Electrical_parts.CableTrayType import CableTrayType from Electrical_parts.CapitalizationForLoadNames import CapitalizationForLoadNames from Electrical_parts.CircuitLoadCalculationMethod import CircuitLoadCalculationMethod from Electrical_parts.CircuitSequence import CircuitSequence from Electrical_parts.CircuitType import CircuitType from Electrical_parts.Conduit import Conduit from Electrical_parts.ConduitRun import ConduitRun from Electrical_parts.ConduitSettings import ConduitSettings from Electrical_parts.ConduitSize import ConduitSize from Electrical_parts.ConduitSizeIterator import ConduitSizeIterator from Electrical_parts.ConduitSizes import ConduitSizes from Electrical_parts.ConduitSizeSettingIterator import ConduitSizeSettingIterator from Electrical_parts.ConduitSizeSettings import ConduitSizeSettings from Electrical_parts.ConduitType import ConduitType from Electrical_parts.CorrectionFactor import CorrectionFactor from Electrical_parts.CorrectionFactorSet import CorrectionFactorSet from Electrical_parts.CorrectionFactorSetIterator import CorrectionFactorSetIterator from Electrical_parts.DistributionSysType import DistributionSysType from Electrical_parts.DistributionSysTypeSet import DistributionSysTypeSet from Electrical_parts.DistributionSysTypeSetIterator import DistributionSysTypeSetIterator from Electrical_parts.ElectricalDemandFactorDefinition import ElectricalDemandFactorDefinition from Electrical_parts.ElectricalDemandFactorRule import ElectricalDemandFactorRule from Electrical_parts.ElectricalDemandFactorValue import ElectricalDemandFactorValue from Electrical_parts.ElectricalEquipment import ElectricalEquipment from Electrical_parts.ElectricalLoadClassification import ElectricalLoadClassification from Electrical_parts.ElectricalLoadClassificationData import ElectricalLoadClassificationData from Electrical_parts.ElectricalLoadClassificationSpace import ElectricalLoadClassificationSpace from Electrical_parts.ElectricalPhase import ElectricalPhase from Electrical_parts.ElectricalPhaseConfiguration import ElectricalPhaseConfiguration from Electrical_parts.ElectricalSetting import ElectricalSetting from Electrical_parts.ElectricalSystem import ElectricalSystem from Electrical_parts.ElectricalSystemSet import ElectricalSystemSet from Electrical_parts.ElectricalSystemSetIterator import ElectricalSystemSetIterator from Electrical_parts.ElectricalSystemType import ElectricalSystemType from Electrical_parts.GroundConductorSize import GroundConductorSize from Electrical_parts.GroundConductorSizeSet import GroundConductorSizeSet from Electrical_parts.GroundConductorSizeSetIterator import GroundConductorSizeSetIterator from Electrical_parts.InsulationType import InsulationType from Electrical_parts.InsulationTypeSet import InsulationTypeSet from Electrical_parts.InsulationTypeSetIterator import InsulationTypeSetIterator from Electrical_parts.LightingDevice import LightingDevice from Electrical_parts.LightingFixture import LightingFixture from Electrical_parts.LoadClassification import LoadClassification from Electrical_parts.LoadClassificationType import LoadClassificationType from Electrical_parts.NeutralMode import NeutralMode from Electrical_parts.PanelConfiguration import PanelConfiguration from Electrical_parts.PanelScheduleData import PanelScheduleData from Electrical_parts.PanelSchedulePhaseLoadType import PanelSchedulePhaseLoadType from Electrical_parts.PanelScheduleSheetInstance import PanelScheduleSheetInstance from Electrical_parts.PanelScheduleTemplate import PanelScheduleTemplate from Electrical_parts.PanelScheduleType import PanelScheduleType from Electrical_parts.PanelScheduleView import PanelScheduleView from Electrical_parts.PowerFactorStateType import PowerFactorStateType from Electrical_parts.TemperatureRatingType import TemperatureRatingType from Electrical_parts.TemperatureRatingTypeSet import TemperatureRatingTypeSet from Electrical_parts.TemperatureRatingTypeSetIterator import TemperatureRatingTypeSetIterator from Electrical_parts.VoltageType import VoltageType from Electrical_parts.VoltageTypeSet import VoltageTypeSet from Electrical_parts.VoltageTypeSetIterator import VoltageTypeSetIterator from Electrical_parts.Wire import Wire from Electrical_parts.WireConduitType import WireConduitType from Electrical_parts.WireConduitTypeSet import WireConduitTypeSet from Electrical_parts.WireConduitTypeSetIterator import WireConduitTypeSetIterator from Electrical_parts.WireMaterialType import WireMaterialType from Electrical_parts.WireMaterialTypeSet import WireMaterialTypeSet from Electrical_parts.WireMaterialTypeSetIterator import WireMaterialTypeSetIterator from Electrical_parts.WireSet import WireSet from Electrical_parts.WireSetIterator import WireSetIterator from Electrical_parts.WireSize import WireSize from Electrical_parts.WireSizeSet import WireSizeSet from Electrical_parts.WireSizeSetIterator import WireSizeSetIterator from Electrical_parts.WireType import WireType from Electrical_parts.WireTypeSet import WireTypeSet from Electrical_parts.WireTypeSetIterator import WireTypeSetIterator from Electrical_parts.WiringType import WiringType

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1

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null

0

1

0

1

0

5

73817ae9d1de17889ee9e3c018c81f83495b2e3b

32

py

Python

crmsystem/hosting/__init__.py

iomegak12/pythondockertry

dd91dc57a09141f94cb0a73e18a8ad9da4d5aa85

[ "MIT" ]

null

crmsystem/hosting/__init__.py

iomegak12/pythondockertry

dd91dc57a09141f94cb0a73e18a8ad9da4d5aa85

[ "MIT" ]

null

crmsystem/hosting/__init__.py

iomegak12/pythondockertry

dd91dc57a09141f94cb0a73e18a8ad9da4d5aa85

[ "MIT" ]

null

from .crmsystem_host import app

16

31

0.84375

5

32

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1

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0

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0

null

0

1

0

1

0

5

73a2e789d58e80833a25e31412732b46825246d3

31

py

Python

skfda/inference/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

147

2019-05-10T20:46:42.000Z

2022-03-25T17:23:19.000Z

skfda/inference/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

306

2019-04-26T08:56:05.000Z

2022-03-30T11:12:48.000Z

skfda/inference/__init__.py

jiduque/scikit-fda

5ea71e78854801b259aa3a01eb6b154aa63bf54b

[ "BSD-3-Clause" ]

38

2019-09-03T17:24:04.000Z

2022-01-06T05:09:18.000Z

from . import anova, hotelling

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73ff17472dc5969985a0c1c162ec9c4a5f3ab1ac

2,195

py

Python

imap_cli/tests/test_imapcli.py

salewski/imap-cli

2a318e278121ba306552e2d215bd126f34f8490f

[ "MIT" ]

60

2015-05-06T12:44:41.000Z

2021-09-28T18:12:50.000Z

imap_cli/tests/test_imapcli.py

salewski/imap-cli

2a318e278121ba306552e2d215bd126f34f8490f

[ "MIT" ]

17

2015-05-12T06:57:11.000Z

2021-01-15T17:11:07.000Z

imap_cli/tests/test_imapcli.py

salewski/imap-cli

2a318e278121ba306552e2d215bd126f34f8490f

[ "MIT" ]

15

2017-02-03T12:10:39.000Z

2022-02-23T18:53:32.000Z

# -*- coding: utf-8 -*- """Test helpers""" import imaplib import unittest import imap_cli from imap_cli import tests class ImapCLITest(unittest.TestCase): def setUp(self): imaplib.IMAP4 = tests.ImapConnectionMock() imaplib.IMAP4_SSL = tests.ImapConnectionMock() def test_change_dir(self): self.imap_account = imaplib.IMAP4_SSL() self.imap_account.login() imap_cli.change_dir(self.imap_account, 'Test') def test_change_dir_twice(self): self.imap_account = imaplib.IMAP4_SSL() self.imap_account.login() assert imap_cli.change_dir(self.imap_account, 'Test') == '1' assert imap_cli.change_dir(self.imap_account, 'INBOX') == '1' def test_connect(self): self.imap_account = imap_cli.connect('hostname', 'username', 'password') assert isinstance(self.imap_account, tests.ImapConnectionMock) def test_connect_no_ssl(self): self.imap_account = imap_cli.connect('hostname', 'username', 'password', ssl=False) assert isinstance(self.imap_account, tests.ImapConnectionMock) def test_connect_sasl_auth(self): self.imap_account = imap_cli.connect('hostname', 'username', sasl_auth='XOAUTH2', sasl_ir='12345abcde') assert isinstance(self.imap_account, tests.ImapConnectionMock) def test_wrong_change_dir(self): self.imap_account = imaplib.IMAP4_SSL() self.imap_account.login() assert imap_cli.change_dir(self.imap_account, 'NotADirectory') == -1 def test_disconnect(self): self.imap_account = imaplib.IMAP4_SSL() self.imap_account.login() imap_cli.disconnect(self.imap_account) assert self.imap_account.state == 'LOGOUT' def test_disconnect_selected_state(self): self.imap_account = imaplib.IMAP4_SSL() self.imap_account.login() imap_cli.change_dir(self.imap_account, 'Test') imap_cli.disconnect(self.imap_account) assert self.imap_account.state == 'LOGOUT'

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null

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null

0

5

fb414b25dc94850b253642a4719cfd935654976e

45

py

Python

ice/error/human_readable_error.py

reavessm/Ice

e78d046abfd6006b1a81d1cbdb516b7c3e141ac9

[ "MIT" ]

578

2015-01-02T12:43:52.000Z

2022-03-27T23:45:32.000Z

ice/error/human_readable_error.py

raphaelcastaneda/Ice

b380de7fc7830251b883fb55c46fea894058afa3

[ "MIT" ]

271

2015-01-05T01:56:38.000Z

2021-08-14T02:51:24.000Z

ice/error/human_readable_error.py

raphaelcastaneda/Ice

b380de7fc7830251b883fb55c46fea894058afa3

[ "MIT" ]

156

2015-01-07T15:43:20.000Z

2021-12-11T19:10:44.000Z

class HumanReadableError(Exception): pass

11.25

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null

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null

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5

fb5e9e1fb86cf41fc9802eb10f8d63054a21f97b

23,470

py

Python

tests/resources/fetcher_inputs/fetcher_test_data.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

null

tests/resources/fetcher_inputs/fetcher_test_data.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

null

tests/resources/fetcher_inputs/fetcher_test_data.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

1

2019-09-20T01:08:33.000Z

MULTI_SIGNAL_CSV_DATA = """ symbol,date,signal ibm,1/1/06,1 ibm,2/1/06,0 ibm,3/1/06,0 ibm,4/1/06,0 ibm,5/1/06,1 ibm,6/1/06,1 ibm,7/1/06,1 ibm,8/1/06,1 ibm,9/1/06,0 ibm,10/1/06,1 ibm,11/1/06,1 ibm,12/1/06,5 ibm,1/1/07,1 ibm,2/1/07,0 ibm,3/1/07,1 ibm,4/1/07,0 ibm,5/1/07,1 dell,1/1/06,1 dell,2/1/06,0 dell,3/1/06,0 dell,4/1/06,0 dell,5/1/06,1 dell,6/1/06,1 dell,7/1/06,1 dell,8/1/06,1 dell,9/1/06,0 dell,10/1/06,1 dell,11/1/06,1 dell,12/1/06,5 dell,1/1/07,1 dell,2/1/07,0 dell,3/1/07,1 dell,4/1/07,0 dell,5/1/07,1 """.strip() AAPL_CSV_DATA = """ symbol,date,signal aapl,1/1/06,1 aapl,2/1/06,0 aapl,3/1/06,0 aapl,4/1/06,0 aapl,5/1/06,1 aapl,6/1/06,1 aapl,7/1/06,1 aapl,8/1/06,1 aapl,9/1/06,0 aapl,10/1/06,1 aapl,11/1/06,1 aapl,12/1/06,5 aapl,1/1/07,1 aapl,2/1/07,0 aapl,3/1/07,1 aapl,4/1/07,0 aapl,5/1/07,1 """.strip() # times are expected in UTC AAPL_MINUTE_CSV_DATA = """ symbol,date,signal aapl,1/4/06 5:31AM, 1 aapl,1/4/06 11:30AM, 2 aapl,1/5/06 5:31AM, 1 aapl,1/5/06 11:30AM, 3 aapl,1/9/06 5:31AM, 1 aapl,1/9/06 11:30AM, 4 """.strip() AAPL_IBM_CSV_DATA = """ symbol,date,signal aapl,1/1/06,1 aapl,2/1/06,0 aapl,3/1/06,0 aapl,4/1/06,0 aapl,5/1/06,1 aapl,6/1/06,1 aapl,7/1/06,1 aapl,8/1/06,1 aapl,9/1/06,0 aapl,10/1/06,1 aapl,11/1/06,1 aapl,12/1/06,5 aapl,1/1/07,1 aapl,2/1/07,0 aapl,3/1/07,1 aapl,4/1/07,0 aapl,5/1/07,1 ibm,1/1/06,1 ibm,2/1/06,0 ibm,3/1/06,0 ibm,4/1/06,0 ibm,5/1/06,1 ibm,6/1/06,1 ibm,7/1/06,1 ibm,8/1/06,1 ibm,9/1/06,0 ibm,10/1/06,1 ibm,11/1/06,1 ibm,12/1/06,5 ibm,1/1/07,1 ibm,2/1/07,0 ibm,3/1/07,1 ibm,4/1/07,0 ibm,5/1/07,1 """.strip() CPIAUCSL_DATA = """ Date,Value 2007-12-01,211.445 2007-11-01,210.834 2007-10-01,209.19 2007-09-01,208.547 2007-08-01,207.667 2007-07-01,207.603 2007-06-01,207.234 2007-05-01,206.755 2007-04-01,205.904 2007-03-01,205.288 2007-02-01,204.226 2007-01-01,203.437 2006-12-01,203.1 2006-11-01,202.0 2006-10-01,201.9 2006-09-01,202.8 2006-08-01,203.8 2006-07-01,202.9 2006-06-01,201.8 2006-05-01,201.3 2006-04-01,200.7 2006-03-01,199.7 2006-02-01,199.4 2006-01-01,199.3 """.strip() PALLADIUM_DATA = """ Date,Hong Kong 8:30,Hong Kong 14:00,London 08:00,New York 9:30,New York 15:00 2007-12-31,367.0,367.0,368.0,368.0,368.0 2007-12-28,366.0,366.0,365.0,368.0,368.0 2007-12-27,367.0,367.0,366.0,363.0,367.0 2007-12-26,,,,365.0,365.0 2007-12-24,351.0,357.0,357.0,357.0,365.0 2007-12-21,356.0,356.0,354.0,357.0,357.0 2007-12-20,357.0,356.0,354.0,356.0,356.0 2007-12-19,359.0,359.0,359.0,356.0,358.0 2007-12-18,357.0,356.0,356.0,359.0,359.0 2007-12-17,353.0,353.0,351.0,354.0,360.0 2007-12-14,347.0,347.0,347.0,347.0,355.0 2007-12-13,349.0,349.0,349.0,349.0,347.0 2007-12-12,348.0,349.0,349.0,351.0,349.0 2007-12-11,346.0,346.0,346.0,348.0,350.0 2007-12-10,346.0,346.0,346.0,348.0,348.0 2007-12-07,348.0,348.0,348.0,346.0,346.0 2007-12-06,350.0,350.0,352.0,348.0,348.0 2007-12-05,350.0,350.0,352.0,351.0,351.0 2007-12-04,349.0,349.0,352.0,351.0,351.0 2007-12-03,350.0,350.0,354.0,350.0,350.0 2007-11-30,345.0,345.0,347.0,353.0,350.0 2007-11-29,348.0,348.0,348.0,347.0,345.0 2007-11-28,350.0,347.0,347.0,348.0,348.0 2007-11-27,356.0,356.0,358.0,354.0,350.0 2007-11-26,357.0,357.0,360.0,360.0,360.0 2007-11-23,353.0,354.0,357.0,355.0, 2007-11-22,359.0,359.0,359.0,358.0, 2007-11-21,364.0,364.0,366.0,365.0,359.0 2007-11-20,360.0,359.0,362.0,364.0,364.0 2007-11-19,366.0,365.0,365.0,365.0,361.0 2007-11-16,368.0,366.0,368.0,369.0,366.0 2007-11-15,373.0,372.0,372.0,368.0,368.0 2007-11-14,372.0,372.0,372.0,373.0,373.0 2007-11-13,365.0,365.0,368.0,372.0,372.0 2007-11-12,373.0,370.0,370.0,366.0,366.0 2007-11-09,376.0,375.0,373.0,373.0,373.0 2007-11-08,376.0,376.0,373.0,376.0,376.0 2007-11-07,379.0,379.0,383.0,378.0,378.0 2007-11-06,374.0,374.0,374.0,379.0,379.0 2007-11-05,376.0,376.0,376.0,376.0,374.0 2007-11-02,372.0,371.0,371.0,371.0,376.0 2007-11-01,374.0,374.0,374.0,374.0,374.0 2007-10-31,369.0,369.0,371.0,372.0,372.0 2007-10-30,373.0,372.0,373.0,371.0,371.0 2007-10-29,373.0,375.0,375.0,376.0,373.0 2007-10-26,364.0,368.0,370.0,373.0,373.0 2007-10-25,360.0,360.0,360.0,364.0,368.0 2007-10-24,364.0,364.0,364.0,360.0,360.0 2007-10-23,361.0,361.0,364.0,366.0,366.0 2007-10-22,367.0,362.0,361.0,361.0,361.0 2007-10-19,,,374.0,372.0,370.0 2007-10-18,373.0,373.0,374.0,373.0,373.0 2007-10-17,372.0,372.0,370.0,373.0,373.0 2007-10-16,375.0,375.0,375.0,372.0,372.0 2007-10-15,379.0,379.0,380.0,382.0,375.0 2007-10-12,378.0,378.0,378.0,379.0,379.0 2007-10-11,375.0,375.0,376.0,381.0,384.0 2007-10-10,365.0,365.0,367.0,377.0,377.0 2007-10-09,365.0,363.0,362.0,362.0,365.0 2007-10-08,369.0,369.0,367.0,366.0,365.0 2007-10-05,369.0,369.0,371.0,369.0,369.0 2007-10-04,359.0,359.0,360.0,362.0,369.0 2007-10-03,352.0,350.0,352.0,352.0,359.0 2007-10-02,358.0,357.0,356.0,352.0,352.0 2007-10-01,,,349.0,355.0,360.0 2007-09-28,345.0,345.0,345.0,346.0,348.0 2007-09-27,342.0,342.0,342.0,343.0,345.0 2007-09-26,,,341.0,340.0,343.0 2007-09-25,342.0,341.0,343.0,341.0,341.0 2007-09-24,340.0,340.0,342.0,342.0,342.0 2007-09-21,341.0,341.0,342.0,342.0,340.0 2007-09-20,335.0,335.0,335.0,338.0,341.0 2007-09-19,333.0,333.0,335.0,335.0,335.0 2007-09-18,333.0,333.0,334.0,333.0,333.0 2007-09-17,331.0,331.0,331.0,333.0,333.0 2007-09-14,334.0,333.0,333.0,333.0,331.0 2007-09-13,336.0,336.0,336.0,334.0,334.0 2007-09-12,336.0,336.0,336.0,336.0,336.0 2007-09-11,333.0,335.0,335.0,336.0,336.0 2007-09-10,337.0,337.0,337.0,336.0,333.0 2007-09-07,336.0,336.0,338.0,337.0,337.0 2007-09-06,333.0,333.0,336.0,336.0,336.0 2007-09-05,334.0,334.0,334.0,336.0,333.0 2007-09-04,333.0,333.0,334.0,334.0,334.0 2007-09-03,334.0,334.0,335.0,334.0, 2007-08-31,331.0,333.0,334.0,333.0,333.0 2007-08-30,331.0,331.0,332.0,331.0,331.0 2007-08-29,329.0,327.0,329.0,329.0,331.0 2007-08-28,331.0,331.0,334.0,331.0,331.0 2007-08-27,330.0,331.0,331.0,331.0,331.0 2007-08-24,326.0,326.0,327.0,325.0,330.0 2007-08-23,322.0,322.0,326.0,330.0,326.0 2007-08-22,321.0,319.0,319.0,322.0,322.0 2007-08-21,331.0,331.0,329.0,328.0,325.0 2007-08-20,331.0,331.0,331.0,331.0,331.0 2007-08-17,334.0,334.0,334.0,335.0,331.0 2007-08-16,348.0,346.0,345.0,338.0,329.0 2007-08-15,354.0,354.0,352.0,348.0,348.0 2007-08-14,357.0,357.0,356.0,351.0,354.0 2007-08-13,355.0,355.0,354.0,356.0,358.0 2007-08-10,361.0,357.0,357.0,350.0,358.0 2007-08-09,364.0,364.0,364.0,361.0,361.0 2007-08-08,362.0,362.0,362.0,364.0,364.0 2007-08-07,365.0,365.0,363.0,360.0,363.0 2007-08-06,365.0,365.0,365.0,365.0,365.0 2007-08-03,366.0,366.0,365.0,365.0,367.0 2007-08-02,365.0,365.0,365.0,368.0,366.0 2007-08-01,367.0,366.0,366.0,365.0,367.0 2007-07-31,367.0,367.0,365.0,367.0,367.0 2007-07-30,363.0,362.0,361.0,365.0,367.0 2007-07-27,365.0,365.0,364.0,363.0,363.0 2007-07-26,366.0,366.0,365.0,365.0,365.0 2007-07-25,368.0,368.0,368.0,366.0,366.0 2007-07-24,372.0,372.0,372.0,370.0,368.0 2007-07-23,372.0,372.0,372.0,372.0,372.0 2007-07-20,372.0,372.0,372.0,372.0,372.0 2007-07-19,370.0,369.0,369.0,370.0,372.0 2007-07-18,368.0,368.0,367.0,367.0,370.0 2007-07-17,368.0,368.0,368.0,368.0,365.0 2007-07-16,369.0,369.0,368.0,368.0,368.0 2007-07-13,370.0,370.0,370.0,369.0,369.0 2007-07-12,369.0,369.0,368.0,370.0,370.0 2007-07-11,369.0,369.0,369.0,369.0,369.0 2007-07-10,369.0,369.0,369.0,369.0,367.0 2007-07-09,367.0,367.0,366.0,370.0,369.0 2007-07-06,366.0,366.0,365.0,365.0,367.0 2007-07-05,366.0,366.0,366.0,367.0,366.0 2007-07-04,366.0,368.0,368.0,366.0, 2007-07-03,368.0,370.0,370.0,368.0,366.0 2007-07-02,,,369.0,368.0,368.0 2007-06-29,368.0,368.0,368.0,368.0,368.0 2007-06-28,367.0,367.0,368.0,368.0,368.0 2007-06-27,366.0,366.0,366.0,368.0,364.0 2007-06-26,372.0,372.0,370.0,368.0,366.0 2007-06-25,377.0,377.0,376.0,373.0,372.0 2007-06-22,376.0,376.0,375.0,377.0,377.0 2007-06-21,375.0,375.0,374.0,376.0,376.0 2007-06-20,373.0,373.0,371.0,375.0,377.0 2007-06-19,,,372.0,371.0,371.0 2007-06-18,370.0,371.0,373.0,373.0,373.0 2007-06-15,370.0,369.0,369.0,369.0,372.0 2007-06-14,367.0,367.0,369.0,369.0,369.0 2007-06-13,369.0,369.0,367.0,365.0,369.0 2007-06-12,368.0,368.0,371.0,369.0,369.0 2007-06-11,367.0,367.0,367.0,368.0,368.0 2007-06-08,369.0,368.0,368.0,371.0,369.0 2007-06-07,370.0,370.0,370.0,369.0,371.0 2007-06-06,370.0,370.0,370.0,368.0,368.0 2007-06-05,372.0,372.0,372.0,372.0,368.0 2007-06-04,376.0,374.0,374.0,372.0,372.0 2007-06-01,370.0,370.0,370.0,373.0,373.0 2007-05-31,368.0,368.0,368.0,370.0,370.0 2007-05-30,370.0,369.0,369.0,367.0,367.0 2007-05-29,370.0,369.0,369.0,371.0,368.0 2007-05-28,368.0,368.0,368.0,, 2007-05-25,368.0,368.0,368.0,367.0,367.0 2007-05-24,,,376.0,376.0,368.0 2007-05-23,375.0,375.0,378.0,376.0,376.0 2007-05-22,374.0,374.0,374.0,378.0,378.0 2007-05-21,364.0,364.0,365.0,368.0,374.0 2007-05-18,362.0,361.0,361.0,364.0,364.0 2007-05-17,359.0,359.0,359.0,359.0,362.0 2007-05-16,363.0,363.0,362.0,362.0,359.0 2007-05-15,362.0,362.0,362.0,358.0,362.0 2007-05-14,368.0,368.0,368.0,364.0,362.0 2007-05-11,361.0,363.0,362.0,364.0,367.0 2007-05-10,370.0,370.0,366.0,363.0,363.0 2007-05-09,376.0,376.0,373.0,372.0,370.0 2007-05-08,378.0,378.0,378.0,376.0,376.0 2007-05-07,378.0,378.0,381.0,381.0,381.0 2007-05-04,376.0,374.0,374.0,376.0,376.0 2007-05-03,373.0,373.0,373.0,376.0,376.0 2007-05-02,373.0,373.0,373.0,372.0,375.0 2007-05-01,,,371.0,369.0,374.0 2007-04-30,373.0,373.0,373.0,373.0,373.0 2007-04-27,373.0,372.0,372.0,374.0,374.0 2007-04-26,380.0,380.0,380.0,376.0,373.0 2007-04-25,377.0,377.0,377.0,380.0,380.0 2007-04-24,384.0,384.0,384.0,383.0,379.0 2007-04-23,386.0,386.0,386.0,382.0,386.0 2007-04-20,378.0,378.0,378.0,385.0,387.0 2007-04-19,383.0,382.0,377.0,377.0,377.0 2007-04-18,377.0,377.0,378.0,377.0,382.0 2007-04-17,376.0,376.0,376.0,376.0,379.0 2007-04-16,380.0,381.0,381.0,376.0,376.0 2007-04-13,371.0,371.0,371.0,374.0,380.0 2007-04-12,367.0,367.0,369.0,371.0,371.0 2007-04-11,360.0,360.0,363.0,366.0,369.0 2007-04-10,358.0,358.0,360.0,360.0,360.0 2007-04-09,,,,355.0,355.0 2007-04-05,,,355.0,353.0,355.0 2007-04-04,354.0,354.0,353.0,355.0,355.0 2007-04-03,353.0,353.0,354.0,354.0,354.0 2007-04-02,355.0,355.0,355.0,353.0,355.0 2007-03-30,354.0,354.0,356.0,355.0,355.0 2007-03-29,355.0,356.0,356.0,355.0,355.0 2007-03-28,355.0,356.0,356.0,356.0,356.0 2007-03-27,355.0,355.0,357.0,355.0,355.0 2007-03-26,354.0,354.0,355.0,355.0,357.0 2007-03-23,355.0,355.0,355.0,355.0,358.0 2007-03-22,354.0,354.0,353.0,356.0,356.0 2007-03-21,352.0,352.0,352.0,352.0,350.0 2007-03-20,352.0,352.0,352.0,352.0,352.0 2007-03-19,352.0,352.0,352.0,352.0,352.0 2007-03-16,352.0,352.0,352.0,352.0,352.0 2007-03-15,349.0,349.0,349.0,352.0,352.0 2007-03-14,351.0,349.0,348.0,349.0,349.0 2007-03-13,352.0,352.0,352.0,351.0,351.0 2007-03-12,353.0,353.0,353.0,352.0,352.0 2007-03-09,353.0,351.0,353.0,353.0,353.0 2007-03-08,349.0,349.0,349.0,353.0,355.0 2007-03-07,349.0,348.0,348.0,348.0,348.0 2007-03-06,342.0,343.0,345.0,345.0,350.0 2007-03-05,344.0,342.0,340.0,340.0,345.0 2007-03-02,351.0,351.0,351.0,349.0,349.0 2007-03-01,351.0,354.0,352.0,355.0,351.0 2007-02-28,347.0,348.0,348.0,350.0,350.0 2007-02-27,357.0,356.0,356.0,351.0,356.0 2007-02-26,358.0,359.0,359.0,357.0,357.0 2007-02-23,347.0,348.0,348.0,355.0,360.0 2007-02-22,346.0,346.0,346.0,350.0,350.0 2007-02-21,339.0,339.0,340.0,339.0,346.0 2007-02-20,,,342.0,337.0,337.0 2007-02-19,,,343.0,342.0,342.0 2007-02-16,344.0,343.0,343.0,340.0,343.0 2007-02-15,345.0,343.0,343.0,344.0,344.0 2007-02-14,343.0,343.0,343.0,345.0,347.0 2007-02-13,340.0,339.0,339.0,339.0,343.0 2007-02-12,338.0,338.0,340.0,338.0,340.0 2007-02-09,343.0,343.0,343.0,338.0,342.0 2007-02-08,344.0,344.0,344.0,339.0,342.0 2007-02-07,344.0,346.0,345.0,346.0,346.0 2007-02-06,340.0,340.0,342.0,344.0,344.0 2007-02-05,337.0,336.0,336.0,340.0,343.0 2007-02-02,344.0,344.0,343.0,341.0,341.0 2007-02-01,341.0,341.0,341.0,344.0,344.0 2007-01-31,341.0,340.0,340.0,334.0,341.0 2007-01-30,343.0,341.0,343.0,336.0,342.0 2007-01-29,349.0,349.0,350.0,342.0,346.0 2007-01-26,353.0,352.0,351.0,351.0,351.0 2007-01-25,350.0,350.0,350.0,353.0,353.0 2007-01-24,351.0,350.0,350.0,348.0,348.0 2007-01-23,345.0,345.0,347.0,350.0,350.0 2007-01-22,343.0,343.0,343.0,344.0,347.0 2007-01-19,340.0,340.0,341.0,341.0,344.0 2007-01-18,340.0,342.0,342.0,342.0,342.0 2007-01-17,335.0,335.0,333.0,334.0,343.0 2007-01-16,332.0,332.0,332.0,334.0,337.0 2007-01-15,334.0,336.0,335.0,332.0,332.0 2007-01-12,331.0,331.0,331.0,331.0,335.0 2007-01-11,331.0,331.0,331.0,333.0,333.0 2007-01-10,333.0,333.0,334.0,331.0,331.0 2007-01-09,333.0,333.0,336.0,329.0,329.0 2007-01-08,335.0,335.0,335.0,333.0,333.0 2007-01-05,340.0,340.0,340.0,342.0,336.0 2007-01-04,337.0,337.0,337.0,340.0,343.0 2007-01-03,338.0,336.0,336.0,342.0,342.0 2007-01-02,337.0,337.0,334.0,336.0,336.0 2006-12-29,327.0,327.0,327.0,327.0,337.0 2006-12-28,326.0,326.0,328.0,327.0,326.0 2006-12-27,326.0,328.0,328.0,328.0,326.0 2006-12-26,,,,327.0,327.0 2006-12-22,325.0,325.0,327.0,327.0,327.0 2006-12-21,326.0,326.0,327.0,325.0,325.0 2006-12-20,328.0,328.0,328.0,326.0,326.0 2006-12-19,324.0,324.0,325.0,322.0,326.0 2006-12-18,325.0,325.0,326.0,324.0,324.0 2006-12-15,330.0,329.0,329.0,327.0,325.0 2006-12-14,328.0,328.0,328.0,330.0,330.0 2006-12-13,329.0,329.0,330.0,328.0,328.0 2006-12-12,332.0,332.0,332.0,329.0,329.0 2006-12-11,329.0,329.0,329.0,329.0,329.0 2006-12-08,330.0,329.0,329.0,332.0,336.0 2006-12-07,328.0,326.0,326.0,328.0,328.0 2006-12-06,333.0,331.0,331.0,328.0,328.0 2006-12-05,330.0,330.0,329.0,333.0,333.0 2006-12-04,330.0,330.0,330.0,330.0,330.0 2006-12-01,330.0,330.0,330.0,328.0,328.0 2006-11-30,324.0,323.0,323.0,330.0,330.0 2006-11-29,326.0,326.0,328.0,321.0,321.0 2006-11-28,329.0,328.0,328.0,326.0,326.0 2006-11-27,330.0,329.0,329.0,329.0,329.0 2006-11-24,326.0,326.0,326.0,330.0, 2006-11-23,328.0,328.0,327.0,326.0, 2006-11-22,330.0,330.0,328.0,328.0,328.0 2006-11-21,323.0,327.0,327.0,330.0,330.0 2006-11-20,320.0,320.0,322.0,323.0,323.0 2006-11-17,321.0,321.0,321.0,318.0,320.0 2006-11-16,320.0,320.0,322.0,323.0,323.0 2006-11-15,321.0,321.0,321.0,317.0,320.0 2006-11-14,326.0,325.0,324.0,324.0,321.0 2006-11-13,333.0,333.0,333.0,326.0,326.0 2006-11-10,338.0,338.0,338.0,335.0,333.0 2006-11-09,329.0,329.0,328.0,331.0,338.0 2006-11-08,333.0,333.0,334.0,327.0,327.0 2006-11-07,334.0,332.0,332.0,335.0,335.0 2006-11-06,340.0,340.0,340.0,330.0,335.0 2006-11-03,326.0,326.0,325.0,330.0,333.0 2006-11-02,327.0,326.0,326.0,324.0,326.0 2006-11-01,323.0,323.0,324.0,326.0,326.0 2006-10-31,325.0,325.0,325.0,318.0,323.0 2006-10-30,,,325.0,325.0,325.0 2006-10-27,324.0,324.0,324.0,321.0,323.0 2006-10-26,325.0,324.0,324.0,323.0,326.0 2006-10-25,322.0,322.0,322.0,319.0,319.0 2006-10-24,319.0,318.0,318.0,320.0,323.0 2006-10-23,326.0,326.0,326.0,319.0,319.0 2006-10-20,337.0,337.0,334.0,329.0,329.0 2006-10-19,331.0,331.0,331.0,330.0,337.0 2006-10-18,320.0,320.0,320.0,326.0,334.0 2006-10-17,324.0,326.0,326.0,321.0,321.0 2006-10-16,318.0,321.0,320.0,324.0,324.0 2006-10-13,309.0,309.0,309.0,316.0,316.0 2006-10-12,305.0,308.0,308.0,310.0,310.0 2006-10-11,299.0,299.0,301.0,305.0,309.0 2006-10-10,304.0,308.0,308.0,299.0,299.0 2006-10-09,302.0,302.0,304.0,304.0,304.0 2006-10-06,301.0,301.0,301.0,297.0,297.0 2006-10-05,297.0,299.0,299.0,301.0,301.0 2006-10-04,300.0,298.0,298.0,302.0,297.0 2006-10-03,315.0,315.0,314.0,305.0,305.0 2006-10-02,,,322.0,315.0,315.0 2006-09-29,321.0,323.0,323.0,318.0,318.0 2006-09-28,320.0,323.0,323.0,323.0,323.0 2006-09-27,318.0,318.0,320.0,317.0,320.0 2006-09-26,318.0,318.0,319.0,318.0,318.0 2006-09-25,319.0,318.0,319.0,316.0,316.0 2006-09-22,310.0,310.0,313.0,325.0,322.0 2006-09-21,308.0,308.0,308.0,309.0,309.0 2006-09-20,307.0,307.0,308.0,311.0,311.0 2006-09-19,317.0,316.0,316.0,319.0,310.0 2006-09-18,313.0,313.0,313.0,306.0,312.0 2006-09-15,311.0,311.0,314.0,315.0,315.0 2006-09-14,317.0,317.0,317.0,332.0,326.0 2006-09-13,310.0,310.0,310.0,321.0,318.0 2006-09-12,311.0,323.0,322.0,320.0,314.0 2006-09-11,330.0,322.0,321.0,317.0,317.0 2006-09-08,347.0,345.0,345.0,323.0,330.0 2006-09-07,350.0,350.0,353.0,348.0,348.0 2006-09-06,351.0,351.0,351.0,351.0,356.0 2006-09-05,347.0,347.0,347.0,351.0,351.0 2006-09-04,346.0,346.0,347.0,346.0, 2006-09-01,348.0,345.0,346.0,346.0,346.0 2006-08-31,340.0,340.0,342.0,343.0,343.0 2006-08-30,339.0,341.0,340.0,339.0,340.0 2006-08-29,341.0,343.0,342.0,338.0,340.0 2006-08-28,345.0,345.0,345.0,345.0,345.0 2006-08-25,345.0,345.0,345.0,346.0,346.0 2006-08-24,345.0,345.0,347.0,348.0,348.0 2006-08-23,340.0,340.0,340.0,345.0,345.0 2006-08-22,347.0,347.0,346.0,340.0,340.0 2006-08-21,335.0,338.0,338.0,341.0,347.0 2006-08-18,332.0,334.0,333.0,335.0,335.0 2006-08-17,333.0,337.0,338.0,341.0,337.0 2006-08-16,326.0,325.0,324.0,334.0,337.0 2006-08-15,317.0,320.0,319.0,322.0,327.0 2006-08-14,320.0,320.0,320.0,314.0,319.0 2006-08-11,320.0,320.0,322.0,324.0,324.0 2006-08-10,326.0,326.0,327.0,326.0,324.0 2006-08-09,320.0,320.0,320.0,324.0,327.0 2006-08-08,327.0,325.0,324.0,320.0,320.0 2006-08-07,327.0,327.0,328.0,324.0,324.0 2006-08-04,324.0,324.0,324.0,327.0,327.0 2006-08-03,330.0,326.0,327.0,324.0,324.0 2006-08-02,319.0,319.0,322.0,325.0,330.0 2006-08-01,316.0,316.0,316.0,319.0,319.0 2006-07-31,315.0,315.0,317.0,313.0,316.0 2006-07-28,320.0,318.0,318.0,315.0,315.0 2006-07-27,315.0,315.0,318.0,320.0,320.0 2006-07-26,315.0,315.0,315.0,315.0,315.0 2006-07-25,314.0,314.0,315.0,314.0,317.0 2006-07-24,309.0,309.0,309.0,309.0,314.0 2006-07-21,308.0,311.0,310.0,310.0,310.0 2006-07-20,317.0,315.0,316.0,315.0,315.0 2006-07-19,308.0,308.0,311.0,311.0,318.0 2006-07-18,320.0,320.0,319.0,318.0,316.0 2006-07-17,333.0,333.0,333.0,321.0,321.0 2006-07-14,331.0,331.0,331.0,331.0,331.0 2006-07-13,330.0,328.0,328.0,331.0,331.0 2006-07-12,330.0,330.0,330.0,330.0,330.0 2006-07-11,318.0,320.0,323.0,326.0,330.0 2006-07-10,325.0,323.0,323.0,320.0,320.0 2006-07-07,329.0,329.0,329.0,327.0,327.0 2006-07-06,328.0,324.0,326.0,323.0,329.0 2006-07-05,328.0,328.0,330.0,328.0,328.0 2006-07-04,325.0,328.0,327.0,326.0, 2006-07-03,322.0,326.0,326.0,329.0, 2006-06-30,320.0,320.0,320.0,316.0,322.0 2006-06-29,309.0,309.0,307.0,314.0,314.0 2006-06-28,310.0,310.0,313.0,314.0,314.0 2006-06-27,318.0,320.0,320.0,318.0,318.0 2006-06-26,308.0,305.0,309.0,320.0,320.0 2006-06-23,310.0,304.0,305.0,306.0,310.0 2006-06-22,315.0,318.0,320.0,320.0,316.0 2006-06-21,303.0,306.0,308.0,311.0,315.0 2006-06-20,292.0,297.0,296.0,301.0,305.0 2006-06-19,307.0,304.0,303.0,302.0,297.0 2006-06-16,300.0,306.0,305.0,310.0,307.0 2006-06-15,290.0,290.0,292.0,300.0,300.0 2006-06-14,277.0,274.0,275.0,288.0,293.0 2006-06-13,313.0,308.0,307.0,286.0,277.0 2006-06-12,320.0,320.0,316.0,321.0,316.0 2006-06-09,317.0,313.0,313.0,327.0,327.0 2006-06-08,342.0,336.0,333.0,331.0,320.0 2006-06-07,348.0,346.0,346.0,335.0,343.0 2006-06-06,359.0,359.0,359.0,350.0,350.0 2006-06-05,356.0,356.0,358.0,363.0,363.0 2006-06-02,340.0,343.0,342.0,351.0,356.0 2006-06-01,347.0,345.0,345.0,340.0,340.0 2006-05-31,,,358.0,358.0,345.0 2006-05-30,352.0,350.0,355.0,359.0,358.0 2006-05-29,357.0,352.0,350.0,, 2006-05-26,355.0,353.0,354.0,354.0,354.0 2006-05-25,348.0,348.0,350.0,350.0,350.0 2006-05-24,358.0,362.0,365.0,352.0,352.0 2006-05-23,343.0,342.0,343.0,355.0,362.0 2006-05-22,350.0,345.0,345.0,340.0,340.0 2006-05-19,366.0,369.0,373.0,347.0,352.0 2006-05-18,372.0,375.0,376.0,380.0,375.0 2006-05-17,379.0,379.0,382.0,390.0,380.0 2006-05-16,368.0,370.0,366.0,379.0,379.0 2006-05-15,395.0,395.0,397.0,370.0,375.0 2006-05-12,400.0,396.0,398.0,407.0,399.0 2006-05-11,390.0,397.0,395.0,400.0,400.0 2006-05-10,394.0,397.0,398.0,390.0,390.0 2006-05-09,375.0,375.0,378.0,384.0,394.0 2006-05-08,380.0,380.0,381.0,377.0,375.0 2006-05-05,,,383.0,382.0,382.0 2006-05-04,379.0,379.0,378.0,379.0,379.0 2006-05-03,386.0,386.0,388.0,384.0,379.0 2006-05-02,377.0,377.0,380.0,380.0,384.0 2006-05-01,,,,380.0,380.0 2006-04-28,360.0,363.0,363.0,364.0,377.0 2006-04-27,368.0,365.0,367.0,364.0,364.0 2006-04-26,366.0,366.0,367.0,361.0,368.0 2006-04-25,356.0,355.0,355.0,362.0,362.0 2006-04-24,359.0,359.0,363.0,360.0,360.0 2006-04-21,344.0,348.0,347.0,352.0,359.0 2006-04-20,368.0,372.0,374.0,365.0,349.0 2006-04-19,366.0,364.0,364.0,371.0,374.0 2006-04-18,364.0,360.0,360.0,361.0,361.0 2006-04-17,,,,358.0,358.0 2006-04-13,347.0,342.0,341.0,346.0,349.0 2006-04-12,340.0,344.0,343.0,347.0,347.0 2006-04-11,359.0,359.0,360.0,359.0,345.0 2006-04-10,351.0,354.0,355.0,359.0,359.0 2006-04-07,352.0,352.0,354.0,351.0,351.0 2006-04-06,341.0,341.0,344.0,352.0,352.0 2006-04-05,,,336.0,341.0,341.0 2006-04-04,342.0,339.0,337.0,338.0,342.0 2006-04-03,332.0,337.0,338.0,341.0,345.0 2006-03-31,349.0,349.0,348.0,332.0,332.0 2006-03-30,338.0,341.0,343.0,349.0,349.0 2006-03-29,340.0,337.0,337.0,333.0,338.0 2006-03-28,340.0,344.0,345.0,340.0,340.0 2006-03-27,333.0,333.0,334.0,341.0,341.0 2006-03-24,321.0,321.0,320.0,326.0,333.0 2006-03-23,323.0,321.0,321.0,317.0,322.0 2006-03-22,317.0,318.0,322.0,320.0,324.0 2006-03-21,320.0,318.0,316.0,315.0,318.0 2006-03-20,318.0,318.0,319.0,317.0,317.0 2006-03-17,316.0,316.0,315.0,318.0,318.0 2006-03-16,315.0,314.0,314.0,316.0,316.0 2006-03-15,305.0,305.0,307.0,318.0,318.0 2006-03-14,300.0,300.0,300.0,302.0,306.0 2006-03-13,288.0,291.0,290.0,292.0,300.0 2006-03-10,289.0,289.0,289.0,288.0,288.0 2006-03-09,280.0,282.0,282.0,285.0,285.0 2006-03-08,291.0,289.0,289.0,285.0,282.0 2006-03-07,296.0,296.0,296.0,299.0,292.0 2006-03-06,307.0,304.0,302.0,302.0,297.0 2006-03-03,300.0,300.0,300.0,305.0,305.0 2006-03-02,297.0,297.0,296.0,294.0,300.0 2006-03-01,291.0,291.0,289.0,290.0,297.0 2006-02-28,284.0,284.0,285.0,288.0,291.0 2006-02-27,286.0,290.0,290.0,285.0,284.0 2006-02-24,283.0,285.0,286.0,286.0,286.0 2006-02-23,289.0,286.0,287.0,288.0,286.0 2006-02-22,293.0,293.0,293.0,292.0,289.0 2006-02-21,292.0,290.0,291.0,291.0,293.0 2006-02-20,292.0,292.0,292.0,292.0,292.0 2006-02-17,279.0,279.0,280.0,285.0,290.0 2006-02-16,276.0,276.0,278.0,275.0,279.0 2006-02-15,282.0,285.0,287.0,285.0,279.0 2006-02-14,273.0,270.0,274.0,278.0,282.0 2006-02-13,283.0,278.0,277.0,282.0,276.0 2006-02-10,304.0,298.0,297.0,296.0,285.0 2006-02-09,293.0,297.0,295.0,300.0,300.0 2006-02-08,288.0,288.0,287.0,290.0,290.0 2006-02-07,309.0,309.0,309.0,297.0,290.0 2006-02-06,317.0,317.0,320.0,305.0,312.0 2006-02-03,309.0,310.0,310.0,317.0,317.0 2006-02-02,294.0,296.0,295.0,300.0,305.0 2006-02-01,294.0,293.0,293.0,294.0,294.0 2006-01-31,,,282.0,293.0,295.0 2006-01-30,,,277.0,278.0,278.0 2006-01-27,275.0,275.0,276.0,275.0,275.0 2006-01-26,279.0,279.0,280.0,275.0,275.0 2006-01-25,275.0,275.0,275.0,279.0,279.0 2006-01-24,278.0,278.0,278.0,276.0,276.0 2006-01-23,276.0,278.0,277.0,278.0,278.0 2006-01-20,279.0,278.0,277.0,280.0,277.0 2006-01-19,273.0,275.0,275.0,273.0,277.0 2006-01-18,282.0,276.0,275.0,273.0,273.0 2006-01-17,289.0,286.0,286.0,281.0,283.0 2006-01-16,283.0,285.0,285.0,289.0,289.0 2006-01-13,273.0,273.0,273.0,275.0,281.0 2006-01-12,274.0,274.0,274.0,273.0,273.0 2006-01-11,274.0,274.0,274.0,271.0,274.0 2006-01-10,279.0,278.0,278.0,277.0,274.0 2006-01-09,272.0,272.0,274.0,275.0,278.0 2006-01-06,264.0,265.0,262.0,269.0,272.0 2006-01-05,274.0,274.0,272.0,263.0,263.0 2006-01-04,272.0,272.0,272.0,272.0,274.0 2006-01-03,260.0,262.0,262.0,267.0,267.0 """.strip() FETCHER_UNIVERSE_DATA = """ date,symbol 1/9/2006,aapl 1/9/2006,ibm 1/9/2006,msft 1/11/2006,aapl 1/11/2006,ibm 1/11/2006,msft 1/11/2006,yhoo """.strip() NON_ASSET_FETCHER_UNIVERSE_DATA = """ date,symbol 1/9/2006,foobarbaz 1/9/2006,bazfoobar 1/9/2006,barbazfoo 1/11/2006,foobarbaz 1/11/2006,bazfoobar 1/11/2006,barbazfoo 1/11/2006,foobarbaz """.strip() FETCHER_ALTERNATE_COLUMN_HEADER = "ARGLEBARGLE" FETCHER_UNIVERSE_DATA_TICKER_COLUMN = FETCHER_UNIVERSE_DATA.replace( "symbol", FETCHER_ALTERNATE_COLUMN_HEADER)

34.464023

77

0.681764

7,311

23,470

2.18438

0.033238

0.080776

0.017846

0.01603

0.690795

0.507952

0.293175

0.180589

0.093676

0.078334

0

0.663354

0.031146

23,470

680

78

34.514706

0.038964

0.001065

0

0.125186

0

0.733234

0.980805

0.865504

0

1

0

false

0

null

0

1

0

1

0

1

null

0

5

fb5ea22bc915d377f52c18e7cc591c1c2366468a

141

py

Python

__init__.py

federicozaiter/LogClass

62c1c9c61294625bdb3d99dc01b6adc7b735c4ab

[ "MIT" ]

159

2020-02-19T00:19:23.000Z

2022-03-30T08:40:08.000Z

__init__.py

WeibinMeng/LogClass-1

8edbaf4377374e2aac5e7057987e1d047b83ff2f

[ "MIT" ]

3

2021-06-09T04:30:35.000Z

2022-01-09T23:26:07.000Z

__init__.py

WeibinMeng/LogClass-1

8edbaf4377374e2aac5e7057987e1d047b83ff2f

[ "MIT" ]

41

2020-02-19T00:19:26.000Z

2022-03-28T08:02:22.000Z

__all__ = ["utils", "logclass"] from .preprocess import * from .feature_engineering import * from .models import * from .reporting import *

20.142857

34

0.737589

16

141

6.1875

0.625

0.30303

0

0.148936

141

6

35

23.5

0.825

0

0.092199

0

1

0

false

0

0.8

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

fb62005e26f5f656c89f92646218f30f51b175ac

152

py

Python

lib/JumpScale/baselib/cache/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

null

lib/JumpScale/baselib/cache/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

4

2016-08-25T12:08:39.000Z

2018-04-12T12:36:01.000Z

lib/JumpScale/baselib/cache/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

3

2016-03-08T07:49:34.000Z

2018-10-19T13:56:43.000Z

from JumpScale import j def cb(): from .Cache import * return CacheFactory() j.base.loader.makeAvailable(j, 'db') j.db._register('cache', cb)

16.888889

36

0.684211

22

152

4.681818

0.636364

0.058252

0

0.171053

152

8

37

19

0.81746

0

0.046053

0

1

0.166667

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fb6ae0cda8e89a4e7fce579b43549ded65e96427

117

py

Python

hla/__init__.py

Biomedical-Genetics/hla-genotyper

91d8568de3b3383efe3c4d2e087668e4b9ab8595

[ "MIT" ]

4

2018-03-05T14:05:08.000Z

2020-10-30T16:05:45.000Z

hla/__init__.py

Biomedical-Genetics/hla-genotyper

91d8568de3b3383efe3c4d2e087668e4b9ab8595

[ "MIT" ]

1

2018-03-05T14:04:34.000Z

2018-03-05T15:41:05.000Z

hla/__init__.py

Biomedical-Genetics/hla-genotyper

91d8568de3b3383efe3c4d2e087668e4b9ab8595

[ "MIT" ]

null

import sys import genotyper as gt def main(): """Entry point for the application script""" gt.main(sys.argv)

19.5

48

0.692308

18

117

4.5

0.777778

0

0.196581

117

5

49

23.4

0.861702

0.324786

0

1

0.25

true

0

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fb78b3034bcecd8deb288b1fe8d95b5dff5665ba

265

py

Python

tests/test_post_processor.py

kachark/FormFlight

94189581ecd28ab5d9d30e2b171a3fa3296029a7

[ "MIT" ]

5

2019-11-03T06:35:28.000Z

2021-05-25T16:21:28.000Z

tests/test_post_processor.py

kachark/FormFlight

94189581ecd28ab5d9d30e2b171a3fa3296029a7

[ "MIT" ]

null

tests/test_post_processor.py

kachark/FormFlight

94189581ecd28ab5d9d30e2b171a3fa3296029a7

[ "MIT" ]

null

import pytest import numpy as np import pandas as pd import DOT_assignment.engine import DOT_assignment.assignments import DOT_assignment.controls import DOT_assignment.run import DOT_assignment.dynamics import DOT_assignment.agents import DOT_assignment.setup

17.666667

33

0.867925

38

265

5.868421

0.421053

0.282511

0.596413

0

0.10566

265

14

34

18.928571

0.940928

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

fb7f0a2eeeb61a5b6034901871c8d870236404a9

35

py

Python

client_admin/menu/__init__.py

HatchCanada/django-client-admin

69ef136ea6bc9b5676dc3547d4322b8b217d2f2f

[ "Apache-2.0" ]

16

2015-01-15T23:42:32.000Z

2021-01-04T08:57:46.000Z

client_admin/menu/__init__.py

HatchCanada/django-client-admin

69ef136ea6bc9b5676dc3547d4322b8b217d2f2f

[ "Apache-2.0" ]

null

client_admin/menu/__init__.py

HatchCanada/django-client-admin

69ef136ea6bc9b5676dc3547d4322b8b217d2f2f

[ "Apache-2.0" ]

8

2015-02-06T09:00:23.000Z

2019-02-13T17:00:18.000Z

# Kept for backwards compatibility

17.5

34

0.828571

4

35

7.25

1

0

0.142857

35

1

35

0.966667

0.914286

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

fb805af812213e48c7e50f3f5372dee72c4a4215

1,454

py

Python

pj8.py

Ziggareto/project_euler_solns

e52066b9ffec97005bad0f26a3f0e08760d5c5cb

[ "MIT" ]

null

pj8.py

Ziggareto/project_euler_solns

e52066b9ffec97005bad0f26a3f0e08760d5c5cb

[ "MIT" ]

null

pj8.py

Ziggareto/project_euler_solns

e52066b9ffec97005bad0f26a3f0e08760d5c5cb

[ "MIT" ]

null

#pj8 from pj_euler import mathStuff x = """73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450""" y = '' for char in x: if not char == '\n': y+= char biggest = 0 biggestString = '' for num in range(len(y)-12): numString = y[num:num+13] temp = 1 for char in numString: temp *= int(char) if temp > biggest: biggest = temp biggestString = numString print(biggestString) print(biggest)

33.045455

58

0.842503

73

1,454

16.767123

0.643836

0.011438

0.014706

0

0.790424

0.123796

1,454

43

59

33.813953

0.17033

0.002063

0

0.725657

0.710732

0

1

0

false

0

0.027027

0

0.027027

0.054054

0

1

null

0

1

0

1

0

1

null

0

5

fb89b9ee6056502603ef680c86f2b758916e1ba7

212

py

Python

pyload/load.py

lrterry/py-load

c06ef979ee1761c5b9df642f5af5119da7ec09fe

[ "Apache-2.0" ]

null

pyload/load.py

lrterry/py-load

c06ef979ee1761c5b9df642f5af5119da7ec09fe

[ "Apache-2.0" ]

null

pyload/load.py

lrterry/py-load

c06ef979ee1761c5b9df642f5af5119da7ec09fe

[ "Apache-2.0" ]

null

class BaseLoadTester(object): def __init__(self, config): self.config = config def before(self): raise NotImplementedError() def on_result(self): raise NotImplementedError()

21.2

35

0.65566

21

212

6.380952

0.571429

0.149254

0.41791

0

0.254717

212

9

36

23.555556

0.848101

0

0.285714

0

1

0.428571

false

0

0.571429

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

fb8bd230ad9060d3b92d7a34b0c0fcc8870cc654

124

py

Python

HLTrigger/HLTanalyzers/test/test_hltrigreport_run_lumi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

HLTrigger/HLTanalyzers/test/test_hltrigreport_run_lumi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

HLTrigger/HLTanalyzers/test/test_hltrigreport_run_lumi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

from test_hltrigreport_base_cfg import process process.hlTrigReport.resetBy = "run" process.hlTrigReport.reportBy = "lumi"

24.8

46

0.830645

15

124

6.666667

0.733333

0.38

0

0.08871

124

4

47

31

0.884956

0

0.056452

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

fbb664c0ea7f42fa05ee1a117842b4328f585eb7

17

py

Python

fancy_tests/tests/models.py

YPCrumble/django-fancy-cache

6efe6317ecd8c2ebf4357fef284f79e3e21afed6

[ "BSD-3-Clause" ]

31

2015-08-03T23:59:54.000Z

2022-02-15T19:43:22.000Z

fancy_tests/tests/models.py

YPCrumble/django-fancy-cache

6efe6317ecd8c2ebf4357fef284f79e3e21afed6

[ "BSD-3-Clause" ]

46

2015-03-14T19:02:34.000Z

2022-03-31T00:23:25.000Z

fancy_tests/tests/models.py

YPCrumble/django-fancy-cache

6efe6317ecd8c2ebf4357fef284f79e3e21afed6

[ "BSD-3-Clause" ]

12

2015-07-27T21:56:00.000Z

2020-11-02T09:39:23.000Z

# lonely in here

8.5

16

0.705882

3

17

4

1

0

0.235294

17

1

17

0.923077

0.823529

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

fbb7df791c9bb8b6592dc68cf38823cd1d975dc5

755

py

Python

build/geometry/kdl_conversions/catkin_generated/pkg.develspace.context.pc.py

EurobotMDX/eurobot_2020_odroid_cam

ddd9a17d53899f1c615816fd74512c112ecad188

[ "MIT" ]

4

2019-10-26T18:48:51.000Z

2020-02-27T19:31:36.000Z

build/geometry/kdl_conversions/catkin_generated/pkg.develspace.context.pc.py

EurobotMDX/eurobot_2020_odroid_cam

ddd9a17d53899f1c615816fd74512c112ecad188

[ "MIT" ]

null

build/geometry/kdl_conversions/catkin_generated/pkg.develspace.context.pc.py

EurobotMDX/eurobot_2020_odroid_cam

ddd9a17d53899f1c615816fd74512c112ecad188

[ "MIT" ]

1

2019-10-26T18:50:48.000Z

# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/home/ros/lidar_ws/src/geometry/kdl_conversions/include;/opt/ros/kinetic/share/orocos_kdl/../../include;/usr/include/eigen3".split(';') if "/home/ros/lidar_ws/src/geometry/kdl_conversions/include;/opt/ros/kinetic/share/orocos_kdl/../../include;/usr/include/eigen3" != "" else [] PROJECT_CATKIN_DEPENDS = "geometry_msgs".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lkdl_conversions;/opt/ros/kinetic/lib/liborocos-kdl.so.1.3.2".split(';') if "-lkdl_conversions;/opt/ros/kinetic/lib/liborocos-kdl.so.1.3.2" != "" else [] PROJECT_NAME = "kdl_conversions" PROJECT_SPACE_DIR = "/home/ros/lidar_ws/devel" PROJECT_VERSION = "1.11.9"

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1

null

0

5

fbce315166e8fedba3fe68b076bf2c0e8e0c57a4

158

py

Python

accounts/forms/__init__.py

kevangel79/b2note

e38789afc389c31b46bba652b4fe98f0e5c2f49e

[ "MIT" ]

null

accounts/forms/__init__.py

kevangel79/b2note

e38789afc389c31b46bba652b4fe98f0e5c2f49e

[ "MIT" ]

null

accounts/forms/__init__.py

kevangel79/b2note

e38789afc389c31b46bba652b4fe98f0e5c2f49e

[ "MIT" ]

1

2020-04-04T11:52:11.000Z

from register import RegistrationForm from old_register import OldRegistrationForm from authenticate import AuthenticationForm from profile import ProfileForm

39.5

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83d55f92211e0f11e50296743ed52e4756674b93

99

py

Python

skater/core/local_interpretation/lime/lime_tabular.py

RPUTHUMA/Skater

317460b88065b41eebe6790e9efdbb0595cbe450

[ "UPL-1.0" ]

718

2017-05-19T22:49:40.000Z

2019-03-27T06:40:54.000Z

skater/core/local_interpretation/lime/lime_tabular.py

quant1729/Skater

b46a4abe3465ddc7b19ffc762ad45d1414b060a6

[ "UPL-1.0" ]

114

2017-05-24T16:55:59.000Z

2019-03-27T12:48:18.000Z

skater/core/local_interpretation/lime/lime_tabular.py

quant1729/Skater

b46a4abe3465ddc7b19ffc762ad45d1414b060a6

[ "UPL-1.0" ]

121

2017-05-22T17:20:19.000Z

2019-03-21T15:06:19.000Z

""" Making LimeTabularExplainer Accessible """ from lime.lime_tabular import LimeTabularExplainer

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0

1

0

1

0

5

83eb514f328590d0ab9c9a0ee2688df9f090d21e

30

py

Python

reddit_login/__init__.py

Chromadream/reddit-login

dc594eca3ac32dbc7cde2e47cd3881d20e7cd7ef

[ "BSD-3-Clause" ]

null

reddit_login/__init__.py

Chromadream/reddit-login

dc594eca3ac32dbc7cde2e47cd3881d20e7cd7ef

[ "BSD-3-Clause" ]

1

2021-06-01T22:52:46.000Z

reddit_login/__init__.py

Chromadream/reddit-login

dc594eca3ac32dbc7cde2e47cd3881d20e7cd7ef

[ "BSD-3-Clause" ]

null

from .login import login_flow

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null

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1

0

1

0

5

f7b0507138a5e7e776b460b1540515a6d41a5447

4,247

py

Python

agent0/ddpg/model.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

[ "MIT" ]

null

agent0/ddpg/model.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

[ "MIT" ]

null

agent0/ddpg/model.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

[ "MIT" ]

null

from itertools import chain import numpy as np import torch import torch.nn as nn from torch.distributions import Normal def init(m, gain=1.0): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): nn.init.orthogonal_(m.weight.data, gain) nn.init.zeros_(m.bias.data) class DDPGMLP(nn.Module): def __init__(self, num_inputs, action_dim, max_action, hidden_size=256): super(DDPGMLP, self).__init__() self.max_action = max_action self.v = nn.Sequential( nn.Linear(num_inputs + action_dim, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 1) ) self.p = nn.Sequential( nn.Linear(num_inputs, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, action_dim), nn.Tanh() ) self.apply(lambda m: init(m, np.sqrt(2))) def act(self, x): return self.p(x) * self.max_action def action_value(self, state, action): return self.v(torch.cat([state, action], dim=1)) def get_policy_params(self): return self.p.parameters() def get_value_params(self): return self.v.parameters() class SACMLP(nn.Module): LOG_STD_MAX = 2 LOG_STD_MIN = -20 eps = 1e-6 def __init__(self, num_inputs, action_dim, max_action, hidden_size=256): super(SACMLP, self).__init__() self.max_action = max_action self.v = nn.Sequential( nn.Linear(num_inputs + action_dim, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 1) ) self.v2 = nn.Sequential( nn.Linear(num_inputs + action_dim, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 1) ) self.p = nn.Sequential( nn.Linear(num_inputs, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, action_dim * 2) ) self.apply(lambda m: init(m, np.sqrt(2))) def act(self, x): action_mean, action_log_std = torch.chunk(self.p(x), 2, dim=-1) action_log_std = action_log_std.clamp(self.LOG_STD_MIN, self.LOG_STD_MAX) dist = Normal(action_mean, action_log_std.exp()) xs = dist.rsample() action = xs.tanh() * self.max_action action_log_prob = dist.log_prob(xs) - torch.log(1 - action.pow(2) + self.eps) entropy = action_log_prob.sum(-1, keepdim=True).neg() return action, entropy, action_mean.tanh() * self.max_action def action_value(self, state, action): x = torch.cat([state, action], dim=1) return self.v(x), self.v2(x) def get_policy_params(self): return self.p.parameters() def get_value_params(self): return chain(self.v.parameters(), self.v2.parameters()) class TD3MLP(nn.Module): def __init__(self, num_inputs, action_dim, max_action, hidden_size=256): super(TD3MLP, self).__init__() self.max_action = max_action self.v = nn.Sequential( nn.Linear(num_inputs + action_dim, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 1) ) self.v2 = nn.Sequential( nn.Linear(num_inputs + action_dim, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 1) ) self.p = nn.Sequential( nn.Linear(num_inputs, hidden_size), nn.Tanh(), nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, action_dim), nn.Tanh() ) self.apply(lambda m: init(m, np.sqrt(2))) def act(self, x): return self.p(x) * self.max_action def action_value(self, state, action): x = torch.cat([state, action], dim=1) return self.v(x), self.v2(x) def get_policy_params(self): return self.p.parameters() def get_value_params(self): return chain(self.v.parameters(), self.v2.parameters())

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null

0

5

f7bc8636782ea4fa6c13b1a0e68b3a1a235df0b6

70

py

Python

efficient_rl/oo_mdp_operations/__init__.py

rlagywjd802/efficient_rl

6a82bfc10d814f5d36a7c211d645aa35ea380acf

[ "MIT" ]

8

2020-06-25T10:16:48.000Z

2022-02-15T09:12:04.000Z

efficient_rl/oo_mdp_operations/__init__.py

rlagywjd802/efficient_rl

6a82bfc10d814f5d36a7c211d645aa35ea380acf

[ "MIT" ]

null

efficient_rl/oo_mdp_operations/__init__.py

rlagywjd802/efficient_rl

6a82bfc10d814f5d36a7c211d645aa35ea380acf

[ "MIT" ]

2

2020-12-30T07:39:38.000Z

2021-04-12T14:57:13.000Z

from efficient_rl.oo_mdp_operations.OperationsClass import Operations

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0

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70

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5

f7e4b36de2a446bc468631ea406410c15917a43d

13,222

py

Python

storagemgr/storage/tests/tests_archive.py

akgrant43/storagemgr

1d0bec363d93802b2133d8d58475f69c4418ad67

[ "Apache-2.0" ]

null

storagemgr/storage/tests/tests_archive.py

akgrant43/storagemgr

1d0bec363d93802b2133d8d58475f69c4418ad67

[ "Apache-2.0" ]

null

storagemgr/storage/tests/tests_archive.py

akgrant43/storagemgr

1d0bec363d93802b2133d8d58475f69c4418ad67

[ "Apache-2.0" ]

null

""" Test the archiver functionality. """ from shutil import copy2, rmtree from os.path import isdir, isfile, join, getmtime from os import makedirs, remove from datetime import datetime from django.conf import settings from django.test import TestCase from storage.models import RootPath, File, Hash from storage.scan import QuickScan from storage.archiver import Archiver, ImageArchiver, VideoArchiver class ArchiveTests(TestCase): fixtures = ['initial_data'] def setUp(self): # Get tmp directory tmpdirs = ['/run/shm', '/tmp'] self.tmpdir = None for d in tmpdirs: if isdir(d): self.tmpdir = d break self.assertNotEqual(self.tmpdir, None, msg="Unable to find temporary directory") # Create initial directories to manage self.rootdir = join(self.tmpdir, 'storagemgr_tests') if isdir(self.rootdir): # Delete the directory and start again rmtree(self.rootdir) makedirs(self.rootdir) self.test_data = join(settings.PROJECT_DIR, 'storage', 'test_data') # Copy initial files in self.file1_src = join(self.test_data, "File1.txt") copy2(self.file1_src, self.rootdir) self.file2_src = join(self.test_data, "File2.txt") copy2(self.file2_src, self.rootdir) self.file2 = join(self.rootdir, "File2.txt") # Initial DB population self.rootpath = RootPath(path=self.rootdir) self.rootpath.save() scanner = QuickScan() scanner.scan() return def test_initial_archive(self): """Archive the first test directory and ensure file is added""" #import pdb; pdb.set_trace() archive1 = join(self.test_data, "archive1") archiver = Archiver(archive1, self.rootdir) archiver.archive() # Should have 5 files - 3 text, 1 image, 1 video self.assertEqual(File.objects.count(), 5) f3 = File.objects.get(name='File3.txt') self.assertEqual(f3.hash.digest, '89e8719f26e37bbfaed29026404741ef10014ed304c0fe956a876bcfd49b822f') return def test_archive_deleted(self): """Check: 1. Archiving a deleted file doesn't re-add it to the archive """ # Delete image2, rescan and confirm deleted remove(self.file2) scanner = QuickScan() scanner.scan() f2 = File.objects.get(name='File2.txt') self.assertIsNotNone(f2.deleted, "Expected File2.txt to be deleted") # Archive archive2, which contains a copy of image2, # which should not be archived archive2 = join(self.test_data, "archive2") archiver = Archiver(archive2, self.rootdir) archiver.archive() # There should still be four objects self.assertEqual(File.objects.count(), 4) # The hash of file2 should only have the deleted file fhash = Hash.objects.get( digest=f2.hash.digest) files = File.objects.filter(hash=fhash) self.assertEqual(files.count(), 1) self.assertIsNotNone(files[0].deleted) return def test_rearchive(self): """Check: 1. Re-archiving the same directory doesn't modify the database. """ #import pdb; pdb.set_trace() archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() files = File.objects.all() self.assertEqual(files.count(), 3) orig_dates = set([x.mod_date for x in files]) archiver.archive() files = File.objects.all() new_dates = set([x.mod_date for x in files]) self.assertEqual(orig_dates, new_dates) return def test_same_date(self): """Check: 1. Two files with the same date (but different hash) are given unique names. """ #import pdb; pdb.set_trace() # Archive image3.png archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() # Archive image4.png archive3 = join(self.test_data, "archive3") archiver = ImageArchiver(archive3, self.rootdir) archiver.archive() i3 = File.objects.get(name='IMG-20131214-084900-0.png') i4 = File.objects.get(name='IMG-20131214-084900-0-1.png') self.assertEqual(i3.date, i4.date) return def test_no_overwrite(self): """Check: 1. The system doesn't overwrite a file with the target name. """ #import pdb; pdb.set_trace() dest_dir = join(self.rootdir, "2013", "12Dec") makedirs(dest_dir) copy2(join(self.test_data, "archive1", "image3.png"), join(dest_dir, "IMG-20131214-084900-0.png")) archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() self.assertTrue(isfile(join(dest_dir, "IMG-20131214-084900-0-1.png")), "Didn't find IMG-20131214-084900-0-1.png") return class ImageArchiveTests(TestCase): fixtures = ['initial_data'] def setUp(self): # Get tmp directory tmpdirs = ['/run/shm', '/tmp'] self.tmpdir = None for d in tmpdirs: if isdir(d): self.tmpdir = d break self.assertNotEqual(self.tmpdir, None, msg="Unable to find temporary directory") # Create initial directories to manage self.rootdir = join(self.tmpdir, 'storagemgr_tests') if isdir(self.rootdir): # Delete the directory and start again rmtree(self.rootdir) makedirs(self.rootdir) self.test_data = join(settings.PROJECT_DIR, 'storage', 'test_data') # Copy initial files in self.image1_src = join(self.test_data, "image1.png") copy2(self.image1_src, self.rootdir) self.image2_src = join(self.test_data, "image2.png") copy2(self.image2_src, self.rootdir) self.image2 = join(self.rootdir, "image2.png") # Initial DB population self.rootpath = RootPath(path=self.rootdir) self.rootpath.save() scanner = QuickScan() scanner.scan() return def test_initial_archive(self): """Archive the first test directory and ensure file is added""" #import pdb; pdb.set_trace() archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() self.assertEqual(File.objects.count(), 3) i3 = File.objects.get(name='IMG-20131214-084900-0.png') self.assertEqual(i3.hash.digest, #'e60698d93e7b7f6955efce729a8fbab2399cbd13fa7b13ac3c2bdc7ffb419ef4') '2c2ddf743172fd763dcb71a17e699481d1eb568cfdb0443a1c7a229f64865983') return def test_archive_deleted(self): """Check: 1. Archiving a deleted file doesn't re-add it to the archive """ #import pdb; pdb.set_trace() # Delete image2, rescan and confirm deleted remove(self.image2) scanner = QuickScan() scanner.scan() i2 = File.objects.get(name='image2.png') self.assertTrue(i2.deleted, "Expected image2.png to be deleted") # Archive archive2, which contains a copy of image2, # which should not be archived archive2 = join(self.test_data, "archive2") archiver = ImageArchiver(archive2, self.rootdir) archiver.archive() # There should still be three objects self.assertEqual(File.objects.count(), 2) # The hash of image2 should only have the deleted file fhash = Hash.objects.get( # Old digest on whole file #digest='245346fa2da665e78e4e36994bb9f0bd654ad8ef4d2f4622fca361280935fd8f') digest='26a2ca1108565fb5df7b4a70660c5017334c7c8074c5528492a679859c119121') files = File.objects.filter(hash=fhash) self.assertEqual(files.count(), 1) self.assertIsNotNone(files[0].deleted) return def test_rearchive(self): """Check: 1. Re-archiving the same directory doesn't modify the database. """ archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() files = File.objects.all() self.assertEqual(files.count(), 3) orig_dates = set([x.mod_date for x in files]) archiver.archive() files = File.objects.all() new_dates = set([x.mod_date for x in files]) self.assertEqual(orig_dates, new_dates) return def test_same_date(self): """Check: 1. Two files with the same date (but different hash) are given unique names. """ # Archive image3.png archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() # Archive image4.png archive3 = join(self.test_data, "archive3") archiver = ImageArchiver(archive3, self.rootdir) archiver.archive() i3 = File.objects.get(name='IMG-20131214-084900-0.png') i4 = File.objects.get(name='IMG-20131214-084900-0-1.png') self.assertEqual(i3.date, i4.date) return def test_no_overwrite(self): """Check: 1. The system doesn't overwrite a file with the target name. """ dest_dir = join(self.rootdir, "2013", "12Dec") makedirs(dest_dir) copy2(join(self.test_data, "archive1", "image3.png"), join(dest_dir, "IMG-20131214-084900-0.png")) archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() self.assertTrue(isfile(join(dest_dir, "IMG-20131214-084900-0-1.png")), "Didn't find IMG-20131214-084900-0-1.png") return def test_accumulate_keywords(self): """Check: 1. Archiving a duplicate photo with additional tags adds the new tags to the existing photo. """ #import pdb; pdb.set_trace() archive1 = join(self.test_data, "archive1") archiver = ImageArchiver(archive1, self.rootdir) archiver.archive() files = File.objects.all() self.assertEqual(files.count(), 3) # Test image3.png has tag3. i3 = File.objects.get(name='IMG-20131214-084900-0.png') self.assertEqual(len(i3.keywords), 1) self.assertEqual(i3.keyword_names[0], u'tag3') # Archive4 has same image, but with tags tag1 and tag2 archive4 = join(self.test_data, "archive4") archiver4 = ImageArchiver(archive4, self.rootdir) archiver4.archive() # Test image3.png has tag1 and tag2 have been added. i3 = File.objects.get(name='IMG-20131214-084900-0.png') self.assertEqual(len(i3.keywords), 3) self.assertEqual(set(i3.keyword_names), set(['tag1', 'tag2', 'tag3'])) return class VideoArchiveTests(TestCase): fixtures = ['initial_data'] def setUp(self): # Get tmp directory tmpdirs = ['/run/shm', '/tmp'] self.tmpdir = None for d in tmpdirs: if isdir(d): self.tmpdir = d break self.assertNotEqual(self.tmpdir, None, msg="Unable to find temporary directory") # Create initial directories to manage self.rootdir = join(self.tmpdir, 'storagemgr_tests') if isdir(self.rootdir): # Delete the directory and start again rmtree(self.rootdir) makedirs(self.rootdir) self.test_data = join(settings.PROJECT_DIR, 'storage', 'test_data') # Copy initial files in self.image1_src = join(self.test_data, "image1.png") copy2(self.image1_src, self.rootdir) self.image1 = join(self.rootdir, "image1.png") # Initial DB population self.rootpath = RootPath(path=self.rootdir) self.rootpath.save() scanner = QuickScan() scanner.scan() return def test_initial_archive(self): """Archive the first test directory and ensure file is added""" #import pdb; pdb.set_trace() archive1 = join(self.test_data, "archive1") archiver = VideoArchiver(archive1, self.rootdir) archiver.archive() self.assertEqual(File.objects.count(), 2) # # The test video doesn't have any metadata, derive the filename # from the modification date # mtime = getmtime(join(archive1, "video1.mp4")) fdate = datetime.fromtimestamp(mtime) newname = "VID-" + fdate.strftime("%Y%m%d-%H%M%S-") + \ str(fdate.microsecond) + ".mp4" v1 = File.objects.get(name=newname) self.assertEqual(v1.hash.digest, 'fdbc165cb15f5d94679d11cd9e264816d78f6560cda2b575b838b2a95be12185') return

35.447721

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5

f7e552f3463d050f19e9eb3f71c2c901fab0f8c9

39

py

Python

dmb/data/loaders/__init__.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

160

2019-11-16T13:59:21.000Z

2022-03-28T07:52:59.000Z

dmb/data/loaders/__init__.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

22

2019-11-22T02:14:18.000Z

2022-01-24T10:16:14.000Z

dmb/data/loaders/__init__.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

38

2019-12-27T14:01:01.000Z

2022-03-12T11:40:11.000Z

from .builder import build_data_loader

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1

39

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

790b7011c7cbd959fb9330d296ab2129185ff98d

2,670

py

Python

waves_gateway/model/polling_delay_config.py

NeolithEra/WavesGatewayFramework

e7ba892427e1d0444f2bfdc2922c45ff5f4c4add

[ "MIT" ]

25

2018-03-04T07:49:21.000Z

2022-03-28T05:20:50.000Z

waves_gateway/model/polling_delay_config.py

NeolithEra/WavesGatewayFramework

e7ba892427e1d0444f2bfdc2922c45ff5f4c4add

[ "MIT" ]

22

2018-03-25T13:19:45.000Z

2020-11-28T17:21:08.000Z

waves_gateway/model/polling_delay_config.py

NeolithEra/WavesGatewayFramework

e7ba892427e1d0444f2bfdc2922c45ff5f4c4add

[ "MIT" ]

31

2018-03-25T09:45:13.000Z

2022-03-24T05:32:18.000Z

""" PollingDelayConfig """ from typing import Any class PollingDelayConfig(object): """ Summarized configuration for the polling_delay settings in the Gateway Application. """ DEFAULT_MIN_TRANSACTION_POLLING_DELAY_S = 0.0 DEFAULT_MAX_TRANSACTION_POLLING_DELAY_S = 60.0 DEFAULT_MIN_ATTEMPT_LIST_WORKER_DELAY_S = 0.1 DEFAULT_MAX_ATTEMPT_LIST_WORKER_DELAY_S = 60.0 def __init__(self, coin_min_polling_delay_s: float = DEFAULT_MIN_TRANSACTION_POLLING_DELAY_S, coin_max_polling_delay_s: float = DEFAULT_MAX_TRANSACTION_POLLING_DELAY_S, waves_min_polling_delay_s: float = DEFAULT_MIN_TRANSACTION_POLLING_DELAY_S, waves_max_polling_delay_s: float = DEFAULT_MAX_TRANSACTION_POLLING_DELAY_S, attempt_list_worker_min_polling_delay_s: float = DEFAULT_MIN_ATTEMPT_LIST_WORKER_DELAY_S, attempt_list_worker_max_polling_delay_s: float = DEFAULT_MAX_ATTEMPT_LIST_WORKER_DELAY_S) -> None: self._coin_polling_delay_s_min = coin_min_polling_delay_s self._coin_polling_delay_s_max = coin_max_polling_delay_s self._waves_polling_delay_s_min = waves_min_polling_delay_s self._waves_polling_delay_s_max = waves_max_polling_delay_s self._attempt_list_worker_min_polling_delay_s = attempt_list_worker_min_polling_delay_s self._attempt_list_worker_max_polling_delay_s = attempt_list_worker_max_polling_delay_s @staticmethod def from_single_polling_delay(polling_delay_s: float) -> Any: return PollingDelayConfig( coin_min_polling_delay_s=polling_delay_s, coin_max_polling_delay_s=polling_delay_s, waves_min_polling_delay_s=polling_delay_s, waves_max_polling_delay_s=polling_delay_s, attempt_list_worker_min_polling_delay_s=polling_delay_s, attempt_list_worker_max_polling_delay_s=polling_delay_s) @property def waves_max_polling_delay_s(self) -> float: return self._waves_polling_delay_s_max @property def waves_min_polling_delay_s(self) -> float: return self._waves_polling_delay_s_min @property def coin_min_polling_delay_s(self) -> float: return self._coin_polling_delay_s_min @property def coin_max_polling_delay_s(self) -> float: return self._coin_polling_delay_s_max @property def attempt_list_worker_min_polling_delay_s(self) -> float: return self._attempt_list_worker_min_polling_delay_s @property def attempt_list_worker_max_polling_delay_s(self) -> float: return self._attempt_list_worker_max_polling_delay_s

41.71875

115

0.762921

378

2,670

4.71164

0.103175

0.178551

0.357664

0.125772

0.862437

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0.731611

0.601909

0.440764

0.411567

0

0.004634

0.19176

2,670

63

116

42.380952

0.820667

0.038202

0

0.130435

0

1

0.173913

false

0

0.021739

0.152174

0.456522

0

null

0

1

0

1

0

null

0

1

0

5

f70b5953932541c87b991bde53d94cc13d35e857

36

py

Python

tests/__init__.py

quarkslab/ziphyr

f4b6f258b88ed5b4c8c1e0557ddd01e63f225407

[ "Apache-2.0" ]

3

2020-12-13T10:52:50.000Z

2021-11-15T10:45:00.000Z

tests/__init__.py

quarkslab/ziphyr

f4b6f258b88ed5b4c8c1e0557ddd01e63f225407

[ "Apache-2.0" ]

null

tests/__init__.py

quarkslab/ziphyr

f4b6f258b88ed5b4c8c1e0557ddd01e63f225407

[ "Apache-2.0" ]

1

2021-11-14T02:56:49.000Z

"""Unit test package for ziphyr."""

18

35

0.666667

5

36

4.8

1

0

0.138889

36

1

36

0.774194

0.805556

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

f7881991ef2ae4a7bb28118def5709bee514b6a3

87

py

Python

extra_hours/shared/value_objects.py

flaviogf/extra_hours

9b235f06b1f111219c2b85d293728aa2f986206a

[ "MIT" ]

null

extra_hours/shared/value_objects.py

flaviogf/extra_hours

9b235f06b1f111219c2b85d293728aa2f986206a

[ "MIT" ]

null

extra_hours/shared/value_objects.py

flaviogf/extra_hours

9b235f06b1f111219c2b85d293728aa2f986206a

[ "MIT" ]

null

from pyflunt.notifications import Notifiable class ValueObject(Notifiable): pass

14.5

44

0.804598

9

87

7.777778

0.888889

0

0.149425

87

5

45

17.4

0.945946

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

e3932807142489ea644607a1dfdd39a2db865169

97

py

Python

lackawanna/collection/admin.py

allyjweir/lackawanna

54ce5088a0fb828b056fb2ca2ac2924489fd33f1

[ "BSD-3-Clause" ]

3

2015-11-03T03:07:28.000Z

2016-02-03T00:32:14.000Z

lackawanna/collection/admin.py

allyjweir/lackawanna

54ce5088a0fb828b056fb2ca2ac2924489fd33f1

[ "BSD-3-Clause" ]

11

2015-10-20T19:05:34.000Z

2019-05-17T13:47:30.000Z

lackawanna/collection/admin.py

allyjweir/lackawanna

54ce5088a0fb828b056fb2ca2ac2924489fd33f1

[ "BSD-3-Clause" ]

null

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

19.4

32

0.835052

13

97

6.230769

0.692308

0

0.103093

97

5

33

19.4

0.931034

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e3bb334b5fafae302bdd73bf5c7274f9b9cb0f25

142

py

Python

flarestack/data/public/__init__.py

grburgess/flarestack

6f94b9493d5470539e2705e473c84683720122cc

[ "MIT" ]

1

2021-04-19T06:26:03.000Z

flarestack/data/public/__init__.py

Raimer/flarestack

60659d368db93ead7b53addf3af9f1e8ac3a52bc

[ "MIT" ]

null

flarestack/data/public/__init__.py

Raimer/flarestack

60659d368db93ead7b53addf3af9f1e8ac3a52bc

[ "MIT" ]

1

2022-03-01T06:11:46.000Z

from flarestack.data.public.icecube.all_sky_point_source.all_sky_3_year \ import icecube_ps_3_year icecube_ps_3_year = icecube_ps_3_year

28.4

73

0.859155

26

142

4.115385

0.5

0.186916

0.280374

0.392523

0

0.031008

0.091549

142

4

74

35.5

0.79845

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

58082132b167acc804473399abdd10ef7e6d0ec7

408

py

Python

simba/errors.py

joebentley/simba

dd1b7bc6d22ad96566898dd1851cfa210462cb00

[ "MIT" ]

8

2020-03-19T10:59:25.000Z

2022-01-22T22:33:07.000Z

simba/errors.py

joebentley/simba

dd1b7bc6d22ad96566898dd1851cfa210462cb00

[ "MIT" ]

1

2022-01-22T11:24:45.000Z

simba/errors.py

joebentley/simba

dd1b7bc6d22ad96566898dd1851cfa210462cb00

[ "MIT" ]

1

2020-03-19T13:27:41.000Z

class DimensionError(Exception): """Represents an error involving matrix dimensions.""" class CoefficientError(Exception): """Represents an error involving transfer function coefficients.""" class StateSpaceError(Exception): """Represents a miscellaneous error involving `StateSpace`.""" class ResultError(Exception): """Represents an error involving the result of some calculation."""

27.2

71

0.754902

40

408

7.7

0.575

0.246753

0.204545

0.253247

0.340909

0

0.142157

408

14

72

29.142857

0.88

0.561275

0

1

0

true

0

1

0

null

1

0

null

0

1

0

1

0

5

582daa62807245d45a97d7bbd0f917377024ee8d

428

py

Python

djangoLogin/loginproject/models.py

roygoswamisuvankar/djangoLogin

e8a9a142e5bfb5b50fa90ea974877151a936eba1

[ "MIT" ]

null

djangoLogin/loginproject/models.py

roygoswamisuvankar/djangoLogin

e8a9a142e5bfb5b50fa90ea974877151a936eba1

[ "MIT" ]

null

djangoLogin/loginproject/models.py

roygoswamisuvankar/djangoLogin

e8a9a142e5bfb5b50fa90ea974877151a936eba1

[ "MIT" ]

null

from django.db import models # Create your models here. class employee(models.Model): name = models.CharField(max_length=20) phone = models.CharField(max_length=10) email = models.CharField(max_length=20) class student(models.Model): name = models.CharField(max_length=20) phone = models.CharField(max_length=20) email = models.CharField(max_length=20) password = models.CharField(max_length=200)

28.533333

47

0.740654

59

428

5.254237

0.372881

0.33871

0.406452

0.541935

0.658065

0.651613

0.451613

0

0.041322

0.151869

428

14

48

30.571429

0.812672

0.056075

0

0.4

0

1

0

false

0.1

0

1

0

null

1

0

null

0

1

0

1

0

5

582de98ed99c66c4e5930ba9f5cff979c3a98b02

35

py

Python

sumuka/drop_all_tables.py

kds119/eBay-Opportunity-Hack-Blr-2014

3bb0d35c88f4001a2fc50dca6349b06bc7f2f33d

[ "MIT" ]

1

2021-03-02T09:07:43.000Z

sumuka/drop_all_tables.py

ebayohblr2014/eBay-Opportunity-Hack-Blr-2014

3bb0d35c88f4001a2fc50dca6349b06bc7f2f33d

[ "MIT" ]

null

sumuka/drop_all_tables.py

ebayohblr2014/eBay-Opportunity-Hack-Blr-2014

3bb0d35c88f4001a2fc50dca6349b06bc7f2f33d

[ "MIT" ]

2

2015-02-05T06:16:58.000Z

2015-02-05T16:10:46.000Z

from models import * db.drop_all()

11.666667

20

0.742857

6

35

4.166667

1

0

0.142857

35

2

21

17.5

0.833333

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

582f0ebc9caea5b574b80619184e9cc38bb986f6

228

py

Python

OFTF/Dice.py

aessek/OFTF

6d6c8c1270c216bb794c851d66212cf9814cb94b

[ "MIT" ]

null

OFTF/Dice.py

aessek/OFTF

6d6c8c1270c216bb794c851d66212cf9814cb94b

[ "MIT" ]

null

OFTF/Dice.py

aessek/OFTF

6d6c8c1270c216bb794c851d66212cf9814cb94b

[ "MIT" ]

null

import random class Dice: def __init__(self): pass def roll(self, num_dice): outcome = list() for i in range(num_dice): outcome.append(random.randrange(1,7)) return outcome

17.538462

49

0.578947

29

228

4.344828

0.724138

0.111111

0.222222

0

0.013072

0.328947

228

13

50

17.538462

0.810458

0

1

0.222222

false

0.111111

0

0.555556

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5885a30bef4725c2a3be66821a30ddbd0fcdc52e

802

py

Python

lib/django-1.5/django/contrib/gis/geos/__init__.py

MiCHiLU/google_appengine_sdk

3da9f20d7e65e26c4938d2c4054bc4f39cbc5522

[ "Apache-2.0" ]

790

2015-01-03T02:13:39.000Z

2020-05-10T19:53:57.000Z

django/contrib/gis/geos/__init__.py

mradziej/django

5d38965743a369981c9a738a298f467f854a2919

[ "BSD-3-Clause" ]

1,361

2015-01-08T23:09:40.000Z

2020-04-14T00:03:04.000Z

django/contrib/gis/geos/__init__.py

mradziej/django

5d38965743a369981c9a738a298f467f854a2919

[ "BSD-3-Clause" ]

155

2015-01-08T22:59:31.000Z

2020-04-08T08:01:53.000Z

""" The GeoDjango GEOS module. Please consult the GeoDjango documentation for more details: http://geodjango.org/docs/geos.html """ from django.contrib.gis.geos.geometry import GEOSGeometry, wkt_regex, hex_regex from django.contrib.gis.geos.point import Point from django.contrib.gis.geos.linestring import LineString, LinearRing from django.contrib.gis.geos.polygon import Polygon from django.contrib.gis.geos.collections import GeometryCollection, MultiPoint, MultiLineString, MultiPolygon from django.contrib.gis.geos.error import GEOSException, GEOSIndexError from django.contrib.gis.geos.io import WKTReader, WKTWriter, WKBReader, WKBWriter from django.contrib.gis.geos.factory import fromfile, fromstr from django.contrib.gis.geos.libgeos import geos_version, geos_version_info, GEOS_PREPARE

53.466667

109

0.835411

109

802

6.091743

0.440367

0.135542

0.230422

0.271084

0.325301

0

0.084788

802

14

110

57.285714

0.904632

0.158354

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

58867867c80228f7ec4c7ed4bd87430ec01d982e

17

py

Python

examples/example1.py

telemetrix/sample_tr50_python

be88ecf38417486325203e281a9ecebffe1b3f69

[ "MIT" ]

null

examples/example1.py

telemetrix/sample_tr50_python

be88ecf38417486325203e281a9ecebffe1b3f69

[ "MIT" ]

null

examples/example1.py

telemetrix/sample_tr50_python

be88ecf38417486325203e281a9ecebffe1b3f69

[ "MIT" ]

null

print("example1")

17

0.764706

2

17

6.5

1

0

0.058824

0

17

1

17

0.705882

0

0.444444

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

58a405bea03b5e36a684daffd37d7aefd68bf1fb

575

py

Python

data_sets/data_set_reader.py

bryceklinker/learning-machine-learning

70b0d6aae091b781f20e3abe219c25aaef2d7be6

[ "MIT" ]

null

data_sets/data_set_reader.py

bryceklinker/learning-machine-learning

70b0d6aae091b781f20e3abe219c25aaef2d7be6

[ "MIT" ]

null

data_sets/data_set_reader.py

bryceklinker/learning-machine-learning

70b0d6aae091b781f20e3abe219c25aaef2d7be6

[ "MIT" ]

null

from os.path import join, dirname from pandas import read_csv from data_sets.data_set import DataSet ROOT_PATH = join(dirname(__file__), 'files') def read_data_set(name: str, dependent_variable_vector_index=-1) -> DataSet: data_frame = read_csv(join(ROOT_PATH, "{}.csv".format(name))) features = data_frame.iloc[:, 0:dependent_variable_vector_index].values dependent_variable_vector = data_frame.iloc[:, dependent_variable_vector_index].values return DataSet(data_frame=data_frame, features=features, dependent_variable_vector=dependent_variable_vector)

38.333333

113

0.798261

81

575

5.271605

0.382716

0.238876

0.323185

0.196721

0.159251

0

0.003884

0.104348

575

14

114

41.071429

0.825243

0

0.01913

0

1

0.111111

false

0

0.333333

0

0.555556

0

null

1

0

null

0

1

0

1

0

5

5492771f7f34d74ff2a91499e303810fdc776866

94

py

Python

data_structures/stack/__init__.py

kirkirey/programming-for-linguists

d97c59738713fab725073e9c88c7321119a648fc

[ "Apache-2.0" ]

null

data_structures/stack/__init__.py

kirkirey/programming-for-linguists

d97c59738713fab725073e9c88c7321119a648fc

[ "Apache-2.0" ]

null

data_structures/stack/__init__.py

kirkirey/programming-for-linguists

d97c59738713fab725073e9c88c7321119a648fc

[ "Apache-2.0" ]

4

2021-02-09T12:00:34.000Z

2021-05-21T18:59:38.000Z

""" Programming for linguists Stack module """ from data_structures.stack.stack import Stack

13.428571

45

0.787234

12

94

6.083333

0.75

0

0.12766

94

6

46

15.666667

0.890244

0.414894

0

1

0

true

0

1

0

1

0

1

0

null

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1

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null

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1

0

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