xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

a37d6077908b7b772bc007466596f71de1834f6c

74

py

Python

test_QandT.py

Jul-Tedyputro/python-sample-vscode-flask-tutorial

8878615add25cad7ee59c804d5aba1e86e5077e2

[ "MIT" ]

null

test_QandT.py

Jul-Tedyputro/python-sample-vscode-flask-tutorial

8878615add25cad7ee59c804d5aba1e86e5077e2

[ "MIT" ]

null

test_QandT.py

Jul-Tedyputro/python-sample-vscode-flask-tutorial

8878615add25cad7ee59c804d5aba1e86e5077e2

[ "MIT" ]

null

def test_eggplantGUI(): print ('Mr Moritz is in action') assert False

18.5

34

0.716216

11

74

4.727273

1

0

0.189189

74

3

35

24.666667

0.866667

0

0.297297

0

0.333333

1

0.333333

true

0

0.333333

1

0

null

0

1

0

null

0

1

0

4

a383a3e2c3c02b404a91c75a2ae26afbd581d269

254

py

Python

web/WebView/admin.py

shinoyasan/intelli-switch

d32fc1617c5c145e0bb67bafd05acd292a761d4c

[ "MIT" ]

12

2021-01-28T02:45:41.000Z

2022-02-13T16:27:15.000Z

web/WebView/admin.py

shinoyasan/intelli-switch

d32fc1617c5c145e0bb67bafd05acd292a761d4c

[ "MIT" ]

null

web/WebView/admin.py

shinoyasan/intelli-switch

d32fc1617c5c145e0bb67bafd05acd292a761d4c

[ "MIT" ]

3

2021-02-01T03:47:38.000Z

2021-03-04T10:31:53.000Z

from django.contrib import admin from .models import ServerInfo,SampleData,DeviceControl,UserApp # Register your models here. admin.site.register(ServerInfo) admin.site.register(SampleData) admin.site.register(DeviceControl) admin.site.register(UserApp)

31.75

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254

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0.317757

0

0.066929

254

8

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true

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null

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1

0

null

0

1

0

1

0

4

6e7225c8632d4f0a73564353db4d878ee8e3ec87

28,125

py

Python

specification/tools/VMHprocessMappings1.py

iptc/video-metadata-hub

e3b03f7197801fd413999d9d6e483a4477796f81

[ "MIT" ]

1

2021-09-28T10:56:19.000Z

specification/tools/VMHprocessMappings1.py

iptc/video-metadata-hub

e3b03f7197801fd413999d9d6e483a4477796f81

[ "MIT" ]

12

2021-06-17T08:35:45.000Z

2022-02-09T16:09:01.000Z

specification/tools/VMHprocessMappings1.py

iptc/video-metadata-hub

e3b03f7197801fd413999d9d6e483a4477796f81

[ "MIT" ]

null

#!/usr/bin/env python3 """ Python script for retrieving IPTC Video Metadata Hub mapping data from a Google sheet The retrieved data are transformed in HTML as saved as HTML page. For IPTC-internal use Creator: Michael Steidl History: 2016-11-25 mws: project started, download and HTML output ok 2020-06-15 BQ: Updated and checked into GitHub """ from __future__ import print_function import pickle import os import sys from googleapiclient.discovery import build from google_auth_oauthlib.flow import InstalledAppFlow from google.auth.transport.requests import Request from lxml import etree as ET SCOPES = 'https://www.googleapis.com/auth/spreadsheets.readonly' CLIENT_SECRET_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'client_secret.json') APPLICATION_NAME = 'Video Metadata Hub Documentation Generator' # Constant values StdVersion = "1.3" HeaderAppendix = "" # could be " - D-R-A-F-T - " IPTCApprovalDate = "13 May 2020" IPTCRevisionDate = "13 May 2020" CopyrightYear = "2020" def get_credentials(): """Gets valid user credentials from storage. If nothing has been stored, or if the stored credentials are invalid, the OAuth2 flow is completed to obtain the new credentials. Returns: Credentials, the obtained credential. """ creds = None # The file token.pickle stores the user's access and refresh tokens, and is # created automatically when the authorization flow completes for the first # time. if os.path.exists('token.pickle'): with open('token.pickle', 'rb') as token: creds = pickle.load(token) # If there are no (valid) credentials available, let the user log in. if not creds or not creds.valid: if creds and creds.expired and creds.refresh_token: creds.refresh(Request()) else: flow = InstalledAppFlow.from_client_secrets_file( CLIENT_SECRET_FILE, SCOPES) creds = flow.run_local_server(port=0) # Save the credentials for the next run with open('token.pickle', 'wb') as token: pickle.dump(creds, token) return creds def createSpecificMapping(valuesProp, headingtext1, headingtext2, findmoreaturl, mapIdx, filename): # create the HTML document xroot = ET.Element('html') head = ET.SubElement(xroot, 'head') title = ET.SubElement(head, 'title') title.text = 'Video Metadata Hub Mapping' metachset = ET.SubElement(head, 'meta', {'http-equiv': "Content-Type", 'content': "text/html; charset=utf-8"}) csslink1 = ET.SubElement(head, 'link', {'type': 'text/css', 'rel': 'stylesheet', 'href': 'iptcspecs1.css'}) body = ET.SubElement(xroot, 'body') pageheader = ET.SubElement(body, 'h1', {'class':'pageheader'}) iptcanc = ET.SubElement(pageheader, 'a', {'href':'https://iptc.org'}) iptcimg = ET.SubElement(iptcanc, 'img', {'src':'https://iptc.org/download/resources/logos/iptc-gr_70x70.jpg', 'align':'left', 'border':'0'}) pageheader.text = headingtext1 seeotherdoc1 = ET.SubElement(body, 'p', {'class':'note1'}) seeotherdoc1.text = 'Return to ' seeotherdoc1link1 = ET.SubElement(seeotherdoc1, 'a', {'href':'IPTC-VideoMetadataHub-mapping-Rec_'+StdVersion+'.html'}) seeotherdoc1link1.text = 'all recommended mappings of the Video Metadata Hub.' seeotherdoc2 = ET.SubElement(body, 'p', {'class':'note1'}) seeotherdoc2.text = 'See the ' seeotherdoc1link2 = ET.SubElement(seeotherdoc2, 'a', {'href':'IPTC-VideoMetadataHub-props-Rec_'+StdVersion+'.html'}) seeotherdoc1link2.text = 'specification of Video Metadata Hub properties' docdate = ET.SubElement(body, 'p', {'class':'note1'}) docdate.text = 'Mapping recommended on ' + IPTCApprovalDate + '. Document revision as of ' + IPTCRevisionDate + '.' copyrightnotice = ET.fromstring('Copyright © ' + CopyrightYear + ', <a href="https://iptc.org">IPTC</a> - all rights reserved. Published under the Creative Commons Attribution 4.0 license <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>') body.append(copyrightnotice) mappedstdnote = ET.SubElement(body, 'p', {'class':'note1'}) mappedstdnote.text = 'In this table the columns with a blue header are defined by the Video Metadata Hub, the column with the green header is defined by ' + headingtext2 propnote1 = ET.fromstring('Note on the column headers: EBUcore: based on the EBU Core Metadata Standard. XMP: based on the ISO XMP standard. PVMD: a specification of JSON properties for Photo and Video MetaData by IPTC (aka phovidmd).') body.append(propnote1) if not valuesProp: print('No Property data found.') else: table = ET.SubElement(body, 'table', {'class':'spec1 vmhmapping'}) thead = ET.SubElement(table, 'thead') throw = ET.SubElement(thead, 'tr') thcol1 = ET.SubElement(throw, 'th', {'class':'hdrcol1'}) thcol1.text = 'Property Group' thcol2 = ET.SubElement(throw, 'th', {'class':'hdrcol2'}) thcol2.text = 'Property Name' thcol3 = ET.SubElement(throw, 'th', {'class':'hdrcol3'}) thcol3.text = 'Definition / Semantics' """ thcol4 = ET.SubElement(throw, 'th', {'class':'hdrcol4'}) thcol4.text = 'Basic Type/Cardinality' """ thcol5 = ET.SubElement(throw, 'th', {'class':'hdrcol5'}) thcol5.text = 'EBUcore' thcol6 = ET.SubElement(throw, 'th', {'class':'hdrcol6'}) thcol6.text = 'XMP' thcol7 = ET.SubElement(throw, 'th', {'class':'hdrcol7'}) thcol7.text = 'PVMD JSON' thcol8 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol8.text = headingtext2 # second row with "find more at ..." links throw = ET.SubElement(thead, 'tr') thcol1 = ET.SubElement(throw, 'td', {'class':'hdrcol1'}) thcol1.text = ' ' thcol2 = ET.SubElement(throw, 'td', {'class':'hdrcol2'}) thcol2.text = ' ' thcol3 = ET.SubElement(throw, 'td', {'class':'hdrcol3'}) thcol3.text = ' ' """ thcol4 = ET.SubElement(throw, 'td', {'class':'hdrcol4'}) thcol4.text = '' """ moreatlink = valuesProp[0][4] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][5] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][6] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][mapIdx] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) tbody = ET.SubElement(table, 'tbody') for rowcounter in range(2, 186): xrow = ET.SubElement(tbody, 'tr') teststr = valuesProp[rowcounter][0] if teststr == 'Property Structures (PS)': xrow.set('style', 'background-color: #009999;') if teststr.find('PS', 0) == 0: xrow.set('style', 'background-color: #00cccc;') xcell1 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][0] except: valstr = ' ' xcell1.text = valstr xcell2 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][1] except: valstr = ' ' xcell2.text = valstr xcell3 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][2] except: valstr = ' ' xcell3.text = valstr """ xcell4 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][3] except: valstr = ' ' xcell4.text = valstr """ xcell5 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][4] except: valstr = ' ' xcell5.text = valstr xcell6 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][5] except: valstr = ' ' xcell6.text = valstr xcell7 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][6] except: valstr = ' ' xcell7.text = valstr xcell8 = ET.SubElement(xrow, 'td', { 'class': 'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][mapIdx] except: valstr = ' ' xcell8.text = valstr with open(filename, 'w') as file: file.write(ET.tostring(xroot, pretty_print=True).decode()) def main(): credentials = get_credentials() service = build('sheets', 'v4', credentials=credentials) spreadsheetId = '1TgfvHcsbGvJqmF0iUUnaL-RAdd1lbentmb2LhcM8SDk' rangeName = 'MappingRec 1.3.1!A4:R' result1 = service.spreadsheets().values().get( spreadsheetId=spreadsheetId, range=rangeName).execute() valuesProp = result1.get('values', []) # create the HTML document xroot = ET.Element('html') head = ET.SubElement(xroot, 'head') title = ET.SubElement(head, 'title') title.text = 'Video Metadata Hub Mapping' metachset = ET.SubElement(head, 'meta', {'http-equiv': "Content-Type", 'content': "text/html; charset=utf-8"}) csslink1 = ET.SubElement(head, 'link', {'type': 'text/css', 'rel': 'stylesheet', 'href': 'iptcspecs1.css'}) body = ET.SubElement(xroot, 'body') pageheader = ET.SubElement(body, 'h1', {'class':'pageheader'}) iptcanc = ET.SubElement(pageheader, 'a', {'href':'https://iptc.org'}) iptcimg = ET.SubElement(iptcanc, 'img', {'src':'https://iptc.org/download/resources/logos/iptc-gr_70x70.jpg', 'align':'left', 'border':'0'}) pageheader.text = 'IPTC Video Metadata Hub - Recommendation '+ StdVersion +' / all Mappings' + HeaderAppendix seeotherdoc1 = ET.SubElement(body, 'p', {'class':'note1'}) seeotherdoc1.text = 'See the ' seeotherdoc1link1 = ET.SubElement(seeotherdoc1, 'a', {'href':'IPTC-VideoMetadataHub-props-Rec_'+StdVersion+'.html'}) seeotherdoc1link1.text = 'specification of Video Metadata Hub properties' docdate = ET.SubElement(body, 'p', {'class':'note1'}) docdate.text = 'Mapping recommended on ' + IPTCApprovalDate + '. Document revision as of ' + IPTCRevisionDate + '.' copyrightnotice = ET.fromstring('Copyright © '+ CopyrightYear + ', <a href="https://iptc.org">IPTC</a> - all rights reserved. Published under the Creative Commons Attribution 4.0 license <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>') body.append(copyrightnotice) mappedstdnote = ET.SubElement(body, 'p', {'class':'note1'}) mappedstdnote.text = 'In this table the columns with a blue header are defined by the Video Metadata Hub, the columns with the green or amber headers are defined by other standards or tools.' propnote1 = ET.fromstring('Note on the column headers: EBUcore: based on the EBU Core Metadata Standard. XMP: based on the ISO XMP standard. PVMD: a specification of JSON properties for Photo and Video MetaData by IPTC (aka phovidmd).') body.append(propnote1) docnote1 = ET.SubElement(body, 'p', {'class':'smallnote1'}) docnote1.text = 'The header of mappings to other standards provides a link to a table including only this mapping (better for printing)' if not valuesProp: print('No Property data found.') else: table = ET.SubElement(body, 'table', {'class':'spec1 vmhmapping'}) thead = ET.SubElement(table, 'thead') throw = ET.SubElement(thead, 'tr') thcol1 = ET.SubElement(throw, 'th', {'class':'hdrcol1'}) thcol1.text = 'Property Group' thcol2 = ET.SubElement(throw, 'th', {'class':'hdrcol2'}) thcol2.text = 'Property Name' thcol3 = ET.SubElement(throw, 'th', {'class':'hdrcol3'}) thcol3.text = 'Definition / Semantics' """ thcol4 = ET.SubElement(throw, 'th', {'class':'hdrcol4'}) thcol4.text = 'Basic Type/Cardinality' """ thcol5 = ET.SubElement(throw, 'th', {'class':'hdrcol5'}) thcol5.text = 'EBUcore' thcol6 = ET.SubElement(throw, 'th', {'class':'hdrcol6'}) thcol6.text = 'XMP' thcol7 = ET.SubElement(throw, 'th', {'class':'hdrcol7'}) thcol7.text = 'IPTC PVMD JSON' thcol8 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol8link = ET.SubElement(thcol8,'a', {'href':'IPTC-VideoMetadataHub-mapping-AppleQT-Rec_'+StdVersion+'.html'}) thcol8link.text = 'Apple Quicktime' thcol9 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc2'}) thcol9link = ET.SubElement(thcol9,'a', {'href':'IPTC-VideoMetadataHub-mapping-MPEG7-Rec_'+StdVersion+'.html'}) thcol9link.text = 'MPEG 7' thcol10 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol10link = ET.SubElement(thcol10,'a', {'href':'IPTC-VideoMetadataHub-mapping-NewsMLG2-Rec_'+StdVersion+'.html'}) thcol10link.text = 'NewsML-G2' thcol11 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc2'}) thcol11link = ET.SubElement(thcol11,'a', {'href':'IPTC-VideoMetadataHub-mapping-PBCore21-Rec_'+StdVersion+'.html'}) thcol11link.text = 'PB Core 2.1' thcol12 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol12link = ET.SubElement(thcol12,'a', {'href':'IPTC-VideoMetadataHub-mapping-SchemaOrg-Rec_'+StdVersion+'.html'}) thcol12link.text = 'Schema.org' # new in 2018-03 thcol13 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc2'}) thcol13link = ET.SubElement(thcol13,'a', {'href':'IPTC-VideoMetadataHub-mapping-SonyXDCAM-Rec_'+StdVersion+'.html'}) thcol13link.text = 'Sony XDCAM & Planning' thcol14 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol14link = ET.SubElement(thcol14,'a', {'href':'IPTC-VideoMetadataHub-mapping-Panasonic-SMPTEP2-Rec_'+StdVersion+'.html'}) thcol14link.text = 'Panasonic/SMPTE P2' thcol15 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc2'}) thcol15link = ET.SubElement(thcol15,'a', {'href':'IPTC-VideoMetadataHub-mapping-CanonVClip-Rec_'+StdVersion+'.html'}) thcol15link.text = 'Canon VideoClip XML' thcol16 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc'}) thcol16link = ET.SubElement(thcol16,'a', {'href':'IPTC-VideoMetadataHub-mapping-exiftool-Rec_'+StdVersion+'.html'}) thcol16link.text = 'exiftool field ids' thcol17 = ET.SubElement(throw, 'th', {'class':'hdrcolNoniptc2'}) thcol17link = ET.SubElement(thcol17,'a', {'href':'IPTC-VideoMetadataHub-mapping-EIDR-Rec_'+StdVersion+'.html'}) thcol17link.text = 'EIDR Data Fields 2.0' # second row with "find more at ..." links throw = ET.SubElement(thead, 'tr') thcol1 = ET.SubElement(throw, 'td', {'class':'hdrcol1'}) thcol1.text = ' ' thcol2 = ET.SubElement(throw, 'td', {'class':'hdrcol2'}) thcol2.text = ' ' thcol3 = ET.SubElement(throw, 'td', {'class':'hdrcol3'}) thcol3.text = ' ' """ thcol4 = ET.SubElement(throw, 'td', {'class':'hdrcol4'}) thcol4.text = '' """ moreatlink = valuesProp[0][4] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][5] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][6] colcode = ET.fromstring( '<td class="hdrcolIptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) moreatlink = valuesProp[0][7] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) moreatlink = valuesProp[0][9] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc2"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc2"> </td>') throw.append(colcode) moreatlink = valuesProp[0][10] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) moreatlink = valuesProp[0][11] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc2"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc2"> </td>') throw.append(colcode) moreatlink = valuesProp[0][12] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) moreatlink = valuesProp[0][13] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc2"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc2"> </td>') throw.append(colcode) moreatlink = valuesProp[0][14] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) moreatlink = valuesProp[0][15] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc2"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc2"> </td>') throw.append(colcode) moreatlink = valuesProp[0][16] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc"> </td>') throw.append(colcode) moreatlink = valuesProp[0][17] if moreatlink != '': colcode = ET.fromstring( '<td class="hdrcolNoniptc2"><a href="' + moreatlink + '" target="_blank">Find more about it at ...</a></td>') throw.append(colcode) else: colcode = ET.fromstring( '<td class="hdrcolNoniptc2"> </td>') throw.append(colcode) tbody = ET.SubElement(table, 'tbody') for rowcounter in range(2, 186): xrow = ET.SubElement(tbody, 'tr') teststr = valuesProp[rowcounter][0] if teststr == 'Property Structures (PS)': xrow.set('style', 'background-color: #009999;') if teststr.find('PS', 0) == 0: xrow.set('style', 'background-color: #00cccc;') xcell1 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][0] except: valstr = ' ' xcell1.text = valstr xcell2 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][1] except: valstr = ' ' xcell2.text = valstr xcell3 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][2] except: valstr = ' ' xcell3.text = valstr """ xcell4 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][3] except: valstr = ' ' xcell4.text = valstr """ xcell5 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][4] except: valstr = ' ' xcell5.text = valstr xcell6 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][5] except: valstr = ' ' xcell6.text = valstr xcell7 = ET.SubElement(xrow, 'td', {'class':'bgdcolIptc'}) try: valstr = valuesProp[rowcounter][6] except: valstr = ' ' xcell7.text = valstr xcell8 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][7] except: valstr = ' ' xcell8.text = valstr xcell9 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc2'}) try: valstr = valuesProp[rowcounter][9] except: valstr = ' ' xcell9.text = valstr xcell10 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][10] except: valstr = ' ' xcell10.text = valstr xcell11 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc2'}) try: valstr = valuesProp[rowcounter][11] except: valstr = ' ' xcell11.text = valstr xcell12 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][12] except: valstr = ' ' xcell12.text = valstr xcell13 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc2'}) try: valstr = valuesProp[rowcounter][13] except: valstr = ' ' xcell13.text = valstr xcell14 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][14] except: valstr = ' ' xcell14.text = valstr xcell15 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc2'}) try: valstr = valuesProp[rowcounter][15] except: valstr = ' ' xcell15.text = valstr xcell16 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc'}) try: valstr = valuesProp[rowcounter][16] except: valstr = ' ' xcell16.text = valstr xcell17 = ET.SubElement(xrow, 'td', {'class':'bgdcolNoniptc2'}) try: valstr = valuesProp[rowcounter][17] except: valstr = ' ' xcell17.text = valstr filename = "IPTC-VideoMetadataHub-mapping-Rec_"+StdVersion+".html" with open(filename, 'w') as file: file.write(ET.tostring(xroot, pretty_print=True).decode()) moreatlink = valuesProp[0][7] createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - Apple Quicktime', 'Apple Quicktime', moreatlink, 7, 'IPTC-VideoMetadataHub-mapping-AppleQT-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - MPEG 7', 'MPEG 7', moreatlink, 9,'IPTC-VideoMetadataHub-mapping-MPEG7-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - NewsML-G2', 'NewsML-G2', moreatlink, 10,'IPTC-VideoMetadataHub-mapping-NewsMLG2-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - PB Core 2.1', 'PB Core 2.1', moreatlink, 11,'IPTC-VideoMetadataHub-mapping-PBCore21-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - Schema.org', 'Schema.org', moreatlink, 12,'IPTC-VideoMetadataHub-mapping-SchemaOrg-Rec_'+StdVersion+'.html') # new in 2018-03 createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - Sony Cameras ', 'Sony XDCAM & Planning', moreatlink, 13,'IPTC-VideoMetadataHub-mapping-SonyXDCAM-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - Panasonic Cameras', 'Panasonic/SMPTE P2', moreatlink, 14,'IPTC-VideoMetadataHub-mapping-Panasonic-SMPTEP2-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - Canon Cameras', 'Canon VideoClip XML', moreatlink, 15,'IPTC-VideoMetadataHub-mapping-CanonVClip-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - exiftool', 'exiftool field id', moreatlink, 16,'IPTC-VideoMetadataHub-mapping-exiftool-Rec_'+StdVersion+'.html') createSpecificMapping(valuesProp, 'IPTC Video Metadata Hub - Recommendation ' + StdVersion + HeaderAppendix + '/ Mapping VMHub - EIDR Data Fields 2.0', 'EIDR Data Fields 2.0', moreatlink, 17,'IPTC-VideoMetadataHub-mapping-EIDR-Rec_'+StdVersion+'.html') if __name__ == '__main__': main()

47.913118

321

0.589547

2,918

28,125

5.656271

0.154558

0.077795

0.03399

0.035626

0.77728

0.764314

0.735414

0.686398

0.667979

0.655135

0

0.024909

0.263467

28,125

586

322

47.994881

0.77176

0.0368

0

0.703625

0

0.014925

0.308196

0.066828

0

1

0.006397

false

0

0.017058

0

0.025586

0.012793

0

null

0

1

0

1

0

1

0

null

0

4

6e73a8b5a5431029c3e98fe181d67f59ff8b3071

160

py

Python

imgtopdf/__init__.py

AVIPAGHADAR1729/imgtopdf

642f83632e99685d71ad601593cd907814237f92

[ "MIT" ]

null

imgtopdf/__init__.py

AVIPAGHADAR1729/imgtopdf

642f83632e99685d71ad601593cd907814237f92

[ "MIT" ]

null

imgtopdf/__init__.py

AVIPAGHADAR1729/imgtopdf

642f83632e99685d71ad601593cd907814237f92

[ "MIT" ]

null

from .imgtopdf import get_images_and_convert # https://towardsdatascience.com/how-to-build-your-first-python-package-6a00b02635c9

9.411765

84

0.6875

18

160

5.944444

1

0

0.072581

0.225

160

17

84

9.411765

0.790323

0.5125

0

1

0

true

0

1

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

4

6e8c2309c9aa1289d95c411b897881690a7bd531

364

py

Python

dl4nlp_pos_tagging/models/modules/seq2seq_encoders/bi_feedforward_encoder.py

michaeljneely/model-uncertainty-pos-tagging

4ed3e1677b2514f162120a7c785d6a9147503106

[ "MIT" ]

1

2021-09-22T15:04:13.000Z

dl4nlp_pos_tagging/models/modules/seq2seq_encoders/bi_feedforward_encoder.py

michaeljneely/model-uncertainty-pos-tagging

4ed3e1677b2514f162120a7c785d6a9147503106

[ "MIT" ]

null

dl4nlp_pos_tagging/models/modules/seq2seq_encoders/bi_feedforward_encoder.py

michaeljneely/model-uncertainty-pos-tagging

4ed3e1677b2514f162120a7c785d6a9147503106

[ "MIT" ]

null

from overrides import overrides from allennlp.modules.seq2seq_encoders.feedforward_encoder import FeedForwardEncoder from allennlp.modules.seq2seq_encoders.seq2seq_encoder import Seq2SeqEncoder @Seq2SeqEncoder.register("bi-feedforward") class BiFeedForwardEncoder(FeedForwardEncoder): @overrides def is_bidirectional(self) -> bool: return True

30.333333

84

0.824176

37

364

7.972973

0.594595

0.081356

0.128814

0.176271

0.230508

0

0.015528

0.115385

364

11

85

33.090909

0.900621

0

0.038462

0

1

0.125

false

0

0.375

0.125

0.75

0

null

0

1

0

null

0

1

0

4

6e8dbc55e5ccc100670611d0c6cb2d264bf5d9af

160

py

Python

descarteslabs/common/graft/interpreter/__init__.py

descarteslabs/descarteslabs-python

efc874d6062603dc424c9646287a9b1f8636e7ac

[ "Apache-2.0" ]

167

2017-03-23T22:16:58.000Z

2022-03-08T09:19:30.000Z

descarteslabs/common/graft/interpreter/__init__.py

descarteslabs/descarteslabs-python

efc874d6062603dc424c9646287a9b1f8636e7ac

[ "Apache-2.0" ]

93

2017-03-23T22:11:40.000Z

2021-12-13T18:38:53.000Z

descarteslabs/common/graft/interpreter/__init__.py

descarteslabs/descarteslabs-python

efc874d6062603dc424c9646287a9b1f8636e7ac

[ "Apache-2.0" ]

46

2017-03-25T19:12:14.000Z

2021-08-15T18:04:29.000Z

from .interpreter import interpret from . import exceptions from .scopedchainmap import ScopedChainMap __all__ = ["interpret", "exceptions", "ScopedChainMap"]

26.666667

55

0.8

15

160

8.266667

0.466667

0

0.1125

160

5

56

32

0.873239

0

0.20625

0

1

0

false

0

0.75

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6ea29c1b9fd896844cdc66b6ce489925ab495d5c

50

py

Python

src/__init__.py

iki-taichi/tf-keras-transformer

613122705583c0274b0c9be0993f3bbeb240932d

[ "MIT" ]

5

2019-08-03T07:56:30.000Z

2020-07-04T09:00:23.000Z

src/__init__.py

iki-taichi/tf-keras-transformer

613122705583c0274b0c9be0993f3bbeb240932d

[ "MIT" ]

1

2019-10-15T16:50:11.000Z

src/__init__.py

iki-taichi/tf-keras-transformer

613122705583c0274b0c9be0993f3bbeb240932d

[ "MIT" ]

4

2019-06-15T03:13:47.000Z

2020-08-03T09:04:14.000Z

# coding:utf-8 #from .custom_callbacks import *

10

32

0.72

7

50

5

1

0

0.02381

0.16

50

4

33

12.5

0.809524

0.86

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

4

6eba1497ffef85ead898a77e1a2828205020b8e1

39

py

Python

ordenenumeros.py

EBERTONSCHIPPNIK/Pequenos-codigospy

b9cc49a1cce372df2ef5217cb93766fafd9e405a

[ "MIT" ]

null

ordenenumeros.py

EBERTONSCHIPPNIK/Pequenos-codigospy

b9cc49a1cce372df2ef5217cb93766fafd9e405a

[ "MIT" ]

null

ordenenumeros.py

EBERTONSCHIPPNIK/Pequenos-codigospy

b9cc49a1cce372df2ef5217cb93766fafd9e405a

[ "MIT" ]

null

lista = [3,2,1] print(sorted(lista))

13

20

0.615385

7

39

3.428571

0.857143

0

0.090909

0.153846

39

3

20

13

0.636364

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

6ee97f02804d6fe12be5b749e25e662ef9fc939d

199

py

Python

discord/ext/ui/item.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

discord/ext/ui/item.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

discord/ext/ui/item.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

from typing import Any, Callable import discord class Item: def to_discord(self) -> Any: pass def check(self, func: Callable[[discord.Interaction], bool]) -> 'Item': pass

16.583333

75

0.638191

25

199

5.04

0.64

0

0.251256

199

11

76

18.090909

0.845638

0

0.285714

0

0.020101

0

1

0.285714

false

0.285714

0

0.714286

0

1

0

null

0

null

0

1

0

1

0

1

0

4

42b288fb2bbcbbfb9085736893e5f574a1c07957

150

py

Python

bims/views/under_development.py

Christiaanvdm/django-bims

f92a63156c711b2d53c5f8ea06867cd64cee9eb9

[ "MIT" ]

null

bims/views/under_development.py

Christiaanvdm/django-bims

f92a63156c711b2d53c5f8ea06867cd64cee9eb9

[ "MIT" ]

null

bims/views/under_development.py

Christiaanvdm/django-bims

f92a63156c711b2d53c5f8ea06867cd64cee9eb9

[ "MIT" ]

null

# coding=utf-8 from django.views.generic import TemplateView class UnderDevelopmentView(TemplateView): template_name = 'under_development.html'

21.428571

45

0.806667

17

150

7

0.941176

0

0.007519

0.113333

150

6

46

25

0.887218

0.08

0

0.161765

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6e35f54e54bf012c2e57ad14a6b064d3914856f4

328

py

Python

py/test_pat.py

frasertweedale/drill

4e71b5348b633fd9beecb243c046f19ddfe131fe

[ "MIT" ]

1

2020-09-02T17:25:26.000Z

py/test_pat.py

frasertweedale/drill

4e71b5348b633fd9beecb243c046f19ddfe131fe

[ "MIT" ]

null

py/test_pat.py

frasertweedale/drill

4e71b5348b633fd9beecb243c046f19ddfe131fe

[ "MIT" ]

null

import unittest from . import pat class PatTestCase(unittest.TestCase): def test_pat(self): self.assertTrue(pat.match('a*', '')) self.assertFalse(pat.match('.', '')) self.assertTrue(pat.match('ab*', 'a')) self.assertTrue(pat.match('a.', 'ab')) self.assertTrue(pat.match('a', 'a'))

25.230769

46

0.591463

40

328

4.825

0.375

0.207254

0.352332

0.455959

0.357513

0

0.204268

328

12

47

27.333333

0.739464

0

0.039634

0

0.555556

1

0.111111

false

0

0.222222

0

0.444444

0

null

1

0

null

0

1

0

4

6e46af7713bd465def2bc46a62a6f5a2877b31be

186

py

Python

napari_svg/__init__.py

Carreau/napari-svg

f5e83f65121a079f0aa012380d58793920f325c8

[ "BSD-3-Clause" ]

1

2020-04-13T12:20:00.000Z

napari_svg/__init__.py

Carreau/napari-svg

f5e83f65121a079f0aa012380d58793920f325c8

[ "BSD-3-Clause" ]

1

2020-05-23T19:07:00.000Z

2020-05-23T20:11:54.000Z

napari_svg/__init__.py

Carreau/napari-svg

f5e83f65121a079f0aa012380d58793920f325c8

[ "BSD-3-Clause" ]

1

2020-05-23T18:33:27.000Z

from .hook_implementations import ( napari_get_writer, napari_write_image, napari_write_labels, napari_write_points, napari_write_shapes, napari_write_vectors, )

20.666667

35

0.763441

22

186

5.863636

0.590909

0.426357

0

0.188172

186

8

36

23.25

0.854305

0

1

0

true

0

0.125

0

0.125

0

1

0

null

1

0

1

0

null

0

1

0

4

2807886c6e4a2a28a8260e941eaf956810fe8636

246

py

Python

xmpath/translate.py

xmake-io/pxmake

c5ca995e1afa840d54b513e8b2f193de463a3606

[ "Apache-2.0" ]

1

2021-08-15T21:26:10.000Z

xmpath/translate.py

xmake-io/pxmake

c5ca995e1afa840d54b513e8b2f193de463a3606

[ "Apache-2.0" ]

null

xmpath/translate.py

xmake-io/pxmake

c5ca995e1afa840d54b513e8b2f193de463a3606

[ "Apache-2.0" ]

null

from os.path import expanduser from os import sep from re import split from functools import reduce from xmtrace import xmtrace @xmtrace def xm_path_translate(lua, ph): return expanduser(reduce(lambda a, b: a + sep + b, split(r"\\|/", ph)))

24.6

75

0.739837

40

246

4.5

0.525

0.066667

0

0.162602

246

9

76

27.333333

0.873786

0

0.01626

0

1

0.125

false

0

0.625

0.125

0.875

0

null

0

1

0

null

0

1

0

4

2843c67d3495ed413d600d4c112625c4b89b76e8

250

py

Python

blog/admin/__init__.py

hentt30/education4all

8f930ade7303fe65355cfe4b2ba66787acad93b4

[ "MIT" ]

null

blog/admin/__init__.py

hentt30/education4all

8f930ade7303fe65355cfe4b2ba66787acad93b4

[ "MIT" ]

null

blog/admin/__init__.py

hentt30/education4all

8f930ade7303fe65355cfe4b2ba66787acad93b4

[ "MIT" ]

2

2021-06-18T08:13:17.000Z

2021-12-03T05:08:41.000Z

""" Admin access page settings """ from django.contrib import admin from blog.models import get_model_factory from .posts_admin import PostAdmin # Register your models here. admin.site.register(get_model_factory('PostsFactory').create(), PostAdmin)

25

74

0.804

34

250

5.764706

0.617647

0.081633

0.153061

0

0.104

250

9

75

27.777778

0.875

0.216

0

0.06383

0

1

0

true

0

0.75

0

0.75

0

null

0

1

0

null

0

1

0

1

0

1

0

4

2857dbde0cc754d9c0768c1f84e4dad01de21f93

31

py

Python

arviz/plots/backends/__init__.py

Ban-zee/arviz

2b31d7318da063cc26f0e41b0f86830d80df0558

[ "Apache-2.0" ]

null

arviz/plots/backends/__init__.py

Ban-zee/arviz

2b31d7318da063cc26f0e41b0f86830d80df0558

[ "Apache-2.0" ]

null

arviz/plots/backends/__init__.py

Ban-zee/arviz

2b31d7318da063cc26f0e41b0f86830d80df0558

[ "Apache-2.0" ]

null

"""ArviZ plotting backends."""

15.5

30

0.677419

3

31

7

1

0

0.096774

31

1

31

0.75

0.774194

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

285a8b4142b061327f98eac18337b5a9999755b9

109

py

Python

Part 1/Chapter 4/example 1.1.py

MineSelf2016/PythonInEconomicManagement

e61a69a5d22dc88a3faf88db72c3819abcc134bf

[ "MIT" ]

null

Part 1/Chapter 4/example 1.1.py

MineSelf2016/PythonInEconomicManagement

e61a69a5d22dc88a3faf88db72c3819abcc134bf

[ "MIT" ]

null

Part 1/Chapter 4/example 1.1.py

MineSelf2016/PythonInEconomicManagement

e61a69a5d22dc88a3faf88db72c3819abcc134bf

[ "MIT" ]

null

score = 92 print("优秀") if score >= 90 else print("及格") a = 1 b = 2 print(type(a)) print(type(b)) print(a/b)

12.111111

43

0.59633

23

109

2.826087

0.565217

0.276923

0

0.067416

0.183486

109

9

44

12.111111

0.662921

0

0.036364

0

1

0

false

0

0.571429

1

0

null

1

0

1

0

null

0

1

0

4

2864fc965ab0b030370035eb0463988806dbc0ac

707

py

Python

plico/utils/loop.py

lbusoni/plico

e4bab48fcc7767a50dcac13644b5e1d6175ca5f0

[ "MIT" ]

null

plico/utils/loop.py

lbusoni/plico

e4bab48fcc7767a50dcac13644b5e1d6175ca5f0

[ "MIT" ]

7

2021-08-30T17:18:34.000Z

2022-03-25T22:42:20.000Z

plico/utils/loop.py

lbusoni/plico

e4bab48fcc7767a50dcac13644b5e1d6175ca5f0

[ "MIT" ]

null

import abc from six import with_metaclass class Loop(with_metaclass(abc.ABCMeta, object)): @abc.abstractmethod def name(self): assert False @abc.abstractmethod def close(self): assert False @abc.abstractmethod def open(self): assert False @abc.abstractmethod def isClosed(self): assert False @abc.abstractmethod def performOnePass(self): assert False @abc.abstractmethod def getConvergenceStepCount(self): assert False @abc.abstractmethod def hasConverged(self): assert False class LoopException(Exception): def __init__(self, message): Exception.__init__(self, message)

17.675

48

0.660537

74

707

6.175676

0.351351

0.260394

0.306346

0.236324

0.459519

0

0.264498

707

39

49

18.128205

0.878846

0

0.518519

0

0.259259

1

0.296296

false

0.037037

0.074074

0

0.444444

0

null

1

0

null

0

1

0

4

28776eb8f03b21f285acf937666fc68e5bc8f34b

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py

Python

virtual/bin/django-admin.py

vinnyotach7/insta-photo

07bc4f870fa119f96b7fbbaeb0982d6902bc41a4

[ "MIT" ]

null

virtual/bin/django-admin.py

vinnyotach7/insta-photo

07bc4f870fa119f96b7fbbaeb0982d6902bc41a4

[ "MIT" ]

null

virtual/bin/django-admin.py

vinnyotach7/insta-photo

07bc4f870fa119f96b7fbbaeb0982d6902bc41a4

[ "MIT" ]

null

#!/home/moringaschool/Documents/django projects/insta-moringa/virtual/bin/python3.6 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

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2878266ec7b83cbe1361c5b9c375e3bfd1d5507a

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py

Python

pelayanan/apps.py

diaksizz/Adisatya

1b20e523aede6ab3e8effb1ca63adf72016a6839

[ "MIT" ]

null

pelayanan/apps.py

diaksizz/Adisatya

1b20e523aede6ab3e8effb1ca63adf72016a6839

[ "MIT" ]

7

2021-03-30T14:04:35.000Z

2022-01-13T03:07:50.000Z

pelayanan/apps.py

diaksizz/Adisatya

1b20e523aede6ab3e8effb1ca63adf72016a6839

[ "MIT" ]

null

from django.apps import AppConfig class PelayananConfig(AppConfig): name = 'pelayanan'

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954eef887a982e473fee330ceda4a0756c075d30

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py

Python

qingmi/utils/functional.py

xiongxianzhu/qingmi

ae5a446abec3982ebf2c5dde8546ef72f9453137

[ "BSD-3-Clause" ]

20

2018-05-22T09:29:40.000Z

2020-12-11T04:53:15.000Z

qingmi/utils/functional.py

xiongxianzhu/qingmi

ae5a446abec3982ebf2c5dde8546ef72f9453137

[ "BSD-3-Clause" ]

65

2019-03-07T02:43:06.000Z

2021-01-07T03:43:52.000Z

qingmi/utils/functional.py

xiongxianzhu/qingmi

ae5a446abec3982ebf2c5dde8546ef72f9453137

[ "BSD-3-Clause" ]

6

2019-03-08T06:39:47.000Z

2021-07-01T11:02:56.000Z

class Promise: """ Base class for the proxy class created in the closure of the lazy function. It's used to recognize promises in code. """ pass

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95640f337672b95f112bad4c0ac277cda9e4288d

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py

Python

flex/http/cors.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

flex/http/cors.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

flex/http/cors.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

from flask_cors import CORS from flex.conf import config cors = CORS(origins=config.CORS_ORIGINS, supports_credentials=True)

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py

Python

deliravision/torch/models/gans/context_conditional/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

4

2019-08-03T09:56:50.000Z

2019-09-05T09:32:06.000Z

deliravision/torch/models/gans/context_conditional/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

23

2019-08-03T14:16:47.000Z

2019-10-22T10:15:10.000Z

deliravision/torch/models/gans/context_conditional/__init__.py

delira-dev/vision_torch

d944aa67d319bd63a2add5cb89e8308413943de6

[ "BSD-2-Clause" ]

null

from deliravision.models.gans.context_conditional.context_cond_gan import \ ContextConditionalGAN

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95b461d2bbf0d1512b375805e80d08bd1b14c33e

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py

Python

tests/unit/baskerville_tests/models_tests/pipeline_task_tests/tests_task_base.py

deflect-ca/baskerville

9659f4b39ab66fcf5329a4eccff15e97245b04f0

[ "CC-BY-4.0" ]

2

2021-12-03T11:26:38.000Z

2022-01-12T22:24:29.000Z

tests/unit/baskerville_tests/models_tests/pipeline_task_tests/tests_task_base.py

deflect-ca/baskerville

9659f4b39ab66fcf5329a4eccff15e97245b04f0

[ "CC-BY-4.0" ]

3

2022-01-19T15:17:37.000Z

2022-03-22T04:55:22.000Z

tests/unit/baskerville_tests/models_tests/pipeline_task_tests/tests_task_base.py

deflect-ca/baskerville

9659f4b39ab66fcf5329a4eccff15e97245b04f0

[ "CC-BY-4.0" ]

null

# Copyright (c) 2020, eQualit.ie inc. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from unittest import mock from baskerville.models.config import BaskervilleConfig from tests.unit.baskerville_tests.helpers.spark_testing_base import \ SQLTestCaseLatestSpark from tests.unit.baskerville_tests.helpers.utils import test_baskerville_conf class TestTask(SQLTestCaseLatestSpark): def setUp(self): super().setUp() self.test_conf = test_baskerville_conf self.baskerville_config = BaskervilleConfig(self.test_conf).validate() def _helper_task_set_up(self, steps=()): from baskerville.models.pipeline_tasks.tasks_base import Task self.task = Task( self.baskerville_config, steps ) def test_initialize(self): self._helper_task_set_up() step_one = mock.MagicMock() step_two = mock.MagicMock() self.task.steps = [step_one, step_two] with mock.patch.object( self.task.service_provider, 'initialize_db_tools_service' ) as mock_initialize_db_tools_service: with mock.patch.object( self.task.service_provider, 'initialize_spark_service' ) as mock_initialize_spark_service: self.task.initialize() mock_initialize_db_tools_service.assert_called_once() mock_initialize_spark_service.assert_called_once() step_one.initialize.assert_called_once() step_two.initialize.assert_called_once() def test_run(self): step_one = mock.MagicMock() step_two = mock.MagicMock() mock_steps = [step_one, step_two] self._helper_task_set_up(mock_steps) self.task.run() for step in mock_steps: step.set_df.assert_called_once() step.set_df.return_value.run.assert_called_once() self.assertTrue(len(self.task.remaining_steps) == 0) def test_finish_up(self): self._helper_task_set_up() with mock.patch.object( self.task.service_provider, 'finish_up' ) as mock_finish_up: self.task.finish_up() mock_finish_up.assert_called_once() def test_reset(self): self._helper_task_set_up() with mock.patch.object( self.task.service_provider, 'reset' ) as mock_reset: self.task.reset() mock_reset.assert_called_once() class TestCacheTask(SQLTestCaseLatestSpark): def setUp(self): super().setUp() self.test_conf = test_baskerville_conf self.baskerville_config = BaskervilleConfig(self.test_conf).validate() def _helper_task_set_up(self, steps=()): from baskerville.models.pipeline_tasks.tasks_base import CacheTask self.task = CacheTask( self.baskerville_config, steps ) def test_initialize(self): self._helper_task_set_up() step_one = mock.MagicMock() step_two = mock.MagicMock() self.task.steps = [step_one, step_two] with mock.patch.object( self.task.service_provider, 'initialize_db_tools_service' ) as mock_initialize_db_tools_service: with mock.patch.object( self.task.service_provider, 'initialize_spark_service' ) as mock_initialize_spark_service: with mock.patch.object( self.task.service_provider, 'initialize_request_set_cache_service' ) as mock_initialize_request_set_cache_service: self.task.initialize() mock_initialize_db_tools_service.assert_called_once() mock_initialize_spark_service.assert_called_once() mock_initialize_request_set_cache_service.\ assert_called_once() step_one.initialize.assert_called_once() step_two.initialize.assert_called_once() class TestMLTask(SQLTestCaseLatestSpark): def setUp(self): super().setUp() self.test_conf = test_baskerville_conf self.baskerville_config = BaskervilleConfig(self.test_conf).validate() def _helper_task_set_up(self, steps=()): from baskerville.models.pipeline_tasks.tasks_base import MLTask self.task = MLTask( self.baskerville_config, steps ) def test_initialize(self): self._helper_task_set_up() step_one = mock.MagicMock() step_two = mock.MagicMock() self.task.steps = [step_one, step_two] self.task.service_provider = mock.MagicMock() self.task.initialize() self.task.service_provider.initialize_db_tools_service\ .assert_called_once() self.task.service_provider\ .initialize_spark_service.assert_called_once() self.task.service_provider.initialize_request_set_cache_service. \ assert_called_once() self.task.service_provider.initalize_ml_services.assert_called_once() step_one.initialize.assert_called_once() step_two.initialize.assert_called_once()

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allink_core/core_apps/allink_legacy_redirect/config.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

5

2017-03-13T08:49:45.000Z

2022-03-05T20:05:56.000Z

allink_core/core_apps/allink_legacy_redirect/config.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

28

2019-10-21T08:32:18.000Z

2022-02-10T13:16:38.000Z

allink_core/core_apps/allink_legacy_redirect/config.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from django.apps import AppConfig class AllinkLegacyConfig(AppConfig): name = 'allink_core.core_apps.allink_legacy_redirect' verbose_name = "Legacy Redirect"

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95c648711164955b4c3300c034a9f596208b0d25

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py

Python

lab3/es3/to_bike_webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

lab3/es3/to_bike_webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

lab3/es3/to_bike_webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

import cherrypy import json import requests class BikeSharing(): exposed=True @cherrypy.tools.json_out() def GET(self,*uri,**params): if len(uri)==0: self.json_data = requests.get("https://api.citybik.es/v2/networks/to-bike").json() return json.loads(json.dumps(self.json_data,default=lambda x: x.__dict__)) if uri[0]=="order_slots": self.json_data = requests.get("https://api.citybik.es/v2/networks/to-bike").json() self.json_out=[] if "N" in params: self.N=int(params["N"]) else: self.N=10 if "order" in params: if params["order"]=="ascend": self.json_data['network']['stations'] = sorted(self.json_data['network']['stations'], key=lambda k: int(k.get('empty_slots', 0)), reverse=False) if params["order"]=="descend": self.json_data['network']['stations'] = sorted(self.json_data["network"]["stations"], key=lambda k: int(k.get('empty_slots', 0)), reverse=True) else: self.json_data['network']['stations'] = sorted(self.json_data["network"]["stations"], key=lambda k: int(k.get('empty_slots', 0)), reverse=True) for i in range(0,self.N): self.json_out.append(self.json_data["network"]["stations"][i]) return json.loads(json.dumps(self.json_out,default=lambda x: x.__dict__)) if uri[0]=="order_bikes": self.json_data = requests.get("https://api.citybik.es/v2/networks/to-bike").json() self.json_out=[] if "N" in params: self.N=int(params["N"]) else: self.N=10 if "order" in params: if params["order"]=="ascend": self.json_data['network']['stations'] = sorted(self.json_data['network']['stations'], key=lambda k: int(k.get('free_bikes', 0)), reverse=False) if params["order"]=="descend": self.json_data['network']['stations'] = sorted(self.json_data['network']['stations'], key=lambda k: int(k.get('free_bikes', 0)), reverse=True) else: self.json_data['network']['stations'] = sorted(self.json_data['network']['stations'], key=lambda k: int(k.get('free_bikes', 0)), reverse=True) for i in range(0,self.N): self.json_out.append(self.json_data['network']['stations'][i]) return json.loads(json.dumps(self.json_out,default=lambda x: x.__dict__)) if uri[0]=="count_bikes_slots": self.json_data = requests.get("https://api.citybik.es/v2/networks/to-bike").json() self.bikes=0 self.slots=0 if "lat" and "lon" in params: self.lat=float(params["lat"]) self.lon=float(params["lon"]) else: return "District number not set" for i in range(0,len(self.json_data["network"]["stations"])): if ((float(self.json_data["network"]["stations"][i]["latitude"])<self.lat+0.005 and float(self.json_data["network"]["stations"][i]["latitude"])>self.lat-0.005) and (float(self.json_data["network"]["stations"][i]["longitude"])<self.lon+0.01 and float(self.json_data["network"]["stations"][i]["longitude"])>self.lon-0.01)): self.bikes+=int(self.json_data["network"]["stations"][i]["free_bikes"]) self.slots+=int(self.json_data["network"]["stations"][i]["empty_slots"]) self.json_out={"latitude":float(params["lat"]),"longitude":float(params["lon"]),"bikes":self.bikes,"slots":self.slots} return json.loads(json.dumps(self.json_out,default=lambda x: x.__dict__)) if __name__ == '__main__': conf = { '/': { 'request.dispatch': cherrypy.dispatch.MethodDispatcher(), 'tools.sessions.on': True } } cherrypy.tree.mount(BikeSharing(), '/', conf) cherrypy.config.update({'server.socket_host': '0.0.0.0'}) cherrypy.config.update({'server.socket_port': 9090}) cherrypy.engine.start() cherrypy.engine.block()

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Python

.env-cbre/bin/django-admin.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

.env-cbre/bin/django-admin.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

.env-cbre/bin/django-admin.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

#!/Users/yutthachaithongkumchum/myproject/app-cbre-exxon/app-cbre-exxon/.env-cbre/bin/python3 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

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py

Python

PetService/apps.py

sifullahrakin/HelloPaw

dc01827076b59bb145ccfb92aa4a5cdda97683e7

[ "MIT" ]

null

PetService/apps.py

sifullahrakin/HelloPaw

dc01827076b59bb145ccfb92aa4a5cdda97683e7

[ "MIT" ]

null

PetService/apps.py

sifullahrakin/HelloPaw

dc01827076b59bb145ccfb92aa4a5cdda97683e7

[ "MIT" ]

null

from django.apps import AppConfig class PetserviceConfig(AppConfig): name = 'PetService'

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250ec70e5d9a97d0228ec0739efdb46b30a01b71

85

py

Python

gh_build.py

sonvt1710/manga-py

848a78e93b890af0c92056a1a9fc7f6ce5707cf6

[ "MIT" ]

337

2019-08-27T16:14:50.000Z

2022-03-29T09:58:22.000Z

gh_build.py

sonvt1710/manga-py

848a78e93b890af0c92056a1a9fc7f6ce5707cf6

[ "MIT" ]

225

2019-08-25T15:02:01.000Z

2022-03-31T06:36:09.000Z

gh_build.py

sonvt1710/manga-py

848a78e93b890af0c92056a1a9fc7f6ce5707cf6

[ "MIT" ]

41

2019-10-04T13:28:02.000Z

2022-03-19T08:18:34.000Z

#!/usr/bin/python3 # -*- coding: utf-8 -*- from helpers.gh_pages import main main()

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Python

app/routes/models/form_model.py

mampilly/fileaccess

1bc0af992653c0b427f9c9f8aafd362b0fca3b43

[ "MIT" ]

null

app/routes/models/form_model.py

mampilly/fileaccess

1bc0af992653c0b427f9c9f8aafd362b0fca3b43

[ "MIT" ]

null

app/routes/models/form_model.py

mampilly/fileaccess

1bc0af992653c0b427f9c9f8aafd362b0fca3b43

[ "MIT" ]

null

from pydantic import BaseModel from fastapi.param_functions import Body from typing import Optional class FormModel(BaseModel): first_name: str = None second_name: str

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py

Python

modules/2.79/bpy/types/TextureNodeCurveRGB.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/TextureNodeCurveRGB.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/TextureNodeCurveRGB.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

TextureNodeCurveRGB.mapping = None

9.25

34

0.810811

3

37

10

1

0

0.135135

37

3

35

12.333333

0.9375

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

c25542e063541cf37440c290075ae0b2b9d38f08

115

py

Python

WebMirror/management/rss_parser_funcs/feed_parse_extractKendalblackBlogspotCom.py

fake-name/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

193

2016-08-02T22:04:35.000Z

2022-03-09T20:45:41.000Z

WebMirror/management/rss_parser_funcs/feed_parse_extractKendalblackBlogspotCom.py

fake-name/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

533

2016-08-23T20:48:23.000Z

2022-03-28T15:55:13.000Z

WebMirror/management/rss_parser_funcs/feed_parse_extractKendalblackBlogspotCom.py

rrosajp/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

19

2015-08-13T18:01:08.000Z

2021-07-12T17:13:09.000Z

def extractKendalblackBlogspotCom(item): ''' DISABLED Parser for 'kendalblack.blogspot.com' ''' return None

14.375

40

0.73913

11

115

7.727273

1

0

0.147826

115

8

41

14.375

0.867347

0.408696

0

1

0.5

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c266e21e1b60f10837c5ee8875bb015d24e6bbf1

413

py

Python

web/src/auth.py

computer-geek64/guardian

b6aa05074c8f63b7b4e9dfc642f03ba750e32640

[ "MIT" ]

null

web/src/auth.py

computer-geek64/guardian

b6aa05074c8f63b7b4e9dfc642f03ba750e32640

[ "MIT" ]

null

web/src/auth.py

computer-geek64/guardian

b6aa05074c8f63b7b4e9dfc642f03ba750e32640

[ "MIT" ]

null

# auth.py import os import json import hashlib authentication_credentials = json.loads(os.environ['AUTHENTICATION_CREDENTIALS']) def authenticate(username, password): if username is None or password is None: return False password_hash = hashlib.sha512(password.encode()).hexdigest() return username in authentication_credentials and password_hash == authentication_credentials[username]

24.294118

107

0.779661

48

413

6.583333

0.541667

0.316456

0

0.008523

0.1477

413

16

108

25.8125

0.889205

0.016949

0

0.064356

0

1

0.111111

false

0.444444

0.333333

0

0.666667

0

null

1

0

null

0

1

0

1

0

4

c27c7b85bef69eec7f1661780c39fa1813738a76

358

py

Python

src/triggers/recommendation_trigger.py

jherrerotardon/spies

ec855b3c1bd207c8ee2beb829e446fa575354c59

[ "Apache-2.0" ]

null

src/triggers/recommendation_trigger.py

jherrerotardon/spies

ec855b3c1bd207c8ee2beb829e446fa575354c59

[ "Apache-2.0" ]

null

src/triggers/recommendation_trigger.py

jherrerotardon/spies

ec855b3c1bd207c8ee2beb829e446fa575354c59

[ "Apache-2.0" ]

null

from pyframework.triggers.abstract_trigger import AbstractTrigger from src.commands.fire.base_fire import Event class RecommendationTrigger(AbstractTrigger): ACTION_KEY_PREFIX = AbstractTrigger.ACTION_KEY_PREFIX + ':' + 'download' EVENT_TASK = Event.RECOMMENDATION_DOWNLOAD_TASK.value EVENT_ACTION = Event.RECOMMENDATION_DOWNLOAD_ACTION.value

35.8

76

0.829609

40

358

7.125

0.525

0.147368

0.168421

0.210526

0

0.106145

358

9

77

39.777778

0.890625

0

0.02514

0

1

0

false

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

4

c292e1dcbb3aeb1b053e7bfaf6e9940f0c0794ff

123

py

Python

src/models.py

VasudhaJha/URLShortner

c28e852a524eb52406bd050b3e4f346fa237a36c

[ "MIT" ]

null

src/models.py

VasudhaJha/URLShortner

c28e852a524eb52406bd050b3e4f346fa237a36c

[ "MIT" ]

null

src/models.py

VasudhaJha/URLShortner

c28e852a524eb52406bd050b3e4f346fa237a36c

[ "MIT" ]

null

from pydantic import BaseModel class URL(BaseModel): long_url: str class ShortURL(BaseModel): short_url: str

15.375

30

0.723577

16

123

5.4375

0.625

0.137931

0

0.211382

123

8

31

15.375

0.896907

0

1

0

true

0

0.2

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c2cafc4f9ff31f75f8818e65ddee5cd5d06ca6d9

108

py

Python

profile_app_mod/apps.py

kurniantoska/medicalwebapp_project

a2e36a44b598ad2989c207f950a89c02d987e00d

[ "BSD-3-Clause" ]

1

2019-10-22T02:12:49.000Z

profile_app_mod/apps.py

kurniantoska/medicalwebapp_project

a2e36a44b598ad2989c207f950a89c02d987e00d

[ "BSD-3-Clause" ]

3

2020-06-05T18:30:35.000Z

2021-06-10T20:31:09.000Z

profile_app_mod/apps.py

kurniantoska/medicalwebapp_project

a2e36a44b598ad2989c207f950a89c02d987e00d

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig class ProfileAppModConfig(AppConfig): name = 'profile_app_mod'

18

38

0.75

12

108

6.583333

0.916667

0

0.185185

108

5

39

21.6

0.897727

0

0.145631

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c2e2d7dd11ea75f5970ce8d2520d4e7031181352

90

py

Python

hangman.py

juank27/Hangman_python

39d2117bf207581691bed5c9b625c486d29ef47e

[ "MIT" ]

null

hangman.py

juank27/Hangman_python

39d2117bf207581691bed5c9b625c486d29ef47e

[ "MIT" ]

null

hangman.py

juank27/Hangman_python

39d2117bf207581691bed5c9b625c486d29ef47e

[ "MIT" ]

null

print("hola a todos") for i in range(0,100): print("Hola") print("Hola otra vez")

22.5

25

0.611111

16

90

3.4375

0.75

0.490909

0

0.056338

0.211111

90

4

25

22.5

0.71831

0

0.329545

0

1

0

false

0

0.75

1

0

null

1

0

1

0

null

0

1

0

4

c2edf9bb96e7e3cd5e041a82496e51eef83c6a05

121

py

Python

positive no in range.py

KrutikaSoor/print-all-positive-no.in-range

61892e4d735bf82576312c009f60411e7a43d2ac

[ "MIT" ]

null

positive no in range.py

KrutikaSoor/print-all-positive-no.in-range

61892e4d735bf82576312c009f60411e7a43d2ac

[ "MIT" ]

null

positive no in range.py

KrutikaSoor/print-all-positive-no.in-range

61892e4d735bf82576312c009f60411e7a43d2ac

[ "MIT" ]

null

list1=[12,-7,5,64,-14] list2=[12,14,-95,3] for i in list1: if i>0: print(i) for j in list2: if j>0: print(j)

13.444444

22

0.553719

29

121

2.310345

0.551724

0.179104

0

0.225806

0.231405

121

8

23

15.125

0.494624

0

1

0

false

0

0.25

1

0

null

0

1

0

1

0

1

0

null

0

4

c2f48358c6829bc521b9857586c1bf0e1032a3c0

49

py

Python

OpenAttack/data/test.py

e-tornike/OpenAttack

b19c53af2e01f096505f8ebb8f48a54388295003

[ "MIT" ]

444

2020-07-14T12:13:26.000Z

2022-03-28T02:46:30.000Z

OpenAttack/data/test.py

e-tornike/OpenAttack

b19c53af2e01f096505f8ebb8f48a54388295003

[ "MIT" ]

50

2020-07-15T01:34:42.000Z

2022-01-24T12:19:19.000Z

OpenAttack/data/test.py

e-tornike/OpenAttack

b19c53af2e01f096505f8ebb8f48a54388295003

[ "MIT" ]

86

2020-08-02T13:16:45.000Z

2022-03-27T06:22:04.000Z

NAME = "test" DOWNLOAD = "/TAADToolbox/test.pkl"

16.333333

34

0.693878

6

49

5.666667

0.833333

0

0.122449

49

2

35

24.5

0.790698

0

0.510204

0.428571

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

6c3448d61b9d525a3d0cfd85877dd708519dfcee

729

py

Python

meraki_sdk/models/device_policy_enum.py

meraki/meraki-python-sdk

9894089eb013318243ae48869cc5130eb37f80c0

[ "MIT" ]

37

2019-04-24T14:01:33.000Z

2022-01-28T01:37:21.000Z

meraki_sdk/models/device_policy_enum.py

ankita66666666/meraki-python-sdk

9894089eb013318243ae48869cc5130eb37f80c0

[ "MIT" ]

10

2019-07-09T16:35:11.000Z

2021-12-07T03:47:53.000Z

meraki_sdk/models/device_policy_enum.py

ankita66666666/meraki-python-sdk

9894089eb013318243ae48869cc5130eb37f80c0

[ "MIT" ]

17

2019-04-30T23:53:21.000Z

2022-02-07T22:57:44.000Z

# -*- coding: utf-8 -*- """ meraki_sdk This file was automatically generated for meraki by APIMATIC v2.0 ( https://apimatic.io ). """ class DevicePolicyEnum(object): """Implementation of the 'DevicePolicy' enum. The policy to apply to the specified client. Can be 'Whitelisted', 'Blocked', 'Normal' or 'Group policy'. Required. Attributes: WHITELISTED: TODO: type description here. BLOCKED: TODO: type description here. NORMAL: TODO: type description here. ENUM_GROUP POLICY: TODO: type description here. """ WHITELISTED = 'Whitelisted' BLOCKED = 'Blocked' NORMAL = 'Normal' ENUM_GROUP_POLICY = 'Group policy'

22.78125

95

0.625514

78

729

5.794872

0.551282

0.097345

0.168142

0.20354

0

0.005671

0.274348

729

31

96

23.516129

0.848771

0.657064

0

1

0

0.220859

0

0.129032

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

4

6c64e8abb731e86a9317708f71e530472211a481

265

py

Python

github/models.py

billryan/github-rss

000fb186c66a0ef2fb234649e10bd1bf157f63fd

[ "MIT" ]

null

github/models.py

billryan/github-rss

000fb186c66a0ef2fb234649e10bd1bf157f63fd

[ "MIT" ]

null

github/models.py

billryan/github-rss

000fb186c66a0ef2fb234649e10bd1bf157f63fd

[ "MIT" ]

null

from django.db import models class Repo(models.Model): repo_url = models.URLField(max_length=200) owner = models.CharField(max_length=200) repo = models.CharField(max_length=200) def __unicode__(self): return self.owner + '/' + self.repo

24.090909

46

0.698113

36

265

4.916667

0.527778

0.152542

0.20339

0.271186

0.305085

0

0.04186

0.188679

265

10

47

26.5

0.781395

0

0.003774

0

1

0.142857

false

0

0.142857

1

0

null

0

1

0

null

0

1

0

4

6c64f3b860d8f1f50a7c8234c20610ae62b635c5

329

py

Python

example3.py

djinn/python-duckduckgo

e4bb5729cdf8c1e086226760af01e2c0c7dbb500

[ "BSD-3-Clause" ]

2

2015-02-19T10:41:31.000Z

2021-11-12T11:42:48.000Z

example3.py

djinn/python-duckduckgo

e4bb5729cdf8c1e086226760af01e2c0c7dbb500

[ "BSD-3-Clause" ]

null

example3.py

djinn/python-duckduckgo

e4bb5729cdf8c1e086226760af01e2c0c7dbb500

[ "BSD-3-Clause" ]

null

from duckduckgo import query def wikipedia_presence(text): """Find if a query has wikipedia article""" return query(text).abstract.url if query(text).abstract != None and query(text).abstract.source == 'Wikipedia' else None if __name__ == '__main__': import sys print wikipedia_presence(' '.join(sys.argv[1:]))

32.9

124

0.714286

45

329

5

0.6

0.12

0.226667

0

0.00361

0.158055

329

9

125

36.555556

0.808664

0

0.062937

0

null

0

0.333333

null

0.166667

0

null

0

1

0

1

0

null

0

1

0

1

0

4

6669c63ec370f0d0bf4e7cf00d020b0eee9a8c8c

353

py

Python

replaybuffer/utils.py

mattbev/replaybuffer

ed2f2bd8e10ab6f118bda49d8c4b26d257bcb5c5

[ "MIT" ]

null

replaybuffer/utils.py

mattbev/replaybuffer

ed2f2bd8e10ab6f118bda49d8c4b26d257bcb5c5

[ "MIT" ]

null

replaybuffer/utils.py

mattbev/replaybuffer

ed2f2bd8e10ab6f118bda49d8c4b26d257bcb5c5

[ "MIT" ]

null

from typing import Iterable, Tuple def remove_nones(*arrays: Iterable) -> Tuple[Iterable]: """ Take inputted arrays that may contain None values, and return copies without Nones. Returns: tuple[Iterable]: New arrays with only non-None values """ return tuple([[i for i in array if i is not None] for array in arrays])

27.153846

75

0.68272

51

353

4.705882

0.627451

0.108333

0

0.235127

353

12

76

29.416667

0.888889

0.427762

0

1

0.333333

true

0

0.333333

0

1

0

null

0

null

0

1

0

1

0

1

0

4

666abd2400392f590093f17cdeaae2457e29958b

950

py

Python

phy/cluster/tests/conftest.py

m-beau/phy

755082af4e123dc057b8edca138652f901d0c8b1

[ "BSD-3-Clause" ]

null

phy/cluster/tests/conftest.py

m-beau/phy

755082af4e123dc057b8edca138652f901d0c8b1

[ "BSD-3-Clause" ]

null

phy/cluster/tests/conftest.py

m-beau/phy

755082af4e123dc057b8edca138652f901d0c8b1

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Test fixtures.""" #------------------------------------------------------------------------------ # Imports #------------------------------------------------------------------------------ from pytest import fixture from phy.io.array import (get_closest_clusters, ) #------------------------------------------------------------------------------ # Fixtures #------------------------------------------------------------------------------ @fixture def cluster_ids(): return [0, 1, 2, 10, 11, 20, 30] # i, g, N, i, g, N, N @fixture def cluster_groups(): return {0: 'noise', 1: 'good', 10: 'mua', 11: 'good'} @fixture def quality(): def quality(c): return c return quality @fixture def similarity(cluster_ids): sim = lambda c, d: (c * 1.01 + d) def similarity(c): return get_closest_clusters(c, cluster_ids, sim) return similarity

21.590909

79

0.382105

87

950

4.08046

0.471264

0.112676

0.101408

0

0.027344

0.191579

950

43

80

22.093023

0.434896

0.415789

0

0.2

0

0.02952

0

1

0.3

false

0

0.1

0.2

0.7

0

null

0

1

0

null

0

1

0

1

0

4

666ee62c62edcf3d4cd56d09470ad6c25ed530c6

50

py

Python

tests/co_sim_io/python/__init__.py

KratosMultiphysics/CoSimIO

cb4578dc338a3215d377e03d9f7cea007c87bfd6

[ "BSD-4-Clause" ]

15

2020-04-17T17:25:47.000Z

2022-02-02T09:28:56.000Z

tests/integration_tutorials/python/mpi/__init__.py

KratosMultiphysics/CoSimIO

cb4578dc338a3215d377e03d9f7cea007c87bfd6

[ "BSD-4-Clause" ]

84

2020-04-29T17:22:04.000Z

2022-02-14T12:24:59.000Z

tests/integration_tutorials/python/mpi/__init__.py

KratosMultiphysics/CoSimIO

cb4578dc338a3215d377e03d9f7cea007c87bfd6

[ "BSD-4-Clause" ]

2

2021-03-02T04:15:05.000Z

2022-01-15T11:59:22.000Z

# this is needed for the python unittest discovery

50

0.82

8

50

5.125

1

0

0.16

50

1

50

0.97619

0.96

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

4

66a9b896387bf96f4917f56c03e45c26b0acf1fb

694

py

Python

custom_components/ge_kitchen/devices/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

custom_components/ge_kitchen/devices/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

custom_components/ge_kitchen/devices/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

import logging from typing import Type from gekitchensdk.erd import ErdApplianceType from .base import ApplianceApi from .oven import OvenApi from .fridge import FridgeApi from .dishwasher import DishwasherApi _LOGGER = logging.getLogger(__name__) def get_appliance_api_type(appliance_type: ErdApplianceType) -> Type: _LOGGER.debug(f"Found device type: {appliance_type}") """Get the appropriate appliance type""" if appliance_type == ErdApplianceType.OVEN: return OvenApi if appliance_type == ErdApplianceType.FRIDGE: return FridgeApi if appliance_type == ErdApplianceType.DISH_WASHER: return DishwasherApi # Fallback return ApplianceApi

28.916667

69

0.76513

78

694

6.615385

0.435897

0.151163

0.224806

0.180233

0

0.174352

694

23

70

30.173913

0.900524

0.011527

0

0.054348

0

1

0.058824

false

0

0.411765

0

0.705882

0

null

0

1

0

null

0

1

0

1

0

4

66cd7ba9850325ca160977d19d5e1d9f829c4ddb

21

py

Python

esp8266/platform.py

pythings/PythingsOS

276b41a32af7fa0d5395b2bb308e611f784f9711

[ "Apache-2.0" ]

11

2020-01-15T14:25:48.000Z

2021-11-25T04:21:18.000Z

esp8266/platform.py

Pythings/PythingsOS

276b41a32af7fa0d5395b2bb308e611f784f9711

[ "Apache-2.0" ]

8

2021-02-04T16:41:57.000Z

2022-03-29T21:57:15.000Z

esp8266/platform.py

pythings/PythingsOS

276b41a32af7fa0d5395b2bb308e611f784f9711

[ "Apache-2.0" ]

null

platform = 'esp8266'

10.5

20

0.714286

2

21

7.5

1

0

0.222222

0.142857

21

1

21

0.611111

0

0.333333

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

66d369f3c34a7ae1107b0ab17b6c370805300977

235

py

Python

python_modules/models/Result.py

martijnbroekman/OfficeHeatlth

7673c4cd5147f0c917869d28c5fd87d80aa93929

[ "MIT" ]

null

python_modules/models/Result.py

martijnbroekman/OfficeHeatlth

7673c4cd5147f0c917869d28c5fd87d80aa93929

[ "MIT" ]

null

python_modules/models/Result.py

martijnbroekman/OfficeHeatlth

7673c4cd5147f0c917869d28c5fd87d80aa93929

[ "MIT" ]

null

class Result: def __init__(self, face_detected, emotions=None, posture=None, fatigue=None): self.face_detected = face_detected self.emotions = emotions self.posture = posture self.fatigue = fatigue

29.375

81

0.676596

27

235

5.62963

0.407407

0.236842

0.210526

0

0.242553

235

7

82

33.571429

0.853933

0

1

0.166667

false

0

0.333333

0

1

0

null

1

0

1

0

null

0

4

66eb69c3968e7d18867984bc5ed54179b53dddbc

98

py

Python

test_pytube.py

Tom-Niesytto/YouTubeDownload

d5391d174f064026efc0b21cece2e3d60af7daf8

[ "MIT" ]

null

test_pytube.py

Tom-Niesytto/YouTubeDownload

d5391d174f064026efc0b21cece2e3d60af7daf8

[ "MIT" ]

null

test_pytube.py

Tom-Niesytto/YouTubeDownload

d5391d174f064026efc0b21cece2e3d60af7daf8

[ "MIT" ]

null

from pytube import YouTube YouTube('http://youtube.com/watch?v=9bZkp7q19f0').streams[0].download()

49

71

0.785714

14

98

5.5

0.857143

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0.040816

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71

49

0.755319

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0

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0

null

0

1

0

null

0

1

0

1

0

4

66ebfa43d3f643cb88ccc46d871565a088dcda7d

58

py

Python

run.py

talos-org/server

6be199fcaf836415b7d32ffb2cee911a9d600395

[ "MIT" ]

1

2019-01-17T20:43:14.000Z

run.py

talos-org/server

6be199fcaf836415b7d32ffb2cee911a9d600395

[ "MIT" ]

42

2018-11-13T06:13:55.000Z

2019-07-27T19:18:23.000Z

run.py

talos-org/server

6be199fcaf836415b7d32ffb2cee911a9d600395

[ "MIT" ]

1

2019-03-26T12:55:01.000Z

from app import app app.run(host='0.0.0.0', port="5000")

14.5

36

0.655172

13

58

2.923077

0.615385

0.157895

0

0.156863

0.12069

58

3

37

19.333333

0.588235

0

0.189655

0

1

0

true

0

0.5

0

0.5

0

1

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null

0

1

0

null

0

1

0

1

0

4

dd202a82a46675a6cded977f11b5495e3094818c

129

py

Python

correios/__init__.py

rennancockles/rastreio-correios

689f2d5ea26e45983834b2192d249c35e3db90aa

[ "MIT" ]

2

2021-11-16T16:54:19.000Z

2022-03-17T19:10:08.000Z

correios/__init__.py

rennancockles/rastreio-correios

689f2d5ea26e45983834b2192d249c35e3db90aa

[ "MIT" ]

null

correios/__init__.py

rennancockles/rastreio-correios

689f2d5ea26e45983834b2192d249c35e3db90aa

[ "MIT" ]

null

from correios.entities import Objeto from correios.main import Correios __version__ = "0.1.4" __all__ = ["Objeto", "Correios"]

18.428571

36

0.751938

17

129

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0

0.026786

0.131783

129

6

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null

1

0

1

0

null

0

1

0

4

dd33079c75c147480313b52087ef6c990b8e2948

5,147

py

Python

swarm_cli/cli_swarm.py

sungazer-io/swarm-cli

da8f06611ccaad7b072c069fbc73656b77833f8b

[ "MIT" ]

null

swarm_cli/cli_swarm.py

sungazer-io/swarm-cli

da8f06611ccaad7b072c069fbc73656b77833f8b

[ "MIT" ]

null

swarm_cli/cli_swarm.py

sungazer-io/swarm-cli

da8f06611ccaad7b072c069fbc73656b77833f8b

[ "MIT" ]

null

from typing import List import click from swarm_cli.lib import SwarmModeState, load_env_files, run_cmd @click.group() @click.option('--environment', '-e', multiple=True, required=False) @click.pass_context def swarm(ctx: click.Context, environment: List[str]): load_env_files(environment) state = SwarmModeState() state.initFromFile('swarm-config.yml') ctx.obj = state @swarm.group() @click.pass_context def preset(ctx: click.Context): state: SwarmModeState = ctx.obj @preset.command('ls') @click.option('--preset', '-p', help="Select a preset", required=False) @click.pass_context def preset_ls(ctx: click.Context, preset: str): state: SwarmModeState = ctx.obj if preset: state.ensure_preset(preset) for k, v in state.cfg['presets'][preset]['stacks'].items(): click.secho("{}:{}".format(k, v['variant'])) else: for preset in state.cfg['presets'].keys(): click.secho("Preset {}".format(preset)) for k, v in state.cfg['presets'][preset]['stacks'].items(): click.secho(" - {}:{}".format(k, v['variant'])) @preset.command('deploy') @click.option('--preset', '-p', help="Select a preset", required=True) @click.option('--dry-run', is_flag=True) @click.pass_context def preset_deploy(ctx: click.Context, preset: str = None, dry_run=False): state: SwarmModeState = ctx.obj state.ensure_preset(preset) preset_data = state.cfg['presets'][preset] load_env_files(preset_data.get('env_files', []), ignore_missing=True) stacks = state.cfg['presets'][preset]['stacks'] for k, v in stacks.items(): name, variant = k, v['variant'] cmd = ' '.join(['docker', 'stack', 'deploy', state.build_deploy_sequence_for_stack(name, variant), name]) run_cmd(cmd, dry_run=dry_run, env=state.get_environment_for_stack(preset, name, variant)) @preset.command('build') @click.option('--preset', '-p', help="Select a preset", required=True) @click.option('--dry-run', is_flag=True) @click.pass_context def preset_build(ctx: click.Context, preset: str = None, dry_run=False): state: SwarmModeState = ctx.obj state.ensure_preset(preset) preset_data = state.cfg['presets'][preset] load_env_files(preset_data.get('env_files', []), ignore_missing=True) stacks = state.cfg['presets'][preset]['stacks'] for k, v in stacks.items(): name, variant = k, v['variant'] state.prepare_build_folder(preset, name, variant) cmd = ' '.join(['docker-compose', state.build_compose_sequence_for_stack(name, variant), 'build']) run_cmd(cmd, dry_run=dry_run, cwd=state.get_build_folder(preset, name, variant), env=state.get_environment_for_stack(preset, name, variant) ) @preset.command('push') @click.option('--preset', '-p', help="Select a preset", required=True) @click.option('--dry-run', is_flag=True) @click.pass_context def preset_push(ctx: click.Context, preset: str = None, dry_run=False): state: SwarmModeState = ctx.obj state.ensure_preset(preset) preset_data = state.cfg['presets'][preset] load_env_files(preset_data.get('env_files', []), ignore_missing=True) stacks = state.cfg['presets'][preset]['stacks'] for k, v in stacks.items(): name, variant = k, v['variant'] cmd = ' '.join(['docker-compose', state.build_compose_sequence_for_stack(name, variant), 'push']) run_cmd(cmd, dry_run=dry_run, env=state.get_environment_for_stack(preset, name, variant) ) # @swarm.group() # def stack(): # pass # # # @stack.command('ls') # @click.pass_context # def stack_ls(ctx: click.Context): # state: SwarmModeState = ctx.obj # click.echo('Available stacks:') # for stack_name in sorted(state.layered_stacks.keys()): # click.echo(stack_name) # for stack_variant in sorted(state.layered_stacks[stack_name].keys()): # click.echo("\t {}".format(stack_variant)) # # # @stack.command('deploy') # @click.argument('name_variant', nargs=-1) # @click.option('--dump-cmd', is_flag=True) # @click.pass_context # def stack_deploy(ctx: click.Context, name_variant: str, dump_cmd: str): # state: SwarmModeState = ctx.obj # for name_variant_elem in name_variant: # name, variant = name_variant_elem.split(':') # state.ensure_stack_exists(name, variant) # cmd = ' '.join(['docker', 'stack', 'deploy', state.build_deploy_sequence_for_stack(name, variant), name]) # env = state.get_environment_for_stack(preset, name, variant) # run_cmd(cmd, dry_run=dump_cmd, env=env) # # # @stack.command('setup') # @click.argument('name_variant', nargs=-1) # @click.option('--dump-cmd', is_flag=True) # @click.pass_context # def stack_setup(ctx: click.Context, name_variant: str, dump_cmd: str): # state: SwarmModeState = ctx.obj # for name_variant_elem in name_variant: # name, variant = name_variant_elem.split(':') # state.ensure_stack_exists(name, variant) # state.ensure_preconditions(name, variant, dump_cmd=dump_cmd)

38.410448

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5,147

4.806735

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0.043862

0.052087

0.788608

0.721596

0.704539

0.673774

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0.176025

5,147

133

116

38.699248

0.773638

0.28502

0

0.556962

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1

0.075949

false

0.075949

0.037975

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0.113924

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null

0

1

0

1

0

null

0

1

0

4

dd44136641483aec5e58869e582e925a4afdf07f

107

py

Python

scts/__init__.py

deniscapeto/SimpleCorreiosTrackingService

e96bcec580dc6cd2cc89c0e8e038270d40d19164

[ "MIT" ]

null

scts/__init__.py

deniscapeto/SimpleCorreiosTrackingService

e96bcec580dc6cd2cc89c0e8e038270d40d19164

[ "MIT" ]

12

2020-06-05T23:26:54.000Z

2021-10-02T09:36:41.000Z

scts/__init__.py

deniscapeto/SimpleCorreiosTrackingService

e96bcec580dc6cd2cc89c0e8e038270d40d19164

[ "MIT" ]

1

2019-10-11T00:32:06.000Z

from django import setup setup() from scts.factory.build_app import build_app # noqa app = build_app()

13.375

52

0.757009

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107

4.588235

0.529412

0.307692

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0.168224

107

7

53

15.285714

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0

1

0

false

0

0.5

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0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

dd4640e671657dc1c95ae56ba8493acc45a399a2

93

py

Python

bc_website/__main__.py

beginner-codes/website

37d2787ff9350c0969d01edcd9b239c860c5d359

[ "MIT" ]

1

2021-08-05T20:22:33.000Z

bc_website/__main__.py

beginner-codes/website

37d2787ff9350c0969d01edcd9b239c860c5d359

[ "MIT" ]

12

2021-08-05T20:37:10.000Z

2021-11-08T06:20:39.000Z

bc_website/__main__.py

beginner-codes/website

37d2787ff9350c0969d01edcd9b239c860c5d359

[ "MIT" ]

1

2021-08-05T21:06:49.000Z

import uvicorn uvicorn.run("bc_website.app:app", host="localhost", port=5000, reload=True)

18.6

75

0.752688

14

93

4.928571

0.857143

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0.047059

0.086022

93

4

76

23.25

0.764706

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true

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0.5

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0.5

0

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0

null

0

1

0

null

0

1

0

1

0

4

dd48c34c58d01914bdce95063cac113a26dfa99e

179

py

Python

web_wrapper/context_processors.py

musicmetadata/web-wrapper

ffc8423769b9d7d1fc57ac2865373ec89ae83192

[ "MIT" ]

1

2019-12-21T12:14:51.000Z

web_wrapper/context_processors.py

musicmetadata/web-wrapper

ffc8423769b9d7d1fc57ac2865373ec89ae83192

[ "MIT" ]

2

2019-12-05T16:23:40.000Z

2020-06-23T07:54:37.000Z

web_wrapper/context_processors.py

musicmetadata/web-wrapper

ffc8423769b9d7d1fc57ac2865373ec89ae83192

[ "MIT" ]

1

2020-12-25T16:37:38.000Z

from django.conf import settings def features(request): return { 'CWR2_AVAILABLE': settings.CWR2_AVAILABLE, 'CWR3_AVAILABLE': settings.CWR3_AVAILABLE, }

19.888889

50

0.692737

19

179

6.315789

0.631579

0.216667

0

0.028571

0.217877

179

8

51

22.375

0.828571

0

0.156425

0

1

0.166667

false

0

0.166667

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

dd7325f505ab2300a98892114fa72bc9daf0d40c

129

py

Python

01. Variable/021.py

MaksonViini/Aprendendo-Python

8d8422f793e4ea9f81fa4ed0e4101bcfc2ba3c99

[ "MIT" ]

1

2020-09-20T23:18:47.000Z

01. Variable/021.py

MaksonViini/Aprendendo-Python

8d8422f793e4ea9f81fa4ed0e4101bcfc2ba3c99

[ "MIT" ]

null

01. Variable/021.py

MaksonViini/Aprendendo-Python

8d8422f793e4ea9f81fa4ed0e4101bcfc2ba3c99

[ "MIT" ]

1

2020-09-20T23:18:49.000Z

#Tocando um MP3 from pygame import mixer mixer.init() mixer.music.load('EX021.mp3') #Adicione o nome da musica mixer.music.play()

25.8

56

0.767442

22

129

4.5

0.772727

0.20202

0

0.043478

0.108527

129

5

57

25.8

0.817391

0.302326

0

0.101124

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1

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true

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0.25

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0.25

0

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0

null

1

0

1

0

null

0

1

0

4

dd73f1e2e89e6ff8832edc49aebbd60bc238b60a

165

py

Python

python/setup.py

SamChill/drunkardswalk

5c30b9dfdfba7df0fb34679a039534b6f84cfcc8

[ "MIT" ]

3

2017-11-08T01:53:44.000Z

2019-04-24T06:55:41.000Z

python/setup.py

SamChill/drunkardswalk

5c30b9dfdfba7df0fb34679a039534b6f84cfcc8

[ "MIT" ]

null

python/setup.py

SamChill/drunkardswalk

5c30b9dfdfba7df0fb34679a039534b6f84cfcc8

[ "MIT" ]

null

#!/usr/bin/env python from setuptools import setup from os.path import dirname, abspath, join setup(name='drunkardswalk', packages=['drunkardswalk'], )

20.625

42

0.709091

20

165

5.85

0.8

0

0.169697

165

7

43

23.571429

0.854015

0.121212

0

0.180556

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

1

0

4

dd74be1482e927f336a388abc03138f8ca7ef313

217

py

Python

examples/docs_snippets/docs_snippets_tests/concepts_tests/io_management_tests/test_subselection.py

kstennettlull/dagster

dd6f57e170ff03bf145f1dd1417e0b2c3156b1d6

[ "Apache-2.0" ]

null

examples/docs_snippets/docs_snippets_tests/concepts_tests/io_management_tests/test_subselection.py

kstennettlull/dagster

dd6f57e170ff03bf145f1dd1417e0b2c3156b1d6

[ "Apache-2.0" ]

null

examples/docs_snippets/docs_snippets_tests/concepts_tests/io_management_tests/test_subselection.py

kstennettlull/dagster

dd6f57e170ff03bf145f1dd1417e0b2c3156b1d6

[ "Apache-2.0" ]

null

from docs_snippets.concepts.io_management.subselection import ( execute_full, execute_subselection, ) def test_execute_job(): execute_full() def test_execute_subselection(): execute_subselection()

16.692308

63

0.769585

24

217

6.541667

0.541667

0.363057

0.178344

0

0.152074

217

12

64

18.083333

0.853261

0

1

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true

0

0.125

0

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0

1

0

null

1

0

null

0

1

0

4

06edec272e6f323369bf14129deee41c42b943fc

114

py

Python

post_office/__init__.py

LeGast00n/django-post_office

cfff8a9e824e3352fa897d20b8531723791ebfd3

[ "MIT" ]

null

post_office/__init__.py

LeGast00n/django-post_office

cfff8a9e824e3352fa897d20b8531723791ebfd3

[ "MIT" ]

null

post_office/__init__.py

LeGast00n/django-post_office

cfff8a9e824e3352fa897d20b8531723791ebfd3

[ "MIT" ]

null

VERSION = (1, 1, 1) from .backends import EmailBackend from .models import PRIORITY from .utils import send_mail

19

34

0.77193

17

114

5.117647

0.647059

0.045977

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0.03125

0.157895

114

5

35

22.8

0.875

0

1

0

false

0

0.75

0

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

4

06f8bac11e775f5c5215020296ea436249d1f719

153

py

Python

LNU_OS/main.py

JessyTsu1/DL_Backup

e553525bdd8eba8ac6f8082f50de63862950d460

[ "Apache-2.0" ]

null

LNU_OS/main.py

JessyTsu1/DL_Backup

e553525bdd8eba8ac6f8082f50de63862950d460

[ "Apache-2.0" ]

null

LNU_OS/main.py

JessyTsu1/DL_Backup

e553525bdd8eba8ac6f8082f50de63862950d460

[ "Apache-2.0" ]

1

2021-12-15T15:03:43.000Z

from process import * #结构体 from out import * #界面窗口 # from config import originate, target import time import os if __name__ == '__main__': run()

12.75

38

0.699346

21

153

4.714286

0.714286

0

0.215686

153

11

39

13.909091

0.825

0.287582

0

0.07767

0

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true

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null

0

null

0

1

0

1

0

1

0

4

6632a527632eb2a27cd322d36cc4b99408f7159d

566

py

Python

src/Bicho.py

victorlujan/Dise-odeSoftwarePatrones

b9845cc1c4abdc44867c90b9e9784246e57f16b3

[ "MIT" ]

null

src/Bicho.py

victorlujan/Dise-odeSoftwarePatrones

b9845cc1c4abdc44867c90b9e9784246e57f16b3

[ "MIT" ]

null

src/Bicho.py

victorlujan/Dise-odeSoftwarePatrones

b9845cc1c4abdc44867c90b9e9784246e57f16b3

[ "MIT" ]

null

class Bicho: def __init__(self): self.vida=0 self.modo = None self.ataque = 10 self.posicion = None def hablar(self): self.modo.hablar() def dormir(self): self.modo.dormir() def atacar(self): self.modo.atacar() def esPerezoso(self): return self.modo.esPerezoso() def esAgresivo(self): return self.modo.esAgresivo() def recorrer(self): self.modo.printOn() def actua(self): self.modo.actua(self) def mover(self): self.modo.mover()

19.517241

37

0.567138

68

566

4.661765

0.323529

0.227129

0.113565

0

0.007712

0.312721

566

28

38

20.214286

0.807198

0

1

0.409091

false

0

0.090909

0.545455

0.045455

0

null

1

0

null

0

1

0

1

0

4

b07e6f3d1f847b30e6670f0c09e2b6575e496439

289

py

Python

pybamm/models/submodels/thermal/x_lumped/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

1

2019-10-29T19:06:04.000Z

pybamm/models/submodels/thermal/x_lumped/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

pybamm/models/submodels/thermal/x_lumped/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

from .base_x_lumped import BaseModel from .x_lumped_no_current_collectors import NoCurrentCollector from .x_lumped_0D_current_collectors import CurrentCollector0D from .x_lumped_1D_current_collectors import CurrentCollector1D from .x_lumped_2D_current_collectors import CurrentCollector2D

48.166667

62

0.913495

38

289

6.473684

0.421053

0.142276

0.178862

0

0.022305

0.069204

289

5

63

57.8

0.892193

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

b0aaeebdb1d78612402cfdbf50a6d8583b2e7d5c

100

py

Python

doobi/doobi_pack/apps.py

bryanopew/doobi

45e98c7a0a8aceea7b13665da4d57f161fe78725

[ "MIT" ]

null

doobi/doobi_pack/apps.py

bryanopew/doobi

45e98c7a0a8aceea7b13665da4d57f161fe78725

[ "MIT" ]

null

doobi/doobi_pack/apps.py

bryanopew/doobi

45e98c7a0a8aceea7b13665da4d57f161fe78725

[ "MIT" ]

null

from django.apps import AppConfig class DoobiPackConfig(AppConfig): name = 'doobi.doobi_pack'

16.666667

33

0.77

12

100

6.333333

0.833333

0

0.15

100

5

34

20

0.894118

0

0.16

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b0b38613cada6570ab2206e440d6b3a6a88cf3fe

166

py

Python

py_tdlib/constructors/user_profile_photo.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/user_profile_photo.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/user_profile_photo.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class userProfilePhoto(Type): id = None # type: "int64" added_date = None # type: "int32" sizes = None # type: "vector<photoSize>"

20.75

42

0.674699

21

166

5.285714

0.714286

0.216216

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0.029851

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166

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false

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null

1

0

1

0

null

0

1

0

4

b0e865e3d917656be94ae9180ed5791cbde11b1c

578

py

Python

pillow_heif/__init__.py

bigcat88/pillow_heif

45fc1b3accd0da4be52b279083dc725d0e02eb87

[ "Apache-2.0" ]

20

2021-09-15T10:03:31.000Z

2022-03-27T22:51:57.000Z

pillow_heif/__init__.py

bigcat88/pillow_heif

45fc1b3accd0da4be52b279083dc725d0e02eb87

[ "Apache-2.0" ]

8

2021-10-29T18:47:18.000Z

2022-03-22T15:41:47.000Z

pillow_heif/__init__.py

bigcat88/pillow_heif

45fc1b3accd0da4be52b279083dc725d0e02eb87

[ "Apache-2.0" ]

4

2021-11-01T10:25:50.000Z

2022-03-11T03:45:57.000Z

from .constants import * # pylint: disable=unused-wildcard-import from .reader import HeifFile, UndecodedHeifFile, check, read, open # pylint: disable=redefined-builtin,unused-import from .writer import write # pylint: disable=unused-import from .error import HeifError # pylint: disable=unused-import from .as_opener import register_heif_opener, check_heif_magic # pylint: disable=unused-import from . import _libheif # pylint: disable=import-self __version__ = "0.1.4" def libheif_version(): return _libheif.ffi.string(_libheif.lib.heif_get_version()).decode()

41.285714

117

0.780277

76

578

5.736842

0.486842

0.178899

0.174312

0.172018

0.199541

0

0.005894

0.119377

578

13

118

44.461538

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0.111111

false

0

0.666667

0.111111

0.888889

0

null

0

1

0

null

0

1

0

4

9fe6eade2553c13d4774cadac8231414559ffbcb

270

py

Python

roles/aliasses/molecule/default/tests/test_default.py

PW999/home-assistant-ansible

fe14d5390712abfe19194bae1af4a9378e1c10af

[ "Apache-2.0" ]

null

roles/aliasses/molecule/default/tests/test_default.py

PW999/home-assistant-ansible

fe14d5390712abfe19194bae1af4a9378e1c10af

[ "Apache-2.0" ]

10

2021-08-08T17:59:04.000Z

2022-02-05T09:45:06.000Z

roles/aliasses/molecule/default/tests/test_default.py

PW999/home-assistant-ansible

fe14d5390712abfe19194bae1af4a9378e1c10af

[ "Apache-2.0" ]

null

import os import testinfra.utils.ansible_runner testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner( os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all') def test_alias(host): host.run_expect([0], 'sudo -u molecule /bin/bash -vilc ll')

27

64

0.740741

37

270

5.189189

0.72973

0.145833

0.21875

0.28125

0

0.004292

0.137037

270

9

65

30

0.819742

0

0.233716

0.088123

0

1

0.166667

false

0

0.333333

0

0.5

0

null

0

1

0

1

0

null

0

1

0

4

b002c602385fbb0947a1ea20ac9464e307a614ae

159

py

Python

Modulo 01/exercicos/d003.py

euyag/python-cursoemvideo

d2f684854d926e38ea193816a6c7d2c48d25aa3d

[ "MIT" ]

2

2021-06-22T00:15:11.000Z

2021-08-02T11:28:56.000Z

Modulo 01/exercicos/d003.py

euyag/python-cursoemvideo

d2f684854d926e38ea193816a6c7d2c48d25aa3d

[ "MIT" ]

null

Modulo 01/exercicos/d003.py

euyag/python-cursoemvideo

d2f684854d926e38ea193816a6c7d2c48d25aa3d

[ "MIT" ]

null

print('===== DESAFIO 003 =====') n1 = int(input('digite um valor: ')) n2 = int(input('digite um valor: ')) s = n1 + n2 print(f'a soma entre {n1} e {n2} é {s}')

31.8

40

0.559748

28

159

3.178571

0.607143

0.179775

0.314607

0.359551

0.47191

0

0.068702

0.176101

159

5

40

31.8

0.610687

0

0.54375

0

1

0

false

0

0.4

1

0

null

0

1

0

1

0

null

0

4

b0114ee1ad2ba99baea331bfcd86c8a3128cfd37

136

py

Python

src/aiofiles/__init__.py

q0w/aiofiles

d010ff4d789598213334a32ec3d3f55caaab766c

[ "Apache-2.0" ]

1,947

2015-04-01T20:44:36.000Z

2022-03-31T23:14:38.000Z

src/aiofiles/__init__.py

q0w/aiofiles

d010ff4d789598213334a32ec3d3f55caaab766c

[ "Apache-2.0" ]

133

2015-04-01T21:06:54.000Z

2022-03-31T22:37:34.000Z

venv/lib/python3.8/site-packages/aiofiles/__init__.py

HCDigitalScholarship/migration-encounters

08e705f8ed1b4d4e00d2c1112a8b5d30bf2ebd4d

[ "MIT" ]

162

2015-04-01T21:01:09.000Z

2022-03-16T04:36:56.000Z

"""Utilities for asyncio-friendly file handling.""" from .threadpool import open from . import tempfile __all__ = ["open", "tempfile"]

22.666667

51

0.735294

16

136

6

0.75

0

0.132353

136

5

52

27.2

0.813559

0.330882

0

0.141176

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b02eb724ab52c3722d06948ea9c270d4d2835327

7,847

py

Python

HDPython/hdl_converter.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

null

HDPython/hdl_converter.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

null

HDPython/hdl_converter.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

1

2021-10-20T20:08:16.000Z

def get_dependency_objects(obj, dep_list): return obj.__hdl_converter__.get_dependency_objects(obj, dep_list) def ops2str(obj, ops): return obj.__hdl_converter__.ops2str(ops) def get_MemfunctionCalls(obj): return obj.__hdl_converter__.get_MemfunctionCalls(obj) def FlagFor_TemplateMissing(obj): obj.__hdl_converter__.FlagFor_TemplateMissing(obj) def reset_TemplateMissing(obj): obj.__hdl_converter__.reset_TemplateMissing(obj) def isTemplateMissing(obj): return obj.__hdl_converter__.isTemplateMissing(obj) def IsSucessfullConverted(obj): return obj.__hdl_converter__.IsSucessfullConverted(obj) def convert_all_packages(obj, ouputFolder, x, FilesDone): return obj.__hdl_converter__.convert_all_packages(obj, ouputFolder, x, FilesDone) def convert_all_entities(obj, ouputFolder, x, FilesDone): return obj.__hdl_converter__.convert_all_entities(obj, ouputFolder, x, FilesDone) def convert_all_impl(obj, ouputFolder, FilesDone): return obj.__hdl_converter__.convert_all_impl(obj, ouputFolder, FilesDone) def convert_all(obj, ouputFolder): return obj.__hdl_converter__.convert_all(obj, ouputFolder) def get_primary_object(obj): return obj.__hdl_converter__.get_primary_object(obj) def get_packet_file_name(obj): return obj.__hdl_converter__.get_packet_file_name(obj) def get_packet_file_content(obj): return obj.__hdl_converter__.get_packet_file_content(obj) def get_enity_file_content(obj): return obj.__hdl_converter__.get_enity_file_content(obj) def get_entity_file_name(obj): return obj.__hdl_converter__.get_entity_file_name(obj) def get_type_simple(obj): return obj.__hdl_converter__.get_type_simple(obj) def get_type_simple_template(obj): return obj.__hdl_converter__.get_type_simple_template(obj) def impl_constructor(obj): return obj.__hdl_converter__.impl_constructor(obj) def parse_file(obj): return obj.__hdl_converter__.parse_file(obj) def impl_includes(obj, name, parent): return obj.__hdl_converter__.impl_includes(obj, name, parent) def def_includes(obj, name, parent): return obj.__hdl_converter__.def_includes(obj, name, parent) def def_record_Member(obj, name, parent, Inout=None): return obj.__hdl_converter__.def_record_Member(obj, name, parent, Inout) def def_record_Member_Default(obj, name, parent, Inout=None): return obj.__hdl_converter__.def_record_Member_Default(obj, name, parent, Inout) def def_packet_header(obj, name, parent): return obj.__hdl_converter__.def_packet_header(obj, name, parent) def def_packet_body(obj, name, parent): return obj.__hdl_converter__.def_packet_body(obj, name, parent) def impl_entity_port(obj, name): return obj.__hdl_converter__.impl_entity_port(obj, name) def impl_function_argument(obj, func_arg, arg): return obj.__hdl_converter__.impl_function_argument(obj, func_arg, arg) def impl_get_attribute(obj, attName,parent = None): return obj.__hdl_converter__.impl_get_attribute(obj, attName, parent) def impl_slice(obj, sl, astParser=None): return obj.__hdl_converter__.impl_slice(obj, sl, astParser) def impl_compare(obj, ops, rhs, astParser=None): return obj.__hdl_converter__.impl_compare(obj, ops, rhs, astParser) def impl_add(obj, args): return obj.__hdl_converter__.impl_add(obj, args) def impl_sub(obj, args): return obj.__hdl_converter__.impl_sub(obj, args) def impl_to_bool(obj, astParser): return obj.__hdl_converter__.impl_to_bool(obj, astParser) def impl_bit_and(obj, rhs, astParser): return obj.__hdl_converter__.impl_bit_and(obj, rhs, astParser) def function_name_modifier(obj, name, varSigSuffix): return obj.__hdl_converter__.function_name_modifier(obj, name, varSigSuffix) def impl_get_value(obj, ReturnToObj=None, astParser=None): return obj.__hdl_converter__.impl_get_value(obj, ReturnToObj, astParser) def impl_reasign_type(obj): return obj.__hdl_converter__.impl_reasign_type(obj) def impl_reasign(obj, rhs, astParser=None, context_str=None): return obj.__hdl_converter__.impl_reasign(obj, rhs, astParser, context_str) def impl_reasign_rshift_(obj, rhs, astParser=None, context_str=None): return obj.__hdl_converter__.impl_reasign_rshift_(obj, rhs, astParser, context_str) def get_call_member_function(obj, name, args): return obj.__hdl_converter__.get_call_member_function(obj, name, args) def impl_function_call(obj, name, args, astParser=None): return obj.__hdl_converter__.impl_function_call(obj=obj, name=name, args=args, astParser=astParser) def impl_symbol_instantiation(obj, VarSymb="variable"): return obj.__hdl_converter__.impl_symbol_instantiation(obj, VarSymb) def impl_architecture_header(obj): prepare_for_conversion(obj) return obj.__hdl_converter__.impl_architecture_header(obj) def impl_architecture_body(obj): return obj.__hdl_converter__.impl_architecture_body(obj) def impl_add(obj,args): return obj.__hdl_converter__.impl_add(obj, args) def impl_sub(obj,args): return obj.__hdl_converter__.impl_sub(obj, args) def impl_multi(obj,args): return obj.__hdl_converter__.impl_multi(obj, args) def def_entity_port(obj): prepare_for_conversion(obj) return obj.__hdl_converter__.def_entity_port(obj) def impl_process_header(obj): return obj.__hdl_converter__.impl_process_header(obj) def impl_process_sensitivity_list(obj): return obj.__hdl_converter__.impl_process_sensitivity_list(obj) def impl_process_pull(obj,clk): return obj.__hdl_converter__.impl_process_pull(obj,clk) def impl_process_push(obj,clk): return obj.__hdl_converter__.impl_process_push(obj,clk) def impl_enter_rising_edge(obj): return obj.__hdl_converter__.impl_enter_rising_edge(obj) def impl_exit_rising_edge(obj): return obj.__hdl_converter__.impl_exit_rising_edge(obj) def get_assiment_op(obj): return obj.__hdl_converter__.get_assiment_op(obj) def get_Inout(obj,parent): return obj.__hdl_converter__.get_Inout(obj,parent) def InOut_t2str2(obj, inOut): return obj.__hdl_converter__.InOut_t2str2(inOut) def InOut_t2str(obj): return obj.__hdl_converter__.InOut_t2str(obj) def get_default_value(obj): return obj.__hdl_converter__.get_default_value(obj) def extract_conversion_types(obj, exclude_class_type=None, filter_inout=None): return obj.__hdl_converter__.extract_conversion_types(obj, exclude_class_type, filter_inout) def get_Name_array(obj): return obj.__hdl_converter__.get_Name_array(obj) def length(obj): return obj.__hdl_converter__.length(obj) def to_arglist(obj, name, parent, withDefault=False, astParser=None): return obj.__hdl_converter__.to_arglist(obj, name, parent, withDefault, astParser) def get_inout_type_recursive(obj): return obj.__hdl_converter__.get_inout_type_recursive(obj) def Has_pushpull_function(obj, pushpull): return obj.__hdl_converter__.Has_pushpull_function(obj, pushpull) def get_free_symbols(obj, name, parent_list=[]): return obj.__hdl_converter__.get_free_symbols(obj,name, parent_list) def get_component_suffix(obj, Inout_type, varsignal_type): return obj.__hdl_converter__.get_component_suffix(obj, Inout_type, varsignal_type) def prepare_for_conversion(obj): return obj.__hdl_converter__.prepare_for_conversion(obj) def get_HDL_name(obj, parent,suffix): return obj.__hdl_converter__.get_HDL_name(obj,parent,suffix) def impl_get_init_values(obj,parent=None, InOut_Filter=None, VaribleSignalFilter = None,ForceExpand=False): return obj.__hdl_converter__.impl_get_init_values(obj, parent, InOut_Filter, VaribleSignalFilter ,ForceExpand) def get_extractedTypes(obj): primary = get_primary_object(obj) prepare_for_conversion(primary) return primary.__hdl_converter__.extractedTypes

27.925267

114

0.795973

1,120

7,847

5.000893

0.108036

0.154258

0.190145

0.258704

0.784146

0.611676

0.396715

0.252455

0.135333

0.103196

0

0.001152

0.114948

7,847

280

115

28.025

0.805328

0

0.067568

0

0.00102

0

1

0.486486

false

0

0.452703

0.959459

0

null

0

1

0

null

0

1

0

1

0

4

b0395013daa1abb48d72d80560781bf8ef91a6af

2,757

py

Python

centinel/unit_test/test_http.py

mikiec84/centinel

39fc263e71e85135fa3d65513e1d417ef76388ea

[ "MIT" ]

29

2015-02-12T22:39:27.000Z

2022-01-25T13:03:18.000Z

centinel/unit_test/test_http.py

mikiec84/centinel

39fc263e71e85135fa3d65513e1d417ef76388ea

[ "MIT" ]

158

2015-01-03T02:29:58.000Z

2021-02-05T18:35:56.000Z

centinel/unit_test/test_http.py

mikiec84/centinel

39fc263e71e85135fa3d65513e1d417ef76388ea

[ "MIT" ]

22

2015-02-11T05:08:49.000Z

2022-01-25T13:03:33.000Z

import pytest import os from ..primitives import http class TestHTTPMethods: def test_url_not_exist(self): """ test if _get_http_request(args...) returns failure for an invalid url. """ file_name = "data/invalid_hosts.txt" fd = open(file_name, 'r') for line in fd: line = line.rstrip('\n') res = http._get_http_request(line) assert res is not None assert 'failure' in res['response'].keys() fd.close() def test_url_exist(self): """ test if _get_http_request(args..) returns valid contents from a valid url. """ file_name = "data/valid_hosts.txt" fd = open(file_name, 'r') for line in fd: line = line.rstrip('\n') res = http._get_http_request(line) assert res is not None assert 'failure' not in res['response'].keys() fd.close() def test_batch_url_invalid_hosts(self): """ test _get_http_request(arg...) primitive when a list of invaid domain name is passed to get_requests_batch(args...). """ invalid_hosts_file_name = "data/invalid_hosts.txt" fd = open(invalid_hosts_file_name, 'r') lines = [line.rstrip('\n') for line in fd] results = http.get_requests_batch(lines) assert results is not None # assert failure for inValid Hosts for key, result in results.items(): assert result is not None assert 'failure' in result['response'].keys() fd.close() def test_batch_url_valid_hosts(self): """ test _get_http_request(arg...) primitive when a list of valid domain name is passed to get_requests_batch(args...). """ valid_hosts_file_name = "data/valid_hosts.txt" fd = open(valid_hosts_file_name, 'r') lines = [line.rstrip('\n') for line in fd] results = http.get_requests_batch(lines) assert results is not None # assert no failure for valid hosts for key,result in results.items(): assert result is not None assert 'failure' not in result['response'].keys() fd.close() def test_batch_url_thread_error(self): """ test if thread takes long time to finish TODO: choose url that gives thread error """ #file_name = "data/input_file.txt" #fd = open(file_name, 'r') #lines = [line.rstrip('\n') for line in fd] #result = http.get_requests_batch(lines) #assert result is not None #assert 'error' in result #assert result['error'] is "Threads took too long to finish." #fd.close()

34.037037

77

0.587232

363

2,757

4.272727

0.209366

0.05158

0.040619

0.067698

0.751773

0.74726

0.70793

0.704062

0.594455

0.491296

0

0.311208

2,757

81

78

34.037037

0.816746

0.285092

0

0.55814

0

0.086331

0.02435

0

0.012346

0.232558

1

0.116279

false

0

0.069767

0

0.209302

0

null

0

1

0

null

0

1

0

4

b043a3e6c49bebd9a1ab89eaebbf5edf82a12c79

760

py

Python

tests/test_encoding_validators/test_are_sources_in_utf.py

SerejkaSJ/fiasko_bro

dfb8c30109f317c1e5b6d211e002fd148695809e

[ "MIT" ]

25

2018-01-24T10:45:35.000Z

2020-12-05T21:47:20.000Z

tests/test_encoding_validators/test_are_sources_in_utf.py

SerejkaSJ/fiasko_bro

dfb8c30109f317c1e5b6d211e002fd148695809e

[ "MIT" ]

110

2018-01-21T12:25:13.000Z

2021-06-10T19:27:22.000Z

tests/test_encoding_validators/test_are_sources_in_utf.py

SerejkaSJ/fiasko_bro

dfb8c30109f317c1e5b6d211e002fd148695809e

[ "MIT" ]

13

2017-12-12T22:19:01.000Z

2019-01-29T18:08:05.000Z

from fiasko_bro import defaults from fiasko_bro.pre_validation_checks import file_not_in_utf8 def test_file_not_in_utf8_fail(encoding_repo_path): directories_to_skip = defaults.VALIDATION_PARAMETERS['directories_to_skip'] output = file_not_in_utf8(encoding_repo_path, directories_to_skip) assert isinstance(output, str) def test_file_not_in_utf8_ok(general_repo_path): directories_to_skip = defaults.VALIDATION_PARAMETERS['directories_to_skip'] output = file_not_in_utf8(general_repo_path, directories_to_skip) assert output is None def test_file_not_in_utf8_uses_whitelist(encoding_repo_path): directories_to_skip = ['win1251'] output = file_not_in_utf8(encoding_repo_path, directories_to_skip) assert output is None

36.190476

79

0.828947

114

760

4.982456

0.289474

0.183099

0.239437

0.160211

0.783451

0.580986

0.484155

0

0.016369

0.115789

760

20

80

38

0.828869

0

0.428571

0

0.059211

0

0.214286

1

0.214286

false

0

0.142857

0

0.357143

0

null

0

1

0

1

0

null

0

1

0

4

b068f5b5070b6daa67c7d700aba8e849c2a13712

106

py

Python

stubs/esp32_1_10_0/btree.py

jmannau/micropython-stubber

8930e8a0038192fd259b31a193d1da3b2501256a

[ "MIT" ]

null

stubs/esp32_1_10_0/btree.py

jmannau/micropython-stubber

8930e8a0038192fd259b31a193d1da3b2501256a

[ "MIT" ]

null

stubs/esp32_1_10_0/btree.py

jmannau/micropython-stubber

8930e8a0038192fd259b31a193d1da3b2501256a

[ "MIT" ]

null

"Module 'btree' on firmware 'v1.10-247-g0fb15fc3f on 2019-03-29'" DESC = 2 INCL = 1 def open(): pass

15.142857

65

0.650943

19

106

3.631579

0.947368

0

0.235294

0.198113

106

6

66

17.666667

0.576471

0.59434

0

0.2

0.6

0.2

0

1

0.2

false

0.2

0

0.2

0

1

0

null

0

1

0

1

0

null

0

1

0

4

c688e3e75f76cd628395dbb666887416facdcb9f

90

py

Python

main.py

Pommers/LCExtract

e9cbe4f0057fd4288b2b9feba44135e4a32df65c

[ "MIT" ]

null

main.py

Pommers/LCExtract

e9cbe4f0057fd4288b2b9feba44135e4a32df65c

[ "MIT" ]

null

main.py

Pommers/LCExtract

e9cbe4f0057fd4288b2b9feba44135e4a32df65c

[ "MIT" ]

null

from src.LCExtract.LCExtract import LCExtract if __name__ == '__main__': LCExtract()

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c6ac846d724d750ec36e961256dd9afbef7fec11

53

py

Python

brainstat/tutorial/__init__.py

rmarkello/BrainStat

f34ffa01274aabf411feb801a3ea1869f8a22d11

[ "BSD-3-Clause" ]

null

brainstat/tutorial/__init__.py

rmarkello/BrainStat

f34ffa01274aabf411feb801a3ea1869f8a22d11

[ "BSD-3-Clause" ]

null

brainstat/tutorial/__init__.py

rmarkello/BrainStat

f34ffa01274aabf411feb801a3ea1869f8a22d11

[ "BSD-3-Clause" ]

null

"""Functions required for the BrainStat Tutorials"""

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null

0

1

0

4

c6be883ddfe6e99b1bba02d2e1c9ae0dd5f68dd1

6,495

py

Python

straxen/gain_models.py

cheryonthetop/straxen

3291c0fb7203dd42ed6c260f528f011c6b7a8391

[ "BSD-3-Clause" ]

null

straxen/gain_models.py

cheryonthetop/straxen

3291c0fb7203dd42ed6c260f528f011c6b7a8391

[ "BSD-3-Clause" ]

null

straxen/gain_models.py

cheryonthetop/straxen

3291c0fb7203dd42ed6c260f528f011c6b7a8391

[ "BSD-3-Clause" ]

null

import numpy as np import strax import straxen export, __all__ = strax.exporter() __all__ += ['ADC_TO_E', 'FIXED_TO_PE'] # Convert from ADC * samples to electrons emitted by PMT # see pax.dsputils.adc_to_pe for calculation ADC_TO_E = 17142.81741 @export def get_to_pe(run_id, gain_model, n_tpc_pmts): if not isinstance(gain_model, tuple): raise ValueError(f"gain_model must be a tuple") if not len(gain_model) == 2: raise ValueError(f"gain_model must have two elements: " f"the model type and its specific configuration") model_type, model_conf = gain_model # Convert from ADC * samples to electrons emitted by PMT # see pax.dsputils.adc_to_pe for calculation adc_to_e = 17142.81741 if model_type == 'disabled': # Somebody messed up raise RuntimeError("Attempt to use a disabled gain model") if model_type == 'to_pe_per_run': # Load a npy file specifing a run_id -> to_pe array to_pe_file = model_conf x = straxen.get_resource(to_pe_file, fmt='npy') run_index = np.where(x['run_id'] == int(run_id))[0] if not len(run_index): # Gains not known: using placeholders run_index = [-1] to_pe = x[run_index[0]]['to_pe'] elif model_type == 'to_pe_constant': if model_conf in FIXED_TO_PE: return FIXED_TO_PE[model_conf] # Uniform gain, specified as a to_pe factor to_pe = np.ones(n_tpc_pmts, dtype=np.float32) * model_conf else: raise NotImplementedError(f"Gain model type {model_type} not implemented") if len(to_pe) != n_tpc_pmts: raise ValueError( f"Gain model {gain_model} resulted in a to_pe " f"of length {len(to_pe)}, but n_tpc_pmts is {n_tpc_pmts}!") return to_pe # Specific gain models, fixed forever. Do not remove or alter models here! FIXED_TO_PE = { # First gain calibration, PMTs at 1300 V. # https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:giovo:first_led_run '1300V_20200428': np.array([0.00241, 0.0064, 0.01071, 0.01465, 0.00812, 0.03647, 0.00384, 0.0025, 0.00385, 0.00546, 0.00456, 0.00755, 0.0197, 0.00521, 0.00672, 0.04181, 0.00647, 0.00652, 0.00849, 0.01027, 0.00581, 0.01491, 0.0072, 0.00739, 0.00952, 0.00599, 0.00801, 0.00482, 0.02414, 0.01633, 0.00745, 0.00667, 0.00783, 0.01905, 0.00974, 0.00836, 0.0064, 0.00593, 0.00531, 0.00742, 0.01199, 0.00717, 0.01045, 0.01224, 0.01014, 0.0102, 0.01014, 0.00686, 0.00917, 0.01033, 0.00606, 0.00708, 0.00723, 0.00527, 0.00675, 0.01309, 0.00779, 0.01052, 0.00828, 0.00503, 0.00828, 0.02198, 0.00688, 0.00942, 0.00652, 0.01078, 0.0098, 0.00619, 0.0061, 0.01207, 0.00446, 0.00628, 0.00937, 0.00652, 0.00828, 0.00828, 0.01394, 0.01602, 0.01014, 0.00947, 0.01158, 0.00801, 0.00635, 0.01319, 0.00542, 0.01003, 0.00745, 0.00749, 0.0084, 0.00564, 0.00828, 0.00691, 0.00828, 0.00828, 0.00879, 0.00683, 0.0084, 0.01371, 0.00974, 0.00664, 0.00832, 0.01045, 0.01078, 0.00626, 0.00772, 0.00546, 0.00974, 0.00828, 0.00828, 0.00488, 0.00969, 0.00553, 0.01199, 0.01092, 0.00745, 0.00612, 0.00942, 0.00898, 0.00783, 0.01681, 0.00755, 0.01045, 0.00828, 0.00828, 0.00564, 0.00828, 0.00828, 0.01681, 0.00797, 0.00649, 0.00759, 0.00861, 0.01143, 0.00733, 0.00736, 0.01216, 0.01681, 0.00703, 0.01216, 0.00917, 0.00828, 0.00828, 0.00523, 0.00828, 0.00657, 0.01394, 0.00794, 0.00801, 0.00705, 0.01633, 0.00769, 0.00824, 0.02116, 0.00739, 0.00937, 0.00898, 0.00963, 0.01113, 0.007, 0.00783, 0.00602, 0.00717, 0.00519, 0.01843, 0.00509, 0.00521, 0.00353, 0.00487, 0.00678, 0.00879, 0.03297, 0.02721, 0.00985, 0.00549, 0.00969, 0.0056, 0.00667, 0.00861, 0.02484, 0.00697, 0.02637, 0.00759, 0.00449, 0.0011, 0.00857, 0.00828, 0.0127, 0.00688, 0.00468, 0.00606, 0.0217, 0.01843, 0.00595, 0.00581, 0.0087, 0.00599, 0.0072, 0.00423, 0.00683, 0.00644, 0.00947, 0.00755, 0.01151, 0.0084, 0.01309, 0.00717, 0.00548, 0.0112, 0.00597, 0.00912, 0.00623, 0.00922, 0.00449, 0.00888, 0.00769, 0.00927, 0.01279, 0.00765, 0.01207, 0.00809, 0.00902, 0.01602, 0.0056, 0.00765, 0.00794, 0.00551, 0.01174, 0.00509, 0.00577, 0.00597, 0.01207, 0.0112, 0.00644, 0.01027, 0.00991, 0.00575, 0.00433, 0.00828, 0.00812, 0.01948, 0.0084, 0.00591, 0.00664, 0.00691, 0.00776, 0.00548, 0.00786, 0.02348, 0.0087, 0.01092, 0.00772, 0.00786, 0.00717, 0.02198, 0.0082, 0.00902, 0.01405, 0.00688, 0.00708, 0.00985, 0.00902, 0.00694, 0.01279, 0.0098, 0.00779, 0.01033, 0.02597, 0.00729, 0.01429, 0.01039, 0.02041, 0.00542, 0.01052, 0.01617, 0.01052, 0.01106, 0.00937, 0.00828, 0.00866, 0.00884, 0.00844, 0.00902, 0.01361, 0.00776, 0.00476, 0.02317, 0.01008, 0.0135, 0.00898, 0.00647, 0.00828, 0.00861, 0.00614, 0.00958, 0.01052, 0.00617, 0.01926, 0.01453, 0.02484, 0.00652, 0.00969, 0.01039, 0.03117, 0.00828, 0.0047, 0.00805, 0.00828, 0.00614, 0.02956, 0.00907, 0.01587, 0.00947, 0.00524, 0.00717, 0.0381, 0.00902, 0.00853, 0.00917, 0.0068, 0.00828, 0.01014, 0.00922, 0.00828, 0.01884, 0.00714, 0.03499, 0.00501, 0.03571, 0.00912, 0.02065, 0.01251, 0.00568, 0.00902, 0.00783, 0.01099, 0.00521, 0.00828, 0.00686, 0.02597, 0.00912, 0.01045, 0.00893, 0.00599, 0.00812, 0.00456, 0.00714, 0.00729, 0.01174, 0.0112, 0.00451, 0.00828, 0.00942, 0.0072, 0.00932, 0.00828, 0.00937, 0.00521, 0.02597, 0.00515, 0.00927, 0.00726, 0.00659, 0.00717, 0.00917, 0.01309, 0.00801, 0.01242, 0.00664, 0.01199, 0.00739, 0.00697, 0.02956, 0.00551, 0.00591, 0.0067, 0.00474, 0.03361, 0.007, 0.01242, 0.01071, 0.00749, 0.02857, 0.00591, 0.00844, 0.00583, 0.01233, 0.00828, 0.01309, 0.00711, 0.01233, 0.00628, 0.02637, 0.00902, 0.0061, 0.01884, 0.00686, 0.00952, 0.00974, 0.00425, 0.01544, 0.0051, 0.0197, 0.0044, 0.00678, 0.01587, 0.01135, 0.00527, 0.00985, 0.01003, 0.00585, 0.00664, 0.00893, 0.00657, 0.00801, 0.01158, 0.00571, 0.00523, 0.00801, 0.00776, 0.02721, 0.00853, 0.01045, 0.0061, 0.00446, 0.00523, 0.00694, 0.00478, 0.00828, 0.00733, 0.00752, 0.0051, 0.02116, 0.01617, 0.00828, 0.0112, 0.00776, 0.00577, 0.00675, 0.03117, 0.01382, 0.0084, 0.00536, 0.00902, 0.00875, 0.03499, 0.00675, 0.0079, 0.00506, 0.02484, 0.00717, 0.01158, 0.01106, 0.00463, 0.00521, 0.00762, 0.01329, 0.00947, 0.02017, 0.00577, 0.00551, 0.0061, 0.00733, 0.00649, 0.01174, 0.00527, 0.01289, 0.00659, 0.00849, 0.00902, 0.00642, 0.01199, 0.01065, 0.00779, 0.00879, 0.00801, 0.01008, 0.01085, 0.01182, 0.00853, 0.02484, 0.00786, 0.00875, 0.02857, 0.00776, 0.00755, 0.00836, 0.01339, 0.00769, 0.01135, 0.02956, 0.00703, 0.00672, 0.01371, 0.02597]) }

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c6c813afc455f3b68be01f9399c579d60cfaa71f

60

py

Python

test/integration/targets/script/files/no_shebang.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

37

2017-08-15T15:02:43.000Z

2021-07-23T03:44:31.000Z

test/integration/targets/script/files/no_shebang.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

12

2018-01-10T05:25:25.000Z

2021-11-28T06:55:48.000Z

test/integration/targets/script/files/no_shebang.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

49

2017-08-15T09:52:13.000Z

2022-03-21T17:11:54.000Z

import sys sys.stdout.write("Script with shebang omitted")

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1

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05d317d5688bdb5e5e7075bf0340da01de9972bb

359

py

Python

Mathematics/106bombyx/usage.py

667MARTIN/Epitech

81095d8e7d54e9abd95541ee3dfcc3bc85d5cf0e

[ "MIT" ]

40

2018-01-28T14:23:27.000Z

2022-03-05T15:57:47.000Z

Mathematics/106bombyx/usage.py

667MARTIN/Epitech

81095d8e7d54e9abd95541ee3dfcc3bc85d5cf0e

[ "MIT" ]

1

2021-10-05T09:03:51.000Z

Mathematics/106bombyx/usage.py

667MARTIN/Epitech

81095d8e7d54e9abd95541ee3dfcc3bc85d5cf0e

[ "MIT" ]

73

2019-01-07T18:47:00.000Z

2022-03-31T08:48:38.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- ## usage.py for usage in /home/rodrig_1/rendu/Maths/103architecte ## ## Made by gwendoline rodriguez ## Login <rodrig_1@epitech.net> ## ## Started on Sun Dec 7 16:31:56 2014 gwendoline rodriguez ## Last update Sun Feb 22 18:32:16 2015 gwendoline rodriguez ## print "Usage: ./106bombyx k(integer >= 1 and <= 4)."

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null

0

1

0

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4

05d5ea8100cc04852e143c63434b59ba93e68e2b

411

py

Python

I Coding Dojo/Solution/test_dojo.py

ComputerSocietyIFB/Dojo

2e09637e1325e8204be4e35e0937ec49b8256df2

[ "MIT" ]

null

I Coding Dojo/Solution/test_dojo.py

ComputerSocietyIFB/Dojo

2e09637e1325e8204be4e35e0937ec49b8256df2

[ "MIT" ]

null

I Coding Dojo/Solution/test_dojo.py

ComputerSocietyIFB/Dojo

2e09637e1325e8204be4e35e0937ec49b8256df2

[ "MIT" ]

null

from dojo import * def test_valid_input(): assert entrada_valida(1) == False assert entrada_valida('ABCDEF GHI') == True assert entrada_valida('') == False def test_get_indice(): assert get_indice('A') == 0 assert get_indice(' ') == 8 assert get_indice('J') == 3 def test_converte(): assert converte('PUZZLES') == '7889999_9999555337777' assert converte('OI PESSOAL') == '66644407337777_77776662555'

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05e6afe5bdcddb8f3fc880a72b40f31a91c9e30c

1,395

py

Python

SHIMON/api/api_calls.py

dosisod/SHIMON

bdcafc1d1036390e1872d0f17bbda511891e02dc

[ "MIT" ]

null

SHIMON/api/api_calls.py

dosisod/SHIMON

bdcafc1d1036390e1872d0f17bbda511891e02dc

[ "MIT" ]

1

2020-03-06T07:17:27.000Z

SHIMON/api/api_calls.py

dosisod/SHIMON

bdcafc1d1036390e1872d0f17bbda511891e02dc

[ "MIT" ]

null

from SHIMON.api.external import api_recent, api_friends, api_allfor from SHIMON.api.error import error, error_200, error_202, error_400 from SHIMON.api.unlock import ApiUnlock from SHIMON.api.send_msg import ApiSendMsg from SHIMON.api.delete_msg import ApiDeleteMsg from SHIMON.api.save import ApiSave from SHIMON.api.lock import ApiLock from SHIMON.api.change_pwd import ApiChangePwd from SHIMON.api.new_key import ApiNewKey from SHIMON.api.msg_policy import ApiMsgPolicy from SHIMON.api.expiration_timer import ApiExpirationTimer from SHIMON.api.theme import ApiTheme from SHIMON.api.devmode import ApiDevmode from SHIMON.api.nuke import ApiNuke from SHIMON.api.fresh_js import ApiFreshJs from SHIMON.api.fresh_css import ApiFreshCss from SHIMON.api.status import ApiStatus from SHIMON.api.ping import ApiPing from SHIMON.api.friends import ApiFriends from SHIMON.api.recent import ApiRecent from SHIMON.api.allfor import ApiAllfor from SHIMON.api.add_friend import ApiAddFriend from SHIMON.api.api_base import ApiBase apicalls = [ ApiUnlock(), ApiSendMsg(), ApiDeleteMsg(), ApiSave(), ApiLock(), ApiChangePwd(), ApiNewKey(), ApiMsgPolicy(), ApiExpirationTimer(), ApiTheme(), ApiDevmode(), ApiNuke(), ApiFreshJs(), ApiFreshCss(), ApiStatus(), ApiPing(), ApiFriends(), ApiRecent(), ApiAllfor(), ApiAddFriend(), ]

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af01a24dd48b27e455d5a11e4e22049ff562ced0

181

py

Python

Nduja/user_info_retriever/__init__.py

herrBez/Nduja

51f93c6a8827ddf8605f88cf062d524b0ca5cebf

[ "BSD-3-Clause" ]

2

2019-07-12T00:52:39.000Z

2020-02-13T17:09:07.000Z

Nduja/user_info_retriever/__init__.py

herrBez/Nduja

51f93c6a8827ddf8605f88cf062d524b0ca5cebf

[ "BSD-3-Clause" ]

2

2018-05-04T09:28:37.000Z

2019-11-09T13:37:00.000Z

Nduja/user_info_retriever/__init__.py

herrBez/Nduja

51f93c6a8827ddf8605f88cf062d524b0ca5cebf

[ "BSD-3-Clause" ]

2

2018-12-04T11:33:31.000Z

2021-09-07T20:13:52.000Z

"""This package contains the definition and implementation of the user info retriever classes, i.e., classes that fetches from the relative APIs the information about an account"""

45.25

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4

af2bf4a10ec6c785f3cae15398f4c31df3f63bd9

176

py

Python

9term/fipt/P2PLending/reviews/forms.py

nik-sergeson/bsuir-informatics-labs

14805fb83b8e2324580b6253158565068595e804

[ "Apache-2.0" ]

null

9term/fipt/P2PLending/reviews/forms.py

nik-sergeson/bsuir-informatics-labs

14805fb83b8e2324580b6253158565068595e804

[ "Apache-2.0" ]

null

9term/fipt/P2PLending/reviews/forms.py

nik-sergeson/bsuir-informatics-labs

14805fb83b8e2324580b6253158565068595e804

[ "Apache-2.0" ]

null

from django.forms import ModelForm from P2PLending.reviews.models import Review class ReviewForm(ModelForm): class Meta: model = Review fields = ['text']

19.555556

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null

0

1

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1

0

4

af3449e99e3851cb666128207a6c1bd7f22efd0a

698

py

Python

PySARibbon/__init__.py

Bllose/SARibbon-pyqt5

98052f0a8862515eecbece2c681387c4655b0db5

[ "MIT" ]

3

2021-11-26T07:05:38.000Z

2022-03-20T15:16:04.000Z

PySARibbon/__init__.py

Bllose/SARibbon-pyqt5

98052f0a8862515eecbece2c681387c4655b0db5

[ "MIT" ]

null

PySARibbon/__init__.py

Bllose/SARibbon-pyqt5

98052f0a8862515eecbece2c681387c4655b0db5

[ "MIT" ]

2

2021-09-21T13:25:45.000Z

2022-03-03T08:14:01.000Z

# -*- coding: utf-8 -*- """ @Module __init__.py @Author ROOT """ from .SAFramelessHelper import SAFramelessHelper from .SARibbonBar import SARibbonBar from .SARibbonButtonGroupWidget import SARibbonButtonGroupWidget from .SARibbonCategory import SARibbonCategory from .SARibbonCategoryLayout import SARibbonCategoryLayout from .SARibbonContextCategory import SARibbonContextCategory from .SARibbonGallery import SARibbonGallery from .SARibbonMainWindow import SARibbonMainWindow from .SARibbonPannel import SARibbonPannel from .SARibbonPannelLayout import SARibbonPannelLayout from .SARibbonQuickAccessBar import SARibbonQuickAccessBar from .SAWindowButtonGroup import SAWindowButtonGroup

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0

null

0

1

0

1

0

1

0

4

af5d1d071152b9648c81c50190a03ebb8b5b5673

86

py

Python

client/hotbox.py

odontomachus/hotbox

d42c48d7f056f2b1f7bd707ad674e737a3c2fe08

[ "MIT" ]

null

client/hotbox.py

odontomachus/hotbox

d42c48d7f056f2b1f7bd707ad674e737a3c2fe08

[ "MIT" ]

null

client/hotbox.py

odontomachus/hotbox

d42c48d7f056f2b1f7bd707ad674e737a3c2fe08

[ "MIT" ]

null

if __name__ == "__main__": from gui import App app = App() app.mainloop()

17.2

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3.909091

0.727273

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86

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21.5

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0

1

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1

0

1

0

null

0

4

bb7c01cedef18581351bbed1c806cb8850d04b28

3,107

py

Python

tests/core/test_base_types.py

balancap/arrowbic

088bb3aff5649f189c935a55c6cdbcc61886f778

[ "Apache-2.0" ]

4

2022-02-08T18:10:35.000Z

2022-02-09T20:28:41.000Z

tests/core/test_base_types.py

balancap/arrowbic

088bb3aff5649f189c935a55c6cdbcc61886f778

[ "Apache-2.0" ]

20

2022-01-11T17:02:14.000Z

2022-02-05T16:53:14.000Z

tests/core/test_base_types.py

balancap/arrowbic

088bb3aff5649f189c935a55c6cdbcc61886f778

[ "Apache-2.0" ]

null

import numpy as np import pyarrow as pa import pytest import arrowbic.extensions from arrowbic.core.base_types import ( from_arrow_to_numpy_dtype, from_arrow_to_python_class, from_numpy_to_arrow_type, is_supported_base_type, ) def test__is_supported_base_type__proper_result() -> None: assert not is_supported_base_type(arrowbic.extensions.IntEnumType()) assert not is_supported_base_type(arrowbic.extensions.TensorType()) def test__from_numpy_to_arrow_type__np_dtype__proper_coverage() -> None: assert from_numpy_to_arrow_type(None) == pa.null() assert from_numpy_to_arrow_type(type(None)) == pa.null() assert from_numpy_to_arrow_type(np.bool_) == pa.bool_() assert from_numpy_to_arrow_type(np.int8) == pa.int8() assert from_numpy_to_arrow_type(np.float32) == pa.float32() assert from_numpy_to_arrow_type(np.dtype(str)) == pa.string() assert from_numpy_to_arrow_type(np.dtype(bytes)) == pa.binary(-1) assert from_numpy_to_arrow_type(np.dtype("datetime64[s]")) == pa.timestamp("s") assert from_numpy_to_arrow_type(np.dtype("timedelta64[ns]")) == pa.duration("ns") with pytest.raises(TypeError): from_numpy_to_arrow_type(np.dtype("O")) def test__from_numpy_to_arrow_type__python_class__proper_coverage() -> None: assert from_numpy_to_arrow_type(None) == pa.null() assert from_numpy_to_arrow_type(type(None)) == pa.null() assert from_numpy_to_arrow_type(bool) == pa.bool_() assert from_numpy_to_arrow_type(int) == pa.int64() assert from_numpy_to_arrow_type(float) == pa.float64() assert from_numpy_to_arrow_type(str) == pa.string() assert from_numpy_to_arrow_type(bytes) == pa.binary(-1) def test__from_arrow_to_numpy_dtype__proper_coverage() -> None: assert from_arrow_to_numpy_dtype(None) == type(None) # noqa: E721 assert from_arrow_to_numpy_dtype(type(None)) == type(None) # noqa: E721 assert from_arrow_to_numpy_dtype(pa.null()) == type(None) # noqa: E721 assert from_arrow_to_numpy_dtype(pa.bool_()) == np.bool_ assert from_arrow_to_numpy_dtype(pa.uint8()) == np.uint8 assert from_arrow_to_numpy_dtype(pa.float32()) == np.float32 assert from_arrow_to_numpy_dtype(pa.string()) == np.dtype(str) assert from_arrow_to_numpy_dtype(pa.binary(-1)) == np.dtype(bytes) assert from_arrow_to_numpy_dtype(pa.timestamp("us")) == np.dtype("datetime64[us]") assert from_arrow_to_numpy_dtype(pa.duration("ns")) == np.dtype("timedelta64[ns]") def test__from_arrow_to_python_class__proper_coverage() -> None: assert from_arrow_to_python_class(pa.null()) == type(None) # noqa: E721 assert from_arrow_to_python_class(pa.float32()) == float # noqa: E721 assert from_arrow_to_python_class(pa.int32()) == int # noqa: E721 assert from_arrow_to_python_class(pa.string()) == str # noqa: E721 assert from_arrow_to_python_class(pa.binary(-1)) == bytes # noqa: E721 assert from_arrow_to_python_class(pa.timestamp("us")) == np.dtype("datetime64[us]") assert from_arrow_to_python_class(pa.duration("ns")) == np.dtype("timedelta64[ns]")

41.426667

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0.745092

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0.152236

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0.489534

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0.022198

0.130029

3,107

74

88

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null

0

1

0

null

0

1

0

1

0

4

bb8cf1fca3e3837b63490012df70f15647da968b

313

py

Python

wallet/errors.py

iesteban/bitcoin_bazaar_backend

2aa7c61d8727dae3a9be4b19c4b2aa49ec7ecaa0

[ "MIT" ]

18

2017-03-08T06:30:55.000Z

2020-05-08T17:30:20.000Z

wallet/errors.py

iesteban/bitcoin_bazaar_backend

2aa7c61d8727dae3a9be4b19c4b2aa49ec7ecaa0

[ "MIT" ]

871

2017-03-06T21:03:59.000Z

2022-03-28T19:46:44.000Z

wallet/errors.py

iesteban/bitcoin_bazaar_backend

2aa7c61d8727dae3a9be4b19c4b2aa49ec7ecaa0

[ "MIT" ]

5

2017-07-07T12:10:47.000Z

2020-05-13T15:57:56.000Z

from django.db import IntegrityError class InsufficientBalance(IntegrityError): """Raised when a wallet has insufficient balance to run an operation. We're subclassing from :mod:`django.db.IntegrityError` so that it is automatically rolled-back during django's transaction lifecycle. """

31.3

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39

313

6.025641

0.846154

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0

0.182109

313

9

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null

0

1

0

1

0

1

0

4

bbc7c7cb6d7d568c5084f050284cf002a5bcd61a

101

py

Python

examples/run_pre_tuned_algorithm/deepar/run.py

arangatang/Crayon

ff1ca68fe676028a8209ad56c108b8d8179ba2d7

[ "MIT" ]

null

examples/run_pre_tuned_algorithm/deepar/run.py

arangatang/Crayon

ff1ca68fe676028a8209ad56c108b8d8179ba2d7

[ "MIT" ]

null

examples/run_pre_tuned_algorithm/deepar/run.py

arangatang/Crayon

ff1ca68fe676028a8209ad56c108b8d8179ba2d7

[ "MIT" ]

null

from crayon import benchmark benchmark("deepar.yml", "deepar", benchmark_id="deepar_100", runs=100)

25.25

70

0.772277

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101

5.428571

0.642857

0

0.065217

0.089109

101

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71

33.666667

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1

0

true

0

0.5

0

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1

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null

0

1

0

null

0

1

0

1

0

4

bbd4ef2bc2bba8befa6288cfc340a6a50999b2c7

216

py

Python

src/airbnb_priceforecaster/features/host_location.py

andersbogsnes/airbnb_priceforecaster

f397c16a08fe7eba9977611f4af5352d234a4624

[ "MIT" ]

null

src/airbnb_priceforecaster/features/host_location.py

andersbogsnes/airbnb_priceforecaster

f397c16a08fe7eba9977611f4af5352d234a4624

[ "MIT" ]

null

src/airbnb_priceforecaster/features/host_location.py

andersbogsnes/airbnb_priceforecaster

f397c16a08fe7eba9977611f4af5352d234a4624

[ "MIT" ]

null

""" host_location ============= Where the host is located. Hypothesis that the host being somewhere else affects the price Text of where the host is located. Could be used to extract features from dtype: string """

24

90

0.731481

33

216

4.757576

0.727273

0.133758

0.152866

0.178344

0.267516

0

0.166667

216

8

91

27

0.872222

0.958333

0

null

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null

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null

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null

1

null

true

0

null

0

null

0

1

0

1

0

1

0

null

0

1

0

4

bbe85ee8e4c269ac93def093a370a21c4e158a9d

1,870

py

Python

{{cookiecutter.project_slug}}/{{cookiecutter.main_app}}/tests/test_{{cookiecutter.main_model|lower}}_status.py

huogerac/cookiecutter-djangofloppyforms

0a2c1d7fe506a5df13aaefde0f716373dbb8194e

[ "BSD-3-Clause" ]

3

2021-03-29T19:11:30.000Z

2021-05-08T13:18:41.000Z

{{cookiecutter.project_slug}}/{{cookiecutter.main_app}}/tests/test_{{cookiecutter.main_model|lower}}_status.py

huogerac/cookiecutter-djangofloppyforms

0a2c1d7fe506a5df13aaefde0f716373dbb8194e

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.main_app}}/tests/test_{{cookiecutter.main_model|lower}}_status.py

huogerac/cookiecutter-djangofloppyforms

0a2c1d7fe506a5df13aaefde0f716373dbb8194e

[ "BSD-3-Clause" ]

2

2021-03-12T15:13:38.000Z

2021-07-01T19:38:11.000Z

from datetime import datetime import pytest from model_bakery import baker from {{cookiecutter.main_app}}.models import {{cookiecutter.main_model}} from {{cookiecutter.main_app}}.services import {{cookiecutter.main_model|lower}}_service def test_should_get_{{cookiecutter.main_model|lower}}_as_pending(db): my_{{cookiecutter.main_model|lower}} = baker.make({{cookiecutter.main_model}}, description='Create an ansible deploy script', due_to=datetime.now()) assert my_{{cookiecutter.main_model|lower}}.status == 'pending' def test_should_get_{{cookiecutter.main_model|lower}}_as_done(db): my_{{cookiecutter.main_model|lower}} = baker.make({{cookiecutter.main_model}}, description='Create an ansible deploy script', due_to=datetime.now()) {{cookiecutter.main_model|lower}}_updated = {{cookiecutter.main_model|lower}}_service.mark_as_done(my_{{cookiecutter.main_model|lower}}.id) assert {{cookiecutter.main_model|lower}}_updated.status == 'done' def test_should_raise_an_erro_for_invalid_{{cookiecutter.main_model|lower}}_id(db): invalid_{{cookiecutter.main_model|lower}} = 0 with pytest.raises(RuntimeError) as error: {{cookiecutter.main_model|lower}} = {{cookiecutter.main_model|lower}}_service.mark_as_done(invalid_{{cookiecutter.main_model|lower}}) assert str(error.value) == f"{{cookiecutter.main_model}} ID: {invalid_{{cookiecutter.main_model|lower}}} invalida" def test_should_mark_as_undone(db): my_{{cookiecutter.main_model|lower}} = baker.make( {{cookiecutter.main_model}}, description='Create an ansible deploy script', due_to=datetime.now(), done=True) {{cookiecutter.main_model|lower}}_updated = {{cookiecutter.main_model|lower}}_service.mark_as_done(my_{{cookiecutter.main_model|lower}}.id) assert {{cookiecutter.main_model|lower}}_updated.status == 'pending'

44.52381

152

0.759358

242

1,870

5.549587

0.219008

0.333582

0.406552

0.722264

0.577066

0.545048

0.479523

0

0.000595

0.101604

1,870

41

153

45.609756

0.79881

0

0.16

0

0.104278

0.037433

0

0.16

0

null

0

0.2

null

0

null

1

0

null

0

1

0

4

bbea09728b7a60a98d5028f548dbb446b1645180

300

py

Python

test_tenacity/main_test.py

Etuloser/python-playground

2b40e88b3b5a2744284c8e1cae2b3917a75bc803

[ "MIT" ]

null

test_tenacity/main_test.py

Etuloser/python-playground

2b40e88b3b5a2744284c8e1cae2b3917a75bc803

[ "MIT" ]

null

test_tenacity/main_test.py

Etuloser/python-playground

2b40e88b3b5a2744284c8e1cae2b3917a75bc803

[ "MIT" ]

null

import unittest from test_tenacity.main import do_something_unreliable class TestMain(unittest.TestCase): def setUp(self) -> None: pass def tearDown(self) -> None: pass def test_do_something_unreliable(self): got = do_something_unreliable() print(got)

20

54

0.683333

36

300

5.472222

0.555556

0.167513

0.319797

0.152284

0

0.24

300

14

55

21.428571

0.864035

0

0.2

0

1

0.3

false

0.2

0

0.6

0.1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

bbf3b90b802450649d8c14713f55b3cadc744f74

74

py

Python

python/seldon_core/__init__.py

juldou/seldon-core

34021ee3ead41c729ff57efd1964ab3f0d37861e

[ "Apache-2.0" ]

3,049

2017-12-21T14:50:09.000Z

2022-03-30T18:14:15.000Z

python/seldon_core/__init__.py

juldou/seldon-core

34021ee3ead41c729ff57efd1964ab3f0d37861e

[ "Apache-2.0" ]

3,678

2017-12-22T16:21:30.000Z

2022-03-31T20:32:31.000Z

python/seldon_core/__init__.py

juldou/seldon-core

34021ee3ead41c729ff57efd1964ab3f0d37861e

[ "Apache-2.0" ]

714

2018-01-03T11:29:49.000Z

2022-03-31T03:49:59.000Z

from seldon_core.version import __version__ from .storage import Storage

18.5

43

0.851351

10

74

5.8

0.6

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0.121622

74

3

44

24.666667

0.892308

0

1

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true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

bbfb331635f6e73b54c8ca18e4bc802a411eedfc

241

py

Python

roobet-Listing1.py

AdamSierakowski/Math-Behind-Roobet-s-Crash-Game

bf804d5742d7bb960b42052aa248181f42b5c3f5

[ "MIT" ]

1

2021-03-10T11:01:51.000Z

roobet-Listing1.py

AdamSierakowski/Math-Behind-Roobet-s-Crash-Game

bf804d5742d7bb960b42052aa248181f42b5c3f5

[ "MIT" ]

null

roobet-Listing1.py

AdamSierakowski/Math-Behind-Roobet-s-Crash-Game

bf804d5742d7bb960b42052aa248181f42b5c3f5

[ "MIT" ]

1

2021-07-10T13:31:16.000Z

import hashlib def prev_hash(hash_code): return hashlib.sha256(hash_code.encode()).hexdigest() def main(): game_hash = 'cc4a75236ecbc038c37729aa5ced461e36155319e88fa375c\ 994933b6a42a0c4' print(prev_hash(game_hash)) main()

16.066667

67

0.763485

26

241

6.846154

0.576923

0.089888

0

0.215311

0.13278

241

14

68

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0

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0.125

0.5

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null

0

1

0

null

0

1

0

1

0

4

a51e15e888b1d3f211aca0c3ec2adcad23363f3a

129

py

Python

home/urls.py

mxpxgx/moiprez.com

8af2bc8ff676b67b5dd773b93721a5e457f89c16

[ "MIT" ]

null

home/urls.py

mxpxgx/moiprez.com

8af2bc8ff676b67b5dd773b93721a5e457f89c16

[ "MIT" ]

null

home/urls.py

mxpxgx/moiprez.com

8af2bc8ff676b67b5dd773b93721a5e457f89c16

[ "MIT" ]

null

# from django.conf.urls import url # from home.views import HomeView # urlpatterns = [ # url(r'^', HomeView.as_view())) # ]

18.428571

36

0.651163

17

129

4.882353

0.764706

0

0.186047

129

7

37

18.428571

0.790476

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0

null

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null

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null

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null

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null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

a52d73617e46dee078fe3895184327cf99dbb572

241

py

Python

aspen_ssh/parser/exceptions.py

thinkwelltwd/aspen_ssh

68cfab56187b63b6e22ab96fefe4c87171f7ccce

[ "Apache-2.0" ]

1

2021-09-09T13:02:36.000Z

aspen_ssh/parser/exceptions.py

thinkwelltwd/aspen_ssh

68cfab56187b63b6e22ab96fefe4c87171f7ccce

[ "Apache-2.0" ]

null

aspen_ssh/parser/exceptions.py

thinkwelltwd/aspen_ssh

68cfab56187b63b6e22ab96fefe4c87171f7ccce

[ "Apache-2.0" ]

null

class SSHCertificateParserError(Exception): pass class UnsupportedKeyTypeError(SSHCertificateParserError): """This key has a type which we do not know how to parse""" class InputTooShortError(SSHCertificateParserError): pass

21.909091

63

0.784232

24

241

7.875

0.791667

0

0.153527

241

10

64

24.1

0.926471

0.219917

0

0.4

0

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true

0.4

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0.6

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0

null

0

null

0

1

0

1

0

4

a52f3df0441eb430930399dd1762ebe5a6d9c5b0

3,399

py

Python

DailyProgrammer/DP20131128C.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

2

2020-12-23T18:59:22.000Z

2021-04-14T13:16:09.000Z

DailyProgrammer/DP20131128C.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

null

DailyProgrammer/DP20131128C.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

null

""" [11/28/13] Challenge #137 [Intermediate / Hard] Banquet Planning https://www.reddit.com/r/dailyprogrammer/comments/1rnrs2/112813_challenge_137_intermediate_hard_banquet/ # [](#IntermediateIcon) *(Intermediate)*: Banquet Planning You and your friends are planning a big banquet, but need to figure out the order in which food will be served. Some food, like a turkey, have to be served after appetizers, but before desserts. Other foods are more simple, like a pecan pie, which can be eaten any time after the main meal. Given a list of foods and the order-relationships they have, print the banquet schedule. If a given food item cannot be placed in this schedule, write an error message for it. # Formal Inputs & Outputs ## Input Description On standard console input, you will be given two space-delimited integers, N and M. N is the number of food items, while M is the number of food-relationships. Food-items are unique single-word lower-case names with optional underscores (the '_' character), while food-relationships are two food items that are space delimited. All food-items will be listed first on their own lines, then all food-relationships will be listed on their own lines afterwards. A food-relationship is where the first item must be served before the second item. Note that in the food-relationships list, some food-item names can use the [wildcard-character](http://en.wikipedia.org/wiki/Wildcard_character) '\*'. You must support this by expanding the rule to fulfill any combination of strings that fit the wildcard. For example, using the items from Sample Input 2, the rule "turkey\* \*_pie" expands to the following four rules: turkey almond_pie turkey_stuffing almond_pie turkey pecan_pie turkey_stuffing pecan_pie A helpful way to think about the wildcard expansion is to use the phrase "any item A must be before any item B". An example would be the food-relationship "\*pie coffee", which can be read as "any pie must be before coffee". Some orderings may be ambiguous: you might have two desserts before coffee, but the ordering of desserts may not be explicit. In such a case, group the items together. ## Output Description Print the correct order of food-items with a preceding index, starting from 1. If there are ambiguous ordering for items, list them together on the same line as a comma-delimited array of food-items. Any items that do not have a relationship must be printed with a warning or error message. # Sample Inputs & Outputs ## Sample Input 1 3 3 salad turkey dessert salad dessert turkey dessert salad turkey ## Sample Output 1 1. salad 2. turkey 3. dessert ## Sample Input 2 8 5 turkey pecan_pie salad crab_cakes almond_pie rice coffee turkey_stuffing turkey_stuffing turkey turkey* *_pie *pie coffee salad turkey* crab_cakes salad ## Sample Output 2 1. crab_cakes 2. salad 3. turkey_stuffing 4. turkey 5. almond_pie, pecan_pie 6. coffee Warning: Rice does not have any ordering. # Author's Note: This challenge has some subtle ordering logic that might be hard to understand at first. Work through sample data 2 by hand to better understand the ordering rules before writing code. Make sure to expand all widecard rules as well. """ def main(): pass if __name__ == "__main__": main()

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1

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null

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1

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4

a55226b3d70c510b89f069856ccaddedee71d98a

431

py

Python

src/question/migrations/0006_auto_20190215_0755.py

DevTeamSCH/vikoverflow-backend

bac0a5f8d0f18bea4d99e0d94ee322feb6a8039e

[ "MIT" ]

null

src/question/migrations/0006_auto_20190215_0755.py

DevTeamSCH/vikoverflow-backend

bac0a5f8d0f18bea4d99e0d94ee322feb6a8039e

[ "MIT" ]

24

2018-10-09T12:34:09.000Z

2022-02-10T11:01:32.000Z

src/question/migrations/0006_auto_20190215_0755.py

DevTeamSCH/vikoverflow-backend

bac0a5f8d0f18bea4d99e0d94ee322feb6a8039e

[ "MIT" ]

null

# Generated by Django 2.1.7 on 2019-02-15 07:55 from django.db import migrations class Migration(migrations.Migration): dependencies = [("question", "0005_merge_20190215_0616")] operations = [ migrations.RemoveField(model_name="answer", name="is_visible"), migrations.RemoveField(model_name="comment", name="is_visible"), migrations.RemoveField(model_name="question", name="is_visible"), ]

28.733333

73

0.707657

52

431

5.692308

0.615385

0.212838

0.263514

0.304054

0.290541

0

0.085635

0.160093

431

14

74

30.785714

0.732044

0.104408

0

1

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0.216146

0.0625

0

1

0

false

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0.125

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0.5

0

null

1

0

1

0

null

0

4

a568ad6dcef13fcee64e95ac026b1a8e9f2f3483

69

py

Python

redis/__init__.py

RuiCoreSci/auth

5a0708ebc86012902e7737d87bf691ab5fd1421c

[ "MIT" ]

null

redis/__init__.py

RuiCoreSci/auth

5a0708ebc86012902e7737d87bf691ab5fd1421c

[ "MIT" ]

null

redis/__init__.py

RuiCoreSci/auth

5a0708ebc86012902e7737d87bf691ab5fd1421c

[ "MIT" ]

null

from redis.client import Redis redis = Redis() __all__ = ['redis']

11.5

30

0.695652

9

69

4.888889

0.555556

0.454545

0

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null

0

1

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4

a56fc512de17dc2fa626e2693007022677fdbddf

70,906

py

Python

multipole-graph-neural-operator/utilities.py

vir-k01/graph-pde

f7bcf22d3f3c58b30769edfa57b86727154850d2

[ "MIT" ]

121

2020-03-13T08:33:29.000Z

2022-03-30T12:57:39.000Z

multipole-graph-neural-operator/utilities.py

vir-k01/graph-pde

f7bcf22d3f3c58b30769edfa57b86727154850d2

[ "MIT" ]

3

2020-04-26T11:52:52.000Z

2022-03-31T15:28:15.000Z

multipole-graph-neural-operator/utilities.py

vir-k01/graph-pde

f7bcf22d3f3c58b30769edfa57b86727154850d2

[ "MIT" ]

36

2020-03-13T08:33:39.000Z

2022-03-31T14:35:27.000Z

import torch import numpy as np import scipy.io import h5py import sklearn.metrics from torch_geometric.data import Data import torch.nn as nn from scipy.ndimage import gaussian_filter ################################################# # # Utilities # ################################################# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # reading data class MatReader(object): def __init__(self, file_path, to_torch=True, to_cuda=False, to_float=True): super(MatReader, self).__init__() self.to_torch = to_torch self.to_cuda = to_cuda self.to_float = to_float self.file_path = file_path self.data = None self.old_mat = None self._load_file() def _load_file(self): try: self.data = scipy.io.loadmat(self.file_path) self.old_mat = True except: self.data = h5py.File(self.file_path) self.old_mat = False def load_file(self, file_path): self.file_path = file_path self._load_file() def read_field(self, field): x = self.data[field] if not self.old_mat: x = x[()] x = np.transpose(x, axes=range(len(x.shape) - 1, -1, -1)) if self.to_float: x = x.astype(np.float32) if self.to_torch: x = torch.from_numpy(x) if self.to_cuda: x = x.cuda() return x def set_cuda(self, to_cuda): self.to_cuda = to_cuda def set_torch(self, to_torch): self.to_torch = to_torch def set_float(self, to_float): self.to_float = to_float # normalization, pointwise gaussian class UnitGaussianNormalizer(object): def __init__(self, x, eps=0.00001): super(UnitGaussianNormalizer, self).__init__() # x could be in shape of ntrain*n or ntrain*T*n or ntrain*n*T self.mean = torch.mean(x, 0) self.std = torch.std(x, 0) self.eps = eps def encode(self, x): x = (x - self.mean) / (self.std + self.eps) return x def decode(self, x, sample_idx=None): if sample_idx is None: std = self.std + self.eps # n mean = self.mean else: if len(self.mean.shape) == len(sample_idx[0].shape): std = self.std[sample_idx] + self.eps # batch*n mean = self.mean[sample_idx] if len(self.mean.shape) > len(sample_idx[0].shape): std = self.std[:,sample_idx]+ self.eps # T*batch*n mean = self.mean[:,sample_idx] # x is in shape of batch*n or T*batch*n x = (x * std) + mean return x def cuda(self): self.mean = self.mean.cuda() self.std = self.std.cuda() def cpu(self): self.mean = self.mean.cpu() self.std = self.std.cpu() # normalization, Gaussian class GaussianNormalizer(object): def __init__(self, x, eps=0.00001): super(GaussianNormalizer, self).__init__() self.mean = torch.mean(x) self.std = torch.std(x) self.eps = eps def encode(self, x): x = (x - self.mean) / (self.std + self.eps) return x def decode(self, x, sample_idx=None): x = (x * (self.std + self.eps)) + self.mean return x def cuda(self): self.mean = self.mean.cuda() self.std = self.std.cuda() def cpu(self): self.mean = self.mean.cpu() self.std = self.std.cpu() # normalization, scaling by range class RangeNormalizer(object): def __init__(self, x, low=0.0, high=1.0): super(RangeNormalizer, self).__init__() mymin = torch.min(x, 0)[0].view(-1) mymax = torch.max(x, 0)[0].view(-1) self.a = (high - low)/(mymax - mymin) self.b = -self.a*mymax + high def encode(self, x): s = x.size() x = x.view(s[0], -1) x = self.a*x + self.b x = x.view(s) return x def decode(self, x): s = x.size() x = x.view(s[0], -1) x = (x - self.b)/self.a x = x.view(s) return x #loss function with rel/abs Lp loss class LpLoss(object): def __init__(self, d=2, p=2, size_average=True, reduction=True): super(LpLoss, self).__init__() #Dimension and Lp-norm type are postive assert d > 0 and p > 0 self.d = d self.p = p self.reduction = reduction self.size_average = size_average def abs(self, x, y): num_examples = x.size()[0] #Assume uniform mesh h = 1.0 / (x.size()[1] - 1.0) all_norms = (h**(self.d/self.p))*torch.norm(x.view(num_examples,-1) - y.view(num_examples,-1), self.p, 1) if self.reduction: if self.size_average: return torch.mean(all_norms) else: return torch.sum(all_norms) return all_norms def rel(self, x, y): num_examples = x.size()[0] diff_norms = torch.norm(x.reshape(num_examples,-1) - y.reshape(num_examples,-1), self.p, 1) y_norms = torch.norm(y.reshape(num_examples,-1), self.p, 1) if self.reduction: if self.size_average: return torch.mean(diff_norms/y_norms) else: return torch.sum(diff_norms/y_norms) return diff_norms/y_norms def __call__(self, x, y): return self.rel(x, y) # A simple feedforward neural network class DenseNet(torch.nn.Module): def __init__(self, layers, nonlinearity, out_nonlinearity=None, normalize=False): super(DenseNet, self).__init__() self.n_layers = len(layers) - 1 assert self.n_layers >= 1 self.layers = nn.ModuleList() for j in range(self.n_layers): self.layers.append(nn.Linear(layers[j], layers[j+1])) if j != self.n_layers - 1: if normalize: self.layers.append(nn.BatchNorm1d(layers[j+1])) self.layers.append(nonlinearity()) if out_nonlinearity is not None: self.layers.append(out_nonlinearity()) def forward(self, x): for _, l in enumerate(self.layers): x = l(x) return x class DenseNet_sin(torch.nn.Module): def __init__(self, layers, nonlinearity, out_nonlinearity=None, normalize=False): super(DenseNet_sin, self).__init__() self.n_layers = len(layers) - 1 assert self.n_layers >= 1 self.layers = nn.ModuleList() for j in range(self.n_layers): self.layers.append(nn.Linear(layers[j], layers[j+1])) def forward(self, x): for j, l in enumerate(self.layers): x = l(x) if j != self.n_layers - 1: x = torch.sin(x) return x # generate graphs on square domain class SquareMeshGenerator(object): def __init__(self, real_space, mesh_size): super(SquareMeshGenerator, self).__init__() self.d = len(real_space) self.s = mesh_size[0] assert len(mesh_size) == self.d if self.d == 1: self.n = mesh_size[0] self.grid = np.linspace(real_space[0][0], real_space[0][1], self.n).reshape((self.n, 1)) else: self.n = 1 grids = [] for j in range(self.d): grids.append(np.linspace(real_space[j][0], real_space[j][1], mesh_size[j])) self.n *= mesh_size[j] self.grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T def ball_connectivity(self, r): pwd = sklearn.metrics.pairwise_distances(self.grid) self.edge_index = np.vstack(np.where(pwd <= r)) self.n_edges = self.edge_index.shape[1] return torch.tensor(self.edge_index, dtype=torch.long) def gaussian_connectivity(self, sigma): pwd = sklearn.metrics.pairwise_distances(self.grid) rbf = np.exp(-pwd**2/sigma**2) sample = np.random.binomial(1,rbf) self.edge_index = np.vstack(np.where(sample)) self.n_edges = self.edge_index.shape[1] return torch.tensor(self.edge_index, dtype=torch.long) def get_grid(self): return torch.tensor(self.grid, dtype=torch.float) def attributes(self, f=None, theta=None): if f is None: if theta is None: edge_attr = self.grid[self.edge_index.T].reshape((self.n_edges,-1)) else: edge_attr = np.zeros((self.n_edges, 2*self.d+2)) edge_attr[:,0:2*self.d] = self.grid[self.edge_index.T].reshape((self.n_edges,-1)) edge_attr[:, 2 * self.d] = theta[self.edge_index[0]] edge_attr[:, 2 * self.d +1] = theta[self.edge_index[1]] else: xy = self.grid[self.edge_index.T].reshape((self.n_edges,-1)) if theta is None: edge_attr = f(xy[:,0:self.d], xy[:,self.d:]) else: edge_attr = f(xy[:,0:self.d], xy[:,self.d:], theta[self.edge_index[0]], theta[self.edge_index[1]]) return torch.tensor(edge_attr, dtype=torch.float) def get_boundary(self): s = self.s n = self.n boundary1 = np.array(range(0, s)) boundary2 = np.array(range(n - s, n)) boundary3 = np.array(range(s, n, s)) boundary4 = np.array(range(2 * s - 1, n, s)) self.boundary = np.concatenate([boundary1, boundary2, boundary3, boundary4]) def boundary_connectivity2d(self, stride=1): boundary = self.boundary[::stride] boundary_size = len(boundary) vertice1 = np.array(range(self.n)) vertice1 = np.repeat(vertice1, boundary_size) vertice2 = np.tile(boundary, self.n) self.edge_index_boundary = np.stack([vertice2, vertice1], axis=0) self.n_edges_boundary = self.edge_index_boundary.shape[1] return torch.tensor(self.edge_index_boundary, dtype=torch.long) def attributes_boundary(self, f=None, theta=None): # if self.edge_index_boundary == None: # self.boundary_connectivity2d() if f is None: if theta is None: edge_attr_boundary = self.grid[self.edge_index_boundary.T].reshape((self.n_edges_boundary,-1)) else: edge_attr_boundary = np.zeros((self.n_edges_boundary, 2*self.d+2)) edge_attr_boundary[:,0:2*self.d] = self.grid[self.edge_index_boundary.T].reshape((self.n_edges_boundary,-1)) edge_attr_boundary[:, 2 * self.d] = theta[self.edge_index_boundary[0]] edge_attr_boundary[:, 2 * self.d +1] = theta[self.edge_index_boundary[1]] else: xy = self.grid[self.edge_index_boundary.T].reshape((self.n_edges_boundary,-1)) if theta is None: edge_attr_boundary = f(xy[:,0:self.d], xy[:,self.d:]) else: edge_attr_boundary = f(xy[:,0:self.d], xy[:,self.d:], theta[self.edge_index_boundary[0]], theta[self.edge_index_boundary[1]]) return torch.tensor(edge_attr_boundary, dtype=torch.float) # generate graphs with sampling class RandomMeshGenerator(object): def __init__(self, real_space, mesh_size, sample_size, attr_features=1): super(RandomMeshGenerator, self).__init__() self.d = len(real_space) self.m = sample_size self.attr_features = attr_features assert len(mesh_size) == self.d if self.d == 1: self.n = mesh_size[0] self.grid = np.linspace(real_space[0][0], real_space[0][1], self.n).reshape((self.n, 1)) else: self.n = 1 grids = [] for j in range(self.d): grids.append(np.linspace(real_space[j][0], real_space[j][1], mesh_size[j])) self.n *= mesh_size[j] self.grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T if self.m > self.n: self.m = self.n self.idx = np.array(range(self.n)) self.grid_sample = self.grid def sample(self): perm = torch.randperm(self.n) self.idx = perm[:self.m] self.grid_sample = self.grid[self.idx] return self.idx def get_grid(self): return torch.tensor(self.grid_sample, dtype=torch.float) def ball_connectivity(self, r, is_forward=False): pwd = sklearn.metrics.pairwise_distances(self.grid_sample) self.edge_index = np.vstack(np.where(pwd <= r)) self.n_edges = self.edge_index.shape[1] if is_forward: print(self.edge_index.shape) self.edge_index = self.edge_index[:, self.edge_index[0] >= self.edge_index[1]] print(self.edge_index.shape) self.n_edges = self.edge_index.shape[1] return torch.tensor(self.edge_index, dtype=torch.long) def torus1d_connectivity(self, r): grid = self.grid_sample pwd0 = sklearn.metrics.pairwise_distances(grid, grid) grid1 = grid grid1[:,0] = grid[:,0]+1 pwd1 = sklearn.metrics.pairwise_distances(grid, grid1) PWD = np.stack([pwd0,pwd1], axis=2) pwd = np.min(PWD, axis=2) self.edge_index = np.vstack(np.where(pwd <= r)) self.n_edges = self.edge_index.shape[1] return torch.tensor(self.edge_index, dtype=torch.long) def gaussian_connectivity(self, sigma): pwd = sklearn.metrics.pairwise_distances(self.grid_sample) rbf = np.exp(-pwd**2/sigma**2) sample = np.random.binomial(1,rbf) self.edge_index = np.vstack(np.where(sample)) self.n_edges = self.edge_index.shape[1] return torch.tensor(self.edge_index, dtype=torch.long) def attributes(self, f=None, theta=None): if f is None: if theta is None: edge_attr = self.grid[self.edge_index.T].reshape((self.n_edges, -1)) else: theta = theta[self.idx] edge_attr = np.zeros((self.n_edges, 2 * self.d + 2*self.attr_features)) edge_attr[:, 0:2 * self.d] = self.grid_sample[self.edge_index.T].reshape((self.n_edges, -1)) edge_attr[:, 2 * self.d : 2 * self.d + self.attr_features] = theta[self.edge_index[0]].view(-1, self.attr_features) edge_attr[:, 2 * self.d + self.attr_features: 2 * self.d + 2*self.attr_features] = theta[self.edge_index[1]].view(-1, self.attr_features) else: xy = self.grid_sample[self.edge_index.T].reshape((self.n_edges, -1)) if theta is None: edge_attr = f(xy[:, 0:self.d], xy[:, self.d:]) else: theta = theta[self.idx] edge_attr = f(xy[:, 0:self.d], xy[:, self.d:], theta[self.edge_index[0]], theta[self.edge_index[1]]) return torch.tensor(edge_attr, dtype=torch.float) # # generate two-level graph class RandomTwoMeshGenerator(object): def __init__(self, real_space, mesh_size, sample_size, induced_point): super(RandomTwoMeshGenerator, self).__init__() self.d = len(real_space) self.m = sample_size self.m_i = induced_point assert len(mesh_size) == self.d if self.d == 1: self.n = mesh_size[0] self.grid = np.linspace(real_space[0][0], real_space[0][1], self.n).reshape((self.n, 1)) else: self.n = 1 grids = [] for j in range(self.d): grids.append(np.linspace(real_space[j][0], real_space[j][1], mesh_size[j])) self.n *= mesh_size[j] self.grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T if self.m > self.n: self.m = self.n self.idx = np.array(range(self.n)) self.idx_i = self.idx self.idx_both = self.idx self.grid_sample = self.grid self.grid_sample_i = self.grid self.grid_sample_both = self.grid def sample(self): perm = torch.randperm(self.n) self.idx = perm[:self.m] self.idx_i = perm[self.m: self.m+self.m_i] self.idx_both = perm[: self.m+self.m_i] self.grid_sample = self.grid[self.idx] self.grid_sample_i = self.grid[self.idx_i] self.grid_sample_both = self.grid[self.idx_both] return self.idx, self.idx_i, self.idx_both def get_grid(self): return torch.tensor(self.grid_sample, dtype=torch.float), \ torch.tensor(self.grid_sample_i, dtype=torch.float), \ torch.tensor(self.grid_sample_both, dtype=torch.float) def ball_connectivity(self, r11, r12, r22): pwd = sklearn.metrics.pairwise_distances(self.grid_sample) pwd12 = sklearn.metrics.pairwise_distances(self.grid_sample, self.grid_sample_i) pwd22 = sklearn.metrics.pairwise_distances(self.grid_sample_i) self.edge_index = np.vstack(np.where(pwd <= r11)) self.edge_index_12 = np.vstack(np.where(pwd12 <= r12)) self.edge_index_12[1,:] = self.edge_index_12[1,:] + self.m self.edge_index_21 = self.edge_index_12[[1,0],:] self.edge_index_22 = np.vstack(np.where(pwd22 <= r22)) + self.m self.n_edges = self.edge_index.shape[1] self.n_edges_12 = self.edge_index_12.shape[1] self.n_edges_22 = self.edge_index_22.shape[1] return torch.tensor(self.edge_index, dtype=torch.long), \ torch.tensor(self.edge_index_12, dtype=torch.long), \ torch.tensor(self.edge_index_21, dtype=torch.long), \ torch.tensor(self.edge_index_22, dtype=torch.long) def attributes(self, theta=None): if theta is None: edge_attr = self.grid_sample_both[self.edge_index.T].reshape((self.n_edges, -1)) edge_attr_12 = self.grid_sample_both[self.edge_index_12.T].reshape((self.n_edges_12, -1)) edge_attr_21 = self.grid_sample_both[self.edge_index_21.T].reshape((self.n_edges_12, -1)) edge_attr_22 = self.grid_sample_both[self.edge_index_22.T].reshape((self.n_edges_22, -1)) else: theta = theta[self.idx_both] edge_attr = np.zeros((self.n_edges, 3 * self.d)) edge_attr[:, 0:2 * self.d] = self.grid_sample_both[self.edge_index.T].reshape((self.n_edges, -1)) edge_attr[:, 2 * self.d] = theta[self.edge_index[0]] edge_attr[:, 2 * self.d + 1] = theta[self.edge_index[1]] edge_attr_12 = np.zeros((self.n_edges_12, 3 * self.d)) edge_attr_12[:, 0:2 * self.d] = self.grid_sample_both[self.edge_index_12.T].reshape((self.n_edges_12, -1)) edge_attr_12[:, 2 * self.d] = theta[self.edge_index_12[0]] edge_attr_12[:, 2 * self.d + 1] = theta[self.edge_index_12[1]] edge_attr_21 = np.zeros((self.n_edges_12, 3 * self.d)) edge_attr_21[:, 0:2 * self.d] = self.grid_sample_both[self.edge_index_21.T].reshape((self.n_edges_12, -1)) edge_attr_21[:, 2 * self.d] = theta[self.edge_index_21[0]] edge_attr_21[:, 2 * self.d + 1] = theta[self.edge_index_21[1]] edge_attr_22 = np.zeros((self.n_edges_22, 3 * self.d)) edge_attr_22[:, 0:2 * self.d] = self.grid_sample_both[self.edge_index_22.T].reshape((self.n_edges_22, -1)) edge_attr_22[:, 2 * self.d] = theta[self.edge_index_22[0]] edge_attr_22[:, 2 * self.d + 1] = theta[self.edge_index_22[1]] return torch.tensor(edge_attr, dtype=torch.float), \ torch.tensor(edge_attr_12, dtype=torch.float), \ torch.tensor(edge_attr_21, dtype=torch.float), \ torch.tensor(edge_attr_22, dtype=torch.float) # generate multi-level graph class RandomMultiMeshGenerator(object): def __init__(self, real_space, mesh_size, level, sample_sizes): super(RandomMultiMeshGenerator, self).__init__() self.d = len(real_space) self.m = sample_sizes self.level = level assert len(sample_sizes) == level assert len(mesh_size) == self.d if self.d == 1: self.n = mesh_size[0] self.grid = np.linspace(real_space[0][0], real_space[0][1], self.n).reshape((self.n, 1)) else: self.n = 1 grids = [] for j in range(self.d): grids.append(np.linspace(real_space[j][0], real_space[j][1], mesh_size[j])) self.n *= mesh_size[j] self.grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T self.idx = [] self.idx_all = None self.grid_sample = [] self.grid_sample_all = None self.edge_index = [] self.edge_index_down = [] self.edge_index_up = [] self.edge_attr = [] self.edge_attr_down = [] self.edge_attr_up = [] self.n_edges_inner = [] self.n_edges_inter = [] def sample(self): self.idx = [] self.grid_sample = [] perm = torch.randperm(self.n) index = 0 for l in range(self.level): self.idx.append(perm[index: index+self.m[l]]) self.grid_sample.append(self.grid[self.idx[l]]) index = index+self.m[l] self.idx_all = perm[:index] self.grid_sample_all = self.grid[self.idx_all] return self.idx, self.idx_all def get_grid(self): grid_out = [] for grid in self.grid_sample: grid_out.append(torch.tensor(grid, dtype=torch.float)) return grid_out, torch.tensor(self.grid_sample_all, dtype=torch.float) def ball_connectivity(self, radius_inner, radius_inter): assert len(radius_inner) == self.level assert len(radius_inter) == self.level - 1 self.edge_index = [] self.edge_index_down = [] self.edge_index_up = [] self.n_edges_inner = [] self.n_edges_inter = [] edge_index_out = [] edge_index_down_out = [] edge_index_up_out = [] index = 0 for l in range(self.level): pwd = sklearn.metrics.pairwise_distances(self.grid_sample[l]) edge_index = np.vstack(np.where(pwd <= radius_inner[l])) + index self.edge_index.append(edge_index) edge_index_out.append(torch.tensor(edge_index, dtype=torch.long)) self.n_edges_inner.append(edge_index.shape[1]) index = index + self.grid_sample[l].shape[0] index = 0 for l in range(self.level-1): pwd = sklearn.metrics.pairwise_distances(self.grid_sample[l], self.grid_sample[l+1]) edge_index = np.vstack(np.where(pwd <= radius_inter[l])) + index edge_index[1, :] = edge_index[1, :] + self.grid_sample[l].shape[0] self.edge_index_down.append(edge_index) edge_index_down_out.append(torch.tensor(edge_index, dtype=torch.long)) self.edge_index_up.append(edge_index[[1,0],:]) edge_index_up_out.append(torch.tensor(edge_index[[1,0],:], dtype=torch.long)) self.n_edges_inter.append(edge_index.shape[1]) index = index + self.grid_sample[l].shape[0] edge_index_out = torch.cat(edge_index_out, dim=1) edge_index_down_out = torch.cat(edge_index_down_out, dim=1) edge_index_up_out = torch.cat(edge_index_up_out, dim=1) return edge_index_out, edge_index_down_out, edge_index_up_out def get_edge_index_range(self): # in order to use graph network's data structure, # the edge index shall be stored as tensor instead of list # we concatenate the edge index list and label the range of each level edge_index_range = torch.zeros((self.level,2), dtype=torch.long) edge_index_down_range = torch.zeros((self.level-1,2), dtype=torch.long) edge_index_up_range = torch.zeros((self.level-1,2), dtype=torch.long) n_edge_index = 0 for l in range(self.level): edge_index_range[l, 0] = n_edge_index n_edge_index = n_edge_index + self.edge_index[l].shape[1] edge_index_range[l, 1] = n_edge_index n_edge_index = 0 for l in range(self.level-1): edge_index_down_range[l, 0] = n_edge_index edge_index_up_range[l, 0] = n_edge_index n_edge_index = n_edge_index + self.edge_index_down[l].shape[1] edge_index_down_range[l, 1] = n_edge_index edge_index_up_range[l, 1] = n_edge_index return edge_index_range, edge_index_down_range, edge_index_up_range def attributes(self, theta=None): self.edge_attr = [] self.edge_attr_down = [] self.edge_attr_up = [] if theta is None: for l in range(self.level): edge_attr = self.grid_sample_all[self.edge_index[l].T].reshape((self.n_edges_inner[l], 2*self.d)) self.edge_attr.append(torch.tensor(edge_attr)) for l in range(self.level - 1): edge_attr_down = self.grid_sample_all[self.edge_index_down[l].T].reshape((self.n_edges_inter[l], 2*self.d)) edge_attr_up = self.grid_sample_all[self.edge_index_up[l].T].reshape((self.n_edges_inter[l], 2*self.d)) self.edge_attr_down.append(torch.tensor(edge_attr_down)) self.edge_attr_up.append(torch.tensor(edge_attr_up)) else: theta = theta[self.idx_all] for l in range(self.level): edge_attr = np.zeros((self.n_edges_inner[l], 2 * self.d + 2)) edge_attr[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index[l].T].reshape( (self.n_edges_inner[l], 2 * self.d)) edge_attr[:, 2 * self.d] = theta[self.edge_index[l][0]] edge_attr[:, 2 * self.d + 1] = theta[self.edge_index[l][1]] self.edge_attr.append(torch.tensor(edge_attr, dtype=torch.float)) for l in range(self.level - 1): edge_attr_down = np.zeros((self.n_edges_inter[l], 2 * self.d + 2)) edge_attr_up = np.zeros((self.n_edges_inter[l], 2 * self.d + 2)) edge_attr_down[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index_down[l].T].reshape( (self.n_edges_inter[l], 2 * self.d)) edge_attr_down[:, 2 * self.d] = theta[self.edge_index_down[l][0]] edge_attr_down[:, 2 * self.d + 1] = theta[self.edge_index_down[l][1]] self.edge_attr_down.append(torch.tensor(edge_attr_down, dtype=torch.float)) edge_attr_up[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index_up[l].T].reshape( (self.n_edges_inter[l], 2 * self.d)) edge_attr_up[:, 2 * self.d] = theta[self.edge_index_up[l][0]] edge_attr_up[:, 2 * self.d + 1] = theta[self.edge_index_up[l][1]] self.edge_attr_up.append(torch.tensor(edge_attr_up, dtype=torch.float)) edge_attr_out = torch.cat(self.edge_attr, dim=0) edge_attr_down_out = torch.cat(self.edge_attr_down, dim=0) edge_attr_up_out = torch.cat(self.edge_attr_up, dim=0) return edge_attr_out, edge_attr_down_out, edge_attr_up_out # generate graph, with split and assemble class RandomGridSplitter(object): def __init__(self, grid, resolution, d=2, m=200, l=1, radius=0.25): super(RandomGridSplitter, self).__init__() self.grid = grid self.resolution = resolution self.n = resolution**d self.d = d self.m = m self.l = l self.radius = radius assert self.n % self.m == 0 self.num = self.n // self.m # number of sub-grid def get_data(self, theta, edge_features=1): data = [] for i in range(self.l): perm = torch.randperm(self.n) perm = perm.reshape(self.num, self.m) for j in range(self.num): idx = perm[j,:].reshape(-1,) grid_sample = self.grid.reshape(self.n,-1)[idx] theta_sample = theta.reshape(self.n,-1)[idx] X = torch.cat([grid_sample,theta_sample],dim=1) pwd = sklearn.metrics.pairwise_distances(grid_sample) edge_index = np.vstack(np.where(pwd <= self.radius)) n_edges = edge_index.shape[1] edge_index = torch.tensor(edge_index, dtype=torch.long) if edge_features == 0: edge_attr = grid_sample[edge_index.T].reshape(n_edges, -1) else: edge_attr = np.zeros((n_edges, 2*self.d+2)) a = theta_sample[:,0] edge_attr[:, :2*self.d] = grid_sample[edge_index.T].reshape(n_edges, -1) edge_attr[:, 2*self.d] = a[edge_index[0]] edge_attr[:, 2*self.d+1] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) data.append(Data(x=X, edge_index=edge_index, edge_attr=edge_attr, split_idx=idx)) print('test', len(data), X.shape, edge_index.shape, edge_attr.shape) return data def assemble(self, pred, split_idx, batch_size2, sigma=1, cuda=False): assert len(pred) == len(split_idx) assert len(pred) == self.num * self.l // batch_size2 out = torch.zeros(self.n, ) if cuda: out = out.cuda() for i in range(len(pred)): pred_i = pred[i].reshape(batch_size2, self.m) split_idx_i = split_idx[i].reshape(batch_size2, self.m) for j in range(batch_size2): pred_ij = pred_i[j,:].reshape(-1,) idx = split_idx_i[j,:].reshape(-1,) out[idx] = out[idx] + pred_ij out = out / self.l # out = gaussian_filter(out, sigma=sigma, mode='constant', cval=0) # out = torch.tensor(out, dtype=torch.float) return out.reshape(-1,) # generate multi-level graph, with split and assemble class RandomMultiMeshSplitter(object): def __init__(self, real_space, mesh_size, level, sample_sizes): super(RandomMultiMeshSplitter, self).__init__() self.d = len(real_space) self.ms = sample_sizes self.m = sample_sizes[0] self.level = level assert len(sample_sizes) == level assert len(mesh_size) == self.d if self.d == 1: self.n = mesh_size[0] self.grid = np.linspace(real_space[0][0], real_space[0][1], self.n).reshape((self.n, 1)) else: self.n = 1 grids = [] for j in range(self.d): grids.append(np.linspace(real_space[j][0], real_space[j][1], mesh_size[j])) self.n *= mesh_size[j] self.grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T self.splits = self.n // self.m # number of sub-grid if self.splits * self.m < self.n: self.splits = self.splits + 1 print('n:',self.n,' m:',self.m, ' number of splits:', self.splits ) self.perm = None self.idx = [] self.idx_all = None self.grid_sample = [] self.grid_sample_all = None self.edge_index = [] self.edge_index_down = [] self.edge_index_up = [] self.edge_attr = [] self.edge_attr_down = [] self.edge_attr_up = [] self.n_edges_inner = [] self.n_edges_inter = [] def sample(self, new_sample=True, index0=0): self.idx = [] self.grid_sample = [] if (new_sample) or (self.perm is None): self.perm = torch.randperm(self.n) index = index0 for l in range(self.level): index = index % self.n index_end = (index+self.ms[l]) % self.n if index < index_end: idx = self.perm[index: index_end] else: idx = torch.cat((self.perm[index: ],self.perm[: index_end]), dim=0) self.idx.append(idx) self.grid_sample.append(self.grid[idx]) index = index_end if index0 < index_end: idx_all = self.perm[index0: index_end] else: idx_all = torch.cat((self.perm[index0:], self.perm[: index_end]), dim=0) self.idx_all = idx_all self.grid_sample_all = self.grid[self.idx_all] return self.idx, self.idx_all def get_grid(self): grid_out = [] for grid in self.grid_sample: grid_out.append(torch.tensor(grid, dtype=torch.float)) return grid_out, torch.tensor(self.grid_sample_all, dtype=torch.float) def ball_connectivity(self, radius_inner, radius_inter): assert len(radius_inner) == self.level assert len(radius_inter) == self.level - 1 self.edge_index = [] self.edge_index_down = [] self.edge_index_up = [] self.n_edges_inner = [] self.n_edges_inter = [] edge_index_out = [] edge_index_down_out = [] edge_index_up_out = [] index = 0 for l in range(self.level): pwd = sklearn.metrics.pairwise_distances(self.grid_sample[l]) edge_index = np.vstack(np.where(pwd <= radius_inner[l])) + index self.edge_index.append(edge_index) edge_index_out.append(torch.tensor(edge_index, dtype=torch.long)) self.n_edges_inner.append(edge_index.shape[1]) index = index + self.grid_sample[l].shape[0] index = 0 for l in range(self.level-1): pwd = sklearn.metrics.pairwise_distances(self.grid_sample[l], self.grid_sample[l+1]) edge_index = np.vstack(np.where(pwd <= radius_inter[l])) + index edge_index[1, :] = edge_index[1, :] + self.grid_sample[l].shape[0] self.edge_index_down.append(edge_index) edge_index_down_out.append(torch.tensor(edge_index, dtype=torch.long)) self.edge_index_up.append(edge_index[[1,0],:]) edge_index_up_out.append(torch.tensor(edge_index[[1,0],:], dtype=torch.long)) self.n_edges_inter.append(edge_index.shape[1]) index = index + self.grid_sample[l].shape[0] edge_index_out = torch.cat(edge_index_out, dim=1) edge_index_down_out = torch.cat(edge_index_down_out, dim=1) edge_index_up_out = torch.cat(edge_index_up_out, dim=1) return edge_index_out, edge_index_down_out, edge_index_up_out def get_edge_index_range(self): # in order to use graph network's data structure, # the edge index shall be stored as tensor instead of list # we concatenate the edge index list and label the range of each level edge_index_range = torch.zeros((self.level,2), dtype=torch.long) edge_index_down_range = torch.zeros((self.level-1,2), dtype=torch.long) edge_index_up_range = torch.zeros((self.level-1,2), dtype=torch.long) n_edge_index = 0 for l in range(self.level): edge_index_range[l, 0] = n_edge_index n_edge_index = n_edge_index + self.edge_index[l].shape[1] edge_index_range[l, 1] = n_edge_index n_edge_index = 0 for l in range(self.level-1): edge_index_down_range[l, 0] = n_edge_index edge_index_up_range[l, 0] = n_edge_index n_edge_index = n_edge_index + self.edge_index_down[l].shape[1] edge_index_down_range[l, 1] = n_edge_index edge_index_up_range[l, 1] = n_edge_index return edge_index_range, edge_index_down_range, edge_index_up_range def attributes(self, theta=None): self.edge_attr = [] self.edge_attr_down = [] self.edge_attr_up = [] if theta is None: for l in range(self.level): edge_attr = self.grid_sample_all[self.edge_index[l].T].reshape((self.n_edges_inner[l], 2*self.d)) self.edge_attr.append(torch.tensor(edge_attr)) for l in range(self.level - 1): edge_attr_down = self.grid_sample_all[self.edge_index_down[l].T].reshape((self.n_edges_inter[l], 2*self.d)) edge_attr_up = self.grid_sample_all[self.edge_index_up[l].T].reshape((self.n_edges_inter[l], 2*self.d)) self.edge_attr_down.append(torch.tensor(edge_attr_down)) self.edge_attr_up.append(torch.tensor(edge_attr_up)) else: theta = theta[self.idx_all] for l in range(self.level): edge_attr = np.zeros((self.n_edges_inner[l], 2 * self.d + 2)) edge_attr[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index[l].T].reshape( (self.n_edges_inner[l], 2 * self.d)) edge_attr[:, 2 * self.d] = theta[self.edge_index[l][0]] edge_attr[:, 2 * self.d + 1] = theta[self.edge_index[l][1]] self.edge_attr.append(torch.tensor(edge_attr, dtype=torch.float)) for l in range(self.level - 1): edge_attr_down = np.zeros((self.n_edges_inter[l], 2 * self.d + 2)) edge_attr_up = np.zeros((self.n_edges_inter[l], 2 * self.d + 2)) edge_attr_down[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index_down[l].T].reshape( (self.n_edges_inter[l], 2 * self.d)) edge_attr_down[:, 2 * self.d] = theta[self.edge_index_down[l][0]] edge_attr_down[:, 2 * self.d + 1] = theta[self.edge_index_down[l][1]] self.edge_attr_down.append(torch.tensor(edge_attr_down, dtype=torch.float)) edge_attr_up[:, 0:2 * self.d] = self.grid_sample_all[self.edge_index_up[l].T].reshape( (self.n_edges_inter[l], 2 * self.d)) edge_attr_up[:, 2 * self.d] = theta[self.edge_index_up[l][0]] edge_attr_up[:, 2 * self.d + 1] = theta[self.edge_index_up[l][1]] self.edge_attr_up.append(torch.tensor(edge_attr_up, dtype=torch.float)) edge_attr_out = torch.cat(self.edge_attr, dim=0) edge_attr_down_out = torch.cat(self.edge_attr_down, dim=0) edge_attr_up_out = torch.cat(self.edge_attr_up, dim=0) return edge_attr_out, edge_attr_down_out, edge_attr_up_out def splitter(self, radius_inner, radius_inter, theta_a, theta_all): # give a test mesh, generate a list of data data = [] index = 0 for i in range(self.splits): if i==0: idx, idx_all = self.sample(new_sample=True, index0=index) else: idx, idx_all = self.sample(new_sample=False, index0=index) index = (index + self.m) % self.n grid, grid_all = self.get_grid() edge_index, edge_index_down, edge_index_up = self.ball_connectivity(radius_inner, radius_inter) edge_index_range, edge_index_down_range, edge_index_up_range = self.get_edge_index_range() edge_attr, edge_attr_down, edge_attr_up = self.attributes(theta=theta_a) x = torch.cat([grid_all, theta_all[idx_all,:] ], dim=1) data.append(Data(x=x, edge_index_mid=edge_index, edge_index_down=edge_index_down, edge_index_up=edge_index_up, edge_index_range=edge_index_range, edge_index_down_range=edge_index_down_range, edge_index_up_range=edge_index_up_range, edge_attr_mid=edge_attr, edge_attr_down=edge_attr_down, edge_attr_up=edge_attr_up, sample_idx=idx[0])) return data def assembler(self, out_list, sample_idx_list, is_cuda=False): assert len(out_list) == self.splits if is_cuda: pred = torch.zeros(self.n, ).cuda() else: pred = torch.zeros(self.n, ) for i in range(self.splits): pred[sample_idx_list[i]] = out_list[i].reshape(-1) return pred # generate graph, with split and assemble with downsample class DownsampleGridSplitter(object): def __init__(self, grid, resolution, r, m=100, radius=0.15, edge_features=1): super(DownsampleGridSplitter, self).__init__() # instead of randomly sample sub-grids, here we downsample sub-grids self.grid = grid.reshape(resolution, resolution,2) # self.theta = theta.reshape(resolution, resolution,-1) # self.y = y.reshape(resolution, resolution,1) self.resolution = resolution if resolution%2==1: self.s = int(((resolution - 1)/r) + 1) else: self.s = int(resolution/r) self.r = r self.n = resolution**2 self.m = m self.radius = radius self.edge_features = edge_features self.index = torch.tensor(range(self.n), dtype=torch.long).reshape(self.resolution, self.resolution) def ball_connectivity(self, grid): pwd = sklearn.metrics.pairwise_distances(grid) edge_index = np.vstack(np.where(pwd <= self.radius)) n_edges = edge_index.shape[1] return torch.tensor(edge_index, dtype=torch.long), n_edges def get_data(self, theta): theta_d = theta.shape[1] theta = theta.reshape(self.resolution, self.resolution, theta_d) data = [] for x in range(self.r): for y in range(self.r): grid_sub = self.grid[x::self.r, y::self.r,:].reshape(-1,2) theta_sub = theta[x::self.r, y::self.r,:].reshape(-1,theta_d) perm = torch.randperm(self.n) m = self.m - grid_sub.shape[0] idx = perm[:m] grid_sample = self.grid.reshape(self.n,-1)[idx] theta_sample = theta.reshape(self.n,-1)[idx] grid_split = torch.cat([grid_sub, grid_sample],dim=0) theta_split = torch.cat([theta_sub, theta_sample],dim=0) X = torch.cat([grid_split,theta_split],dim=1) edge_index, n_edges = self.ball_connectivity(grid_split) edge_attr = np.zeros((n_edges, 4+self.edge_features*2)) a = theta_split[:, :self.edge_features] edge_attr[:, :4] = grid_split[edge_index.T].reshape(n_edges, -1) edge_attr[:, 4:4 + self.edge_features] = a[edge_index[0]] edge_attr[:, 4 + self.edge_features: 4 + self.edge_features * 2] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) split_idx = torch.tensor([x,y],dtype=torch.long).reshape(1,2) data.append(Data(x=X, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx)) print('test', len(data), X.shape, edge_index.shape, edge_attr.shape) return data def sample(self, theta, Y): theta_d = theta.shape[1] theta = theta.reshape(self.resolution, self.resolution, theta_d) Y = Y.reshape(self.resolution, self.resolution) x = torch.randint(0,self.r,(1,)) y = torch.randint(0,self.r,(1,)) grid_sub = self.grid[x::self.r, y::self.r, :].reshape(-1, 2) theta_sub = theta[x::self.r, y::self.r, :].reshape(-1, theta_d) Y_sub = Y[x::self.r, y::self.r].reshape(-1,) index_sub = self.index[x::self.r, y::self.r].reshape(-1,) n_sub = Y_sub.shape[0] if self.m >= n_sub: m = self.m - n_sub perm = torch.randperm(self.n) idx = perm[:m] grid_sample = self.grid.reshape(self.n, -1)[idx] theta_sample = theta.reshape(self.n, -1)[idx] Y_sample = Y.reshape(self.n, )[idx] grid_split = torch.cat([grid_sub, grid_sample], dim=0) theta_split = torch.cat([theta_sub, theta_sample], dim=0) Y_split = torch.cat([Y_sub, Y_sample], dim=0).reshape(-1,) index_split = torch.cat([index_sub, idx], dim=0).reshape(-1,) X = torch.cat([grid_split, theta_split], dim=1) else: grid_split = grid_sub theta_split = theta_sub Y_split = Y_sub.reshape(-1,) index_split = index_sub.reshape(-1,) X = torch.cat([grid_split, theta_split], dim=1) edge_index, n_edges = self.ball_connectivity(grid_split) edge_attr = np.zeros((n_edges, 4+self.edge_features*2)) a = theta_split[:, :self.edge_features] edge_attr[:, :4] = grid_split[edge_index.T].reshape(n_edges, -1) edge_attr[:, 4:4+self.edge_features] = a[edge_index[0]] edge_attr[:, 4+self.edge_features: 4+self.edge_features*2] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) split_idx = torch.tensor([x, y], dtype=torch.long).reshape(1, 2) data = Data(x=X, y=Y_split, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx, sample_idx=index_split) print('train', X.shape, Y_split.shape, edge_index.shape, edge_attr.shape, index_split.shape) return data def assemble(self, pred, split_idx, batch_size2, sigma=1): assert len(pred) == len(split_idx) assert len(pred) == self.r**2 // batch_size2 out = torch.zeros((self.resolution,self.resolution)) for i in range(len(pred)): pred_i = pred[i].reshape(batch_size2, self.m) split_idx_i = split_idx[i] for j in range(batch_size2): pred_ij = pred_i[j,:] x, y = split_idx_i[j] if self.resolution%2==1: if x==0: nx = self.s else: nx = self.s-1 if y==0: ny = self.s else: ny = self.s-1 else: nx = self.s ny = self.s # pred_ij = pred_i[idx : idx + nx * ny] out[x::self.r, y::self.r] = pred_ij[:nx * ny].reshape(nx,ny) out = gaussian_filter(out, sigma=sigma, mode='constant', cval=0) out = torch.tensor(out, dtype=torch.float) return out.reshape(-1,) # generate graph on Torus, with split and assemble class TorusGridSplitter(object): def __init__(self, grid, resolution, r, m=100, radius=0.15, T=None, edge_features=1, ): super(TorusGridSplitter, self).__init__() self.grid = grid.reshape(resolution, resolution,2) # self.theta = theta.reshape(resolution, resolution,-1) # self.y = y.reshape(resolution, resolution,1) self.resolution = resolution if resolution%2==1: self.s = int(((resolution - 1)/r) + 1) else: self.s = int(resolution/r) self.r = r self.n = resolution**2 self.m = m self.T = T self.radius = radius self.edge_features = edge_features self.index = torch.tensor(range(self.n), dtype=torch.long).reshape(self.resolution, self.resolution) def pairwise_difference(self,grid1, grid2): n = grid1.shape[0] x1 = grid1[:,0] y1 = grid1[:,1] x2 = grid2[:,0] y2 = grid2[:,1] X1 = np.tile(x1.reshape(n, 1), [1, n]) X2 = np.tile(x2.reshape(1, n), [n, 1]) X_diff = X1 - X2 Y1 = np.tile(y1.reshape(n, 1), [1, n]) Y2 = np.tile(y2.reshape(1, n), [n, 1]) Y_diff = Y1 - Y2 return X_diff, Y_diff def torus_connectivity(self, grid): pwd0 = sklearn.metrics.pairwise_distances(grid, grid) X_diff0, Y_diff0 = self.pairwise_difference(grid, grid) grid1 = grid grid1[:,0] = grid[:,0]+1 pwd1 = sklearn.metrics.pairwise_distances(grid, grid1) X_diff1, Y_diff1 = self.pairwise_difference(grid, grid1) grid2 = grid grid2[:, 1] = grid[:, 1] + 1 pwd2 = sklearn.metrics.pairwise_distances(grid, grid2) X_diff2, Y_diff2 = self.pairwise_difference(grid, grid2) grid3 = grid grid3[:, :] = grid[:, :] + 1 pwd3 = sklearn.metrics.pairwise_distances(grid, grid3) X_diff3, Y_diff3 = self.pairwise_difference(grid, grid3) grid4 = grid grid4[:, 0] = grid[:, 0] + 1 grid4[:, 1] = grid[:, 1] - 1 pwd4 = sklearn.metrics.pairwise_distances(grid, grid4) X_diff4, Y_diff4 = self.pairwise_difference(grid, grid4) PWD = np.stack([pwd0,pwd1,pwd2,pwd3,pwd4], axis=2) X_DIFF = np.stack([X_diff0,X_diff1,X_diff2,X_diff3,X_diff4], axis=2) Y_DIFF = np.stack([Y_diff0, Y_diff1, Y_diff2, Y_diff3, Y_diff4], axis=2) pwd = np.min(PWD, axis=2) pwd_index = np.argmin(PWD, axis=2) edge_index = np.vstack(np.where(pwd <= self.radius)) pwd_index = pwd_index[np.where(pwd <= self.radius)] PWD_index = (np.where(pwd <= self.radius)[0], np.where(pwd <= self.radius)[1], pwd_index) distance = PWD[PWD_index] X_difference = X_DIFF[PWD_index] Y_difference = Y_DIFF[PWD_index] n_edges = edge_index.shape[1] return torch.tensor(edge_index, dtype=torch.long), n_edges, distance, X_difference, Y_difference def get_data(self, theta, params=None): theta_d = theta.shape[1] theta = theta.reshape(self.resolution, self.resolution, theta_d) data = [] for x in range(self.r): for y in range(self.r): grid_sub = self.grid[x::self.r, y::self.r,:].reshape(-1,2) theta_sub = theta[x::self.r, y::self.r,:].reshape(-1,theta_d) perm = torch.randperm(self.n) m = self.m - grid_sub.shape[0] idx = perm[:m] grid_sample = self.grid.reshape(self.n,-1)[idx] theta_sample = theta.reshape(self.n,-1)[idx] grid_split = torch.cat([grid_sub, grid_sample],dim=0) theta_split = torch.cat([theta_sub, theta_sample],dim=0) X = torch.cat([grid_split,theta_split],dim=1) edge_index, n_edges, distance, X_difference, Y_difference = self.torus_connectivity(grid_split) edge_attr = np.zeros((n_edges, 3+self.edge_features*2)) a = theta_split[:, :self.edge_features] edge_attr[:, 0] = X_difference.reshape(n_edges, ) edge_attr[:, 1] = Y_difference.reshape(n_edges, ) edge_attr[:, 2] = distance.reshape(n_edges, ) edge_attr[:, 3:3 + self.edge_features] = a[edge_index[0]] edge_attr[:, 3 + self.edge_features: 4 + self.edge_features * 2] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) split_idx = torch.tensor([x,y],dtype=torch.long).reshape(1,2) if params==None: data.append(Data(x=X, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx)) else: data.append(Data(x=X, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx, params=params)) print('test', len(data), X.shape, edge_index.shape, edge_attr.shape) return data def sample(self, theta, Y): theta_d = theta.shape[1] theta = theta.reshape(self.resolution, self.resolution, theta_d) Y = Y.reshape(self.resolution, self.resolution) x = torch.randint(0,self.r,(1,)) y = torch.randint(0,self.r,(1,)) grid_sub = self.grid[x::self.r, y::self.r, :].reshape(-1, 2) theta_sub = theta[x::self.r, y::self.r, :].reshape(-1, theta_d) Y_sub = Y[x::self.r, y::self.r].reshape(-1,) index_sub = self.index[x::self.r, y::self.r].reshape(-1,) n_sub = Y_sub.shape[0] if self.m >= n_sub: m = self.m - n_sub perm = torch.randperm(self.n) idx = perm[:m] grid_sample = self.grid.reshape(self.n, -1)[idx] theta_sample = theta.reshape(self.n, -1)[idx] Y_sample = Y.reshape(self.n, )[idx] grid_split = torch.cat([grid_sub, grid_sample], dim=0) theta_split = torch.cat([theta_sub, theta_sample], dim=0) Y_split = torch.cat([Y_sub, Y_sample], dim=0).reshape(-1,) index_split = torch.cat([index_sub, idx], dim=0).reshape(-1,) X = torch.cat([grid_split, theta_split], dim=1) else: grid_split = grid_sub theta_split = theta_sub Y_split = Y_sub.reshape(-1,) index_split = index_sub.reshape(-1,) X = torch.cat([grid_split, theta_split], dim=1) edge_index, n_edges, distance, X_difference, Y_difference = self.torus_connectivity(grid_split) edge_attr = np.zeros((n_edges, 3+self.edge_features*2)) a = theta_split[:, :self.edge_features] edge_attr[:, 0] = X_difference.reshape(n_edges, ) edge_attr[:, 1] = Y_difference.reshape(n_edges, ) edge_attr[:, 2] = distance.reshape(n_edges, ) edge_attr[:, 3:3+self.edge_features] = a[edge_index[0]] edge_attr[:, 3+self.edge_features: 4+self.edge_features*2] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) split_idx = torch.tensor([x, y], dtype=torch.long).reshape(1, 2) data = Data(x=X, y=Y_split, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx, sample_idx=index_split) print('train', X.shape, Y_split.shape, edge_index.shape, edge_attr.shape, index_split.shape) return data def sampleT(self, theta, Y, params=None): theta_d = theta.shape[1] theta = theta.reshape(self.resolution, self.resolution, theta_d) Y = Y.reshape(self.T, self.resolution, self.resolution) x = torch.randint(0, self.r, (1,)) y = torch.randint(0, self.r, (1,)) grid_sub = self.grid[x::self.r, y::self.r, :].reshape(-1, 2) theta_sub = theta[x::self.r, y::self.r, :].reshape(-1, theta_d) Y_sub = Y[:,x::self.r, y::self.r].reshape(self.T,-1) index_sub = self.index[x::self.r, y::self.r].reshape(-1, ) n_sub = Y_sub.shape[1] if self.m >= n_sub: m = self.m - n_sub perm = torch.randperm(self.n) idx = perm[:m] grid_sample = self.grid.reshape(self.n, -1)[idx] theta_sample = theta.reshape(self.n, -1)[idx] Y_sample = Y.reshape(self.T, self.n)[:,idx] grid_split = torch.cat([grid_sub, grid_sample], dim=0) theta_split = torch.cat([theta_sub, theta_sample], dim=0) Y_split = torch.cat([Y_sub, Y_sample], dim=1).reshape(self.T,-1) index_split = torch.cat([index_sub, idx], dim=0).reshape(-1, ) X = torch.cat([grid_split, theta_split], dim=1) else: grid_split = grid_sub theta_split = theta_sub Y_split = Y_sub.reshape(self.T, -1) index_split = index_sub.reshape(-1, ) X = torch.cat([grid_split, theta_split], dim=1) edge_index, n_edges, distance, X_difference, Y_difference = self.torus_connectivity(grid_split) edge_attr = np.zeros((n_edges, 3 + self.edge_features * 2)) a = theta_split[:, :self.edge_features] edge_attr[:, 0] = X_difference.reshape(n_edges, ) edge_attr[:, 1] = Y_difference.reshape(n_edges, ) edge_attr[:, 2] = distance.reshape(n_edges, ) edge_attr[:, 3:3 + self.edge_features] = a[edge_index[0]] edge_attr[:, 3 + self.edge_features: 4 + self.edge_features * 2] = a[edge_index[1]] edge_attr = torch.tensor(edge_attr, dtype=torch.float) split_idx = torch.tensor([x, y], dtype=torch.long).reshape(1, 2) if params==None: data = Data(x=X, y=Y_split, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx, sample_idx=index_split) else: data = Data(x=X, y=Y_split, edge_index=edge_index, edge_attr=edge_attr, split_idx=split_idx, sample_idx=index_split, params=params) print('train', X.shape, Y_split.shape, edge_index.shape, edge_attr.shape, index_split.shape) return data def assemble(self, pred, split_idx, batch_size2, sigma=1): assert len(pred) == len(split_idx) assert len(pred) == self.r**2 // batch_size2 out = torch.zeros((self.resolution,self.resolution)) for i in range(len(pred)): pred_i = pred[i].reshape(batch_size2, self.m) split_idx_i = split_idx[i] for j in range(batch_size2): pred_ij = pred_i[j,:] x, y = split_idx_i[j] if self.resolution%2==1: if x==0: nx = self.s else: nx = self.s-1 if y==0: ny = self.s else: ny = self.s-1 else: nx = self.s ny = self.s # pred_ij = pred_i[idx : idx + nx * ny] out[x::self.r, y::self.r] = pred_ij[:nx * ny].reshape(nx,ny) out = gaussian_filter(out, sigma=sigma, mode='wrap') out = torch.tensor(out, dtype=torch.float) return out.reshape(-1,) def assembleT(self, pred, split_idx, batch_size2, sigma=1): # pred is a list (batches) of list (time seq) assert len(pred) == len(split_idx) assert len(pred[0]) == self.T assert len(pred) == self.r**2 // batch_size2 out = torch.zeros((self.T, self.resolution,self.resolution)) for t in range(self.T): for i in range(len(pred)): pred_i = pred[i][t].reshape(batch_size2, self.m) split_idx_i = split_idx[i] for j in range(batch_size2): pred_ij = pred_i[j,:] x, y = split_idx_i[j] if self.resolution%2==1: if x==0: nx = self.s else: nx = self.s-1 if y==0: ny = self.s else: ny = self.s-1 else: nx = self.s ny = self.s # pred_ij = pred_i[idx : idx + nx * ny] out[t, x::self.r, y::self.r] = pred_ij[:nx * ny].reshape(nx,ny) out = gaussian_filter(out, sigma=sigma, mode='wrap') out = torch.tensor(out, dtype=torch.float) return out.reshape(self.T,self.n) def downsample(data, grid_size, l): data = data.reshape(-1, grid_size, grid_size) data = data[:, ::l, ::l] data = data.reshape(-1, (grid_size // l) ** 2) return data def simple_grid(n_x, n_y): xs = np.linspace(0.0, 1.0, n_x) ys = np.linspace(0.0, 1.0, n_y) # xs = np.array(range(n_x)) # ys = np.array(range(n_y)) grid = np.vstack([xx.ravel() for xx in np.meshgrid(xs, ys)]).T edge_index = [] edge_attr = [] for y in range(n_y): for x in range(n_x): i = y * n_x + x if (x != n_x - 1): edge_index.append((i, i + 1)) edge_attr.append((1, 0, 0)) edge_index.append((i + 1, i)) edge_attr.append((-1, 0, 0)) if (y != n_y - 1): edge_index.append((i, i + n_x)) edge_attr.append((0, 1, 0)) edge_index.append((i + n_x, i)) edge_attr.append((0, -1, 0)) X = torch.tensor(grid, dtype=torch.float) # Exact = torch.tensor(Exact, dtype=torch.float).view(-1) edge_index = torch.tensor(edge_index, dtype=torch.long).transpose(0, 1) edge_attr = torch.tensor(edge_attr, dtype=torch.float) return X, edge_index, edge_attr def grid_edge(n_x, n_y, a=None): if a != None: a = a.reshape(n_x, n_y) xs = np.linspace(0.0, 1.0, n_x) ys = np.linspace(0.0, 1.0, n_y) # xs = np.array(range(n_x)) # ys = np.array(range(n_y)) grid = np.vstack([xx.ravel() for xx in np.meshgrid(xs, ys)]).T edge_index = [] edge_attr = [] for y in range(n_y): for x in range(n_x): i = y * n_x + x if (x != n_x - 1): d = 1 / n_x edge_index.append((i, i + 1)) edge_index.append((i + 1, i )) if a != None: a1 = a[x, y] a2 = a[x + 1, y] edge_attr.append((x / n_x, y / n_y, a1, a2)) edge_attr.append((y/n_y, x/n_x, a2, a1)) if (y != n_y - 1): d = 1 / n_y edge_index.append((i, i + n_x)) edge_index.append((i + n_x, i)) if a != None: a1 = a[x, y] a2 = a[x, y+1] edge_attr.append((x/n_x, y/n_y, a1, a2)) edge_attr.append((y/n_y, x/n_x, a2, a1)) X = torch.tensor(grid, dtype=torch.float) # Exact = torch.tensor(Exact, dtype=torch.float).view(-1) edge_index = torch.tensor(edge_index, dtype=torch.long).transpose(0, 1) edge_attr = torch.tensor(edge_attr, dtype=torch.float) return X, edge_index, edge_attr def grid_edge1d(n_x, a=None): if a != None: a = a.reshape(n_x) xs = np.linspace(0.0, 1.0, n_x) # xs = np.array(range(n_x)) # ys = np.array(range(n_y)) edge_index = [] edge_attr = [] for x in range(n_x): i = x i1 = (x+1)%n_x edge_index.append((i, i1)) edge_index.append((i1, i )) i2 = (x + 2) % n_x edge_index.append((i, i2)) edge_index.append((i2, i )) if a != None: a1 = a[x] a2 = a[x + 1] edge_attr.append((x / n_x, a1, a2)) edge_attr.append((x / n_x, a2, a1)) X = torch.tensor(xs, dtype=torch.float) # Exact = torch.tensor(Exact, dtype=torch.float).view(-1) edge_index = torch.tensor(edge_index, dtype=torch.long).transpose(0, 1) edge_attr = torch.tensor(edge_attr, dtype=torch.float) return X, edge_index, edge_attr def grid_edge_aug(n_x, n_y, a): a = a.reshape(n_x, n_y) xs = np.linspace(0.0, 1.0, n_x) ys = np.linspace(0.0, 1.0, n_y) # xs = np.array(range(n_x)) # ys = np.array(range(n_y)) grid = np.vstack([xx.ravel() for xx in np.meshgrid(xs, ys)]).T edge_index = [] edge_attr = [] for y in range(n_y): for x in range(n_x): i = y * n_x + x if (x != n_x - 1): d = 1 / n_x a1 = a[x, y] a2 = a[x + 1, y] edge_index.append((i, i + 1)) edge_attr.append((d, a1, a2, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) edge_index.append((i + 1, i)) edge_attr.append((d, a2, a1, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) if (y != n_y - 1): d = 1 / n_y a1 = a[x, y] a2 = a[x, y+1] edge_index.append((i, i + n_x)) edge_attr.append((d, a1, a2, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) edge_index.append((i + n_x, i)) edge_attr.append((d, a2, a1, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) X = torch.tensor(grid, dtype=torch.float) # Exact = torch.tensor(Exact, dtype=torch.float).view(-1) edge_index = torch.tensor(edge_index, dtype=torch.long).transpose(0, 1) edge_attr = torch.tensor(edge_attr, dtype=torch.float) return X, edge_index, edge_attr def grid_edge_aug_full(n_x, n_y, r, a): n = n_x * n_y xs = np.linspace(0.0, 1.0, n_x) ys = np.linspace(0.0, 1.0, n_y) grid = np.vstack([xx.ravel() for xx in np.meshgrid(xs, ys)]).T edge_index = [] edge_attr = [] for i1 in range(n): x1 = grid[i1] for i2 in range(n): x2 = grid[i2] d = np.linalg.norm(x1-x2) if(d<=r): a1 = a[i1] a2 = a[i2] edge_index.append((i1, i2)) edge_attr.append((d, a1, a2, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) edge_index.append((i2, i1)) edge_attr.append((d, a2, a1, 1 / np.sqrt(np.abs(a1 * a2)), np.exp(-(d) ** 2), np.exp(-(d / 0.1) ** 2), np.exp(-(d / 0.01) ** 2))) X = torch.tensor(grid, dtype=torch.float) # Exact = torch.tensor(Exact, dtype=torch.float).view(-1) edge_index = torch.tensor(edge_index, dtype=torch.long).transpose(0, 1) edge_attr = torch.tensor(edge_attr, dtype=torch.float) return X, edge_index, edge_attr def multi_grid(depth, n_x, n_y, grid, params): edge_index_global = [] edge_attr_global = [] X_global = [] num_nodes = 0 # build connected graph for l in range(depth): h_x_l = n_x // (2 ** l) h_y_l = n_y // (2 ** l) n_l = h_x_l * h_y_l a = downsample(params, n_x, (2 ** l)) if grid == 'grid': X, edge_index_inner, edge_attr_inner = grid(h_y_l, h_x_l) elif grid == 'grid_edge': X, edge_index_inner, edge_attr_inner = grid_edge(h_y_l, h_x_l, a) elif grid == 'grid_edge_aug': X, edge_index_inner, edge_attr_inner = grid_edge(h_y_l, h_x_l, a) # update index edge_index_inner = edge_index_inner + num_nodes edge_index_global.append(edge_index_inner) edge_attr_global.append(edge_attr_inner) # construct X # if (is_high): # X = torch.cat([torch.zeros(n_l, l * 2), X, torch.zeros(n_l, (depth - 1 - l) * 2)], dim=1) # else: # X_l = torch.tensor(l, dtype=torch.float).repeat(n_l, 1) # X = torch.cat([X, X_l], dim=1) X_global.append(X) # construct edges index1 = torch.tensor(range(n_l), dtype=torch.long) index1 = index1 + num_nodes num_nodes += n_l # #construct inter-graph edge if l != depth-1: index2 = np.array(range(n_l//4)).reshape(h_x_l//2, h_y_l//2) # torch.repeat is different from numpy index2 = index2.repeat(2, axis = 0).repeat(2, axis = 1) index2 = torch.tensor(index2).reshape(-1) index2 = index2 + num_nodes index2 = torch.tensor(index2, dtype=torch.long) edge_index_inter1 = torch.cat([index1,index2], dim=-1).reshape(2,-1) edge_index_inter2 = torch.cat([index2,index1], dim=-1).reshape(2,-1) edge_index_inter = torch.cat([edge_index_inter1, edge_index_inter2], dim=1) edge_attr_inter1 = torch.tensor((0, 0, 1), dtype=torch.float).repeat(n_l, 1) edge_attr_inter2 = torch.tensor((0, 0,-1), dtype=torch.float).repeat(n_l, 1) edge_attr_inter = torch.cat([edge_attr_inter1, edge_attr_inter2], dim=0) edge_index_global.append(edge_index_inter) edge_attr_global.append(edge_attr_inter) X = torch.cat(X_global, dim=0) edge_index = torch.cat(edge_index_global, dim=1) edge_attr = torch.cat(edge_attr_global, dim=0) mask_index = torch.tensor(range(n_x * n_y), dtype=torch.long) # print('create multi_grid with size:', X.shape, edge_index.shape, edge_attr.shape, mask_index.shape) return (X, edge_index, edge_attr, mask_index, num_nodes) def multi_pole_grid1d(theta, theta_d, s, N, is_periodic=False): grid_list = [] theta_list = [] edge_index_list = [] edge_index_list_cuda = [] level = int(np.log2(s) - 1) print(level) for l in range(1, level+1): r_l = 2 ** (l - 1) s_l = s // r_l n_l = s_l print('level',s_l,r_l,n_l) xs = np.linspace(0.0, 1.0, s_l) grid_l = xs grid_l = torch.tensor(grid_l, dtype=torch.float) print(grid_l.shape) grid_list.append(grid_l) theta_l = theta[:,:,:theta_d].reshape(N, s, theta_d) theta_l = theta_l[:, ::r_l, :] theta_l = theta_l.reshape(N, n_l, theta_d) theta_l = torch.tensor(theta_l, dtype=torch.float) print(theta_l.shape) theta_list.append(theta_l) # for the finest level, we construct the nearest neighbors (NN) if l==1: edge_index_nn = [] for x_i in range(s_l): for x in (-1,1): x_j = x_i + x if is_periodic: x_j = x_j % s_l # if (xj, yj) is a valid node if (x_j in range(s_l)): edge_index_nn.append([x_i,x_j]) edge_index_nn = torch.tensor(edge_index_nn, dtype=torch.long) edge_index_nn = edge_index_nn.transpose(0,1) edge_index_list.append(edge_index_nn) edge_index_list_cuda.append(edge_index_nn.cuda()) print('edge', edge_index_nn.shape) # we then compute the interactive neighbors -- their parents are NN but they are not NearestNeighbor edge_index_inter = [] for x_i in range(s_l): for x in range(-3,4): x_j = x_i + x # if (xj, yj) is a valid node if is_periodic: x_j = x_j % s_l if (x_j in range(s_l)): # if (xi, yi), (xj, yj) not NearestNeighbor if abs(x)>=2: # if their parents are NN if abs(x_i//2 - x_j//2)%(s_l//2) <=1: edge_index_inter.append([x_i,x_j]) edge_index_inter = torch.tensor(edge_index_inter, dtype=torch.long) edge_index_inter = edge_index_inter.transpose(0,1) edge_index_list.append(edge_index_inter) edge_index_list_cuda.append(edge_index_inter.cuda()) print('edge_inter', edge_index_inter.shape) print(len(grid_list),len(edge_index_list),len(theta_list)) return grid_list, theta_list, edge_index_list, edge_index_list_cuda def get_edge_attr(grid, theta, edge_index): n_edges = edge_index.shape[1] edge_attr = np.zeros((n_edges, 4)) edge_attr[:, 0:2] = grid[edge_index.transpose(0,1)].reshape((n_edges, -1)) edge_attr[:, 2] = theta[edge_index[0]] edge_attr[:, 3] = theta[edge_index[1]] return torch.tensor(edge_attr, dtype=torch.float)

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a573575eb47ee5f59b1acb1272487d1d173397d0

84

py

Python

test_hello.py

bkwin66/python_testx

45acb6fca740d46855322cf11f1a1197cbdcb82e

[ "Apache-2.0" ]

null

test_hello.py

bkwin66/python_testx

45acb6fca740d46855322cf11f1a1197cbdcb82e

[ "Apache-2.0" ]

null

test_hello.py

bkwin66/python_testx

45acb6fca740d46855322cf11f1a1197cbdcb82e

[ "Apache-2.0" ]

null

print "Hello World" print "Print something else" for i in range (10): print i

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a595db8e6a7ce9570cdc1f94fe4be454e40272a1

89

py

Python

posters/apps.py

postersession/postersession

1c246f820005673af15fd52da56a2011897e953c

[ "MIT" ]

null

posters/apps.py

postersession/postersession

1c246f820005673af15fd52da56a2011897e953c

[ "MIT" ]

null

posters/apps.py

postersession/postersession

1c246f820005673af15fd52da56a2011897e953c

[ "MIT" ]

null

from django.apps import AppConfig class PostersConfig(AppConfig): name = 'posters'

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a59f016df3e6e6d3d87d70a6a34e78535d79e64e

538

py

Python

2017/10_Oct/11/04-isnumeric.py

z727354123/pyCharmTest

9cbd770e19929cb4feb3be2f13b60dc0b1f68b56

[ "Apache-2.0" ]

null

2017/10_Oct/11/04-isnumeric.py

z727354123/pyCharmTest

9cbd770e19929cb4feb3be2f13b60dc0b1f68b56

[ "Apache-2.0" ]

null

2017/10_Oct/11/04-isnumeric.py

z727354123/pyCharmTest

9cbd770e19929cb4feb3be2f13b60dc0b1f68b56

[ "Apache-2.0" ]

null

myStr = '' print(myStr.isalnum()) # False 不支持空 myStr = 'abCC' print(myStr.isalpha()) # True 支持大写 myStr = 'abc*' print(myStr.isalpha()) # False 不支持符号 myStr = 'abc1' print(myStr.isalpha()) # False 不支持包含num print(myStr.isalnum()) # True 支持包含num myStr = '123' print(myStr.isnumeric()) # True 只支持全数字 myStr = '123.123' print(myStr.isnumeric()) # False myStr = '0.1' print(myStr.isnumeric()) # False 不支持 . print(myStr.isalnum()) # False 不支持 . myStr = 'abc123.1' print(myStr.isalnum()) # False 不支持 .

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a5a1a33d59bbc6bd6b2c679b73a2011d21d8a80e

209

py

Python

dbaas/tsuru/admin/__init__.py

jaeko44/python_dbaas

4fafa4ad70200fec1436c326c751761922ec9fa8

[ "BSD-3-Clause" ]

null

dbaas/tsuru/admin/__init__.py

jaeko44/python_dbaas

4fafa4ad70200fec1436c326c751761922ec9fa8

[ "BSD-3-Clause" ]

null

dbaas/tsuru/admin/__init__.py

jaeko44/python_dbaas

4fafa4ad70200fec1436c326c751761922ec9fa8

[ "BSD-3-Clause" ]

1

2017-07-02T08:46:17.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, unicode_literals from django.contrib import admin from .. import models from .bind import BindAdmin admin.site.register(models.Bind, BindAdmin)

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