1-10 #!/usr/bin/env python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene,","#!/usr/bin/env python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs):","import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs) self.friendly","from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene,","python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app,"],"string":"[\n \"Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs) self.friendly =\",\n \"game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs)\",\n \"class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs) self.friendly = False\",\n \"<gh_stars>1-10 #!/usr/bin/env python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene,\",\n \"#!/usr/bin/env python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs):\",\n \"import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs) self.friendly\",\n \"from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene,\",\n \"python from game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app,\"\n]","isConversational":false}}},{"rowIdx":2169,"cells":{"ngram":{"kind":"list like","value":["= [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ),","migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField(","= [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate', ), ]","1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class","import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'),","from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations","unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ]","'0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates',","] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate',","-*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals","by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import","import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [","('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate',","class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate',","utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import","from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [","Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db","# Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from","coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__","on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration):","-*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from","[ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField(","__future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates',","# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10","15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies =","operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate', ),","django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations =","Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations","2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies","dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate',","Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate',"],"string":"[\n \"= [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ),\",\n \"migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField(\",\n \"= [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate', ), ]\",\n \"1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class\",\n \"import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'),\",\n \"from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations\",\n \"unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ]\",\n \"'0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates',\",\n \"] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate',\",\n \"-*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals\",\n \"by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import\",\n \"import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [\",\n \"('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate',\",\n \"class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate',\",\n \"utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import\",\n \"from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [\",\n \"Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db\",\n \"# Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from\",\n \"coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from __future__\",\n \"on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration):\",\n \"-*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10 from\",\n \"[ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField(\",\n \"__future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates',\",\n \"# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-25 15:10\",\n \"15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies =\",\n \"operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ), migrations.RenameField( model_name='rate', old_name='pound_rates', new_name='gbp_rate', ),\",\n \"django.db import migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations =\",\n \"Django 1.11.1 on 2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations\",\n \"2017-06-25 15:10 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies\",\n \"dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate',\",\n \"Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate',\"\n]","isConversational":false}}},{"rowIdx":2170,"cells":{"ngram":{"kind":"list like","value":["using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An improvement layer","files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by:","'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI",":: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System :: Archiving', ],","their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\"","python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >=","API\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\"\"\"\\ quetzal-openapi-client ====================== This is an auto-generated","Quetzal API. An improvement layer on this client exists in the quetzal-client package.","'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic","# coding: utf-8 \"\"\" Quetzal API Quetzal: an API to manage data files","http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME,","metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from setuptools","- Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License',","and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\",","# python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3","0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages #","# noqa: H301 NAME = \"quetzal-openapi-client\" VERSION = \"0.5.0\" # To install the","OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python ::","OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from setuptools import","setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >= 1.15\",","setup, find_packages # noqa: H301 NAME = \"quetzal-openapi-client\" VERSION = \"0.5.0\" # To","quetzal-client package. Quetzal is an API to manage data files and their associated","Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic ::",":: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language ::",":: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved ::","quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification",":: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python",":: 2.7', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python ::","an API to manage data files and their associated metadata. 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An","the quetzal-client package. Quetzal is an API to manage data files and their","by: https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages # noqa: H301 NAME =","its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended","Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System ::","OpenAPI specification of the Quetzal API. An improvement layer on this client exists","\"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal API auto-generated client\", author='<NAME>', author_email=\"<EMAIL>\", url=\"https://github.com/quet.zal/quetzal-openapi-client\", project_urls={ \"Documentation\":","Quetzal is an API to manage data files and their associated metadata. See","and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta',","to manage data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact:","manage data files and their associated metadata. See more at [quetz.al](https://quetz.al) and its","Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent',",":: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language","run the following # # python setup.py install # # prerequisite: setuptools #","\"Code\": \"https://github.com/quetz-al/quetzal-openapi-client\", \"Issue tracker\": \"https://github.com/quetz-al/quetzal-openapi-client/issues\", }, license=\"BSD-3-Clause\", keywords=[\"OpenAPI\", \"OpenAPI-Generator\", \"Quetzal API\"], install_requires=REQUIRES, packages=find_packages(exclude=['test',","BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7',","layer on this client exists in the quetzal-client package. Quetzal is an API","\"\"\" Quetzal API Quetzal: an API to manage data files and their associated","[openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An improvement layer on",":: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet ::","find_packages # noqa: H301 NAME = \"quetzal-openapi-client\" VERSION = \"0.5.0\" # To install","classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License ::",":: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System ::","improvement layer on this client exists in the quetzal-client package. Quetzal is an","Quetzal: an API to manage data files and their associated metadata. OpenAPI spec","manage data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL>","API to manage data files and their associated metadata. OpenAPI spec version: 0.5.0","# To install the library, run the following # # python setup.py install","include_package_data=True, long_description=\"\"\"\\ quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an","install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\"\"\"\\ quetzal-openapi-client ====================== This is an auto-generated package","following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES","3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8',","'Programming Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic ::","'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Programming","To install the library, run the following # # python setup.py install #",":: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends',","from an OpenAPI specification of the Quetzal API. An improvement layer on this","REQUIRES = [\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION,","See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status","documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience ::","API to manage data files and their associated metadata. See more at [quetz.al](https://quetz.al)","\"0.5.0\" # To install the library, run the following # # python setup.py","files and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io).","Generated by: https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages # noqa: H301 NAME","= \"0.5.0\" # To install the library, run the following # # python","of the Quetzal API. An improvement layer on this client exists in the","https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages # noqa: H301 NAME = \"quetzal-openapi-client\"","= [\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal","Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved","API Quetzal: an API to manage data files and their associated metadata. OpenAPI",":: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database","'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming","package. Quetzal is an API to manage data files and their associated metadata.","This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the","[quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 -","utf-8 \"\"\" Quetzal API Quetzal: an API to manage data files and their","Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6',","1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal API auto-generated client\",","= \"quetzal-openapi-client\" VERSION = \"0.5.0\" # To install the library, run the following","# # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES =","# # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >= 1.15\", \"six >=","[\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal API","NAME = \"quetzal-openapi-client\" VERSION = \"0.5.0\" # To install the library, run the","auto-generated client\", author='<NAME>', author_email=\"<EMAIL>\", url=\"https://github.com/quet.zal/quetzal-openapi-client\", project_urls={ \"Documentation\": \"https://quetzal-openapi-client.readthedocs.io\", \"Code\": \"https://github.com/quetz-al/quetzal-openapi-client\", \"Issue tracker\": \"https://github.com/quetz-al/quetzal-openapi-client/issues\",","on this client exists in the quetzal-client package. Quetzal is an API to","'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming","Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python","from setuptools import setup, find_packages # noqa: H301 NAME = \"quetzal-openapi-client\" VERSION =","Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages # noqa:","is an API to manage data files and their associated metadata. See more",">= 1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal API auto-generated client\", author='<NAME>', author_email=\"<EMAIL>\",","<EMAIL> Generated by: https://openapi-generator.tech \"\"\" from setuptools import setup, find_packages # noqa: H301","data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated","data files and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs","setup( name=NAME, version=VERSION, description=\"Quetzal API auto-generated client\", author='<NAME>', author_email=\"<EMAIL>\", url=\"https://github.com/quet.zal/quetzal-openapi-client\", project_urls={ \"Documentation\": \"https://quetzal-openapi-client.readthedocs.io\",","1.10\", \"certifi\", \"python-dateutil\"] setup( name=NAME, version=VERSION, description=\"Quetzal API auto-generated client\", author='<NAME>', author_email=\"<EMAIL>\", url=\"https://github.com/quet.zal/quetzal-openapi-client\",","an API to manage data files and their associated metadata. See more at","metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development","associated metadata. 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See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown',","\"https://quetzal-openapi-client.readthedocs.io\", \"Code\": \"https://github.com/quetz-al/quetzal-openapi-client\", \"Issue tracker\": \"https://github.com/quetz-al/quetzal-openapi-client/issues\", }, license=\"BSD-3-Clause\", keywords=[\"OpenAPI\", \"OpenAPI-Generator\", \"Quetzal API\"], install_requires=REQUIRES,","'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System","# http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"] setup(","Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python","Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System :: Archiving',","[readthedocs documentation](https://quetzal-api.readthedocs.io). \"\"\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience","3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic ::","setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\"urllib3 >= 1.15\", \"six >= 1.10\", \"certifi\", \"python-dateutil\"]","\"https://github.com/quetz-al/quetzal-openapi-client\", \"Issue tracker\": \"https://github.com/quetz-al/quetzal-openapi-client/issues\", }, license=\"BSD-3-Clause\", keywords=[\"OpenAPI\", \"OpenAPI-Generator\", \"Quetzal API\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']),","API. 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OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\"\",\n \"python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >=\",\n \"API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated\",\n \"Quetzal API. An improvement layer on this client exists in the quetzal-client package.\",\n \"'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic\",\n \"# coding: utf-8 \\\"\\\"\\\" Quetzal API Quetzal: an API to manage data files\",\n \"http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME,\",\n \"metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools\",\n \"- Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License',\",\n \"and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\",\",\n \"# python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3\",\n \"0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages #\",\n \"# noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the\",\n \"OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python ::\",\n \"OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import\",\n \"setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\",\",\n \"setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To\",\n \"quetzal-client package. Quetzal is an API to manage data files and their associated\",\n \"Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic ::\",\n \":: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language ::\",\n \":: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved ::\",\n \"quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification\",\n \":: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python\",\n \":: 2.7', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python ::\",\n \"an API to manage data files and their associated metadata. 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An improvement layer on this client exists in the quetzal-client\",\n \"name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\":\",\n \"Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP',\",\n \"description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue\",\n \"client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", },\",\n \"\\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\\",\n \"long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License\",\n \"an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API.\",\n \"auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An\",\n \"the quetzal-client package. Quetzal is an API to manage data files and their\",\n \"by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages # noqa: H301 NAME =\",\n \"its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended\",\n \"Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System ::\",\n \"OpenAPI specification of the Quetzal API. An improvement layer on this client exists\",\n \"\\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\":\",\n \"Quetzal is an API to manage data files and their associated metadata. See\",\n \"and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta',\",\n \"to manage data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact:\",\n \"manage data files and their associated metadata. See more at [quetz.al](https://quetz.al) and its\",\n \"Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent',\",\n \":: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language\",\n \"run the following # # python setup.py install # # prerequisite: setuptools #\",\n \"\\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test',\",\n \"BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7',\",\n \"layer on this client exists in the quetzal-client package. Quetzal is an API\",\n \"\\\"\\\"\\\" Quetzal API Quetzal: an API to manage data files and their associated\",\n \"[openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An improvement layer on\",\n \":: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet ::\",\n \"find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install\",\n \"classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License ::\",\n \":: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System ::\",\n \"improvement layer on this client exists in the quetzal-client package. Quetzal is an\",\n \"Quetzal: an API to manage data files and their associated metadata. OpenAPI spec\",\n \"manage data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL>\",\n \"API to manage data files and their associated metadata. OpenAPI spec version: 0.5.0\",\n \"# To install the library, run the following # # python setup.py install\",\n \"include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an\",\n \"install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package\",\n \"following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES\",\n \"3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8',\",\n \"'Programming Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic ::\",\n \"'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Programming\",\n \"To install the library, run the following # # python setup.py install #\",\n \":: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends',\",\n \"from an OpenAPI specification of the Quetzal API. An improvement layer on this\",\n \"REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION,\",\n \"See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status\",\n \"documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience ::\",\n \"API to manage data files and their associated metadata. See more at [quetz.al](https://quetz.al)\",\n \"\\\"0.5.0\\\" # To install the library, run the following # # python setup.py\",\n \"files and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io).\",\n \"Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages # noqa: H301 NAME\",\n \"= \\\"0.5.0\\\" # To install the library, run the following # # python\",\n \"of the Quetzal API. An improvement layer on this client exists in the\",\n \"https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\"\",\n \"= [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal\",\n \"Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved\",\n \"API Quetzal: an API to manage data files and their associated metadata. OpenAPI\",\n \":: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database\",\n \"'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming\",\n \"package. Quetzal is an API to manage data files and their associated metadata.\",\n \"This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the\",\n \"[quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 -\",\n \"utf-8 \\\"\\\"\\\" Quetzal API Quetzal: an API to manage data files and their\",\n \"Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6',\",\n \"1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\",\",\n \"= \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the library, run the following\",\n \"# # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES =\",\n \"# # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >=\",\n \"[\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API\",\n \"NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the library, run the\",\n \"auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\",\",\n \"on this client exists in the quetzal-client package. Quetzal is an API to\",\n \"'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming\",\n \"Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python\",\n \"from setuptools import setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION =\",\n \"Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages # noqa:\",\n \"is an API to manage data files and their associated metadata. See more\",\n \">= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\",\",\n \"<EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from setuptools import setup, find_packages # noqa: H301\",\n \"data files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated\",\n \"data files and their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs\",\n \"setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\",\",\n \"1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\",\",\n \"an API to manage data files and their associated metadata. See more at\",\n \"metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development\",\n \"associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[\",\n \"setuptools import setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\"\",\n \"\\\"\\\"\\\" from setuptools import setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION\",\n \"Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database ::\",\n \"at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4\",\n \"\\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"],\",\n \"namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from\",\n \"Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language\",\n \"noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the library,\",\n \"\\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={\",\n \"2.7', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7',\",\n \"url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\",\",\n \"library, run the following # # python setup.py install # # prerequisite: setuptools\",\n \"Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating\",\n \"Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python\",\n \"keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This\",\n \"License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming\",\n \"4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD\",\n \"author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\",\",\n \"associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from\",\n \"install the library, run the following # # python setup.py install # #\",\n \"tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True,\",\n \"prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\",\",\n \"long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI\",\n \"import setup, find_packages # noqa: H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" #\",\n \"An improvement layer on this client exists in the quetzal-client package. Quetzal is\",\n \"Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet\",\n \"is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal\",\n \"the following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools\",\n \"client exists in the quetzal-client package. Quetzal is an API to manage data\",\n \":: Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Programming Language ::\",\n \":: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS\",\n \"exists in the quetzal-client package. Quetzal is an API to manage data files\",\n \"OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language ::\",\n \"3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Database :: Front-Ends', 'Topic\",\n \"to manage data files and their associated metadata. See more at [quetz.al](https://quetz.al) and\",\n \"Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System :: Archiving', ], )\",\n \":: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python ::\",\n \"Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Programming Language\",\n \"VERSION = \\\"0.5.0\\\" # To install the library, run the following # #\",\n \"author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\",\",\n \"license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ======================\",\n \"'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator)\",\n \"more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status ::\",\n \"coding: utf-8 \\\"\\\"\\\" Quetzal API Quetzal: an API to manage data files and\",\n \"'Topic :: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System\",\n \"\\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup( name=NAME, version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>',\",\n \"their associated metadata. See more at [quetz.al](https://quetz.al) and its [readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown',\",\n \"\\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES,\",\n \"'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System\",\n \"# http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"] setup(\",\n \"Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python\",\n \"Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic :: System :: Archiving',\",\n \"[readthedocs documentation](https://quetzal-api.readthedocs.io). \\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience\",\n \"3.8', 'Topic :: Database :: Front-Ends', 'Topic :: Internet :: WWW/HTTP', 'Topic ::\",\n \"setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\", \\\"certifi\\\", \\\"python-dateutil\\\"]\",\n \"\\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']),\",\n \"API. An improvement layer on this client exists in the quetzal-client package. Quetzal\",\n \"this client exists in the quetzal-client package. Quetzal is an API to manage\",\n \"and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech\",\n \"# prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six >= 1.10\\\",\",\n \"\\\"\\\"\\\", long_description_content_type='text/markdown', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers',\",\n \":: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python ::\",\n \"H301 NAME = \\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the library, run\",\n \"\\\"quetzal-openapi-client\\\" VERSION = \\\"0.5.0\\\" # To install the library, run the following #\",\n \"\\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an\",\n \"API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\":\",\n \"System :: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language ::\",\n \"in the quetzal-client package. Quetzal is an API to manage data files and\",\n \"'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language\",\n \"Quetzal API Quetzal: an API to manage data files and their associated metadata.\",\n \"version=VERSION, description=\\\"Quetzal API auto-generated client\\\", author='<NAME>', author_email=\\\"<EMAIL>\\\", url=\\\"https://github.com/quet.zal/quetzal-openapi-client\\\", project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\",\",\n \"}, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client\",\n \"\\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'],\",\n \"project_urls={ \\\"Documentation\\\": \\\"https://quetzal-openapi-client.readthedocs.io\\\", \\\"Code\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client\\\", \\\"Issue tracker\\\": \\\"https://github.com/quetz-al/quetzal-openapi-client/issues\\\", }, license=\\\"BSD-3-Clause\\\", keywords=[\\\"OpenAPI\\\", \\\"OpenAPI-Generator\\\", \\\"Quetzal\",\n \"\\\"OpenAPI-Generator\\\", \\\"Quetzal API\\\"], install_requires=REQUIRES, packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is\",\n \"packages=find_packages(exclude=['test', 'docs']), namespace_packages=['quetzal'], include_package_data=True, long_description=\\\"\\\"\\\"\\\\ quetzal-openapi-client ====================== This is an auto-generated package using\",\n \"package using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An improvement\",\n \"the library, run the following # # python setup.py install # # prerequisite:\",\n \"install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = [\\\"urllib3 >= 1.15\\\", \\\"six\"\n]","isConversational":false}}},{"rowIdx":2171,"cells":{"ngram":{"kind":"list like","value":["chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is","subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id':","self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if","media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None","thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id,","if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data =","query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth =","= int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url","[]).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id,","return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data,","split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = []","start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is","for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats(","{}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com',","stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query)","custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if","= [] formats = [] thumbnails = [] subtitles = {} rex =","strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas',","+ video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext","path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue # protected_path =","fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data,","m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in","{} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId')","if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {},","'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds':","re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src:","ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif","'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src')","s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title =","import unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str from","[] formats = [] thumbnails = [] subtitles = {} rex = re.compile(","thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')),","_extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None):","fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj","i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats =","tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token","xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not","video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id =","}) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$',","video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',')","'?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url","= track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc':","'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')),","'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails':","s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats(","not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration'))","= self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if custom_tokenizer_query:","re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com","'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true'","# protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, #","video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height':","= [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations:","+ '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue","for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id':","token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token(","m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []):","self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True)","'3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url':","= self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token:","ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query)","video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in","content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg')","formats locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url","'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv'","if not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else:","= xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails = []","r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file","path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src","data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id =","maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue","return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path =","return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self,","track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt',","f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for","'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj","if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, })","not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token","xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self,","continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these","others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext","ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil':","expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token","# 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token","for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or","supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url')","= xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if","# and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not","video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif","rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src =","formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m',","'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src","'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')),","rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats","[]).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) })","query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg","__future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str","}) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats(","parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data,","or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if","mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if","not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction","continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats':","+ '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path,","is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time +","secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not","= xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url","if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else:","fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com,","thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats","# continue # video_url = rtmp_src + video_url + '?' + token elif","subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or","files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip())","protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url,","ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files","formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com,","if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if","[] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible","if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id,","and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif","or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett':","# r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ #","or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext,","'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url':","raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path]","video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in","media_id, media_id)['media']['tv'] duration = None chapters = [] formats = [] for supported_type","token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def","'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters':","protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue #","== 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls","not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif","formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url'))","start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration': duration,","# 'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token')","_add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') +","mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1))","'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken']","..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration,","Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'):","{'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id,","path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http':","if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True)","urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en',","video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data = path_data.get('protected')","{} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id,","= rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure')","'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url,","= False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id","'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats)","'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image","= xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src:","'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'):","video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src,","'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')),","'?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml(","f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id,","video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return","else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in","= self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats","if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url,","f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url)","tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters:","track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id':","import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none,","video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id']","float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue","chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats)","locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url =","path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id,","urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext':","video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails,","in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang =","ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr':","or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f","query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth,","'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data =","= base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if video_url","self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data =","rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if","{})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url","int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else:","== 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats':","'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query,","{ 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or","'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, #","'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'),","{ 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com #","'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({","# auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id,","self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data,","video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats)","update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data):","self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth,","} mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')),","= self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data","= [] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') #","query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) #","# continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth","video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query =","token = xpath_text(auth, 'token') # if not token: # continue # video_url =","from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none,","not token: # continue # video_url = rtmp_src + video_url + '?' +","int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') ==","{}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not","image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in","or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src =","'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/',","video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path,","} for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return {","'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token') #","= xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live","'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length')","url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or","source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang')","video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url,","}) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext","path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if","ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if","'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments',","video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea='","('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext ==","None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return {","if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return","format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext,","'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')),","default='') urls = [] formats = [] thumbnails = [] subtitles = {}","compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj:","ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src')","= xpath_text(auth, 'token') # if not token: # continue # video_url = rtmp_src","r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path':","'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if not token:","( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, )","= { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if","for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not","track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc',","# aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails","import re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import","_extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s:","# rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = []","# protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage(","'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False))","chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data =","coding: utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE from","video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data:","formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for","= self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp':","media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in","# ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url,","})) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or","'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id)","chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({","secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] +","protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth,","AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext,","video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'):","# if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data,","these files # protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src:","ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return","start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters,","elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext ==","[] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file,","not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId']","xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails = [] subtitles","for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue","if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en'","== 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, {","or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token(","{ 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj:","} def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv']","stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe',","'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def","return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False):","chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats,","'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp':","# Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file in","if not media_src: continue video_url = media_src + video_url if video_url in urls:","TODO Correct extraction for these files # protected_path_data = path_data.get('protected') # if not","video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue","float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {}","in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title':","import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none,","return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles':","video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal)","== 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f)","video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'),","self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query)","for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time","is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if","token: # continue # video_url = rtmp_src + video_url + '?' + token","xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE):","class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def","rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe","f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit():","format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', })","f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext':","fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') #","'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png':","# 'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if not","'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width':","'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url,","'*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, }","track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url,","utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat","[] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl')","= { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if","lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': {","in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False","video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title)","track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({","'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({","continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth =","media_src: continue video_url = media_src + video_url if video_url in urls: continue urls.append(video_url)","re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others for","+ video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {}))","}.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')),","base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if video_url in","self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp,","duration = None chapters = [] formats = [] for supported_type in ('unprotected',","protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( #","'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height':","track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or","self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats =","in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if","= self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg =","media_id)['media']['tv'] duration = None chapters = [] formats = [] for supported_type in","= path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue # protected_path","isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if","if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id,","else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj =","formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data,","ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False)","# ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8':","'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, }","stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue","= self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url","m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter","video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt',","# split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1]","'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, })","formats = [] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?')","f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'):","'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')),","xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) ==","m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id,","xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self,","def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration","ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg:","# protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue","ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url,","if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'),","content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token =","== 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f","tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters","{} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and","elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False))","{}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') ==","xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url +","= [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type,","image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live","'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr']","else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec':","'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea='","+ token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src,","media_src + video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if","# coding: utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE","elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url,","= int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio':","'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'),","'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token:","determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE =","start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time","len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='')","float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time,","elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id)","rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') #","content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token =","path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path':","fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats:","'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number':","if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' +","media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if","'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = {","'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data,","xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live':","_extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration =","} if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'],","elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'],","not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path')","video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m':","= url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue #","'token') # if not token: # continue # video_url = rtmp_src + video_url","in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'):","urls = [] formats = [] thumbnails = [] subtitles = {} rex","data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(),","xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: #","= xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src =","error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token')","mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none',","+ token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url =","# if not token: # continue # video_url = rtmp_src + video_url +","video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data =","ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats","'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f in","# video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data","transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title","'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text,","self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls',","% media_id, media_id)['media']['tv'] duration = None chapters = [] formats = [] for","= self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'],","}) # token = xpath_text(auth, 'token') # if not token: # continue #","# TODO Correct extraction for these files # protected_path_data = path_data.get('protected') # if","def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data,","if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time':","error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url","'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url':","if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data)","[] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {})","is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return","or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url':","in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or","secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] =","m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for","subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files","= {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url,","video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com #","from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands,","= determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files #","if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] =","media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration =","}) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track","= video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id","not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id,","in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore':","continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or","_AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src,","'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url:","= self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4',","'akamai/aifp', default='') urls = [] formats = [] thumbnails = [] subtitles =","= {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files #","= float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start'))","int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json(","def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda","track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang","or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number':","video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url)","[]): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration","rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if","{ 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles,","video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src","m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}),","= rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp","video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url):","fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data:","video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for","m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe':","'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' %","ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime'))","m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data","'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data,","= split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats =","not media_src: continue video_url = media_src + video_url if video_url in urls: continue","ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext","title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')),","auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, #","extraction for these files # protected_path_data = path_data.get('protected') # if not protected_path_data or","duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time =","fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline',","rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id,","streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = []","streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection')","video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url):","video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com","..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none,","else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data,","if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src","if '/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable']","f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url,","'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data","None chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data","continue # video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'):","unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils","fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class","xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data,","ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not","xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else","# token = xpath_text(auth, 'token') # if not token: # continue # video_url","chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if","'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f =","from __future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat import","tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*'",") class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts'))","+ chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration': duration, }","import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr,","video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id #","else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth","# rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') #","'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration':","= [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url =","= float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time':","aifp, # }) # token = xpath_text(auth, 'token') # if not token: #","mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), })","for these files # protected_path_data = path_data.get('protected') # if not protected_path_data or not","self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and","self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id,","ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else:","'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in","'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id,","if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration =","int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE","video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'):","self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id","custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'],","= self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg,","or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure","stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url =","or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, })","'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats,","# if not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex(","f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) ==","if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str):","token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] =","= token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={},","or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt':","query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id","protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # })","int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return","'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({","formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url","fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time","base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url","'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url,","ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters =","'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id","== 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if","not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id","in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None","video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data","'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series':","url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO","if not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( #","xpath_text(auth, 'token') # if not token: # continue # video_url = rtmp_src +","elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if","= float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None:","'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live,","('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not","elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com,","int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({","'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, }","is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id,","title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src","False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or","m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration","video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path","'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False))","int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj =","token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={},","'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure","= streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if","video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token","if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc',","video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue","url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated',","or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image'))","compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query,","# nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', }))","'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live =","auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise","'m3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/'","re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a',","None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration,","from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError,","token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id,","in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext","continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({","ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path)","'/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] =","secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext,","subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format'))","determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data","'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com","ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext ==","int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] =","content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src,","token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return","'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source","ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] =","self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={","= stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url","'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'),","for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext =","xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data),","+ '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data =","continue video_url = media_src + video_url if video_url in urls: continue urls.append(video_url) format_id","in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if","path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src +","elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls',","'m4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for","content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if","'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title,","rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp =","files # protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: #","Correct extraction for these files # protected_path_data = path_data.get('protected') # if not protected_path_data","continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://',","'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4',","mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr']","strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data,","'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt',","'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut')","query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name'])","+ video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not","int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None):","= path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url =","image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data,","TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self,","in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext ==","# if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: #","video_url = media_src + video_url if video_url in urls: continue urls.append(video_url) format_id =","'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive')","stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or","thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for","= video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url':","== 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats(","int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)',","split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] #","format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1)","def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path')","== 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats(","is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description':","video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src']","not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang,","= content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml(","'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail':","= None chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'):","secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not","}) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), }","not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\.[a-z0-9]',","int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url =","'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date':","update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id':","video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), #","xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data,","not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not","aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails =","{ 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'):","float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration","= media_src + video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate')","parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def","path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId':",".adobepass import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text,","= xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src)","video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url,","'?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext","'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token =","if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp',","continue # TODO Correct extraction for these files # protected_path_data = path_data.get('protected') #","files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if","2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls =","re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import (","ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\.akamaihd\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id,","formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = {","ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id,","nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif","self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query","= self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {}","source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url","format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width':","content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if","= rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif","formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds',","format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif","secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default',","video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or","= url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label')","and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url:","image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id,","if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct","= re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext':","= re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others","token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if","'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2:","# }) # token = xpath_text(auth, 'token') # if not token: # continue","self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data,","if not token: # continue # video_url = rtmp_src + video_url + '?'","'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats = []","continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext':","content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src =","'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url:","= path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url,","video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com,","'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration':"],"string":"[\n \"chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is\",\n \"subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id':\",\n \"self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if\",\n \"media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None\",\n \"thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id,\",\n \"if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data =\",\n \"query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth =\",\n \"= int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url\",\n \"[]).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id,\",\n \"return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data,\",\n \"split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = []\",\n \"start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is\",\n \"for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats(\",\n \"{}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com',\",\n \"stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query)\",\n \"custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if\",\n \"= [] formats = [] thumbnails = [] subtitles = {} rex =\",\n \"strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas',\",\n \"+ video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext\",\n \"path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue # protected_path =\",\n \"fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data,\",\n \"m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in\",\n \"{} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId')\",\n \"if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {},\",\n \"'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds':\",\n \"re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src:\",\n \"ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif\",\n \"'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src')\",\n \"s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title =\",\n \"import unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str from\",\n \"[] formats = [] thumbnails = [] subtitles = {} rex = re.compile(\",\n \"thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')),\",\n \"_extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None):\",\n \"fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj\",\n \"i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats =\",\n \"tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token\",\n \"xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not\",\n \"video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id =\",\n \"}) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$',\",\n \"video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',')\",\n \"'?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url\",\n \"= track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc':\",\n \"'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')),\",\n \"'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails':\",\n \"s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats(\",\n \"not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration'))\",\n \"= self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if custom_tokenizer_query:\",\n \"re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com\",\n \"'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true'\",\n \"# protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, #\",\n \"video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height':\",\n \"= [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations:\",\n \"+ '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue\",\n \"for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id':\",\n \"token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token(\",\n \"m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []):\",\n \"self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True)\",\n \"'3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url':\",\n \"= self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token:\",\n \"ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query)\",\n \"video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in\",\n \"content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg')\",\n \"formats locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url\",\n \"'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv'\",\n \"if not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else:\",\n \"= xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails = []\",\n \"r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file\",\n \"path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src\",\n \"data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id =\",\n \"maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue\",\n \"return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path =\",\n \"return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self,\",\n \"track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt',\",\n \"f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for\",\n \"'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj\",\n \"if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, })\",\n \"not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token\",\n \"xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self,\",\n \"continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these\",\n \"others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext\",\n \"ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil':\",\n \"expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token\",\n \"# 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token\",\n \"for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or\",\n \"supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url')\",\n \"= xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if\",\n \"# and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not\",\n \"video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif\",\n \"rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src =\",\n \"formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m',\",\n \"'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src\",\n \"'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')),\",\n \"rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats\",\n \"[]).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) })\",\n \"query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg\",\n \"__future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str\",\n \"}) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats(\",\n \"parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data,\",\n \"or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if\",\n \"mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if\",\n \"not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction\",\n \"continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats':\",\n \"+ '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path,\",\n \"is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time +\",\n \"secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not\",\n \"= xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url\",\n \"if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else:\",\n \"fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com,\",\n \"thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats\",\n \"# continue # video_url = rtmp_src + video_url + '?' + token elif\",\n \"subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or\",\n \"files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip())\",\n \"protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url,\",\n \"ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files\",\n \"formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com,\",\n \"if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if\",\n \"[] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible\",\n \"if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id,\",\n \"and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif\",\n \"or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett':\",\n \"# r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ #\",\n \"or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext,\",\n \"'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url':\",\n \"raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path]\",\n \"video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in\",\n \"media_id, media_id)['media']['tv'] duration = None chapters = [] formats = [] for supported_type\",\n \"token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def\",\n \"'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters':\",\n \"protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue #\",\n \"== 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls\",\n \"not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif\",\n \"formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url'))\",\n \"start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration': duration,\",\n \"# 'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token')\",\n \"_add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') +\",\n \"mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1))\",\n \"'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken']\",\n \"..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration,\",\n \"Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'):\",\n \"{'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id,\",\n \"path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http':\",\n \"if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True)\",\n \"urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en',\",\n \"video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data = path_data.get('protected')\",\n \"{} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id,\",\n \"= rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure')\",\n \"'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url,\",\n \"= False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id\",\n \"'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats)\",\n \"'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image\",\n \"= xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src:\",\n \"'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'):\",\n \"video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src,\",\n \"'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')),\",\n \"'?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml(\",\n \"f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id,\",\n \"video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return\",\n \"else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in\",\n \"= self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats\",\n \"if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url,\",\n \"f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url)\",\n \"tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters:\",\n \"track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id':\",\n \"import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none,\",\n \"video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id']\",\n \"float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue\",\n \"chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats)\",\n \"locations: files/file, files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url =\",\n \"path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id,\",\n \"urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext':\",\n \"video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails,\",\n \"in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang =\",\n \"ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr':\",\n \"or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f\",\n \"query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth,\",\n \"'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data =\",\n \"= base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if video_url\",\n \"self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data =\",\n \"rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if\",\n \"{})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url\",\n \"int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else:\",\n \"== 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats':\",\n \"'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query,\",\n \"{ 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or\",\n \"'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, #\",\n \"'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'),\",\n \"{ 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com #\",\n \"'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({\",\n \"# auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id,\",\n \"self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data,\",\n \"video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats)\",\n \"update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data):\",\n \"self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth,\",\n \"} mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')),\",\n \"= self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data\",\n \"= [] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') #\",\n \"query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) #\",\n \"# continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth\",\n \"video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query =\",\n \"token = xpath_text(auth, 'token') # if not token: # continue # video_url =\",\n \"from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none,\",\n \"not token: # continue # video_url = rtmp_src + video_url + '?' +\",\n \"int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') ==\",\n \"{}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not\",\n \"image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in\",\n \"or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src =\",\n \"'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/',\",\n \"video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path,\",\n \"} for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return {\",\n \"'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token') #\",\n \"= xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live\",\n \"'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length')\",\n \"url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or\",\n \"source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang')\",\n \"video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url,\",\n \"}) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext\",\n \"path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if\",\n \"ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if\",\n \"'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments',\",\n \"video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea='\",\n \"('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext ==\",\n \"None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return {\",\n \"if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return\",\n \"format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext,\",\n \"'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')),\",\n \"default='') urls = [] formats = [] thumbnails = [] subtitles = {}\",\n \"compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj:\",\n \"ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src')\",\n \"= xpath_text(auth, 'token') # if not token: # continue # video_url = rtmp_src\",\n \"r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path':\",\n \"'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if not token:\",\n \"( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, )\",\n \"= { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if\",\n \"for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not\",\n \"track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc',\",\n \"# aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails\",\n \"import re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import\",\n \"_extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s:\",\n \"# rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = []\",\n \"# protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage(\",\n \"'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False))\",\n \"chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data =\",\n \"coding: utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE from\",\n \"video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data:\",\n \"formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for\",\n \"= self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp':\",\n \"media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in\",\n \"# ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url,\",\n \"})) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or\",\n \"'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id)\",\n \"chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({\",\n \"secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] +\",\n \"protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token = xpath_text(auth,\",\n \"AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext,\",\n \"video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'):\",\n \"# if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data,\",\n \"these files # protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src:\",\n \"ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return\",\n \"start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters,\",\n \"elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext ==\",\n \"[] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file,\",\n \"not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId']\",\n \"xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails = [] subtitles\",\n \"for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue\",\n \"if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en'\",\n \"== 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, {\",\n \"or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token(\",\n \"{ 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj:\",\n \"} def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv']\",\n \"stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe',\",\n \"'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def\",\n \"return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False):\",\n \"chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats,\",\n \"'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp':\",\n \"# Possible formats locations: files/file, files/groupFiles/files # and maybe others for video_file in\",\n \"if not media_src: continue video_url = media_src + video_url if video_url in urls:\",\n \"TODO Correct extraction for these files # protected_path_data = path_data.get('protected') # if not\",\n \"video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue\",\n \"float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {}\",\n \"in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title':\",\n \"import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none,\",\n \"return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles':\",\n \"video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal)\",\n \"== 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f)\",\n \"video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'),\",\n \"self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query)\",\n \"for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time\",\n \"is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if\",\n \"token: # continue # video_url = rtmp_src + video_url + '?' + token\",\n \"xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE):\",\n \"class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def\",\n \"rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe\",\n \"f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit():\",\n \"format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', })\",\n \"f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext':\",\n \"fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') #\",\n \"'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png':\",\n \"# 'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if not\",\n \"'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width':\",\n \"'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url,\",\n \"'*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, }\",\n \"track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url,\",\n \"utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat\",\n \"[] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl')\",\n \"= { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if\",\n \"lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': {\",\n \"in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False\",\n \"video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title)\",\n \"track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({\",\n \"'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({\",\n \"continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth =\",\n \"media_src: continue video_url = media_src + video_url if video_url in urls: continue urls.append(video_url)\",\n \"re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others for\",\n \"+ video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {}))\",\n \"}.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')),\",\n \"base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if video_url in\",\n \"self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp,\",\n \"duration = None chapters = [] formats = [] for supported_type in ('unprotected',\",\n \"protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( #\",\n \"'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height':\",\n \"track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or\",\n \"self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats =\",\n \"in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if\",\n \"= self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg =\",\n \"media_id)['media']['tv'] duration = None chapters = [] formats = [] for supported_type in\",\n \"= path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue # protected_path\",\n \"isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if\",\n \"if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id,\",\n \"else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, } mobj =\",\n \"formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data,\",\n \"ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False)\",\n \"# ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8':\",\n \"'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, }\",\n \"stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue\",\n \"= self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url\",\n \"m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter\",\n \"video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt',\",\n \"# split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1]\",\n \"'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url, })\",\n \"formats = [] thumbnails = [] subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?')\",\n \"f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'):\",\n \"'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')),\",\n \"xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) ==\",\n \"m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id,\",\n \"xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self,\",\n \"def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration\",\n \"ap_data['site_name']) auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg:\",\n \"# protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: # continue\",\n \"ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url': video_url,\",\n \"if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'),\",\n \"content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token =\",\n \"== 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f\",\n \"tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters\",\n \"{} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and\",\n \"elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False))\",\n \"{}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') ==\",\n \"xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url +\",\n \"= [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type,\",\n \"image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live\",\n \"'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr']\",\n \"else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec':\",\n \"'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea='\",\n \"+ token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src,\",\n \"media_src + video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if\",\n \"# coding: utf-8 from __future__ import unicode_literals import re from .adobepass import AdobePassIE\",\n \"elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url,\",\n \"= int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio':\",\n \"'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'),\",\n \"'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token:\",\n \"determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE =\",\n \"start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time\",\n \"len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='')\",\n \"float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time,\",\n \"elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id)\",\n \"rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') #\",\n \"content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token =\",\n \"path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path':\",\n \"fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats:\",\n \"'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number':\",\n \"if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' +\",\n \"media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src + video_url if\",\n \"'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = {\",\n \"'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data,\",\n \"xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live':\",\n \"_extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration =\",\n \"} if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'],\",\n \"elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'],\",\n \"not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path')\",\n \"video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m':\",\n \"= url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue #\",\n \"'token') # if not token: # continue # video_url = rtmp_src + video_url\",\n \"in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'):\",\n \"urls = [] formats = [] thumbnails = [] subtitles = {} rex\",\n \"data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(),\",\n \"xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: #\",\n \"= xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src =\",\n \"error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token')\",\n \"mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none',\",\n \"+ token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url =\",\n \"# if not token: # continue # video_url = rtmp_src + video_url +\",\n \"video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data =\",\n \"ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats\",\n \"'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for f in\",\n \"# video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data\",\n \"transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title\",\n \"'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text,\",\n \"self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls',\",\n \"% media_id, media_id)['media']['tv'] duration = None chapters = [] formats = [] for\",\n \"= self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'],\",\n \"}) # token = xpath_text(auth, 'token') # if not token: # continue #\",\n \"# TODO Correct extraction for these files # protected_path_data = path_data.get('protected') # if\",\n \"def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data,\",\n \"if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time':\",\n \"error_msg: raise ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url\",\n \"'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id': format_id, 'url':\",\n \"if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data)\",\n \"[] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {})\",\n \"is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return\",\n \"or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url':\",\n \"in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or\",\n \"secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] =\",\n \"m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url: for\",\n \"subtitles = {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files\",\n \"= {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url,\",\n \"video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com #\",\n \"from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands,\",\n \"= determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files #\",\n \"if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] =\",\n \"media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration =\",\n \"}) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track\",\n \"= video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id\",\n \"not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id,\",\n \"in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore':\",\n \"continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or\",\n \"_AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src,\",\n \"'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url:\",\n \"= self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4',\",\n \"'akamai/aifp', default='') urls = [] formats = [] thumbnails = [] subtitles =\",\n \"= {} rex = re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files #\",\n \"= float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start'))\",\n \"int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json(\",\n \"def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda\",\n \"track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang\",\n \"or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number':\",\n \"video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url)\",\n \"[]): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration\",\n \"rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if\",\n \"{ 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles,\",\n \"video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src\",\n \"m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}),\",\n \"= rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp\",\n \"video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url):\",\n \"fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data:\",\n \"video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for\",\n \"m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe':\",\n \"'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' %\",\n \"ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration = float_or_none(stream_data.get('totalRuntime'))\",\n \"m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data\",\n \"'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data,\",\n \"= split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats =\",\n \"not media_src: continue video_url = media_src + video_url if video_url in urls: continue\",\n \"ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext\",\n \"title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')),\",\n \"auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, #\",\n \"extraction for these files # protected_path_data = path_data.get('protected') # if not protected_path_data or\",\n \"duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time =\",\n \"fatal=fatal) if not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline',\",\n \"rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id,\",\n \"streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = []\",\n \"streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection')\",\n \"video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url):\",\n \"video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com\",\n \"..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none,\",\n \"else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data,\",\n \"if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src\",\n \"if '/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable']\",\n \"f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url,\",\n \"'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data\",\n \"None chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data\",\n \"continue # video_url = rtmp_src + video_url + '?' + token elif video_url.startswith('/secure/'):\",\n \"unicode_literals import re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils\",\n \"fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class\",\n \"xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data,\",\n \"ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not\",\n \"xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else\",\n \"# token = xpath_text(auth, 'token') # if not token: # continue # video_url\",\n \"chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if\",\n \"'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f =\",\n \"from __future__ import unicode_literals import re from .adobepass import AdobePassIE from ..compat import\",\n \"tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*'\",\n \") class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts'))\",\n \"+ chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration': duration, }\",\n \"import compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr,\",\n \"video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id #\",\n \"else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth\",\n \"# rtmp_src = xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') #\",\n \"'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration':\",\n \"= [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url =\",\n \"= float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None: continue chapters.append({ 'start_time':\",\n \"aifp, # }) # token = xpath_text(auth, 'token') # if not token: #\",\n \"mobj = rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), })\",\n \"for these files # protected_path_data = path_data.get('protected') # if not protected_path_data or not\",\n \"self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and\",\n \"self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id,\",\n \"ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else:\",\n \"'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in\",\n \"'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id,\",\n \"if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration =\",\n \"int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE\",\n \"video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'):\",\n \"self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {} video_id\",\n \"custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'],\",\n \"= self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise ExtractorError(error_msg,\",\n \"or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure\",\n \"stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url =\",\n \"or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration, })\",\n \"'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title, 'formats': formats,\",\n \"# if not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex(\",\n \"f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) ==\",\n \"if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str):\",\n \"token: query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] =\",\n \"= token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={},\",\n \"or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt':\",\n \"query = { 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id\",\n \"protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # })\",\n \"int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return\",\n \"'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({\",\n \"formats = [] for supported_type in ('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url\",\n \"fatal=False)) duration = float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time\",\n \"base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url\",\n \"'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url,\",\n \"ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters =\",\n \"'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id\",\n \"== 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if\",\n \"not video_data: return {} video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id\",\n \"in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None\",\n \"video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data\",\n \"'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series':\",\n \"url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO\",\n \"if not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( #\",\n \"xpath_text(auth, 'token') # if not token: # continue # video_url = rtmp_src +\",\n \"elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if\",\n \"= float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or chapter_duration is None:\",\n \"'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live,\",\n \"('unprotected', 'bulkaes'): stream_data = streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not\",\n \"elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com,\",\n \"int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({\",\n \"'hds', fatal=False)) else: f = { 'format_id': format_id, 'url': video_url, 'ext': ext, }\",\n \"is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None): streams_data = self._download_json( 'http://medium.ngtv.io/media/%s/tv' % media_id,\",\n \"title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src\",\n \"False formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or\",\n \"m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats( m3u8_url, media_id, 'mp4', m3u8_id='hls', fatal=False)) duration\",\n \"video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path\",\n \"'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False))\",\n \"int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] = int(format_id) else: mobj =\",\n \"token return video_url + '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={},\",\n \"'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure\",\n \"= streams_data.get(supported_type, {}) m3u8_url = stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if\",\n \"video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token\",\n \"if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc',\",\n \"video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue\",\n \"url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated',\",\n \"or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image'))\",\n \"compat_str from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query,\",\n \"# nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', }))\",\n \"'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live =\",\n \"auth = self._download_xml( tokenizer_src, content_id, query=query) error_msg = xpath_text(auth, 'error/msg') if error_msg: raise\",\n \"'m3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/'\",\n \"re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a',\",\n \"None or chapter_duration is None: continue chapters.append({ 'start_time': start_time, 'end_time': start_time + chapter_duration,\",\n \"from ..utils import ( fix_xml_ampersands, xpath_text, int_or_none, determine_ext, float_or_none, parse_duration, xpath_attr, update_url_query, ExtractorError,\",\n \"token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data = self._download_xml( data_src, video_id,\",\n \"in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext\",\n \"continue urls.append(video_url) format_id = video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({\",\n \"ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path)\",\n \"'/secure/' in video_url and '?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] =\",\n \"secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext,\",\n \"subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format'))\",\n \"determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct extraction for these files # protected_path_data\",\n \"'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com\",\n \"ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext ==\",\n \"int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif isinstance(format_id, compat_str): if format_id.isdigit(): f['tbr'] =\",\n \"content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml( tokenizer_src,\",\n \"token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] = token return\",\n \"'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source\",\n \"ExtractorError(error_msg, expected=True) token = xpath_text(auth, 'token') if not token: return video_url self._AKAMAI_SPE_TOKEN_CACHE[secure_path] =\",\n \"self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]', video_url, 'secure path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={\",\n \"= stream_data.get('secureUrl') or stream_data.get('url') if not m3u8_url: continue if stream_data.get('playlistProtection') == 'spe': m3u8_url\",\n \"'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'),\",\n \"for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext =\",\n \"xpath_text(video_data, 'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data),\",\n \"+ '?hdnea=' + token def _extract_cvp_info(self, data_src, video_id, path_data={}, ap_data={}, fatal=False): video_data =\",\n \"continue video_url = media_src + video_url if video_url in urls: continue urls.append(video_url) format_id\",\n \"in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url: continue ext = determine_ext(video_url) if\",\n \"path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url = media_src +\",\n \"elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4', m3u8_id=format_id or 'hls',\",\n \"'m4a', }) else: f['tbr'] = int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for\",\n \"content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data, 'akamai/src') # if\",\n \"'isLive') == 'true' return { 'id': video_id, 'title': self._live_title(title) if is_live else title,\",\n \"rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp =\",\n \"files # protected_path_data = path_data.get('protected') # if not protected_path_data or not rtmp_src: #\",\n \"Correct extraction for these files # protected_path_data = path_data.get('protected') # if not protected_path_data\",\n \"continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://',\",\n \"'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id, 'mp4',\",\n \"mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext': 'm4a', }) else: f['tbr']\",\n \"strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data,\",\n \"'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt',\",\n \"'ext': { 'scc': 'scc', 'webvtt': 'vtt', 'smptett': 'tt', }.get(source.get('format')) }) thumbnails.extend({ 'id': image.get('cut')\",\n \"query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name'])\",\n \"+ video_url + '?' + token elif video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not\",\n \"int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data, 'episodeNumber')), 'is_live': is_live, } def _extract_ngtv_info(self, media_id, tokenizer_query, ap_data=None):\",\n \"= path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not media_src: continue video_url =\",\n \"image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data,\",\n \"TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def _extract_timestamp(self, video_data): return int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self,\",\n \"in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext ==\",\n \"# if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: #\",\n \"video_url = media_src + video_url if video_url in urls: continue urls.append(video_url) format_id =\",\n \"'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive')\",\n \"stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is None or\",\n \"thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), } for\",\n \"= video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url':\",\n \"== 'png': thumbnails.append({ 'id': format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats(\",\n \"int_or_none(xpath_attr(video_data, 'dateCreated', 'uts')) def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)',\",\n \"split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] #\",\n \"format_id.isdigit(): f['tbr'] = int(format_id) else: mobj = re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1)\",\n \"def _add_akamai_spe_token(self, tokenizer_src, video_url, content_id, ap_data, custom_tokenizer_query=None): secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path')\",\n \"== 'spe': m3u8_url = self._add_akamai_spe_token( 'http://token.ngtv.io/token/token_spe', m3u8_url, media_id, ap_data or {}, tokenizer_query) formats.extend(self._extract_m3u8_formats(\",\n \"is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail': xpath_text(video_data, 'poster'), 'description':\",\n \"video_url.startswith('/secure/'): secure_path_data = path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src']\",\n \"not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang,\",\n \"= content_id if ap_data.get('auth_required'): query['accessToken'] = self._extract_mvpd_auth(ap_data['url'], content_id, ap_data['site_name'], ap_data['site_name']) auth = self._download_xml(\",\n \"'title': self._live_title(title) if is_live else title, 'formats': formats, 'subtitles': subtitles, 'thumbnails': thumbnails, 'thumbnail':\",\n \"= None chapters = [] formats = [] for supported_type in ('unprotected', 'bulkaes'):\",\n \"secure_path = self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not\",\n \"}) thumbnails.extend({ 'id': image.get('cut') or image.get('name'), 'url': image.text, 'width': int_or_none(image.get('width')), 'height': int_or_none(image.get('height')), }\",\n \"not protected_path_data or not rtmp_src: # continue # protected_path = self._search_regex( # r'/mp4:(.+)\\\\.[a-z0-9]',\",\n \"int(mobj.group(1)) formats.append(f) self._sort_formats(formats) for source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url =\",\n \"'poster'), 'description': strip_or_none(xpath_text(video_data, 'description')), 'duration': parse_duration(xpath_text(video_data, 'length') or xpath_text(video_data, 'trt')), 'timestamp': self._extract_timestamp(video_data), 'upload_date':\",\n \"update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = { 'format_id':\",\n \"video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), #\",\n \"xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or video_id # rtmp_src = xpath_text(video_data,\",\n \"not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src = base_path_data.get('media_src') if not\",\n \"aifp = xpath_text(video_data, 'akamai/aifp', default='') urls = [] formats = [] thumbnails =\",\n \"{ 'path': secure_path, } if custom_tokenizer_query: query.update(custom_tokenizer_query) else: query['videoId'] = content_id if ap_data.get('auth_required'):\",\n \"float_or_none(stream_data.get('totalRuntime')) if not chapters: for chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration\",\n \"= media_src + video_url if video_url in urls: continue urls.append(video_url) format_id = video_file.get('bitrate')\",\n \"parse_duration, xpath_attr, update_url_query, ExtractorError, strip_or_none, url_or_none, ) class TurnerBaseIE(AdobePassIE): _AKAMAI_SPE_TOKEN_CACHE = {} def\",\n \"path') # auth = self._download_webpage( # protected_path_data['tokenizer_src'], query={ # 'path': protected_path, # 'videoId':\",\n \".adobepass import AdobePassIE from ..compat import compat_str from ..utils import ( fix_xml_ampersands, xpath_text,\",\n \"= xpath_text(video_data, 'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src)\",\n \"video_file.get('bitrate') if ext in ('scc', 'srt', 'vtt'): subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url,\",\n \"'?hdnea=' in video_url: for f in m3u8_formats: f['_seekable'] = False formats.extend(m3u8_formats) elif ext\",\n \"'path': protected_path, # 'videoId': content_id, # 'aifp': aifp, # }) # token =\",\n \"if len(split_rtmp_src) == 2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp',\",\n \"continue # TODO Correct extraction for these files # protected_path_data = path_data.get('protected') #\",\n \"files/groupFiles/files # and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if\",\n \"2: # rtmp_src = split_rtmp_src[1] # aifp = xpath_text(video_data, 'akamai/aifp', default='') urls =\",\n \"re from .adobepass import AdobePassIE from ..compat import compat_str from ..utils import (\",\n \"ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif re.match(r'https?://[^/]+\\\\.akamaihd\\\\.net/[iz]/', video_url): formats.extend(self._extract_akamai_formats( video_url, video_id,\",\n \"formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds', fatal=False)) else: f = {\",\n \"ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif ext == 'm3u8': m3u8_formats = self._extract_m3u8_formats( video_url, video_id,\",\n \"nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com, s.cdn.turner.com # ssl.cdn.turner.com 'http': 'pmd.cdn.turner.com', })) elif\",\n \"self._search_regex(r'https?://[^/]+(.+/)', video_url, 'secure path') + '*' token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query\",\n \"= self._download_xml( data_src, video_id, transform_source=lambda s: fix_xml_ampersands(s).strip(), fatal=fatal) if not video_data: return {}\",\n \"source in video_data.findall('closedCaptions/source'): for track in source.findall('track'): track_url = url_or_none(track.get('url')) if not track_url\",\n \"format_id, 'url': video_url, 'ext': ext, } mobj = rex.search(video_url) if mobj: f.update({ 'width':\",\n \"content_id, # 'aifp': aifp, # }) # token = xpath_text(auth, 'token') # if\",\n \"= rex.search(video_url) if mobj: f.update({ 'width': int(mobj.group('width')), 'height': int(mobj.group('height')), 'tbr': int_or_none(mobj.group('bitrate')), }) elif\",\n \"formats.extend(m3u8_formats) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( update_url_query(video_url, {'hdcore': '3.7.0'}), video_id, f4m_id=format_id or 'hds',\",\n \"format_id, 'url': video_url, }) elif ext == 'smil': formats.extend(self._extract_smil_formats( video_url, video_id, fatal=False)) elif\",\n \"secure_path_data['media_src'] + video_url, content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default',\",\n \"video_id = video_data.attrib['id'] title = xpath_text(video_data, 'headline', fatal=True) content_id = xpath_text(video_data, 'contentId') or\",\n \"= url_or_none(track.get('url')) if not track_url or track_url.endswith('/big'): continue lang = track.get('lang') or track.get('label')\",\n \"and maybe others for video_file in video_data.findall('.//file'): video_url = url_or_none(video_file.text.strip()) if not video_url:\",\n \"image in video_data.findall('images/image')) is_live = xpath_text(video_data, 'isLive') == 'true' return { 'id': video_id,\",\n \"if not video_url: continue ext = determine_ext(video_url) if video_url.startswith('/mp4:protected/'): continue # TODO Correct\",\n \"= re.match(r'ios_(audio|[0-9]+)$', format_id) if mobj: if mobj.group(1) == 'audio': f.update({ 'vcodec': 'none', 'ext':\",\n \"= re.compile( r'(?P<width>[0-9]+)x(?P<height>[0-9]+)(?:_(?P<bitrate>[0-9]+))?') # Possible formats locations: files/file, files/groupFiles/files # and maybe others\",\n \"token = self._AKAMAI_SPE_TOKEN_CACHE.get(secure_path) if not token: query = { 'path': secure_path, } if\",\n \"'akamai/src') # if rtmp_src: # split_rtmp_src = rtmp_src.split(',') # if len(split_rtmp_src) == 2:\",\n \"# }) # token = xpath_text(auth, 'token') # if not token: # continue\",\n \"self._extract_timestamp(video_data), 'upload_date': xpath_attr(video_data, 'metas', 'version'), 'series': xpath_text(video_data, 'showTitle'), 'season_number': int_or_none(xpath_text(video_data, 'seasonNumber')), 'episode_number': int_or_none(xpath_text(video_data,\",\n \"if not token: # continue # video_url = rtmp_src + video_url + '?'\",\n \"'http://medium.ngtv.io/media/%s/tv' % media_id, media_id)['media']['tv'] duration = None chapters = [] formats = []\",\n \"continue lang = track.get('lang') or track.get('label') or 'en' subtitles.setdefault(lang, []).append({ 'url': track_url, 'ext':\",\n \"content_id, ap_data) elif not re.match('https?://', video_url): base_path_data = path_data.get(ext, path_data.get('default', {})) media_src =\",\n \"'mp4', m3u8_id=format_id or 'hls', fatal=False) if '/secure/' in video_url and '?hdnea=' in video_url:\",\n \"= path_data.get('secure') if not secure_path_data: continue video_url = self._add_akamai_spe_token( secure_path_data['tokenizer_src'], secure_path_data['media_src'] + video_url,\",\n \"video_url, video_id, { 'hds': path_data.get('f4m', {}).get('host'), # nba.cdn.turner.com, ht.cdn.turner.com, ht2.cdn.turner.com # ht3.cdn.turner.com, i.cdn.turner.com,\",\n \"'end_time': start_time + chapter_duration, }) self._sort_formats(formats) return { 'formats': formats, 'chapters': chapters, 'duration':\"\n]","isConversational":false}}},{"rowIdx":2172,"cells":{"ngram":{"kind":"list like","value":["time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key,","= ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol',","ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol,","symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif","elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj':","== 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\":","print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data =","data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser =","'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data,","alpha_vantage.timeseries import TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT',","credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data","json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key']","help=\"the time period you want to download the stock history for\") namespace =","time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window ==","== 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data","'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window ==","'daily', 'daily_adj'], help=\"the time period you want to download the stock history for\")","= ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10))","interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window","parser.add_argument('symbol', type=str, help=\"the stock symbol you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday',","time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday(","help=\"the stock symbol you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'],","ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str,","pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock","\"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want to download\")","type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you want to download the","= credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data,","api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday':","if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window","to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you want","outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the","import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol,","'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser","you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period","= argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want to download\") parser.add_argument('time_window', type=str,","argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want to download\") parser.add_argument('time_window', type=str, choices=[","data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol,","download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you want to","period you want to download the stock history for\") namespace = parser.parse_args() save_dataset(**vars(namespace))","__name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want","time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ ==","TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full')","outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window ==","time period you want to download the stock history for\") namespace = parser.parse_args()","import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key =","choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you want to download the stock","'intraday', 'daily', 'daily_adj'], help=\"the time period you want to download the stock history","import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r'))","== 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily':","== \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want to","parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you want to download","symbol you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time","argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window)","meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full')","credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas')","pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key","import TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'):","pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json',","from alpha_vantage.timeseries import TimeSeries from pprint import pprint import json import argparse def","outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if","time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data,","'daily_adj'], help=\"the time period you want to download the stock history for\") namespace","ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv')","if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you","data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol","= json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if","meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data =","= ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol,","save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts =","from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials =","json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window","def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts","ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full')","want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the time period you","elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__","= TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min',","parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\"the stock symbol you want to download\") parser.add_argument('time_window',","TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials","meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__\": parser = argparse.ArgumentParser()","stock symbol you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\"the","type=str, help=\"the stock symbol you want to download\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily',","output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif","'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data =","data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data"],"string":"[\n \"time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key,\",\n \"= ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol',\",\n \"ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol,\",\n \"symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif\",\n \"elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj':\",\n \"== 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\":\",\n \"print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data =\",\n \"data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser =\",\n \"'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data,\",\n \"alpha_vantage.timeseries import TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT',\",\n \"credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data\",\n \"json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key']\",\n \"help=\\\"the time period you want to download the stock history for\\\") namespace =\",\n \"time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window ==\",\n \"== 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data\",\n \"'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window ==\",\n \"'daily', 'daily_adj'], help=\\\"the time period you want to download the stock history for\\\")\",\n \"= ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10))\",\n \"interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window\",\n \"parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday',\",\n \"time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday(\",\n \"help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'],\",\n \"ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str,\",\n \"pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock\",\n \"\\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to download\\\")\",\n \"type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want to download the\",\n \"= credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data,\",\n \"api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday':\",\n \"if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window\",\n \"to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want\",\n \"outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the\",\n \"import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol,\",\n \"'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser\",\n \"you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period\",\n \"= argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window', type=str,\",\n \"argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[\",\n \"data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol,\",\n \"download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want to\",\n \"period you want to download the stock history for\\\") namespace = parser.parse_args() save_dataset(**vars(namespace))\",\n \"__name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want\",\n \"time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ ==\",\n \"TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full')\",\n \"outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window ==\",\n \"time period you want to download the stock history for\\\") namespace = parser.parse_args()\",\n \"import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key =\",\n \"choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want to download the stock\",\n \"'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want to download the stock history\",\n \"import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r'))\",\n \"== 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily':\",\n \"== \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to\",\n \"parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you want to download\",\n \"symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time\",\n \"argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window)\",\n \"meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full')\",\n \"credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas')\",\n \"pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key\",\n \"import TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'):\",\n \"pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json',\",\n \"from alpha_vantage.timeseries import TimeSeries from pprint import pprint import json import argparse def\",\n \"outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if\",\n \"time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data,\",\n \"'daily_adj'], help=\\\"the time period you want to download the stock history for\\\") namespace\",\n \"ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv')\",\n \"if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you\",\n \"data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol\",\n \"= json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if\",\n \"meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data =\",\n \"= ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol,\",\n \"save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts =\",\n \"from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials =\",\n \"json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key, output_format='pandas') if time_window\",\n \"def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts\",\n \"ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data = ts.get_daily(symbol, outputsize='full')\",\n \"want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the time period you\",\n \"elif time_window == 'daily_adj': data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__\",\n \"= TimeSeries(key=api_key, output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min',\",\n \"parser = argparse.ArgumentParser() parser.add_argument('symbol', type=str, help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window',\",\n \"TimeSeries from pprint import pprint import json import argparse def save_dataset(symbol='MSFT', time_window='daily_adj'): credentials\",\n \"meta_data = ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \\\"__main__\\\": parser = argparse.ArgumentParser()\",\n \"stock symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily', 'daily_adj'], help=\\\"the\",\n \"type=str, help=\\\"the stock symbol you want to download\\\") parser.add_argument('time_window', type=str, choices=[ 'intraday', 'daily',\",\n \"output_format='pandas') if time_window == 'intraday': data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif\",\n \"'daily': data, meta_data = ts.get_daily(symbol, outputsize='full') elif time_window == 'daily_adj': data, meta_data =\",\n \"data, meta_data = ts.get_intraday( symbol=symbol, interval='1min', outputsize='full') elif time_window == 'daily': data, meta_data\"\n]","isConversational":false}}},{"rowIdx":2173,"cells":{"ngram":{"kind":"list like","value":["debug) self.path = path self._used = set() self.archive = None def playback(self): self.archive","result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based","class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \"UTC\"","\"UTC\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON","if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while","def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200):","in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return (fn, format) def","operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0",") recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False,","maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned","1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we are","########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream,","\"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def","from placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID #","assumes, note this will record sts # assume role api calls creds into","= 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError","assume role api calls creds into test data, they will # go stale,","fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused","== \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data,","= False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None):","return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session,","operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data,","zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses:","if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"]","here raise IOError(\"response file ({0}) not found\".format(fn)) return fn def attach(session, data_path, prefix=None,","aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region != default_region: new_session","base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in self._files: next_file =","get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name)","creds into test data, they will # go stale, but its best to","= obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] =","\"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist() if","obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"]","super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath =","pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for","dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts back","= obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] =","here. We can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63","self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response:","os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in self._files: next_file","response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class","return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures","# # Placebo is effectively abandoned upstream, since mitch went back to work","= obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result","########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return","= self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i > index: index =","return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if","assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise","= None # slightly experimental for test recording, using # cross account assumes,","JSON dicts back into objects.\"\"\" # Be careful of shallow copy here target","work at AWS, irony... # These monkeypatch patches represent fixes on trunk of","= zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in","boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else:","AWS, irony... # These monkeypatch patches represent fixes on trunk of that repo","super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation,","path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive","debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime):","import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants import","self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath,","StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID","back to work at AWS, irony... # These monkeypatch patches represent fixes on","playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for","= obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] =","# noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore):","first index and it's not here raise IOError(\"response file ({0}) not found\".format(fn)) return","\"\"\"Convert JSON dicts back into objects.\"\"\" # Be careful of shallow copy here","i > index: index = i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index))","return timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self, dt): return timedelta(0) utc","not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session","import json import os import shutil import zipfile import re from datetime import","trunk of that repo that have not been released # into an extant","{\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def","operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file),","base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name)","(\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug)","# BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since","self).get_next_file_path(service, operation) # couple of double use cases if fn in self._avail: self._avail.remove(fn)","super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" )","pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix:","MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self):","!= 1: self._index[base_name] = 1 else: # we are looking for the first","augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not","It is unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording =","operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None","RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation,","import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test","timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self, dt): return timedelta(0) utc =","re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3","dicts back into objects.\"\"\" # Be careful of shallow copy here target =","self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name","ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if","self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if","BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch","def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath)","self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self,","not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data %s\" % test_dir) session","= BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback()","def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to sanitize response metadata and","we are looking for the first index and it's not here raise IOError(\"response","object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\"","information for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError:","StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise","datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response","\"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize)","service == \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data =","zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool","obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a","\"*.json\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) #","pass # Convert objects to dictionary representation based on type if isinstance(obj, datetime):","return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if","test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda","if self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill):","new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None):","glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if","= \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file","for test recording, using # cross account assumes, note this will record sts","self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir","return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set()","structures as-is return obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record","go stale, but its best to modify before commiting. # Disabled by default.","test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight","test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not","index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\',","copy here target = dict(obj) class_name = None if \"__class__\" in target: class_name","for testing. # Access is available for community project maintainers. ########################################################################### # BEGIN","= dict(obj) class_name = None if \"__class__\" in target: class_name = target.pop(\"__class__\") if","\"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name +","# SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import os","zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)}","augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or","if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region","recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize =","assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return","region. It is unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording","import StringIO import json import os import shutil import zipfile import re from","back into objects.\"\"\" # Be careful of shallow copy here target = dict(obj)","pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4,","= os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in self._files:","The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO","obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute","os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m","def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix,","m: i = int(m.group(\"index\")) if i > index: index = i index +=","session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument is","n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path)","based on type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"]","% fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail:","commiting. # Disabled by default. if 0 and (assume is not False and","boto3 import placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing","self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\"","fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix)","re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data,","self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return","# Record class and module information for deserialization result = {\"__class__\": obj.__class__.__name__} try:","return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation)","+= 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we","TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize #","objects to dictionary representation based on type if isinstance(obj, datetime): result[\"year\"] = obj.year","archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" )","} def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple","creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or","1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn","self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern =","obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based on type","index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name =","= {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects","# # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015","self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly","service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use","os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix:","import boto3 import placebo from botocore.response import StreamingBody from placebo import pill from","service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path:","if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update files","if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not","= attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory:","= StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') #","IOError(\"response file ({0}) not found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill","%s\", base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name, 1)","is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials']","\"output\" ) recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case,","Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt):","= obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read()","region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment):","\"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name","http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),","def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts","setups involve a short lived credential transfer if service == \"portal.sso\": return if","# go stale, but its best to modify before commiting. # Disabled by","self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in","if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path,","return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service,","response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name","= \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file is None:","% self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def",") def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name =","obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise TypeError(\"Type not","they will # go stale, but its best to modify before commiting. #","base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path,","debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False,","of shallow copy here target = dict(obj) class_name = None if \"__class__\" in","RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region)","= \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name","looking for the first index and it's not here raise IOError(\"response file ({0})","\"data\", \"output\" ) recording = False def cleanUp(self): self.pill = None def record_flight_data(self,","Return unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\"","pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name,","if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj","2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return","get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double","Dir for flight data %s\" % test_dir) session = boto3.Session(region_name=region) if not zdata:","These monkeypatch patches represent fixes on trunk of that repo that have not","# Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0)","if m: i = int(m.group(\"index\")) if i > index: index = i index","1 else: # we are looking for the first index and it's not","= None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ##########################################################################",".constants import ACCOUNT_ID # Custodian Test Account. This is used only for testing.","def tzname(self, dt): return \"UTC\" def dst(self, dt): return timedelta(0) utc = UTC()","operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir =","serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self):","\"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self,","return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument is to","if needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\":","= zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue","'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\",","pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self,","community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is","raise RuntimeError(\"Invalid Test Dir for flight data %s\" % test_dir) session = boto3.Session(region_name=region)","zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata","Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import","<gh_stars>1000+ # Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from","def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def","zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\"","metadata and account_ids \"\"\" # aws sso setups involve a short lived credential","self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive","self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None def playback(self):","recording, using # cross account assumes, note this will record sts # assume","is unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True","so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\")","only for testing. # Access is available for community project maintainers. ########################################################################### #","account_ids \"\"\" # aws sso setups involve a short lived credential transfer if","obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record class and module","class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path","needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return","Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import","= super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn in","Placebo is effectively abandoned upstream, since mitch went back to work at AWS,","os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording =","available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # #","datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to sanitize","session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session,","FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument is to allow","zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path,","base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name)","a short lived credential transfer if service == \"portal.sso\": return if 'ResponseMetadata' in","= None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir =","operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases","timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" #","stale, but its best to modify before commiting. # Disabled by default. if","os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close()","data %s\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session,","class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj def","for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass","obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return","if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index","response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data)","or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session:","of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\"","and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session(","class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service,","custom types if needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name","Be careful of shallow copy here target = dict(obj) class_name = None if","target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types","%s\\n\" % fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if","1: self._index[base_name] = 1 else: # we are looking for the first index","test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None,","at AWS, irony... # These monkeypatch patches represent fixes on trunk of that","return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The","stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None:","return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused json","aws sso setups involve a short lived credential transfer if service == \"portal.sso\":","calls creds into test data, they will # go stale, but its best","operation, response_data, http_response=200): \"\"\" Override to sanitize response metadata and account_ids \"\"\" #","response metadata and account_ids \"\"\" # aws sso setups involve a short lived","= self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\")","super(ZippedPill, self).record() # We can't update files in a zip, so copy self.archive.close()","datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200):","# pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp)","to modify before commiting. # Disabled by default. if 0 and (assume is","None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class","pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False)","obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read","os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join(","(assume is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role,","will # go stale, but its best to modify before commiting. # Disabled","= boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir)","repo that have not been released # into an extant version, we carry","self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path),","src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n","fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in","will record sts # assume role api calls creds into test data, they","not files: return super(ZippedPill, self).record() # We can't update files in a zip,","class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\",","isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't recognized","RuntimeError(\"Invalid Test Dir for flight data %s\" % test_dir) session = boto3.Session(region_name=region) if","import re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import","and it's not here raise IOError(\"response file ({0}) not found\".format(fn)) return fn def","= None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill,","experimental for test recording, using # cross account assumes, note this will record","result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"]","placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill,","= zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in","from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import","aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region != default_region:","self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data,","its best to modify before commiting. # Disabled by default. if 0 and","or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if","\"\"\"Convert objects into JSON structures.\"\"\" # Record class and module information for deserialization","0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name","isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize","lived credential transfer if service == \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata']","Disabled by default. if 0 and (assume is not False and fake.assume_role): client","# Convert objects to dictionary representation based on type if isinstance(obj, datetime): result[\"year\"]","test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data %s\" %","= target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom","files in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src =","default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill","dt): return timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self, dt): return timedelta(0)","operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return (","strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata:","# Raise a TypeError if the object isn't recognized raise TypeError(\"Type not serializable\")","self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir","been released # into an extant version, we carry them here. We can","tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody","Override to sanitize response metadata and account_ids \"\"\" # aws sso setups involve","{ os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation):","= os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data","obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result if","= deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback()","pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self,","and module information for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__","response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def","for flight data %s\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill","on type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] =","set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist())","filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data}","return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data","shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill =","# Disabled by default. if 0 and (assume is not False and fake.assume_role):","getattr(module, class_name) for custom types if needed if class_name == \"datetime\": return datetime(tzinfo=utc,","file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i","fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index !=","# We can't update files in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\"","not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region =","self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED)","fixes on trunk of that repo that have not been released # into","BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop)","Account. This is used only for testing. # Access is available for community","prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive =","is used only for testing. # Access is available for community project maintainers.","\"\"\" Override to sanitize response metadata and account_ids \"\"\" # aws sso setups","if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record()","\"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if","response_data, http_response=200): \"\"\" Override to sanitize response metadata and account_ids \"\"\" # aws","record sts # assume role api calls creds into test data, they will","# END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail =","format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn","SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import os import","operation) # couple of double use cases if fn in self._avail: self._avail.remove(fn) else:","= boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and","when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda","response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response,","lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not","\"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized","Raise a TypeError if the object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw","(zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name","if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return","json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service,","__call__(fake, region=None, assume=None): new_session = None # slightly experimental for test recording, using","zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist():","None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn =","from botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from","default. if 0 and (assume is not False and fake.assume_role): client = session.client('sts')","self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name]","file ({0}) not found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill =","self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return (fn, format) def stop(self):","Test Dir for flight data %s\" % test_dir) session = boto3.Session(region_name=region) if not","zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else:","Test Account. This is used only for testing. # Access is available for","datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo","(c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self,","prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat()","n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update files in","# License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\"","self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if","operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\",","double use cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" %","= 1 else: # we are looking for the first index and it's","utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self, dt): return","dict(obj) class_name = None if \"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\"","best to modify before commiting. # Disabled by default. if 0 and (assume","next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] =","\"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n))","False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session =","not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END","n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def","obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second","them here. We can drop this when this issue is resolved # #","= None if \"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\" in obj:","fn.replace('\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn)","else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp)","if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data =","we carry them here. We can drop this when this issue is resolved","pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill","json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, )","index: index = i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self,","= True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir,","os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill","= self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr(","else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None,","= obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based on","raise IOError(\"response file ({0}) not found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False):","files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return","that have not been released # into an extant version, we carry them","load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data =","= json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data,","%s\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir)","but its best to modify before commiting. # Disabled by default. if 0","PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch went","next_file = None while next_file is None: index = self._index.setdefault(base_name, 1) fn =","carry them here. We can drop this when this issue is resolved #","= self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize )","= {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa","glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name =","target = dict(obj) class_name = None if \"__class__\" in target: class_name = target.pop(\"__class__\")","this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0","% (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix,","json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path,","1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation)","n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill,","base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file is","representation based on type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month","self._index[base_name] = 1 else: # we are looking for the first index and","== \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj def serialize(obj):","client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'],","structures.\"\"\" # Record class and module information for deserialization result = {\"__class__\": obj.__class__.__name__}","to allow functional tests to override the default region. It is unused when","MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch went back","to dictionary representation based on type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"]","\"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in","class_name = None if \"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\" in","get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self,","def stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n %s\"","PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\"","placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self,","Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def","Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import","obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream","import shutil import zipfile import re from datetime import datetime, timedelta, tzinfo from","class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for","__init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set()","on trunk of that repo that have not been released # into an","Convert objects to dictionary representation based on type if isinstance(obj, datetime): result[\"year\"] =","stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n %s\" %","for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" %","session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region","= zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive =","pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None, assume=None:","object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name =","import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from","since mitch went back to work at AWS, irony... # These monkeypatch patches","stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath","def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" # Be careful of shallow","an extant version, we carry them here. We can drop this when this","placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session,","= os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def cleanUp(self): self.pill =","in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\"","zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n,","operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\":","region=None, assume=None): new_session = None # slightly experimental for test recording, using #","self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for","here target = dict(obj) class_name = None if \"__class__\" in target: class_name =","'/') if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif","unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" #","zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path,","response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation):","obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\"","return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to sanitize response","Apache-2.0 import fnmatch from io import StringIO import json import os import shutil","\"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert","serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record class and module information for","result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] =","into JSON structures.\"\"\" # Record class and module information for deserialization result =","= json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service,","def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data,","drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License","self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files =","src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill,","if \"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") #","bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise","new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region","default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert","filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file =","This is used only for testing. # Access is available for community project","\"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if","if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test","def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data","index != 1: self._index[base_name] = 1 else: # we are looking for the","import placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing import","save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data","if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data %s\" % test_dir)","\"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file is None: index","'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir","in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID,","AttributeError: pass # Convert objects to dictionary representation based on type if isinstance(obj,","{} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data","import os import shutil import zipfile import re from datetime import datetime, timedelta,","super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files","file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i","None while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name","the default region. It is unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL',","return obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record class and","try: result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary representation","while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name +","assume=None): new_session = None # slightly experimental for test recording, using # cross","UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \"UTC\" def","test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case,","unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return","boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() #","result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0","return StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert objects","super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n","region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir):","= ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj):","return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument","tests to override the default region. It is unused when replaying stored data.","= placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill =","serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO","is effectively abandoned upstream, since mitch went back to work at AWS, irony...","test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill","test data, they will # go stale, but its best to modify before","is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn","using # cross account assumes, note this will record sts # assume role","before commiting. # Disabled by default. if 0 and (assume is not False","result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert","new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True)","BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path,","transfer if service == \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {}","self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake,","# Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io","timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response import","+ \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in self._files: next_file = fn","result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj,","sso setups involve a short lived credential transfer if service == \"portal.sso\": return","self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n","= RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill","for custom types if needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if","new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region`","use %s\\n\" % fn) return (fn, format) def stop(self): result = super(BluePill, self).stop()","{n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record()","new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session","StringIO(target[\"body\"]) # Return unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert objects into","return obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise TypeError(\"Type","in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return","to work at AWS, irony... # These monkeypatch patches represent fixes on trunk","format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n","= os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" )","if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override","for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() #","effectively abandoned upstream, since mitch went back to work at AWS, irony... #","def replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument is to allow functional","self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\",","and (assume is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role(","version, we carry them here. We can drop this when this issue is","# Return unrecognized structures as-is return obj def serialize(obj): \"\"\"Convert objects into JSON","botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants","self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") }","for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo","cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return","by default. if 0 and (assume is not False and fake.assume_role): client =","class and module information for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] =","sts # assume role api calls creds into test data, they will #","os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path,","account assumes, note this will record sts # assume role api calls creds","in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service,","self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session =","placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill):","test_case, zdata=False, region=None): \"\"\" The `region` argument is to allow functional tests to","obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types if needed if class_name ==","response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize","when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache","PATCH # # Placebo is effectively abandoned upstream, since mitch went back to","raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize","client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or","fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\"))","# pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case,","if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return","distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo","import zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util import","= self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/')","noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path","session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill =","We can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 #","files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill,","if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata:","pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return","pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY","c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is","`region` argument is to allow functional tests to override the default region. It","upstream, since mitch went back to work at AWS, irony... # These monkeypatch","os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn,","= json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join(","self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n","super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None def","% test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) #","from io import StringIO import json import os import shutil import zipfile import","import ACCOUNT_ID # Custodian Test Account. This is used only for testing. #","can't update files in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path)","deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass #","of that repo that have not been released # into an extant version,","resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c)","= UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" # Be careful","service, operation, response_data, http_response=200): \"\"\" Override to sanitize response metadata and account_ids \"\"\"","return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read =","path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize),","functional tests to override the default region. It is unused when replaying stored","index = 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files,",") output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def cleanUp(self):","placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian","http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self,","# we are looking for the first index and it's not here raise","have not been released # into an extant version, we carry them here.","Record class and module information for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"]","def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of","filepath) json_data = {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED,","default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file)","# couple of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble","default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir,","if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern","result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"]","%s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] )","n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn, format","get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name)","def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix,","prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill):","response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data =","output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def cleanUp(self): self.pill","to override the default region. It is unused when replaying stored data. \"\"\"","self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return (fn, format) def stop(self): result","os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def cleanUp(self): self.pill = None","# Be careful of shallow copy here target = dict(obj) class_name = None","def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for test recording,","service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads(","pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session","base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name, 1) fn","for the first index and it's not here raise IOError(\"response file ({0}) not","\"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if fn in self._files: next_file = fn self._index[base_name]","went back to work at AWS, irony... # These monkeypatch patches represent fixes","fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if","files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None,","os import shutil import zipfile import re from datetime import datetime, timedelta, tzinfo","None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback()","this will record sts # assume role api calls creds into test data,","def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case)","\"\"\" The `region` argument is to allow functional tests to override the default","= pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None","self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def","new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory()","self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files:","self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we are looking","debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def","TypeError if the object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None","Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH","in target: class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module,","in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i =","boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid","src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive:","cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True","testing. # Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO","i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name","datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour","obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8')","into test data, they will # go stale, but its best to modify","None if \"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\")","def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def","region=None): \"\"\" The `region` argument is to allow functional tests to override the","found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug)","slightly experimental for test recording, using # cross account assumes, note this will","( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if","pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return","False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording","allow functional tests to override the default region. It is unused when replaying","result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path =","mitch went back to work at AWS, irony... # These monkeypatch patches represent","fn = fn.replace('\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] +=","default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill,","http_response=200): \"\"\" Override to sanitize response metadata and account_ids \"\"\" # aws sso","types if needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name ==","FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for test","role api calls creds into test data, they will # go stale, but","not found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix,","= session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'],","shutil import zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util","from .constants import ACCOUNT_ID # Custodian Test Account. This is used only for","a TypeError if the object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw =","used only for testing. # Access is available for community project maintainers. ###########################################################################","\"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types if needed","couple of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use","os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir =","= fn.replace('\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] += 1","zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return","pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account.","n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We","% self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED)","self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update","it's not here raise IOError(\"response file ({0}) not found\".format(fn)) return fn def attach(session,","response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) #","not here raise IOError(\"response file ({0}) not found\".format(fn)) return fn def attach(session, data_path,","abandoned upstream, since mitch went back to work at AWS, irony... # These","self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file)","as-is return obj def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record class","Use getattr(module, class_name) for custom types if needed if class_name == \"datetime\": return","ACCOUNT_ID # Custodian Test Account. This is used only for testing. # Access","= set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files","irony... # These monkeypatch patches represent fixes on trunk of that repo that","fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation)","base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\")","the first index and it's not here raise IOError(\"response file ({0}) not found\".format(fn))","elif index != 1: self._index[base_name] = 1 else: # we are looking for","\"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file =","self).record() # We can't update files in a zip, so copy self.archive.close() os.rename(self.path,","def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording =","zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist()","Custodian Test Account. This is used only for testing. # Access is available","self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental","if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types if","for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m:","- Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self,","return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\"","= {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill,","int(m.group(\"index\")) if i > index: index = i index += 1 return os.path.join(self._data_path,","os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session =","os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data %s\"","None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir,","= obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody):","+ \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name)","test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop)","for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service, operation): fn, format =","super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result","test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir)","zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True)","json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation):","StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result if","import strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo import","elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not","# assume role api calls creds into test data, they will # go","api calls creds into test data, they will # go stale, but its","else: print(\"\\ndouble use %s\\n\" % fn) return (fn, format) def stop(self): result =","https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015 <NAME> class","return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \"*.json\") } def get_next_file_path(self, service,","i = int(m.group(\"index\")) if i > index: index = i index += 1","result[\"microsecond\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream =","record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if","= fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1","%s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill,","# https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015 <NAME>","is to allow functional tests to override the default region. It is unused","return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path","self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return","recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False,","Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json","self.path = path self._used = set() self.archive = None def playback(self): self.archive =","def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return","can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # #","\"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\",","extant version, we carry them here. We can drop this when this issue","# Use getattr(module, class_name) for custom types if needed if class_name == \"datetime\":","2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt):","if the object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize","region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata","== \"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return","not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session","is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright","return \"UTC\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert","debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None","else: # we are looking for the first index and it's not here","involve a short lived credential transfer if service == \"portal.sso\": return if 'ResponseMetadata'","m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i > index: index","deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" # Be careful of shallow copy","UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" # Be careful of","type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day","is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH #","copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive","print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self,","service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data =","http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\"save_response: path=%s\", filepath) json_data = {\"status_code\": http_response, \"data\":","self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file","into an extant version, we carry them here. We can drop this when","into objects.\"\"\" # Be careful of shallow copy here target = dict(obj) class_name","response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self,","not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case,","import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is used","self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive =","monkeypatch patches represent fixes on trunk of that repo that have not been","print(\"\\ndouble use %s\\n\" % fn) return (fn, format) def stop(self): result = super(BluePill,","% self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if","obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to sanitize response metadata","{\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects to","test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path,","playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self):","region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill =","The `region` argument is to allow functional tests to override the default region.","in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't","result = super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail))))","from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This","shallow copy here target = dict(obj) class_name = None if \"__class__\" in target:","aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region !=","# Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY","patches represent fixes on trunk of that repo that have not been released","from distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody from","datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is","= obj.microsecond return result if isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"])","credential transfer if service == \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] =","response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service,","fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else:","\"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False","if service == \"portal.sso\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data","os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill()","debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None):","data, they will # go stale, but its best to modify before commiting.","%s\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path","= client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region","are looking for the first index and it's not here raise IOError(\"response file","result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj,","released # into an extant version, we carry them here. We can drop","update files in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src","!= default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session,","zdata=False, region=None): \"\"\" The `region` argument is to allow functional tests to override","base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m =","io import StringIO import json import os import shutil import zipfile import re","self).stop() if self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class","PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def","def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \"UTC\" def dst(self, dt):","os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for flight data %s\" % test_dir) session =","self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i > index: index = i","fake.region or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if","JSON structures.\"\"\" # Record class and module information for deserialization result = {\"__class__\":","obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond","self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index)) fn = fn.replace('\\\\', '/') if","= re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation,","in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types if needed if","# slightly experimental for test recording, using # cross account assumes, note this","result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"]","def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n","= serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def","= 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern):","if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return (fn,","= set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files =","= os.path.join(self._data_path, base_name + \"*\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path)","sanitize response metadata and account_ids \"\"\" # aws sso setups involve a short","zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir for","True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case)","json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)),","tzname(self, dt): return \"UTC\" def dst(self, dt): return timedelta(0) utc = UTC() def","isinstance(obj, StreamingBody): result[\"body\"] = obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result","and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill","None # slightly experimental for test recording, using # cross account assumes, note","issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 #","modify before commiting. # Disabled by default. if 0 and (assume is not","def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used =","True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir)","# cross account assumes, note this will record sts # assume role api","0 and (assume is not False and fake.assume_role): client = session.client('sts') creds =","= super(BluePill, self).stop() if self._avail: print(\"Unused json files \\n %s\" % (\"\\n\".join(sorted(self._avail)))) return","# These monkeypatch patches represent fixes on trunk of that repo that have","indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation)","to sanitize response metadata and account_ids \"\"\" # aws sso setups involve a","class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) #","object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize","service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path:","fnmatch from io import StringIO import json import os import shutil import zipfile","next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \"_{0}.json\".format(index))","self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: #","# Placebo is effectively abandoned upstream, since mitch went back to work at","os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording","\"\"\" # aws sso setups involve a short lived credential transfer if service","= int(m.group(\"index\")) if i > index: index = i index += 1 return","note this will record sts # assume role api calls creds into test","data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region)","isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to","(fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\"Unused json files","objects into JSON structures.\"\"\" # Record class and module information for deserialization result","utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into objects.\"\"\" # Be","replay_flight_data(self, test_case, zdata=False, region=None): \"\"\" The `region` argument is to allow functional tests","self._files = set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if","test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region)","this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License -","<NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return","+= 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service,","dt): return \"UTC\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj):","or fake.region or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region)","= boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record()","placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\"","class_name) for custom types if needed if class_name == \"datetime\": return datetime(tzinfo=utc, **target)","return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def","= set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not","obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for custom types if needed if class_name","pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None #","boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop)","CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is used only","= None while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path,","= session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill =","session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session,","\"__class__\" in target: class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use","dictionary representation based on type if isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] =","\"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" % self.path, \"r\") self.archive = zipfile.ZipFile(self.path, \"w\",","continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop()","pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class","END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available()","We can't update files in a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" %","if i > index: index = i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name,","\"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),","return super(ZippedPill, self).record() # We can't update files in a zip, so copy","in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1:","response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join(",") return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"] ) def get_new_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service,","module information for deserialization result = {\"__class__\": obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except","attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill","self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir =","if not files: return super(ZippedPill, self).record() # We can't update files in a","new_session = None # slightly experimental for test recording, using # cross account","= \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index","= True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir):","region_name=region or fake.region or default_region) elif region and region != default_region: new_session =","\"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_new_file_path: %s\", base_name) index =","RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill =","cross account assumes, note this will record sts # assume role api calls","in files: continue self.archive.writestr(n, src.read(n)) os.remove(\"%s.tmp\" % self.path) return super(ZippedPill, self).record() def stop(self):","result[\"hour\"] = obj.hour result[\"minute\"] = obj.minute result[\"second\"] = obj.second result[\"microsecond\"] = obj.microsecond return","os.path.dirname(os.path.abspath(__file__)), \"data\", \"output\" ) recording = False def cleanUp(self): self.pill = None def","= os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session","replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region,","save_response(self, service, operation, response_data, http_response=200): \"\"\" Override to sanitize response metadata and account_ids","= os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i >","= path self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path,","if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation)","if class_name == \"datetime\": return datetime(tzinfo=utc, **target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"])","import fnmatch from io import StringIO import json import os import shutil import","isinstance(obj, datetime): result[\"year\"] = obj.year result[\"month\"] = obj.month result[\"day\"] = obj.day result[\"hour\"] =","\\n %s\" % (\"\\n\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False):","self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]),","= placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def","response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path =","operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\",","pill.LOG.debug(\"load_responses: %s\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\"status_code\"]), response_data[\"data\"]","test recording, using # cross account assumes, note this will record sts #","deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail","StringIO import json import os import shutil import zipfile import re from datetime","dt): return timedelta(0) utc = UTC() def deserialize(obj): \"\"\"Convert JSON dicts back into","strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo import pill","if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't","fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn) return (fn, format)","flight data %s\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill =","target: class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name)","class_name = target.pop(\"__class__\") if \"__module__\" in obj: obj.pop(\"__module__\") # Use getattr(module, class_name) for","placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore","= boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill()","if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session,","def serialize(obj): \"\"\"Convert objects into JSON structures.\"\"\" # Record class and module information","result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object","= i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service, operation):","override the default region. It is unused when replaying stored data. \"\"\" if","path self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\")","return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the","self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \"r\") self._files","ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response)","\"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service,","represent fixes on trunk of that repo that have not been released #","short lived credential transfer if service == \"portal.sso\": return if 'ResponseMetadata' in response_data:","set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files =","fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'],","> index: index = i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def","project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively","not been released # into an extant version, we carry them here. We","except AttributeError: pass # Convert objects to dictionary representation based on type if","index = i index += 1 return os.path.join(self._data_path, \"{0}_{1}.json\".format(base_name, index)) def get_next_file_path(self, service,","and account_ids \"\"\" # aws sso setups involve a short lived credential transfer","= {\"status_code\": http_response, \"data\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath)","= attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None, assume=None: session","class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return {","ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used","({0}) not found\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path,","**target) if class_name == \"StreamingBody\": return StringIO(target[\"body\"]) # Return unrecognized structures as-is return","# aws sso setups involve a short lived credential transfer if service ==","json.dumps(response_data, default=serialize) response_data = re.sub(r\"\\b\\d{12}\\b\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize)","0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if","careful of shallow copy here target = dict(obj) class_name = None if \"__class__\"","that repo that have not been released # into an extant version, we","boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region","the object isn't recognized raise TypeError(\"Type not serializable\") pill.FakeHttpResponse.raw = None placebo.pill.serialize =","argument is to allow functional tests to override the default region. It is","= os.path.join( os.path.dirname(os.path.abspath(__file__)), \"placebo_data.zip\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir","# # License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo):","not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\"Invalid Test Dir","record(self): self.archive = zipfile.ZipFile(self.path, \"a\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for","# Custodian Test Account. This is used only for testing. # Access is",") self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\"load_responses: %s\",","attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def","# into an extant version, we carry them here. We can drop this","use cases if fn in self._avail: self._avail.remove(fn) else: print(\"\\ndouble use %s\\n\" % fn)","a zip, so copy self.archive.close() os.rename(self.path, \"%s.tmp\" % self.path) src = zipfile.ZipFile(\"%s.tmp\" %","RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif","pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record()","base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file is None: index =","super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn in self._avail:","if 0 and (assume is not False and fake.assume_role): client = session.client('sts') creds","def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n)","self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service,","License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \"\"\"UTC\"\"\" def","index and it's not here raise IOError(\"response file ({0}) not found\".format(fn)) return fn","= obj.read() obj._raw_stream = StringIO(result[\"body\"]) obj._amount_read = 0 return result if isinstance(obj, bytes):","set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files:","not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir)","os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\"index\")) if i > index:","self.prefix: base_name = \"{0}.{1}\".format(self.prefix, base_name) pill.LOG.debug(\"get_next_file_path: %s\", base_name) next_file = None while next_file","objects.\"\"\" # Be careful of shallow copy here target = dict(obj) class_name =",") placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\", \"placebo\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \"data\",","prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj,","index)) def get_next_file_path(self, service, operation): base_name = \"{0}.{1}\".format(service, operation) if self.prefix: base_name =","data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class","default region. It is unused when replaying stored data. \"\"\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')):","json import os import shutil import zipfile import re from datetime import datetime,","base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \"*\") for file_path in","files: return super(ZippedPill, self).record() # We can't update files in a zip, so","obj.__class__.__name__} try: result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary","pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill"],"string":"[\n \"debug) self.path = path self._used = set() self.archive = None def playback(self): self.archive\",\n \"result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based\",\n \"class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\"\",\n \"\\\"UTC\\\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON\",\n \"if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while\",\n \"def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200):\",\n \"in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format) def\",\n \"operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0\",\n \") recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False,\",\n \"maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned\",\n \"1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we are\",\n \"########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream,\",\n \"\\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def\",\n \"from placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID #\",\n \"assumes, note this will record sts # assume role api calls creds into\",\n \"= 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError\",\n \"assume role api calls creds into test data, they will # go stale,\",\n \"fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused\",\n \"== \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data,\",\n \"= False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None):\",\n \"return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session,\",\n \"operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data,\",\n \"zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses:\",\n \"if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"]\",\n \"here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None,\",\n \"aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region != default_region: new_session\",\n \"base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in self._files: next_file =\",\n \"get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name)\",\n \"creds into test data, they will # go stale, but its best to\",\n \"= obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] =\",\n \"\\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist() if\",\n \"obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"]\",\n \"super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath =\",\n \"pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for\",\n \"dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back\",\n \"= obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] =\",\n \"here. We can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63\",\n \"self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response:\",\n \"os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in self._files: next_file\",\n \"response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class\",\n \"return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures\",\n \"# # Placebo is effectively abandoned upstream, since mitch went back to work\",\n \"= obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result\",\n \"########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return\",\n \"= self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i > index: index =\",\n \"return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if\",\n \"assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise\",\n \"= None # slightly experimental for test recording, using # cross account assumes,\",\n \"JSON dicts back into objects.\\\"\\\"\\\" # Be careful of shallow copy here target\",\n \"work at AWS, irony... # These monkeypatch patches represent fixes on trunk of\",\n \"= zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in\",\n \"boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else:\",\n \"AWS, irony... # These monkeypatch patches represent fixes on trunk of that repo\",\n \"super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation,\",\n \"path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive\",\n \"debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime):\",\n \"import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants import\",\n \"self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath,\",\n \"StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID\",\n \"back to work at AWS, irony... # These monkeypatch patches represent fixes on\",\n \"playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for\",\n \"= obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] =\",\n \"# noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore):\",\n \"first index and it's not here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return\",\n \"\\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be careful of shallow copy here\",\n \"i > index: index = i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index))\",\n \"return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc\",\n \"not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session\",\n \"import json import os import shutil import zipfile import re from datetime import\",\n \"trunk of that repo that have not been released # into an extant\",\n \"{\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def\",\n \"operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file),\",\n \"base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name)\",\n \"(\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug)\",\n \"# BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since\",\n \"self).get_next_file_path(service, operation) # couple of double use cases if fn in self._avail: self._avail.remove(fn)\",\n \"super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" )\",\n \"pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix:\",\n \"MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self):\",\n \"!= 1: self._index[base_name] = 1 else: # we are looking for the first\",\n \"augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not\",\n \"It is unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording =\",\n \"operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None\",\n \"RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation,\",\n \"import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test\",\n \"timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc =\",\n \"re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3\",\n \"dicts back into objects.\\\"\\\"\\\" # Be careful of shallow copy here target =\",\n \"self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name\",\n \"ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if\",\n \"self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if\",\n \"BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch\",\n \"def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath)\",\n \"self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self,\",\n \"not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" % test_dir) session\",\n \"= BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback()\",\n \"def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and\",\n \"we are looking for the first index and it's not here raise IOError(\\\"response\",\n \"object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\"\",\n \"information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError:\",\n \"StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise\",\n \"datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response\",\n \"\\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize)\",\n \"service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data =\",\n \"zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool\",\n \"obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a\",\n \"\\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) #\",\n \"pass # Convert objects to dictionary representation based on type if isinstance(obj, datetime):\",\n \"return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if\",\n \"test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda\",\n \"if self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill):\",\n \"new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None):\",\n \"glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if\",\n \"= \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file\",\n \"for test recording, using # cross account assumes, note this will record sts\",\n \"self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir\",\n \"return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set()\",\n \"structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record\",\n \"go stale, but its best to modify before commiting. # Disabled by default.\",\n \"test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight\",\n \"test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not\",\n \"index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\',\",\n \"copy here target = dict(obj) class_name = None if \\\"__class__\\\" in target: class_name\",\n \"for testing. # Access is available for community project maintainers. ########################################################################### # BEGIN\",\n \"= dict(obj) class_name = None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if\",\n \"\\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name +\",\n \"# SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import os\",\n \"zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)}\",\n \"augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or\",\n \"if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region\",\n \"recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize =\",\n \"assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return\",\n \"region. It is unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording\",\n \"import StringIO import json import os import shutil import zipfile import re from\",\n \"back into objects.\\\"\\\"\\\" # Be careful of shallow copy here target = dict(obj)\",\n \"pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4,\",\n \"= os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in self._files:\",\n \"The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO\",\n \"obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute\",\n \"os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m\",\n \"def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix,\",\n \"m: i = int(m.group(\\\"index\\\")) if i > index: index = i index +=\",\n \"session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is\",\n \"n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path)\",\n \"based on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"]\",\n \"% fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail:\",\n \"commiting. # Disabled by default. if 0 and (assume is not False and\",\n \"boto3 import placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing\",\n \"self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\"\",\n \"fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix)\",\n \"re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data,\",\n \"self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return\",\n \"# Record class and module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try:\",\n \"return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation)\",\n \"+= 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we\",\n \"TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize #\",\n \"objects to dictionary representation based on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year\",\n \"archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" )\",\n \"} def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple\",\n \"creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or\",\n \"1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn\",\n \"self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern =\",\n \"obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based on type\",\n \"index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name =\",\n \"= {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert objects\",\n \"# # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015\",\n \"self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly\",\n \"service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use\",\n \"os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix:\",\n \"import boto3 import placebo from botocore.response import StreamingBody from placebo import pill from\",\n \"service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path:\",\n \"if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update files\",\n \"if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not\",\n \"= attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory:\",\n \"= StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8') #\",\n \"IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill\",\n \"%s\\\", base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name, 1)\",\n \"is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials']\",\n \"\\\"output\\\" ) recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case,\",\n \"Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt):\",\n \"= obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read()\",\n \"region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment):\",\n \"\\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name\",\n \"http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),\",\n \"def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts\",\n \"setups involve a short lived credential transfer if service == \\\"portal.sso\\\": return if\",\n \"# go stale, but its best to modify before commiting. # Disabled by\",\n \"self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in\",\n \"if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path,\",\n \"return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service,\",\n \"response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name\",\n \"= \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file is None:\",\n \"% self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def\",\n \") def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name =\",\n \"obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise TypeError(\\\"Type not\",\n \"they will # go stale, but its best to modify before commiting. #\",\n \"base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path,\",\n \"debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False,\",\n \"of shallow copy here target = dict(obj) class_name = None if \\\"__class__\\\" in\",\n \"RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region)\",\n \"= \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name\",\n \"looking for the first index and it's not here raise IOError(\\\"response file ({0})\",\n \"\\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self): self.pill = None def record_flight_data(self,\",\n \"Return unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\"\",\n \"pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name,\",\n \"if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj\",\n \"2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return\",\n \"get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double\",\n \"Dir for flight data %s\\\" % test_dir) session = boto3.Session(region_name=region) if not zdata:\",\n \"These monkeypatch patches represent fixes on trunk of that repo that have not\",\n \"# Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0)\",\n \"if m: i = int(m.group(\\\"index\\\")) if i > index: index = i index\",\n \"1 else: # we are looking for the first index and it's not\",\n \"= None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ##########################################################################\",\n \".constants import ACCOUNT_ID # Custodian Test Account. This is used only for testing.\",\n \"def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc = UTC()\",\n \"operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir =\",\n \"serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self):\",\n \"\\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self,\",\n \"return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to\",\n \"if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\":\",\n \"= zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue\",\n \"'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\",\",\n \"pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self,\",\n \"community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is\",\n \"raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" % test_dir) session = boto3.Session(region_name=region)\",\n \"zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata\",\n \"Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import\",\n \"<gh_stars>1000+ # Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from\",\n \"def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def\",\n \"zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\"\",\n \"metadata and account_ids \\\"\\\"\\\" # aws sso setups involve a short lived credential\",\n \"self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive\",\n \"self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None def playback(self):\",\n \"recording, using # cross account assumes, note this will record sts # assume\",\n \"is unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True\",\n \"so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\")\",\n \"only for testing. # Access is available for community project maintainers. ########################################################################### #\",\n \"account_ids \\\"\\\"\\\" # aws sso setups involve a short lived credential transfer if\",\n \"obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class and module\",\n \"class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path\",\n \"needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return\",\n \"Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import\",\n \"= super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn in\",\n \"Placebo is effectively abandoned upstream, since mitch went back to work at AWS,\",\n \"os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording =\",\n \"available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # #\",\n \"datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize\",\n \"session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session,\",\n \"FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to allow\",\n \"zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path,\",\n \"base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name)\",\n \"a short lived credential transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in\",\n \"= None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir =\",\n \"operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases\",\n \"timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" #\",\n \"stale, but its best to modify before commiting. # Disabled by default. if\",\n \"os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close()\",\n \"data %s\\\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session,\",\n \"class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj def\",\n \"for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass\",\n \"obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return\",\n \"if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index\",\n \"response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data)\",\n \"or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session:\",\n \"of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\"\",\n \"and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session(\",\n \"class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service,\",\n \"custom types if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name\",\n \"Be careful of shallow copy here target = dict(obj) class_name = None if\",\n \"target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types\",\n \"%s\\\\n\\\" % fn) return (fn, format) def stop(self): result = super(BluePill, self).stop() if\",\n \"1: self._index[base_name] = 1 else: # we are looking for the first index\",\n \"test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None,\",\n \"at AWS, irony... # These monkeypatch patches represent fixes on trunk of that\",\n \"return new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The\",\n \"stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None:\",\n \"return (fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json\",\n \"aws sso setups involve a short lived credential transfer if service == \\\"portal.sso\\\":\",\n \"calls creds into test data, they will # go stale, but its best\",\n \"operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and account_ids \\\"\\\"\\\" #\",\n \"response metadata and account_ids \\\"\\\"\\\" # aws sso setups involve a short lived\",\n \"= self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\")\",\n \"super(ZippedPill, self).record() # We can't update files in a zip, so copy self.archive.close()\",\n \"datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200):\",\n \"# pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp)\",\n \"to modify before commiting. # Disabled by default. if 0 and (assume is\",\n \"None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class\",\n \"pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False)\",\n \"obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read\",\n \"os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join(\",\n \"(assume is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role,\",\n \"will # go stale, but its best to modify before commiting. # Disabled\",\n \"= boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir)\",\n \"repo that have not been released # into an extant version, we carry\",\n \"self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path),\",\n \"src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n\",\n \"fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in\",\n \"will record sts # assume role api calls creds into test data, they\",\n \"not files: return super(ZippedPill, self).record() # We can't update files in a zip,\",\n \"class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\",\",\n \"isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't recognized\",\n \"RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" % test_dir) session = boto3.Session(region_name=region) if\",\n \"import re from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import\",\n \"and it's not here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def\",\n \"= None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill,\",\n \"experimental for test recording, using # cross account assumes, note this will record\",\n \"result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"]\",\n \"placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill,\",\n \"= zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in\",\n \"from datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import\",\n \"aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region != default_region:\",\n \"self).record() def stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data,\",\n \"its best to modify before commiting. # Disabled by default. if 0 and\",\n \"or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if\",\n \"\\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class and module information for deserialization\",\n \"0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name\",\n \"isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize\",\n \"lived credential transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata']\",\n \"Disabled by default. if 0 and (assume is not False and fake.assume_role): client\",\n \"# Convert objects to dictionary representation based on type if isinstance(obj, datetime): result[\\\"year\\\"]\",\n \"test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" %\",\n \"= target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom\",\n \"files in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src =\",\n \"default_region = session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill\",\n \"dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0)\",\n \"operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return (\",\n \"strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata:\",\n \"# Raise a TypeError if the object isn't recognized raise TypeError(\\\"Type not serializable\\\")\",\n \"self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir\",\n \"been released # into an extant version, we carry them here. We can\",\n \"tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody\",\n \"Override to sanitize response metadata and account_ids \\\"\\\"\\\" # aws sso setups involve\",\n \"{ os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation):\",\n \"= os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data\",\n \"obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if\",\n \"= deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback()\",\n \"pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self,\",\n \"and module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__\",\n \"response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def\",\n \"for flight data %s\\\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill\",\n \"on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] =\",\n \"set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist())\",\n \"filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data}\",\n \"return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data\",\n \"shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill =\",\n \"# Disabled by default. if 0 and (assume is not False and fake.assume_role):\",\n \"getattr(module, class_name) for custom types if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc,\",\n \"file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i\",\n \"fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index !=\",\n \"# We can't update files in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\"\",\n \"not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region =\",\n \"self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED)\",\n \"fixes on trunk of that repo that have not been released # into\",\n \"BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop)\",\n \"Account. This is used only for testing. # Access is available for community\",\n \"prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive =\",\n \"is used only for testing. # Access is available for community project maintainers.\",\n \"\\\"\\\"\\\" Override to sanitize response metadata and account_ids \\\"\\\"\\\" # aws sso setups\",\n \"if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record()\",\n \"\\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if\",\n \"response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and account_ids \\\"\\\"\\\" # aws\",\n \"record sts # assume role api calls creds into test data, they will\",\n \"# END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail =\",\n \"format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn\",\n \"SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json import os import\",\n \"operation) # couple of double use cases if fn in self._avail: self._avail.remove(fn) else:\",\n \"= boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and\",\n \"when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda\",\n \"response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response,\",\n \"lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not\",\n \"\\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized\",\n \"Raise a TypeError if the object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw\",\n \"(zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name\",\n \"if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return\",\n \"json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service,\",\n \"__call__(fake, region=None, assume=None): new_session = None # slightly experimental for test recording, using\",\n \"zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist():\",\n \"None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn =\",\n \"from botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from\",\n \"default. if 0 and (assume is not False and fake.assume_role): client = session.client('sts')\",\n \"self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name]\",\n \"file ({0}) not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill =\",\n \"self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format) def stop(self):\",\n \"Test Dir for flight data %s\\\" % test_dir) session = boto3.Session(region_name=region) if not\",\n \"zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else:\",\n \"Test Account. This is used only for testing. # Access is available for\",\n \"datetime import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo\",\n \"(c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self,\",\n \"prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat()\",\n \"n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update files in\",\n \"# License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\"\",\n \"self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if\",\n \"operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\",\",\n \"double use cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" %\",\n \"= 1 else: # we are looking for the first index and it's\",\n \"utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return\",\n \"dict(obj) class_name = None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\"\",\n \"best to modify before commiting. # Disabled by default. if 0 and (assume\",\n \"next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] =\",\n \"\\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n))\",\n \"False and fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session =\",\n \"not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END\",\n \"n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def\",\n \"obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second\",\n \"them here. We can drop this when this issue is resolved # #\",\n \"= None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj:\",\n \"fn.replace('\\\\\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn)\",\n \"else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp)\",\n \"if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data =\",\n \"we carry them here. We can drop this when this issue is resolved\",\n \"pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill\",\n \"json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, )\",\n \"index: index = i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self,\",\n \"= True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir,\",\n \"os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill\",\n \"= self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr(\",\n \"else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None,\",\n \"= obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based on\",\n \"raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False):\",\n \"files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return\",\n \"that have not been released # into an extant version, we carry them\",\n \"load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data =\",\n \"= json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data,\",\n \"%s\\\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir)\",\n \"but its best to modify before commiting. # Disabled by default. if 0\",\n \"PLACEBO MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch went\",\n \"next_file = None while next_file is None: index = self._index.setdefault(base_name, 1) fn =\",\n \"carry them here. We can drop this when this issue is resolved #\",\n \"= self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize )\",\n \"= {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa\",\n \"glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name =\",\n \"target = dict(obj) class_name = None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\")\",\n \"this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0\",\n \"% (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix,\",\n \"json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path,\",\n \"1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation)\",\n \"n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill,\",\n \"base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file is\",\n \"representation based on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month\",\n \"self._index[base_name] = 1 else: # we are looking for the first index and\",\n \"== \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj def serialize(obj):\",\n \"client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'],\",\n \"structures.\\\"\\\"\\\" # Record class and module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__}\",\n \"to allow functional tests to override the default region. It is unused when\",\n \"MONKEY PATCH # # Placebo is effectively abandoned upstream, since mitch went back\",\n \"to dictionary representation based on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"]\",\n \"\\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in\",\n \"class_name = None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in\",\n \"get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self,\",\n \"def stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\"\",\n \"PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\"\",\n \"placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self,\",\n \"Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def\",\n \"Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import\",\n \"obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream\",\n \"import shutil import zipfile import re from datetime import datetime, timedelta, tzinfo from\",\n \"class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for\",\n \"__init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set()\",\n \"on trunk of that repo that have not been released # into an\",\n \"Convert objects to dictionary representation based on type if isinstance(obj, datetime): result[\\\"year\\\"] =\",\n \"stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\" %\",\n \"for n in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" %\",\n \"session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region\",\n \"= zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive =\",\n \"pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None, assume=None:\",\n \"object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name =\",\n \"import datetime, timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from\",\n \"since mitch went back to work at AWS, irony... # These monkeypatch patches\",\n \"stop(self): super(ZippedPill, self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath\",\n \"def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be careful of shallow\",\n \"an extant version, we carry them here. We can drop this when this\",\n \"placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session,\",\n \"= os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self): self.pill =\",\n \"in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\"\",\n \"zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files: continue self.archive.writestr(n,\",\n \"operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\":\",\n \"region=None, assume=None): new_session = None # slightly experimental for test recording, using #\",\n \"self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for\",\n \"here target = dict(obj) class_name = None if \\\"__class__\\\" in target: class_name =\",\n \"'/') if fn in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif\",\n \"unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" #\",\n \"zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path,\",\n \"response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation):\",\n \"obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\"\",\n \"return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response\",\n \"Apache-2.0 import fnmatch from io import StringIO import json import os import shutil\",\n \"\\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert\",\n \"serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class and module information for\",\n \"result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] =\",\n \"into JSON structures.\\\"\\\"\\\" # Record class and module information for deserialization result =\",\n \"= json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service,\",\n \"def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data,\",\n \"drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License\",\n \"self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files =\",\n \"src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill,\",\n \"if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") #\",\n \"bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise\",\n \"new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region\",\n \"default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert\",\n \"filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file =\",\n \"This is used only for testing. # Access is available for community project\",\n \"\\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if\",\n \"if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test\",\n \"def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data\",\n \"index != 1: self._index[base_name] = 1 else: # we are looking for the\",\n \"import placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing import\",\n \"save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data\",\n \"if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" % test_dir)\",\n \"\\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file is None: index\",\n \"'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir\",\n \"in response_data: response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID,\",\n \"AttributeError: pass # Convert objects to dictionary representation based on type if isinstance(obj,\",\n \"{} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data\",\n \"import os import shutil import zipfile import re from datetime import datetime, timedelta,\",\n \"super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files\",\n \"file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i\",\n \"None while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name\",\n \"the default region. It is unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL',\",\n \"return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class and\",\n \"try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary representation\",\n \"while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name +\",\n \"assume=None): new_session = None # slightly experimental for test recording, using # cross\",\n \"UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def\",\n \"test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def replay_flight_data(self, test_case,\",\n \"unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return\",\n \"boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() #\",\n \"result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0\",\n \"return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects\",\n \"super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n\",\n \"region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir):\",\n \"= ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj):\",\n \"return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument\",\n \"tests to override the default region. It is unused when replaying stored data.\",\n \"= placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill =\",\n \"serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO\",\n \"is effectively abandoned upstream, since mitch went back to work at AWS, irony...\",\n \"test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill\",\n \"test data, they will # go stale, but its best to modify before\",\n \"is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn\",\n \"using # cross account assumes, note this will record sts # assume role\",\n \"before commiting. # Disabled by default. if 0 and (assume is not False\",\n \"result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert\",\n \"new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True)\",\n \"BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path,\",\n \"transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {}\",\n \"self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake,\",\n \"# Copyright The Cloud Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io\",\n \"timedelta, tzinfo from distutils.util import strtobool import boto3 import placebo from botocore.response import\",\n \"+ \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in self._files: next_file = fn\",\n \"result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj,\",\n \"sso setups involve a short lived credential transfer if service == \\\"portal.sso\\\": return\",\n \"self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n\",\n \"= RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill\",\n \"for custom types if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if\",\n \"new_session return session return FakeFactory() def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region`\",\n \"use %s\\\\n\\\" % fn) return (fn, format) def stop(self): result = super(BluePill, self).stop()\",\n \"{n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record()\",\n \"new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session\",\n \"StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into\",\n \"return obj.decode('utf8') # Raise a TypeError if the object isn't recognized raise TypeError(\\\"Type\",\n \"in src.namelist(): if n in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return\",\n \"to work at AWS, irony... # These monkeypatch patches represent fixes on trunk\",\n \"format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n\",\n \"= os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" )\",\n \"if isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override\",\n \"for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() #\",\n \"effectively abandoned upstream, since mitch went back to work at AWS, irony... #\",\n \"def replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to allow functional\",\n \"self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\",\",\n \"and (assume is not False and fake.assume_role): client = session.client('sts') creds = client.assume_role(\",\n \"version, we carry them here. We can drop this when this issue is\",\n \"# Return unrecognized structures as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON\",\n \"botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore from .constants\",\n \"self.get_available() def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") }\",\n \"for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo\",\n \"cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return\",\n \"by default. if 0 and (assume is not False and fake.assume_role): client =\",\n \"class and module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] =\",\n \"sts # assume role api calls creds into test data, they will #\",\n \"os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path,\",\n \"account assumes, note this will record sts # assume role api calls creds\",\n \"in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service,\",\n \"self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session =\",\n \"placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill):\",\n \"test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to allow functional tests to\",\n \"obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types if needed if class_name ==\",\n \"response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize\",\n \"when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache\",\n \"PATCH # # Placebo is effectively abandoned upstream, since mitch went back to\",\n \"raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize\",\n \"client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or\",\n \"fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\"))\",\n \"# pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case,\",\n \"if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return\",\n \"distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo\",\n \"import zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util import\",\n \"= self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/')\",\n \"noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path\",\n \"session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill =\",\n \"We can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 #\",\n \"files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill,\",\n \"if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata:\",\n \"pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return\",\n \"pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY\",\n \"c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is\",\n \"`region` argument is to allow functional tests to override the default region. It\",\n \"upstream, since mitch went back to work at AWS, irony... # These monkeypatch\",\n \"os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn,\",\n \"= json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join(\",\n \"self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n\",\n \"super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None def\",\n \"% test_dir) session = boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) #\",\n \"from io import StringIO import json import os import shutil import zipfile import\",\n \"import ACCOUNT_ID # Custodian Test Account. This is used only for testing. #\",\n \"can't update files in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path)\",\n \"deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass #\",\n \"of that repo that have not been released # into an extant version,\",\n \"resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c)\",\n \"= UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be careful\",\n \"service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and account_ids \\\"\\\"\\\"\",\n \"return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read =\",\n \"path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize),\",\n \"functional tests to override the default region. It is unused when replaying stored\",\n \"index = 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files,\",\n \") output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self):\",\n \"placebo import pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian\",\n \"http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self,\",\n \"# we are looking for the first index and it's not here raise\",\n \"have not been released # into an extant version, we carry them here.\",\n \"Record class and module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"]\",\n \"def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of\",\n \"filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED,\",\n \"default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file)\",\n \"# couple of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble\",\n \"default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir,\",\n \"if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern\",\n \"result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"]\",\n \"%s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] )\",\n \"n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format\",\n \"get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name)\",\n \"def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix,\",\n \"prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill):\",\n \"response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data =\",\n \"output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self): self.pill\",\n \"to override the default region. It is unused when replaying stored data. \\\"\\\"\\\"\",\n \"self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format) def stop(self): result\",\n \"os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self): self.pill = None\",\n \"# Be careful of shallow copy here target = dict(obj) class_name = None\",\n \"def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for test recording,\",\n \"service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads(\",\n \"pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session\",\n \"base_name) next_file = None while next_file is None: index = self._index.setdefault(base_name, 1) fn\",\n \"for the first index and it's not here raise IOError(\\\"response file ({0}) not\",\n \"\\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if fn in self._files: next_file = fn self._index[base_name]\",\n \"went back to work at AWS, irony... # These monkeypatch patches represent fixes\",\n \"fn, format = super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if\",\n \"files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None,\",\n \"os import shutil import zipfile import re from datetime import datetime, timedelta, tzinfo\",\n \"None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback()\",\n \"this will record sts # assume role api calls creds into test data,\",\n \"def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case)\",\n \"\\\"\\\"\\\" The `region` argument is to allow functional tests to override the default\",\n \"= pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None\",\n \"self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def\",\n \"new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory()\",\n \"self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if n in files:\",\n \"self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: # we are looking\",\n \"debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def\",\n \"TypeError if the object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None\",\n \"Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH\",\n \"in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module,\",\n \"in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i =\",\n \"boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid\",\n \"src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop() if self.archive:\",\n \"cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True\",\n \"testing. # Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO\",\n \"i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name\",\n \"datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour\",\n \"obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj, bytes): return obj.decode('utf8')\",\n \"into test data, they will # go stale, but its best to modify\",\n \"None if \\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\")\",\n \"def get_available(self): return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def\",\n \"region=None): \\\"\\\"\\\" The `region` argument is to allow functional tests to override the\",\n \"found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug)\",\n \"slightly experimental for test recording, using # cross account assumes, note this will\",\n \"( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if\",\n \"pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return\",\n \"False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording\",\n \"allow functional tests to override the default region. It is unused when replaying\",\n \"result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path =\",\n \"mitch went back to work at AWS, irony... # These monkeypatch patches represent\",\n \"fn = fn.replace('\\\\\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] +=\",\n \"default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill,\",\n \"http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and account_ids \\\"\\\"\\\" # aws sso\",\n \"types if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name ==\",\n \"FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental for test\",\n \"role api calls creds into test data, they will # go stale, but\",\n \"not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix,\",\n \"= session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'],\",\n \"shutil import zipfile import re from datetime import datetime, timedelta, tzinfo from distutils.util\",\n \"from .constants import ACCOUNT_ID # Custodian Test Account. This is used only for\",\n \"a TypeError if the object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw =\",\n \"used only for testing. # Access is available for community project maintainers. ###########################################################################\",\n \"\\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types if needed\",\n \"couple of double use cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use\",\n \"os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir =\",\n \"= fn.replace('\\\\\\\\', '/') if fn in self._files: next_file = fn self._index[base_name] += 1\",\n \"zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return\",\n \"pill from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account.\",\n \"n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We\",\n \"% self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED)\",\n \"self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't update\",\n \"it's not here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def attach(session,\",\n \"response_data['ResponseMetadata'] = {} response_data = json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) #\",\n \"not here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn def attach(session, data_path,\",\n \"abandoned upstream, since mitch went back to work at AWS, irony... # These\",\n \"self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file)\",\n \"as-is return obj def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class\",\n \"Use getattr(module, class_name) for custom types if needed if class_name == \\\"datetime\\\": return\",\n \"ACCOUNT_ID # Custodian Test Account. This is used only for testing. # Access\",\n \"= set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files\",\n \"irony... # These monkeypatch patches represent fixes on trunk of that repo that\",\n \"fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format = super(BluePill, self).get_next_file_path(service, operation)\",\n \"base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\")\",\n \"the first index and it's not here raise IOError(\\\"response file ({0}) not found\\\".format(fn))\",\n \"elif index != 1: self._index[base_name] = 1 else: # we are looking for\",\n \"\\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file =\",\n \"self).record() # We can't update files in a zip, so copy self.archive.close() os.rename(self.path,\",\n \"def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording =\",\n \"zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for n in self.archive.namelist()\",\n \"Custodian Test Account. This is used only for testing. # Access is available\",\n \"self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None # slightly experimental\",\n \"if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types if\",\n \"for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name) if m:\",\n \"- Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self,\",\n \"return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\"\",\n \"= {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill,\",\n \"int(m.group(\\\"index\\\")) if i > index: index = i index += 1 return os.path.join(self._data_path,\",\n \"os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session =\",\n \"os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\"\",\n \"None def record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir,\",\n \"= obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody):\",\n \"+ \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m = self.filename_re.match(file_name)\",\n \"test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop)\",\n \"for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service, operation): fn, format =\",\n \"super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result\",\n \"test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir)\",\n \"zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True)\",\n \"json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation):\",\n \"StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if\",\n \"import strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo import\",\n \"elif region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not\",\n \"# assume role api calls creds into test data, they will # go\",\n \"api calls creds into test data, they will # go stale, but its\",\n \"else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format) def stop(self): result =\",\n \"https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015 <NAME> class\",\n \"return { os.path.join(self.data_path, n) for n in fnmatch.filter(os.listdir(self.data_path), \\\"*.json\\\") } def get_next_file_path(self, service,\",\n \"i = int(m.group(\\\"index\\\")) if i > index: index = i index += 1\",\n \"result[\\\"microsecond\\\"] = obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream =\",\n \"record_flight_data(self, test_case, zdata=False, augment=False, region=None): self.recording = True test_dir = os.path.join(self.placebo_dir, test_case) if\",\n \"= fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1\",\n \"%s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill,\",\n \"# https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright (c) 2015 <NAME>\",\n \"is to allow functional tests to override the default region. It is unused\",\n \"return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path\",\n \"self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return\",\n \"recording = False def cleanUp(self): self.pill = None def record_flight_data(self, test_case, zdata=False, augment=False,\",\n \"Custodian Authors. # SPDX-License-Identifier: Apache-2.0 import fnmatch from io import StringIO import json\",\n \"self.path = path self._used = set() self.archive = None def playback(self): self.archive =\",\n \"def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return\",\n \"can drop this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # #\",\n \"\\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\",\",\n \"extant version, we carry them here. We can drop this when this issue\",\n \"# Use getattr(module, class_name) for custom types if needed if class_name == \\\"datetime\\\":\",\n \"2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt):\",\n \"if the object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize\",\n \"region and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata\",\n \"== \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return\",\n \"not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session\",\n \"is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 # Copyright\",\n \"return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert\",\n \"debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used = set() self.archive = None\",\n \"else: # we are looking for the first index and it's not here\",\n \"involve a short lived credential transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata'\",\n \"m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i > index: index\",\n \"deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be careful of shallow copy\",\n \"UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be careful of\",\n \"type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day\",\n \"is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH #\",\n \"copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive\",\n \"print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self,\",\n \"service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data =\",\n \"http_response=200): filepath = self.get_new_file_path(service, operation) pill.LOG.debug(\\\"save_response: path=%s\\\", filepath) json_data = {\\\"status_code\\\": http_response, \\\"data\\\":\",\n \"self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file\",\n \"into an extant version, we carry them here. We can drop this when\",\n \"into objects.\\\"\\\"\\\" # Be careful of shallow copy here target = dict(obj) class_name\",\n \"response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self,\",\n \"not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case,\",\n \"import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is used\",\n \"self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive =\",\n \"monkeypatch patches represent fixes on trunk of that repo that have not been\",\n \"print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format) def stop(self): result = super(BluePill,\",\n \"% self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\", zipfile.ZIP_DEFLATED) for n in src.namelist(): if\",\n \"obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata\",\n \"{\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert objects to\",\n \"test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill = attach(session, self.archive_path,\",\n \"playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files = set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self):\",\n \"region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill =\",\n \"The `region` argument is to allow functional tests to override the default region.\",\n \"in self.archive.namelist() if n.startswith(self.prefix)} if not files: return super(ZippedPill, self).record() # We can't\",\n \"result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail))))\",\n \"from c7n.testing import CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This\",\n \"shallow copy here target = dict(obj) class_name = None if \\\"__class__\\\" in target:\",\n \"aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region !=\",\n \"# Access is available for community project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY\",\n \"patches represent fixes on trunk of that repo that have not been released\",\n \"from distutils.util import strtobool import boto3 import placebo from botocore.response import StreamingBody from\",\n \"datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is\",\n \"= obj.microsecond return result if isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"])\",\n \"credential transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] =\",\n \"response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service,\",\n \"fn self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else:\",\n \"\\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False\",\n \"if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data: response_data['ResponseMetadata'] = {} response_data\",\n \"os.makedirs(test_dir) session = boto3.Session(region_name=region) default_region = session.region_name if not zdata: pill = RedPill()\",\n \"debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None):\",\n \"data, they will # go stale, but its best to modify before commiting.\",\n \"%s\\\", base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path\",\n \"= client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region\",\n \"are looking for the first index and it's not here raise IOError(\\\"response file\",\n \"result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj,\",\n \"released # into an extant version, we carry them here. We can drop\",\n \"update files in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src\",\n \"!= default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session,\",\n \"zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to allow functional tests to override\",\n \"base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path) m =\",\n \"io import StringIO import json import os import shutil import zipfile import re\",\n \"self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class\",\n \"PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def\",\n \"def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return \\\"UTC\\\" def dst(self, dt):\",\n \"os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for flight data %s\\\" % test_dir) session =\",\n \"self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i > index: index = i\",\n \"fake.region or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region) if\",\n \"JSON structures.\\\"\\\"\\\" # Record class and module information for deserialization result = {\\\"__class__\\\":\",\n \"obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond\",\n \"self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index)) fn = fn.replace('\\\\\\\\', '/') if\",\n \"= re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation,\",\n \"in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types if needed if\",\n \"# slightly experimental for test recording, using # cross account assumes, note this\",\n \"result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"]\",\n \"def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n\",\n \"= serialize placebo.pill.deserialize = deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def\",\n \"= 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern):\",\n \"if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn,\",\n \"= set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files =\",\n \"= os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in fnmatch.filter(self._files, glob_pattern): file_name = os.path.basename(file_path)\",\n \"sanitize response metadata and account_ids \\\"\\\"\\\" # aws sso setups involve a short\",\n \"zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir for\",\n \"True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir = os.path.join(self.placebo_dir, test_case)\",\n \"json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)),\",\n \"tzname(self, dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc = UTC() def\",\n \"isinstance(obj, StreamingBody): result[\\\"body\\\"] = obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result\",\n \"and region != default_region: new_session = boto3.Session(region_name=region) if new_session: assert not zdata new_pill\",\n \"None # slightly experimental for test recording, using # cross account assumes, note\",\n \"issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License - Apache 2.0 #\",\n \"modify before commiting. # Disabled by default. if 0 and (assume is not\",\n \"def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used =\",\n \"True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir)\",\n \"# cross account assumes, note this will record sts # assume role api\",\n \"0 and (assume is not False and fake.assume_role): client = session.client('sts') creds =\",\n \"= super(BluePill, self).stop() if self._avail: print(\\\"Unused json files \\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return\",\n \"# These monkeypatch patches represent fixes on trunk of that repo that have\",\n \"indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation)\",\n \"to sanitize response metadata and account_ids \\\"\\\"\\\" # aws sso setups involve a\",\n \"class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) #\",\n \"object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize = serialize\",\n \"service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path:\",\n \"fnmatch from io import StringIO import json import os import shutil import zipfile\",\n \"next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path, base_name + \\\"_{0}.json\\\".format(index))\",\n \"self._index[base_name] += 1 self._files.add(fn) elif index != 1: self._index[base_name] = 1 else: #\",\n \"# Placebo is effectively abandoned upstream, since mitch went back to work at\",\n \"os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording\",\n \"\\\"\\\"\\\" # aws sso setups involve a short lived credential transfer if service\",\n \"= int(m.group(\\\"index\\\")) if i > index: index = i index += 1 return\",\n \"note this will record sts # assume role api calls creds into test\",\n \"data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region)\",\n \"isinstance(obj, datetime): return obj.isoformat() def save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to\",\n \"(fn, format) def stop(self): result = super(BluePill, self).stop() if self._avail: print(\\\"Unused json files\",\n \"objects into JSON structures.\\\"\\\"\\\" # Record class and module information for deserialization result\",\n \"utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into objects.\\\"\\\"\\\" # Be\",\n \"replay_flight_data(self, test_case, zdata=False, region=None): \\\"\\\"\\\" The `region` argument is to allow functional tests\",\n \"self._files = set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if\",\n \"test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session = boto3.Session(region_name=region)\",\n \"this when this issue is resolved # # https://github.com/garnaat/placebo/issues/63 # # License -\",\n \"<NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return\",\n \"+= 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service,\",\n \"dt): return \\\"UTC\\\" def dst(self, dt): return timedelta(0) utc = UTC() def deserialize(obj):\",\n \"or fake.region or default_region) elif region and region != default_region: new_session = boto3.Session(region_name=region)\",\n \"= boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record()\",\n \"placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\"\",\n \"class_name) for custom types if needed if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target)\",\n \"return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj, datetime): return obj.isoformat() def\",\n \"= set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not\",\n \"obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for custom types if needed if class_name\",\n \"pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def __call__(fake, region=None, assume=None): new_session = None #\",\n \"boto3.Session(region_name=region) if new_session: assert not zdata new_pill = placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop)\",\n \"CustodianTestCore from .constants import ACCOUNT_ID # Custodian Test Account. This is used only\",\n \"= None while next_file is None: index = self._index.setdefault(base_name, 1) fn = os.path.join(self._data_path,\",\n \"= session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill =\",\n \"session.region_name if not zdata: pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session,\",\n \"\\\"__class__\\\" in target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use\",\n \"dictionary representation based on type if isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] =\",\n \"\\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" % self.path, \\\"r\\\") self.archive = zipfile.ZipFile(self.path, \\\"w\\\",\",\n \"continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self): super(ZippedPill, self).stop()\",\n \"pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class\",\n \"END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available()\",\n \"We can't update files in a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" %\",\n \"if i > index: index = i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name,\",\n \"\\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)),\",\n \"return super(ZippedPill, self).record() # We can't update files in a zip, so copy\",\n \"in self._files: next_file = fn self._index[base_name] += 1 self._files.add(fn) elif index != 1:\",\n \"response_data, http_response) class PillTest(CustodianTestCore): archive_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join(\",\n \") return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"] ) def get_new_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service,\",\n \"module information for deserialization result = {\\\"__class__\\\": obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except\",\n \"attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill\",\n \"self.recording = True return lambda region=region, assume=None: boto3.Session(region_name=region) if not zdata: test_dir =\",\n \"if not files: return super(ZippedPill, self).record() # We can't update files in a\",\n \"new_session = None # slightly experimental for test recording, using # cross account\",\n \"= \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index\",\n \"= True test_dir = os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir):\",\n \"region_name=region or fake.region or default_region) elif region and region != default_region: new_session =\",\n \"\\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_new_file_path: %s\\\", base_name) index =\",\n \"RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill =\",\n \"cross account assumes, note this will record sts # assume role api calls\",\n \"in files: continue self.archive.writestr(n, src.read(n)) os.remove(\\\"%s.tmp\\\" % self.path) return super(ZippedPill, self).record() def stop(self):\",\n \"result[\\\"hour\\\"] = obj.hour result[\\\"minute\\\"] = obj.minute result[\\\"second\\\"] = obj.second result[\\\"microsecond\\\"] = obj.microsecond return\",\n \"os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"output\\\" ) recording = False def cleanUp(self): self.pill = None def\",\n \"= os.path.join(self.placebo_dir, test_case) if not (zdata or augment): if os.path.exists(test_dir): shutil.rmtree(test_dir) os.makedirs(test_dir) session\",\n \"replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')): self.recording = True return lambda region=region,\",\n \"save_response(self, service, operation, response_data, http_response=200): \\\"\\\"\\\" Override to sanitize response metadata and account_ids\",\n \"= os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i >\",\n \"= path self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path,\",\n \"if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service, operation)\",\n \"if class_name == \\\"datetime\\\": return datetime(tzinfo=utc, **target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"])\",\n \"import fnmatch from io import StringIO import json import os import shutil import\",\n \"isinstance(obj, datetime): result[\\\"year\\\"] = obj.year result[\\\"month\\\"] = obj.month result[\\\"day\\\"] = obj.day result[\\\"hour\\\"] =\",\n \"\\\\n %s\\\" % (\\\"\\\\n\\\".join(sorted(self._avail)))) return result class ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False):\",\n \"self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]),\",\n \"= placebo.attach(new_session, test_dir, debug=True) new_pill.record() self.addCleanup(new_pill.stop) return new_session return session return FakeFactory() def\",\n \"response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response) class PillTest(CustodianTestCore): archive_path =\",\n \"operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\",\",\n \"pill.LOG.debug(\\\"load_responses: %s\\\", response_file) response_data = json.loads( self.archive.read(response_file), object_hook=pill.deserialize ) return ( pill.FakeHttpResponse(response_data[\\\"status_code\\\"]), response_data[\\\"data\\\"]\",\n \"test recording, using # cross account assumes, note this will record sts #\",\n \"deserialize # END PLACEBO MONKEY ########################################################################## class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail\",\n \"StringIO import json import os import shutil import zipfile import re from datetime\",\n \"dt): return timedelta(0) utc = UTC() def deserialize(obj): \\\"\\\"\\\"Convert JSON dicts back into\",\n \"strtobool import boto3 import placebo from botocore.response import StreamingBody from placebo import pill\",\n \"if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object isn't\",\n \"fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn) return (fn, format)\",\n \"flight data %s\\\" % test_dir) session = boto3.Session(region_name=region) if not zdata: pill =\",\n \"target: class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name)\",\n \"class_name = target.pop(\\\"__class__\\\") if \\\"__module__\\\" in obj: obj.pop(\\\"__module__\\\") # Use getattr(module, class_name) for\",\n \"placebo from botocore.response import StreamingBody from placebo import pill from c7n.testing import CustodianTestCore\",\n \"= boto3.Session(region_name=region) if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill()\",\n \"if not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session,\",\n \"def serialize(obj): \\\"\\\"\\\"Convert objects into JSON structures.\\\"\\\"\\\" # Record class and module information\",\n \"result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the object\",\n \"= i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service, operation):\",\n \"override the default region. It is unused when replaying stored data. \\\"\\\"\\\" if\",\n \"path self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\")\",\n \"return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if the\",\n \"self._used = set() self.archive = None def playback(self): self.archive = zipfile.ZipFile(self.path, \\\"r\\\") self._files\",\n \"ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize) super(RedPill, self).save_response(service, operation, response_data, http_response)\",\n \"\\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath) def load_response(self, service,\",\n \"represent fixes on trunk of that repo that have not been released #\",\n \"short lived credential transfer if service == \\\"portal.sso\\\": return if 'ResponseMetadata' in response_data:\",\n \"set(self.archive.namelist()) return super(ZippedPill, self).playback() def record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files =\",\n \"fake.assume_role): client = session.client('sts') creds = client.assume_role( RoleArn=fake.assume_role, RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'],\",\n \"> index: index = i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def\",\n \"project maintainers. ########################################################################### # BEGIN PLACEBO MONKEY PATCH # # Placebo is effectively\",\n \"not been released # into an extant version, we carry them here. We\",\n \"except AttributeError: pass # Convert objects to dictionary representation based on type if\",\n \"index = i index += 1 return os.path.join(self._data_path, \\\"{0}_{1}.json\\\".format(base_name, index)) def get_next_file_path(self, service,\",\n \"and account_ids \\\"\\\"\\\" # aws sso setups involve a short lived credential transfer\",\n \"= {\\\"status_code\\\": http_response, \\\"data\\\": response_data} self.archive.writestr( filepath, json.dumps(json_data, indent=4, default=pill.serialize), zipfile.ZIP_DEFLATED, ) self._files.add(filepath)\",\n \"= attach(session, self.archive_path, test_case, False) pill.playback() self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) return lambda region=None, assume=None: session\",\n \"class BluePill(pill.Pill): def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return {\",\n \"ZippedPill(pill.Pill): def __init__(self, path, prefix=None, debug=False): super(ZippedPill, self).__init__(prefix, debug) self.path = path self._used\",\n \"({0}) not found\\\".format(fn)) return fn def attach(session, data_path, prefix=None, debug=False): pill = ZippedPill(data_path,\",\n \"**target) if class_name == \\\"StreamingBody\\\": return StringIO(target[\\\"body\\\"]) # Return unrecognized structures as-is return\",\n \"# aws sso setups involve a short lived credential transfer if service ==\",\n \"json.dumps(response_data, default=serialize) response_data = re.sub(r\\\"\\\\b\\\\d{12}\\\\b\\\", ACCOUNT_ID, response_data) # noqa response_data = json.loads(response_data, object_hook=deserialize)\",\n \"0 return result if isinstance(obj, bytes): return obj.decode('utf8') # Raise a TypeError if\",\n \"careful of shallow copy here target = dict(obj) class_name = None if \\\"__class__\\\"\",\n \"that repo that have not been released # into an extant version, we\",\n \"boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif region and region\",\n \"the object isn't recognized raise TypeError(\\\"Type not serializable\\\") pill.FakeHttpResponse.raw = None placebo.pill.serialize =\",\n \"argument is to allow functional tests to override the default region. It is\",\n \"= os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"placebo_data.zip\\\" ) placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir\",\n \"# # License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo):\",\n \"not zdata: test_dir = os.path.join(self.placebo_dir, test_case) if not os.path.exists(test_dir): raise RuntimeError(\\\"Invalid Test Dir\",\n \"record(self): self.archive = zipfile.ZipFile(self.path, \\\"a\\\", zipfile.ZIP_DEFLATED) self._files = set() files = {n for\",\n \"# Custodian Test Account. This is used only for testing. # Access is\",\n \") self._files.add(filepath) def load_response(self, service, operation): response_file = self.get_next_file_path(service, operation) self._used.add(response_file) pill.LOG.debug(\\\"load_responses: %s\\\",\",\n \"attach(session, self.archive_path, test_case, debug=True) pill.record() self.pill = pill self.addCleanup(pill.stop) self.addCleanup(self.cleanUp) class FakeFactory: def\",\n \"# into an extant version, we carry them here. We can drop this\",\n \"use cases if fn in self._avail: self._avail.remove(fn) else: print(\\\"\\\\ndouble use %s\\\\n\\\" % fn)\",\n \"a zip, so copy self.archive.close() os.rename(self.path, \\\"%s.tmp\\\" % self.path) src = zipfile.ZipFile(\\\"%s.tmp\\\" %\",\n \"RoleSessionName='CustodianTest')['Credentials'] new_session = boto3.Session( aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'], region_name=region or fake.region or default_region) elif\",\n \"pill = RedPill() pill.attach(session, test_dir) else: pill = attach(session, self.archive_path, test_case, debug=True) pill.record()\",\n \"base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file is None: index =\",\n \"super(BluePill, self).get_next_file_path(service, operation) # couple of double use cases if fn in self._avail:\",\n \"if 0 and (assume is not False and fake.assume_role): client = session.client('sts') creds\",\n \"def playback(self): super(BluePill, self).playback() self._avail = self.get_available() def get_available(self): return { os.path.join(self.data_path, n)\",\n \"self).stop() if self.archive: self.archive.close() def save_response(self, service, operation, response_data, http_response=200): filepath = self.get_new_file_path(service,\",\n \"License - Apache 2.0 # Copyright (c) 2015 <NAME> class UTC(tzinfo): \\\"\\\"\\\"UTC\\\"\\\"\\\" def\",\n \"index and it's not here raise IOError(\\\"response file ({0}) not found\\\".format(fn)) return fn\",\n \"= obj.read() obj._raw_stream = StringIO(result[\\\"body\\\"]) obj._amount_read = 0 return result if isinstance(obj, bytes):\",\n \"set() files = {n for n in self.archive.namelist() if n.startswith(self.prefix)} if not files:\",\n \"not zdata: pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir)\",\n \"os.path.basename(file_path) m = self.filename_re.match(file_name) if m: i = int(m.group(\\\"index\\\")) if i > index:\",\n \"self.prefix: base_name = \\\"{0}.{1}\\\".format(self.prefix, base_name) pill.LOG.debug(\\\"get_next_file_path: %s\\\", base_name) next_file = None while next_file\",\n \"objects.\\\"\\\"\\\" # Be careful of shallow copy here target = dict(obj) class_name =\",\n \") placebo_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\", \\\"placebo\\\" ) output_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), \\\"data\\\",\",\n \"prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class RedPill(pill.Pill): def datetime_converter(self, obj): if isinstance(obj,\",\n \"index)) def get_next_file_path(self, service, operation): base_name = \\\"{0}.{1}\\\".format(service, operation) if self.prefix: base_name =\",\n \"data_path, prefix=None, debug=False): pill = ZippedPill(data_path, prefix=prefix, debug=debug) pill.attach(session, prefix) return pill class\",\n \"default region. It is unused when replaying stored data. \\\"\\\"\\\" if strtobool(os.environ.get('C7N_FUNCTIONAL', 'no')):\",\n \"json import os import shutil import zipfile import re from datetime import datetime,\",\n \"base_name) index = 0 glob_pattern = os.path.join(self._data_path, base_name + \\\"*\\\") for file_path in\",\n \"files: return super(ZippedPill, self).record() # We can't update files in a zip, so\",\n \"obj.__class__.__name__} try: result[\\\"__module__\\\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary\",\n \"pill = placebo.attach(session, test_dir) # pill = BluePill() # pill.attach(session, test_dir) else: pill\"\n]","isConversational":false}}},{"rowIdx":2174,"cells":{"ngram":{"kind":"list like","value":["<<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<:","_config = \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill:","Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), (","S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02',","21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21","*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack","S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test","ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking:","Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series","( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep',","[], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters):","Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test","series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series:","['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ),","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<:","series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series:","19 - Test Series 20 - Test Series 21 - Test Series 22","- Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: -","import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\"","test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill:","8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8","supported by guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject':","S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test","Test Series 8 - Test Series 9 - Test Series 10 - Test","('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series","S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06',","Test Series 3 - Test Series 4 - Test Series 5 - Test","['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series","Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series","is a tuple of lists: [task name, list of series ID(s) to inject,","['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07',","], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task,","backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series:","0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0","backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking:","S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test","( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16","'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17","['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ),","S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test","'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test","in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple","22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23:","Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17","1 - Test Series 2 - Test Series 3 - Test Series 4","Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test","*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack","Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5","'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series","*nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start","- Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: -","TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing:","*nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes","class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill","Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test","ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by:","), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), (","<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by:","reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100:","*nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start","- Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season","series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series:","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<:","run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in","S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01',","17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18:","[ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04',","5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6:","return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True},","- Test Series 3 - Test Series 4 - Test Series 5 -","series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series:","S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07',","( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ], )","Series 22 - Test Series 23 - Test Series 24 - Test Series","tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill","pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add more","S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'],","[Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'],","<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<:","assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to","Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test","['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test","12 - Test Series 13 - Test Series 14 - Test Series 15","S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test","20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ), (","task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find)","series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series:","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01","), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series","Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test","18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02',","max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns:","*nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill","13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'],","19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06',","series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:","S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'],","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns:","Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test","22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill',","'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test","execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests","test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin:","Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test","14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15:","16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02',","<<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons:","'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05',","['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series","11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'],","20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20","S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03',","19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20:","4 - Test Series 5 - Test Series 6 - Test Series 7","_series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack","15 - Test Series 16 - Test Series 17 - Test Series 18","<<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons:","Test Series 24 - Test Series 25 - Test Series 50 - Test","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01","*nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start","17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18","24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'],","# Each run_parameter is a tuple of lists: [task name, list of series","threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test","8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'],","S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ),","series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series:","( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24","'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ], ) def","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<:","13 - Test Series 14 - Test Series 15 - Test Series 16","Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series","series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:","0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns:","S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01',","== len(result_find) # Tests which require multiple tasks to be executed in order","Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), (","next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01","Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6","Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], )","threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: -","test_series: season_packs: always test_series: - Test Series 1 - Test Series 2 -","= execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) #","Tests which require multiple tasks to be executed in order # Each run_parameter","S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ),","S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test","Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series","10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11:","Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01',","S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05',","['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2","Series 21 - Test Series 22 - Test Series 23 - Test Series","test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin:","0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0","Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ),","20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series","( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill',",") def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]:","Test Series 1 - Test Series 2 - Test Series 3 - Test","2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3:","'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test","( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22","['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test","result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in","), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series","in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns:","16 - Test Series 17 - Test Series 18 - Test Series 19","6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02',","this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert","21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02',","Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16","S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], )","- Test Series 23 - Test Series 24 - Test Series 25 -","Series 23 - Test Series 24 - Test Series 25 - Test Series","yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill","s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin:","series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series:","'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series","S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'],","'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series","Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series","Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series","Test Series 14 - Test Series 15 - Test Series 16 - Test","['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series","test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin:","Test Series 18 - Test Series 19 - Test Series 20 - Test","*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack","reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:","- Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -","Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0","Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test","15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15","'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series","0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0","'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ],","Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20","Series 24 - Test Series 25 - Test Series 50 - Test Series","series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series:","20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series","<<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported by","'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series","absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from","season_packs: always test_series: - Test Series 1 - Test Series 2 - Test","100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0","Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), (","S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test","in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title)","standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series: internal anchors:","S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05',","24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'],","S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test","['Test Series 50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self,","'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series","21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21","Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series","2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'],","'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test","- Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<:","series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:","series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps:","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<:","6 - Test Series 7 - Test Series 8 - Test Series 9","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack","5 - Test Series 6 - Test Series 7 - Test Series 8","test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill:","- Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: -","*nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill","), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series","'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'],","series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series:","Series 5 - Test Series 6 - Test Series 7 - Test Series","S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test","Test Series 6 - Test Series 7 - Test Series 8 - Test","'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series","), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<:","( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series","['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<:","test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap:","- Test Series 1 - Test Series 2 - Test Series 3 -","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<:","'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'],","result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks","Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test","Series 13 - Test Series 14 - Test Series 15 - Test Series","Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series","reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes","in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title)","test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin:","], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for","next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\"","Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series","def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task","series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes","11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12:","Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3","*nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start","11 - Test Series 12 - Test Series 13 - Test Series 14","Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test","17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ), (","max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin:","_series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes","series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series:","Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test","S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<:","Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series","14 - Test Series 15 - Test Series 16 - Test Series 17","Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test","'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test","*nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start","test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin:","def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize(","execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test","'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series","assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be executed","Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), (","( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21","find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ],","['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series","Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test","Test Series 7 - Test Series 8 - Test Series 9 - Test","identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series:","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<:","Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series","S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test","Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series","- Test Series 10 - Test Series 11 - Test Series 12 -","Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20","- Test Series 7 - Test Series 8 - Test Series 9 -","S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test","max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:","next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<:","[], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'],","Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8","21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01',","[ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04',","S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ),","task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be","Test Series 4 - Test Series 5 - Test Series 6 - Test","S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test","['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series","'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series","0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0","19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19","20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20","yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking':","S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test","next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold:","Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series","16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill',","S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test","Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19","Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for","_series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes","*nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start","len(result_find) # Tests which require multiple tasks to be executed in order #","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01","'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series","identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons:","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack","inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [],","), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series","*nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start","24 - Test Series 25 - Test Series 50 - Test Series 100","identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep","0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0","( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9","Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), (","of lists: [task name, list of series ID(s) to inject, list of result(s)","['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ),","100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ],","<gh_stars>0 from __future__ import unicode_literals, division, absolute_import from builtins import * # noqa","S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test","Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test","Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test","- Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: -","Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series:","Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), (","S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'],","Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series","Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22","), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series","lists: [task name, list of series ID(s) to inject, list of result(s) to","division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest","Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series","23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23","'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07',","Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23","14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15","flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config =","series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series:","Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series","( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test","S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test","yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill","Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series","['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ),","Series 8 - Test Series 9 - Test Series 10 - Test Series","Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series","<<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<:","0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01","S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17","S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ),","self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries)","True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1","'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'],","\"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun',","S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20","from flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config","- Test Series 17 - Test Series 18 - Test Series 19 -","reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000","test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start:","season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01","21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22:","S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series:","12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap',","[ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04',","'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'],","( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10","test_series: - Test Series 1 - Test Series 2 - Test Series 3","15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02',","<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<:","'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01',","S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'],","Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series","entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert","series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series:","<<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<:","'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ),","Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series","Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50","Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series","Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3","8 - Test Series 9 - Test Series 10 - Test Series 11","series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:","test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill:","which require multiple tasks to be executed in order # Each run_parameter is","- Test Series 18 - Test Series 19 - Test Series 20 -","Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6","&nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack","4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'],","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack","4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03',","Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), (","self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, )","), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject:","18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19:","50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1:","series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series:","Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), (","disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add more standard","parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start:","be executed in order # Each run_parameter is a tuple of lists: [task","'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test","24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24","threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000","to inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun',","task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for","def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task,","series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series:","_nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill","Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test","1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test","'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test","11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin',","Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13","<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<:","season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold:","*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack","Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50","( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11","'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test","<<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<:","- Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes","15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start',","list of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\",","Series 2 - Test Series 3 - Test Series 4 - Test Series","['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07',","S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<:","24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50:","Series 3 - Test Series 4 - Test Series 5 - Test Series","'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series","Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14","), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series","max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:","S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test","*nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start","Series 12 - Test Series 13 - Test Series 14 - Test Series","S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test","series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series:","yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack:","Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12","'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ),","17 - Test Series 18 - Test Series 19 - Test Series 20","Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack","[ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04',","S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23","18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap',","*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack","- Test Series 16 - Test Series 17 - Test Series 18 -","'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test","'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series","- Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -","test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill:","14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ), (","Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test","9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start',","S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test","17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series","( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12","S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01',","TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons:","- Test Series 14 - Test Series 15 - Test Series 16 -","( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13","Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series","], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test","entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries) ==","'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'],","@pytest.fixture() def config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\" return self._config def","8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9:","S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05',","S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02',","run_parameter is a tuple of lists: [task name, list of series ID(s) to","season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01","S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01',","for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0])","max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns:","Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test","'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07',","Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series","0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0","list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test","- Test Series 19 - Test Series 20 - Test Series 21 -","'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test","import unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin","6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7:","from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture()","( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8","Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series","backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin:","Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series","Series 16 - Test Series 17 - Test Series 18 - Test Series","Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ),","13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill',","0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0","0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0","Test Series 19 - Test Series 20 - Test Series 21 - Test","Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test","executed in order # Each run_parameter is a tuple of lists: [task name,","Test Series 16 - Test Series 17 - Test Series 18 - Test","'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test","<<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<:","50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun:","'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ),","Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14","max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:","14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14","Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17","'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test","[ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series","yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps:","['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series","begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs:","Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test","next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<:","next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<:","test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin:","), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series","reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000","], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test","['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series","ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series:","17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18","Test Series 20 - Test Series 21 - Test Series 22 - Test","__future__ import unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import,","backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start:","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<:","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack","max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack","series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self):","['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series","S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [","max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:","yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps:","Test Series 21 - Test Series 22 - Test Series 23 - Test","release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack',","max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns:","\"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill',","max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns:","*nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start","0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0","2 - Test Series 3 - Test Series 4 - Test Series 5","Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series","), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series","S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test","<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<:","7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8:","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns:","], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def","Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series","25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series:","&series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01:","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns:","Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), (","Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test","- Test Series 8 - Test Series 9 - Test Series 10 -","test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill:","Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns:","S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03',","['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series","yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01:","options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1","['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ),","execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name)","['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [","inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title","Series 4 - Test Series 5 - Test Series 6 - Test Series","0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0","15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16:","- Test Series 22 - Test Series 23 - Test Series 24 -","_series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01","['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series","- Test Series 12 - Test Series 13 - Test Series 14 -","- Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: -","'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series","max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns:","), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), (","S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test","Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), (","S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin',","# Tests which require multiple tasks to be executed in order # Each","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:","0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01","'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series","begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill","Test Series 11 - Test Series 12 - Test Series 13 - Test","20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21","Test Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons:","4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5:","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<:","Series 14 - Test Series 15 - Test Series 16 - Test Series","Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test","*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep","11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11","Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5","Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7","S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test","'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series","Series 15 - Test Series 16 - Test Series 17 - Test Series","_nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack:","test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill:","), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ],","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<:","['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test","a tuple of lists: [task name, list of series ID(s) to inject, list","redefined-builtin import pytest from flexget.entry import Entry # TODO Add more standard tests","'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ],","- Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: -","['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ],","inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\",","( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start',","Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test","'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100","tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill:","by guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name,","*nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start","begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes","( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23","S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17","Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4","season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: -","task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(this_test[2])","24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24","13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14:","series: settings: test_series: season_packs: always test_series: - Test Series 1 - Test Series","Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns:","S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test","max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns:","S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'],","tuple of lists: [task name, list of series ID(s) to inject, list of","0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0","S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [","Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9","test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed:","S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin',","test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill:","- Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: -","- Test Series 5 - Test Series 6 - Test Series 7 -","22 - Test Series 23 - Test Series 24 - Test Series 25","Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series","tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep","<<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<:","&nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill:","<<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<:","'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series","12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13:","test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs:","S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test","&series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by:","['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series","['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ),","S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23","Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series","from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep","S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test","23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ), (","&series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by:","test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start:","3 - Test Series 4 - Test Series 5 - Test Series 6","], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test","Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series","Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19","S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters:",") def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id)","S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test","Series 19 - Test Series 20 - Test Series 21 - Test Series","1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series","Test Series 9 - Test Series 10 - Test Series 11 - Test","0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0","Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series","tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill","1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test","( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series","19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill',","pytest from flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object):","24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24","- Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -","13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02',","7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02',","'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07',","Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11","['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series","), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series","7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'],","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01","from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01","- Test Series 24 - Test Series 25 - Test Series 50 -","12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02',","<<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<:","14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14","18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18","20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21","def config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\" return self._config def inject_series(self,","( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5","next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by:","9 - Test Series 10 - Test Series 11 - Test Series 12","'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14","\"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\" return self._config","Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16","'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'],","- Test Series 9 - Test Series 10 - Test Series 11 -","Test Series 12 - Test Series 13 - Test Series 14 - Test","Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test","), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series","18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06',","'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05',","- Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: -","S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test","17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series","[ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test","['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ),","23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24:","<<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons:","Series 10 - Test Series 11 - Test Series 12 - Test Series","<<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<:","3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'],","20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series","0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0","16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17:","['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series","'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test","S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03',","import pytest from flexget.entry import Entry # TODO Add more standard tests class","Test Series 5 - Test Series 6 - Test Series 7 - Test","Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12","next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold:","packs aren't supported by guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task(","test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0","['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for","['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series","<<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<:","result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100","S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02',","yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill","tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1 -","10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02',","execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task","- Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: -","next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<:","Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs","S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01',","settings: test_series: season_packs: always test_series: - Test Series 1 - Test Series 2","<<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<:","'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03',","['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ),","series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series:","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:","( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15","# TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global:","Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11","Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13","['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find):","test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin:","Series 1 - Test Series 2 - Test Series 3 - Test Series","yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill","*nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill","Test Series 10 - Test Series 11 - Test Series 12 - Test","15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'],","'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series","Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ),","to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'],","Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8","identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack","Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test","Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10","S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'],","( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2","S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05',","<<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<:","2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'],","), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series","Series 18 - Test Series 19 - Test Series 20 - Test Series","unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import","global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons:","Test Series 22 - Test Series 23 - Test Series 24 - Test","*nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes","Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't","9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'],","Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series","14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15","], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in","S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin',","0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0","3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4:","S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test","17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17","from __future__ import unicode_literals, division, absolute_import from builtins import * # noqa pylint:","- Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -","Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test","- Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: -","this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for","['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test","5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin',","order # Each run_parameter is a tuple of lists: [task name, list of","14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series","threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs:","Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series","url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test","8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02',","- Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: -","of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series","- Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: -","['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ),","Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ),","Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series","*nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes","16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'],","S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in","'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'],","50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name,","'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series","Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test","series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series:","Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test","Test Series 15 - Test Series 16 - Test Series 17 - Test","Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22","Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series","S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<:","10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'],","S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'],","'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'],","s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always","S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07',","test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start:","<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<:","max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\"","S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test","'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series","['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2","10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill',","yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes","Test Series 13 - Test Series 14 - Test Series 15 - Test","'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test","22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02',","entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert",") @pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']),","'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'],","2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin',","'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test","internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start:","0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0","yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def","= \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes","- Test Series 2 - Test Series 3 - Test Series 4 -","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack","series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ (","Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), (","inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1 - Test","Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series","\"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start:","- Test Series 13 - Test Series 14 - Test Series 15 -","S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'],","Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series","24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed',","23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23","8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin',","1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold:","Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series","for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find:","*series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported by guessit","S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'],","Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series","100: <<: *series_ep_pack max_reruns: 0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported","23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24","'Test Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test","Series 11 - Test Series 12 - Test Series 13 - Test Series","0 \"\"\" @pytest.fixture() def config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\" return","0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0","S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test","), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), (","'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test","50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id","7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill',","@pytest.mark.parametrize( \"run_parameters\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [","Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series","tasks to be executed in order # Each run_parameter is a tuple of","in order # Each run_parameter is a tuple of lists: [task name, list","S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test","S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03',","( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18","'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test","backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000","Series 6 - Test Series 7 - Test Series 8 - Test Series","'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05',","21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start',","- Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: -","name, list of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize(","100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in","execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \"task_name,inject,result_find\", [","S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series","['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series","3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'],","'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'],","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns:","Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23","noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add","*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01","20 - Test Series 21 - Test Series 22 - Test Series 23","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack","Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test","Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series","test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap:","builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import","- Test Series 11 - Test Series 12 - Test Series 13 -","0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0","Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series","0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0","@pytest.mark.parametrize( \"task_name,inject,result_find\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), (","*nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start","), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series","Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9","Test Series 2 - Test Series 3 - Test Series 4 - Test","S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test","templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start","'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test","series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series:","&nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs:","0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0","22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22","23 - Test Series 24 - Test Series 25 - Test Series 50","], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test","S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test","* # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry #","S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series","'Test Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test","Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series","6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'],","- Test Series 20 - Test Series 21 - Test Series 22 -","), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series","identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01","import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry","[ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self,","S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test","'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series","- Test Series 4 - Test Series 5 - Test Series 6 -","['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test","inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert","11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02',","1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns:","Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series","*nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes","Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test","season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series:","Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns:","- Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: -","yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<:","- Test Series 15 - Test Series 16 - Test Series 17 -","yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000","always test_series: - Test Series 1 - Test Series 2 - Test Series","Test Series 17 - Test Series 18 - Test Series 19 - Test","<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<:","series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series:","S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20","S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject,","- Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: -","test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start:","'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07',","for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which","['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [","test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill:","test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin:","result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require","S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test","'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task,","<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<:","Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series","guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')],","Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series","'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series","15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15","( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19","- Test Series 21 - Test Series 22 - Test Series 23 -","S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test","yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps:","- Test Series 6 - Test Series 7 - Test Series 8 -","9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10:","22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01',","entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) ==","'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ],","aren't supported by guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series',","S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20","# noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO","Each run_parameter is a tuple of lists: [task name, list of series ID(s)","series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series:","from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes","test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task =","S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test","yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking:","['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test","'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series","Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test","Test Series 23 - Test Series 24 - Test Series 25 - Test","<<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<:","S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test","Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test","Series 20 - Test Series 21 - Test Series 22 - Test Series","max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:","Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series","Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test","23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24","Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series","S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test",") ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id","), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test","anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start","1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps:","Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series","'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05',","S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02',","['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series","<<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<:","'Test Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test","1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold:","<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<:","multiple tasks to be executed in order # Each run_parameter is a tuple","_nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons:","Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'],","'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test","), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series","[task name, list of series ID(s) to inject, list of result(s) to find]","test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id)","for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]:","S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test","<<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<:","20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21:","23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series","S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test","config(self): \"\"\"Season packs aren't supported by guessit yet.\"\"\" return self._config def inject_series(self, execute_task,","test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep:","Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series","19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02',","0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0","23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series","5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'],","12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'],","*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack","- Test Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack:","Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), (","0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0","21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21","['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series","run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task =","( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20","5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'],","Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test","17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17","yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1","<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<:","self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries)","'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'],","ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [ ( [","*nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill","<<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<:","ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking:","Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4","18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18","<<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons:","'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ),","14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series","more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates: global: parsing: series: internal","- Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: -","Series 7 - Test Series 8 - Test Series 9 - Test Series","- Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: -","S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test","['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ),","S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test","), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series","Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test","'Test Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test","21 - Test Series 22 - Test Series 23 - Test Series 24","to be executed in order # Each run_parameter is a tuple of lists:","Series 50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task,","0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0","'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'],","( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7","['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series","tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings:","['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ),","- Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: -","S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test","Series 9 - Test Series 10 - Test Series 11 - Test Series","Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \"\"\" templates:","Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series","'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'],","*nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start","require multiple tasks to be executed in order # Each run_parameter is a","Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7","7 - Test Series 8 - Test Series 9 - Test Series 10","of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \"run_parameters\", [","max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns:","Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), (","S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23","Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series","threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs:","Series 17 - Test Series 18 - Test Series 19 - Test Series","*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack","S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test","S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14","Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test","'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test","Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), (","S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin',","18 - Test Series 19 - Test Series 20 - Test Series 21","Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes","Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10","Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), (","10 - Test Series 11 - Test Series 12 - Test Series 13","- Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: -","&series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks:","S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14","Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series","'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ],","\"\"\"Season packs aren't supported by guessit yet.\"\"\" return self._config def inject_series(self, execute_task, release_name):","Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), (","'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test","Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series","23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series","ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start:","['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ),","9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02',","- Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: -","[ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [],","- Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: -","backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs:","'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'],","), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test","max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns:","1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2:","['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test","len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be executed in","), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series","*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack"],"string":"[\n \"<<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<:\",\n \"_config = \\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill:\",\n \"Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), (\",\n \"S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02',\",\n \"21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21\",\n \"*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack\",\n \"S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test\",\n \"ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking:\",\n \"Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series\",\n \"( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep',\",\n \"[], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters):\",\n \"Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test\",\n \"series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series:\",\n \"['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ),\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<:\",\n \"series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series:\",\n \"19 - Test Series 20 - Test Series 21 - Test Series 22\",\n \"- Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: -\",\n \"import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\"\",\n \"test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill:\",\n \"8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8\",\n \"supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject':\",\n \"S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test\",\n \"Test Series 8 - Test Series 9 - Test Series 10 - Test\",\n \"('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series\",\n \"S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06',\",\n \"Test Series 3 - Test Series 4 - Test Series 5 - Test\",\n \"['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series\",\n \"Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series\",\n \"is a tuple of lists: [task name, list of series ID(s) to inject,\",\n \"['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07',\",\n \"], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task,\",\n \"backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series:\",\n \"0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking:\",\n \"S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test\",\n \"( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16\",\n \"'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17\",\n \"['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ),\",\n \"S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test\",\n \"'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test\",\n \"in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple\",\n \"22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23:\",\n \"Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17\",\n \"1 - Test Series 2 - Test Series 3 - Test Series 4\",\n \"Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test\",\n \"*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack\",\n \"Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5\",\n \"'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series\",\n \"*nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start\",\n \"- Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: -\",\n \"TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing:\",\n \"*nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes\",\n \"class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill\",\n \"Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test\",\n \"ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by:\",\n \"), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), (\",\n \"<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by:\",\n \"reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100:\",\n \"*nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start\",\n \"- Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season\",\n \"series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series:\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<:\",\n \"run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in\",\n \"S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01',\",\n \"17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18:\",\n \"[ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04',\",\n \"5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6:\",\n \"return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True},\",\n \"- Test Series 3 - Test Series 4 - Test Series 5 -\",\n \"series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series:\",\n \"S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07',\",\n \"( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ], )\",\n \"Series 22 - Test Series 23 - Test Series 24 - Test Series\",\n \"tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill\",\n \"pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add more\",\n \"S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'],\",\n \"[Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'],\",\n \"<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<:\",\n \"assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to\",\n \"Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test\",\n \"['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test\",\n \"12 - Test Series 13 - Test Series 14 - Test Series 15\",\n \"S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test\",\n \"20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ), (\",\n \"task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find)\",\n \"series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series:\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01\",\n \"), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series\",\n \"Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test\",\n \"18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02',\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns:\",\n \"*nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill\",\n \"13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'],\",\n \"19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06',\",\n \"series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:\",\n \"S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'],\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns:\",\n \"Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test\",\n \"22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill',\",\n \"'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test\",\n \"execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests\",\n \"test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin:\",\n \"Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test\",\n \"14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15:\",\n \"16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02',\",\n \"<<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons:\",\n \"'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05',\",\n \"['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series\",\n \"11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'],\",\n \"20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20\",\n \"S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03',\",\n \"19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20:\",\n \"4 - Test Series 5 - Test Series 6 - Test Series 7\",\n \"_series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack\",\n \"15 - Test Series 16 - Test Series 17 - Test Series 18\",\n \"<<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons:\",\n \"Test Series 24 - Test Series 25 - Test Series 50 - Test\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01\",\n \"*nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start\",\n \"17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18\",\n \"24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'],\",\n \"# Each run_parameter is a tuple of lists: [task name, list of series\",\n \"threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test\",\n \"8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'],\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ),\",\n \"series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series:\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24\",\n \"'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ], ) def\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<:\",\n \"13 - Test Series 14 - Test Series 15 - Test Series 16\",\n \"Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series\",\n \"series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:\",\n \"0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns:\",\n \"S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01',\",\n \"== len(result_find) # Tests which require multiple tasks to be executed in order\",\n \"Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), (\",\n \"next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01\",\n \"Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6\",\n \"Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], )\",\n \"threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: -\",\n \"test_series: season_packs: always test_series: - Test Series 1 - Test Series 2 -\",\n \"= execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) #\",\n \"Tests which require multiple tasks to be executed in order # Each run_parameter\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ),\",\n \"S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test\",\n \"Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series\",\n \"10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11:\",\n \"Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01',\",\n \"S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05',\",\n \"['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2\",\n \"Series 21 - Test Series 22 - Test Series 23 - Test Series\",\n \"test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin:\",\n \"0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0\",\n \"Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ),\",\n \"20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series\",\n \"( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill',\",\n \") def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]:\",\n \"Test Series 1 - Test Series 2 - Test Series 3 - Test\",\n \"2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3:\",\n \"'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22\",\n \"['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test\",\n \"result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in\",\n \"), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series\",\n \"in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns:\",\n \"16 - Test Series 17 - Test Series 18 - Test Series 19\",\n \"6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02',\",\n \"this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert\",\n \"21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02',\",\n \"Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16\",\n \"S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], )\",\n \"- Test Series 23 - Test Series 24 - Test Series 25 -\",\n \"Series 23 - Test Series 24 - Test Series 25 - Test Series\",\n \"yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill\",\n \"s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin:\",\n \"series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series:\",\n \"'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series\",\n \"S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'],\",\n \"'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series\",\n \"Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series\",\n \"Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series\",\n \"Test Series 14 - Test Series 15 - Test Series 16 - Test\",\n \"['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series\",\n \"test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin:\",\n \"Test Series 18 - Test Series 19 - Test Series 20 - Test\",\n \"*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack\",\n \"reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:\",\n \"- Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -\",\n \"Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0\",\n \"Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test\",\n \"15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15\",\n \"'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series\",\n \"0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0\",\n \"'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ],\",\n \"Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20\",\n \"Series 24 - Test Series 25 - Test Series 50 - Test Series\",\n \"series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series:\",\n \"20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series\",\n \"<<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by\",\n \"'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series\",\n \"absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from\",\n \"season_packs: always test_series: - Test Series 1 - Test Series 2 - Test\",\n \"100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0\",\n \"Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), (\",\n \"S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test\",\n \"in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title)\",\n \"standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series: internal anchors:\",\n \"S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05',\",\n \"24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'],\",\n \"S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test\",\n \"['Test Series 50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self,\",\n \"'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series\",\n \"21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21\",\n \"Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series\",\n \"2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'],\",\n \"'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test\",\n \"- Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<:\",\n \"series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:\",\n \"series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps:\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<:\",\n \"6 - Test Series 7 - Test Series 8 - Test Series 9\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack\",\n \"5 - Test Series 6 - Test Series 7 - Test Series 8\",\n \"test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill:\",\n \"- Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: -\",\n \"*nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series\",\n \"'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'],\",\n \"series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series:\",\n \"Series 5 - Test Series 6 - Test Series 7 - Test Series\",\n \"S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test\",\n \"Test Series 6 - Test Series 7 - Test Series 8 - Test\",\n \"'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<:\",\n \"( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series\",\n \"['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<:\",\n \"test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap:\",\n \"- Test Series 1 - Test Series 2 - Test Series 3 -\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<:\",\n \"'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'],\",\n \"result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks\",\n \"Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test\",\n \"Series 13 - Test Series 14 - Test Series 15 - Test Series\",\n \"Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series\",\n \"reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes\",\n \"in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title)\",\n \"test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin:\",\n \"], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for\",\n \"next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\"\",\n \"Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series\",\n \"def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task\",\n \"series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes\",\n \"11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12:\",\n \"Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3\",\n \"*nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start\",\n \"11 - Test Series 12 - Test Series 13 - Test Series 14\",\n \"Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test\",\n \"17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ), (\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin:\",\n \"_series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes\",\n \"series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series:\",\n \"Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test\",\n \"S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<:\",\n \"Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series\",\n \"14 - Test Series 15 - Test Series 16 - Test Series 17\",\n \"Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test\",\n \"'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test\",\n \"*nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start\",\n \"test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin:\",\n \"def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize(\",\n \"execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test\",\n \"'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series\",\n \"assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be executed\",\n \"Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), (\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21\",\n \"find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ],\",\n \"['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series\",\n \"Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test\",\n \"Test Series 7 - Test Series 8 - Test Series 9 - Test\",\n \"identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series:\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<:\",\n \"Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series\",\n \"S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test\",\n \"Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series\",\n \"- Test Series 10 - Test Series 11 - Test Series 12 -\",\n \"Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20\",\n \"- Test Series 7 - Test Series 8 - Test Series 9 -\",\n \"S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:\",\n \"next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<:\",\n \"[], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'],\",\n \"Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8\",\n \"21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01',\",\n \"[ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04',\",\n \"S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ),\",\n \"task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be\",\n \"Test Series 4 - Test Series 5 - Test Series 6 - Test\",\n \"S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test\",\n \"['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series\",\n \"'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19\",\n \"20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series 20\",\n \"yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking':\",\n \"S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test\",\n \"next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold:\",\n \"Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series\",\n \"16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill',\",\n \"S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test\",\n \"Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19\",\n \"Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for\",\n \"_series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes\",\n \"*nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start\",\n \"len(result_find) # Tests which require multiple tasks to be executed in order #\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01\",\n \"'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series\",\n \"identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons:\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack\",\n \"inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [],\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series\",\n \"*nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start\",\n \"24 - Test Series 25 - Test Series 50 - Test Series 100\",\n \"identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep\",\n \"0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0\",\n \"( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9\",\n \"Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), (\",\n \"of lists: [task name, list of series ID(s) to inject, list of result(s)\",\n \"['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ),\",\n \"100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ],\",\n \"<gh_stars>0 from __future__ import unicode_literals, division, absolute_import from builtins import * # noqa\",\n \"S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test\",\n \"Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test\",\n \"Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test\",\n \"- Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: -\",\n \"Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series:\",\n \"Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), (\",\n \"S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'],\",\n \"Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series\",\n \"Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series\",\n \"lists: [task name, list of series ID(s) to inject, list of result(s) to\",\n \"division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest\",\n \"Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series\",\n \"23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test Series 23\",\n \"'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15 S07',\",\n \"Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23\",\n \"14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15\",\n \"flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config =\",\n \"series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series:\",\n \"Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series\",\n \"( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test\",\n \"S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test\",\n \"yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill\",\n \"Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series\",\n \"['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ),\",\n \"Series 8 - Test Series 9 - Test Series 10 - Test Series\",\n \"Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series\",\n \"<<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<:\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01\",\n \"S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17\",\n \"S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ),\",\n \"self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries)\",\n \"True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1\",\n \"'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'],\",\n \"\\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun',\",\n \"S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20\",\n \"from flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config\",\n \"- Test Series 17 - Test Series 18 - Test Series 19 -\",\n \"reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000\",\n \"test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start:\",\n \"season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01\",\n \"21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22:\",\n \"S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series:\",\n \"12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap',\",\n \"[ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04',\",\n \"'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'],\",\n \"( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10\",\n \"test_series: - Test Series 1 - Test Series 2 - Test Series 3\",\n \"15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02',\",\n \"<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<:\",\n \"'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01',\",\n \"S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'],\",\n \"Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series\",\n \"entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert\",\n \"series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series:\",\n \"<<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<:\",\n \"'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ),\",\n \"Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series\",\n \"Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50\",\n \"Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series\",\n \"Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3\",\n \"8 - Test Series 9 - Test Series 10 - Test Series 11\",\n \"series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series:\",\n \"test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill:\",\n \"which require multiple tasks to be executed in order # Each run_parameter is\",\n \"- Test Series 18 - Test Series 19 - Test Series 20 -\",\n \"Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6\",\n \"&nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack\",\n \"4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'],\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack\",\n \"4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03',\",\n \"Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), (\",\n \"self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, )\",\n \"), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject:\",\n \"18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19:\",\n \"50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1:\",\n \"series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series:\",\n \"Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), (\",\n \"disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add more standard\",\n \"parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start:\",\n \"be executed in order # Each run_parameter is a tuple of lists: [task\",\n \"'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test\",\n \"24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series 24\",\n \"threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000\",\n \"to inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun',\",\n \"task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for\",\n \"def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task,\",\n \"series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series:\",\n \"_nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill\",\n \"Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test\",\n \"1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test\",\n \"'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test\",\n \"11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin',\",\n \"Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13\",\n \"<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<:\",\n \"season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold:\",\n \"*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack\",\n \"Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50\",\n \"( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11\",\n \"'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test\",\n \"<<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<:\",\n \"- Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes\",\n \"15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start',\",\n \"list of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\",\",\n \"Series 2 - Test Series 3 - Test Series 4 - Test Series\",\n \"['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07',\",\n \"S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<:\",\n \"24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50:\",\n \"Series 3 - Test Series 4 - Test Series 5 - Test Series\",\n \"'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series\",\n \"Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:\",\n \"S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test\",\n \"*nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start\",\n \"Series 12 - Test Series 13 - Test Series 14 - Test Series\",\n \"S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test\",\n \"series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series:\",\n \"yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack:\",\n \"Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12\",\n \"'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ),\",\n \"17 - Test Series 18 - Test Series 19 - Test Series 20\",\n \"Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack\",\n \"[ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04',\",\n \"S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23\",\n \"18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap',\",\n \"*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack\",\n \"- Test Series 16 - Test Series 17 - Test Series 18 -\",\n \"'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23 S04', 'Test\",\n \"'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series\",\n \"- Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -\",\n \"test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill:\",\n \"14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ), (\",\n \"Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test\",\n \"9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start',\",\n \"S05', 'Test Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test\",\n \"17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test Series\",\n \"( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12\",\n \"S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01',\",\n \"TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons:\",\n \"- Test Series 14 - Test Series 15 - Test Series 16 -\",\n \"( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13\",\n \"Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series\",\n \"], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24 S03E01', 'Test\",\n \"entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries) ==\",\n \"'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'],\",\n \"@pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config def\",\n \"8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9:\",\n \"S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05',\",\n \"S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02',\",\n \"run_parameter is a tuple of lists: [task name, list of series ID(s) to\",\n \"season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01\",\n \"S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01',\",\n \"for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0])\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns:\",\n \"Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05', 'Test\",\n \"'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07',\",\n \"Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series\",\n \"0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0\",\n \"list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test\",\n \"- Test Series 19 - Test Series 20 - Test Series 21 -\",\n \"'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test\",\n \"import unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin\",\n \"6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7:\",\n \"from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture()\",\n \"( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8\",\n \"Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series\",\n \"backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin:\",\n \"Series 24 S06'], ['Test Series 24 S07', 'Test Series 24 S05', 'Test Series\",\n \"Series 16 - Test Series 17 - Test Series 18 - Test Series\",\n \"Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ),\",\n \"13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill',\",\n \"0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0\",\n \"Test Series 19 - Test Series 20 - Test Series 21 - Test\",\n \"Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test\",\n \"executed in order # Each run_parameter is a tuple of lists: [task name,\",\n \"Test Series 16 - Test Series 17 - Test Series 18 - Test\",\n \"'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test\",\n \"<<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<:\",\n \"50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun:\",\n \"'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ),\",\n \"Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:\",\n \"14 S05', 'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14\",\n \"Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17\",\n \"'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test\",\n \"[ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series\",\n \"yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps:\",\n \"['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series\",\n \"begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs:\",\n \"Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test\",\n \"next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<:\",\n \"next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<:\",\n \"test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin:\",\n \"), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series\",\n \"reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000\",\n \"], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test\",\n \"['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series\",\n \"ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series:\",\n \"17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18\",\n \"Test Series 20 - Test Series 21 - Test Series 22 - Test\",\n \"__future__ import unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import,\",\n \"backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start:\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<:\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack\",\n \"series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self):\",\n \"['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series\",\n \"S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:\",\n \"yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps:\",\n \"Test Series 21 - Test Series 22 - Test Series 23 - Test\",\n \"release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack',\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns:\",\n \"\\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill',\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns:\",\n \"*nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start\",\n \"0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"2 - Test Series 3 - Test Series 4 - Test Series 5\",\n \"Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series\",\n \"S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test\",\n \"<<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<:\",\n \"7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8:\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns:\",\n \"], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def\",\n \"Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series\",\n \"25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series:\",\n \"&series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01:\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns:\",\n \"Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), (\",\n \"Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test\",\n \"- Test Series 8 - Test Series 9 - Test Series 10 -\",\n \"test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill:\",\n \"Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns:\",\n \"S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03',\",\n \"['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series\",\n \"yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01:\",\n \"options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1\",\n \"['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ),\",\n \"execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name)\",\n \"['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'], [\",\n \"inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title\",\n \"Series 4 - Test Series 5 - Test Series 6 - Test Series\",\n \"0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0\",\n \"15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16:\",\n \"- Test Series 22 - Test Series 23 - Test Series 24 -\",\n \"_series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01\",\n \"['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series\",\n \"- Test Series 12 - Test Series 13 - Test Series 14 -\",\n \"- Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: -\",\n \"'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns:\",\n \"), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'], ), (\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test\",\n \"Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11 S01'], ), (\",\n \"S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin',\",\n \"# Tests which require multiple tasks to be executed in order # Each\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:\",\n \"0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01\",\n \"'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series\",\n \"begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill\",\n \"Test Series 11 - Test Series 12 - Test Series 13 - Test\",\n \"20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21\",\n \"Test Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons:\",\n \"4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5:\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<:\",\n \"Series 14 - Test Series 15 - Test Series 16 - Test Series\",\n \"Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test\",\n \"*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep\",\n \"11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test Series 11\",\n \"Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5\",\n \"Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7\",\n \"S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test\",\n \"'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series\",\n \"Series 15 - Test Series 16 - Test Series 17 - Test Series\",\n \"_nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack:\",\n \"test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill:\",\n \"), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test Series 50 S02'], ['Test Series 50 S03'], ), ],\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<:\",\n \"['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test\",\n \"a tuple of lists: [task name, list of series ID(s) to inject, list\",\n \"redefined-builtin import pytest from flexget.entry import Entry # TODO Add more standard tests\",\n \"'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ],\",\n \"- Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: -\",\n \"['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ],\",\n \"inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\",\",\n \"( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start',\",\n \"Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test\",\n \"'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100\",\n \"tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill:\",\n \"by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name,\",\n \"*nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start\",\n \"begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23\",\n \"S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17\",\n \"Series 3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4\",\n \"season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: -\",\n \"task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(this_test[2])\",\n \"24 S03E01', 'Test Series 24 S06'], ['Test Series 24 S07', 'Test Series 24\",\n \"13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14:\",\n \"series: settings: test_series: season_packs: always test_series: - Test Series 1 - Test Series\",\n \"Series 23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns:\",\n \"S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns:\",\n \"S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'],\",\n \"tuple of lists: [task name, list of series ID(s) to inject, list of\",\n \"0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0\",\n \"S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [\",\n \"Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9\",\n \"test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed:\",\n \"S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin',\",\n \"test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill:\",\n \"- Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: -\",\n \"- Test Series 5 - Test Series 6 - Test Series 7 -\",\n \"22 - Test Series 23 - Test Series 24 - Test Series 25\",\n \"Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series\",\n \"tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep\",\n \"<<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<:\",\n \"&nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill:\",\n \"<<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<:\",\n \"'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series\",\n \"12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13:\",\n \"test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs:\",\n \"S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test\",\n \"&series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by:\",\n \"['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series\",\n \"['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ),\",\n \"S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series 23\",\n \"Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series\",\n \"from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep\",\n \"S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test\",\n \"23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ), (\",\n \"&series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by:\",\n \"test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start:\",\n \"3 - Test Series 4 - Test Series 5 - Test Series 6\",\n \"], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test\",\n \"Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series\",\n \"Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19\",\n \"S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters:\",\n \") def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id)\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test\",\n \"Series 19 - Test Series 20 - Test Series 21 - Test Series\",\n \"1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series\",\n \"Test Series 9 - Test Series 10 - Test Series 11 - Test\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0\",\n \"Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series\",\n \"tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill\",\n \"1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test\",\n \"( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test Series\",\n \"19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill',\",\n \"pytest from flexget.entry import Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object):\",\n \"24 S02', 'Test Series 24 S03E01', 'Test Series 24 S06'], ['Test Series 24\",\n \"- Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -\",\n \"13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02',\",\n \"7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02',\",\n \"'Test Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07',\",\n \"Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11\",\n \"['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series\",\n \"7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'],\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 18: <<: *series_ep_tracking_pack_begin_s04e01\",\n \"from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01\",\n \"- Test Series 24 - Test Series 25 - Test Series 50 -\",\n \"12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02',\",\n \"<<: *nss_backfill_from_start series: - Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<:\",\n \"14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14\",\n \"18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18\",\n \"20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21\",\n \"def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self,\",\n \"( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5\",\n \"next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by:\",\n \"9 - Test Series 10 - Test Series 11 - Test Series 12\",\n \"'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14\",\n \"\\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config\",\n \"Series 15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16\",\n \"'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'],\",\n \"- Test Series 9 - Test Series 10 - Test Series 11 -\",\n \"Test Series 12 - Test Series 13 - Test Series 14 - Test\",\n \"Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series\",\n \"18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06',\",\n \"'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05',\",\n \"- Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: -\",\n \"S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed', ['Test\",\n \"17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series\",\n \"[ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test\",\n \"['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ),\",\n \"23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24:\",\n \"<<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons:\",\n \"Series 10 - Test Series 11 - Test Series 12 - Test Series\",\n \"<<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<:\",\n \"3 S02'], ['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'],\",\n \"20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series\",\n \"0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17:\",\n \"['Test Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series\",\n \"'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test\",\n \"S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03',\",\n \"import pytest from flexget.entry import Entry # TODO Add more standard tests class\",\n \"Test Series 5 - Test Series 6 - Test Series 7 - Test\",\n \"Series 11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12\",\n \"next_series_seasons: yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold:\",\n \"packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task(\",\n \"test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0\",\n \"['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for\",\n \"['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series\",\n \"<<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<:\",\n \"result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100\",\n \"S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02',\",\n \"yes series: - Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill\",\n \"tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1 -\",\n \"10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02',\",\n \"execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task\",\n \"- Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: -\",\n \"next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<:\",\n \"Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs\",\n \"S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01',\",\n \"settings: test_series: season_packs: always test_series: - Test Series 1 - Test Series 2\",\n \"<<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<:\",\n \"'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03',\",\n \"['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ),\",\n \"series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series:\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns:\",\n \"( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'], ['Test Series 15\",\n \"# TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global:\",\n \"Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11\",\n \"Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13\",\n \"['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find):\",\n \"test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin:\",\n \"Series 1 - Test Series 2 - Test Series 3 - Test Series\",\n \"yes series: - Test Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill\",\n \"*nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill\",\n \"Test Series 10 - Test Series 11 - Test Series 12 - Test\",\n \"15 S04'], ), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'],\",\n \"'Test Series 19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series\",\n \"Series 20 S04', 'Test Series 20 S03', 'Test Series 20 S01', ], ),\",\n \"to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'],\",\n \"Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8\",\n \"identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack\",\n \"Test Series 13: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test\",\n \"Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10\",\n \"S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15 S06'],\",\n \"( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2\",\n \"S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05',\",\n \"<<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<:\",\n \"2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test Series 3 S02'], ['Test Series 3 S03'],\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series\",\n \"Series 18 - Test Series 19 - Test Series 20 - Test Series\",\n \"unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import\",\n \"global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons:\",\n \"Test Series 22 - Test Series 23 - Test Series 24 - Test\",\n \"*nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes\",\n \"Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't\",\n \"9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'],\",\n \"Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series\",\n \"14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series 15\",\n \"], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in\",\n \"S04', 'Test Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin',\",\n \"0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<: *series_ep_pack max_reruns: 0\",\n \"3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4:\",\n \"S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test\",\n \"17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17\",\n \"from __future__ import unicode_literals, division, absolute_import from builtins import * # noqa pylint:\",\n \"- Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: -\",\n \"Test Series 11: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test\",\n \"- Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: -\",\n \"this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for\",\n \"['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test\",\n \"5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin',\",\n \"order # Each run_parameter is a tuple of lists: [task name, list of\",\n \"14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series\",\n \"threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs:\",\n \"Series 14 S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series\",\n \"url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test\",\n \"8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02',\",\n \"- Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: -\",\n \"of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series\",\n \"- Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: -\",\n \"['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'], ),\",\n \"Series 17 S04', 'Test Series 17 S03', 'Test Series 17 S01', ], ),\",\n \"Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test Series\",\n \"*nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes\",\n \"16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'],\",\n \"S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in\",\n \"'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series 21 S07E01'],\",\n \"50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name,\",\n \"'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series\",\n \"Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test\",\n \"series: - Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series:\",\n \"Test Series 10: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill: <<: *nss_backfill_from_start series: - Test\",\n \"Test Series 15 - Test Series 16 - Test Series 17 - Test\",\n \"Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22\",\n \"Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<:\",\n \"10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'],\",\n \"S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21 S04'],\",\n \"'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'],\",\n \"s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always\",\n \"S02', 'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07',\",\n \"test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start:\",\n \"<<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 15: <<:\",\n \"max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\"\",\n \"S05', 'Test Series 18 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test\",\n \"'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series\",\n \"['Test Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2\",\n \"10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill',\",\n \"yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes\",\n \"Test Series 13 - Test Series 14 - Test Series 15 - Test\",\n \"'Test Series 15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test\",\n \"22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02',\",\n \"entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert\",\n \") @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']),\",\n \"'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test Series 12 S03'],\",\n \"2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin',\",\n \"'Test Series 20 S05', 'Test Series 20 S04', 'Test Series 20 S03', 'Test\",\n \"internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start:\",\n \"0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series 7: <<: *series_ep_pack max_reruns: 0\",\n \"yes series: - Test Series 100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def\",\n \"= \\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes\",\n \"- Test Series 2 - Test Series 3 - Test Series 4 -\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack\",\n \"series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ (\",\n \"Series 24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), (\",\n \"inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1 - Test\",\n \"Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series\",\n \"\\\"\\\"\\\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start:\",\n \"- Test Series 13 - Test Series 14 - Test Series 15 -\",\n \"S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'],\",\n \"Series 20 S07', 'Test Series 20 S05', 'Test Series 20 S04', 'Test Series\",\n \"24 S07', 'Test Series 24 S05', 'Test Series 24 S04'], ), ( 'test_next_series_seasons_season_pack_begin_completed',\",\n \"23 S05', 'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23\",\n \"8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin',\",\n \"1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold:\",\n \"Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series\",\n \"for entity_id in inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find:\",\n \"*series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit\",\n \"S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series 8 S03'],\",\n \"Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series\",\n \"100: <<: *series_ep_pack max_reruns: 0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported\",\n \"23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24 S02', 'Test Series 24\",\n \"'Test Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test\",\n \"Series 11 - Test Series 12 - Test Series 13 - Test Series\",\n \"0 \\\"\\\"\\\" @pytest.fixture() def config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return\",\n \"0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test\",\n \"), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), (\",\n \"'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test\",\n \"50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id\",\n \"7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill',\",\n \"@pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [\",\n \"Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series\",\n \"tasks to be executed in order # Each run_parameter is a tuple of\",\n \"in order # Each run_parameter is a tuple of lists: [task name, list\",\n \"S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), ( 'test_next_series_seasons_season_pack_backfill_and_begin', ['Test\",\n \"S07', 'Test Series 14 S05', 'Test Series 14 S04', 'Test Series 14 S03',\",\n \"( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18\",\n \"'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test\",\n \"backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000\",\n \"Series 6 - Test Series 7 - Test Series 8 - Test Series\",\n \"'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test Series 17 S05',\",\n \"21 S07', 'Test Series 21 S05', 'Test Series 21 S04'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start',\",\n \"- Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: -\",\n \"name, list of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize(\",\n \"100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in\",\n \"execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')], 'disable_tracking': True}, ) @pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [\",\n \"S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series\",\n \"['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series\",\n \"3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4 S03'],\",\n \"'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'],\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns:\",\n \"Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23\",\n \"noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO Add\",\n \"*series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01\",\n \"20 - Test Series 21 - Test Series 22 - Test Series 23\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: - Test Series 8: <<: *series_ep_tracking_pack\",\n \"Series 17 S02', 'Test Series 17 S06'], [ 'Test Series 17 S07', 'Test\",\n \"Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series\",\n \"test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap:\",\n \"builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import\",\n \"- Test Series 11 - Test Series 12 - Test Series 13 -\",\n \"0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0\",\n \"Series 1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series\",\n \"0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"@pytest.mark.parametrize( \\\"task_name,inject,result_find\\\", [ ('test_next_series_seasons_season_pack', ['Test Series 1 S02'], ['Test Series 1 S03']), (\",\n \"*nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02', 'Test Series 21 S06', 'Test Series\",\n \"Series 8 S01', 'Test Series 8 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9\",\n \"Test Series 2 - Test Series 3 - Test Series 4 - Test\",\n \"S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02', 'Test Series 12 S03E01'], ['Test\",\n \"templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start\",\n \"'Test Series 21 S06', 'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test\",\n \"series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series:\",\n \"&nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs:\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: - Test Series 20: <<: *series_ep_tracking_pack max_reruns: 0\",\n \"22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22\",\n \"23 - Test Series 24 - Test Series 25 - Test Series 50\",\n \"], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test\",\n \"S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test\",\n \"* # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry #\",\n \"S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'], ['Test Series 2 S01', 'Test Series\",\n \"'Test Series 17 S03', 'Test Series 17 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test\",\n \"Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test Series\",\n \"6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'],\",\n \"- Test Series 20 - Test Series 21 - Test Series 22 -\",\n \"), ( 'test_next_series_seasons_season_pack_from_start_backfill', ['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series\",\n \"identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01\",\n \"import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry\",\n \"[ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self,\",\n \"S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test\",\n \"'Test Series 9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series\",\n \"- Test Series 4 - Test Series 5 - Test Series 6 -\",\n \"['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test Series 8 S01', 'Test\",\n \"inject: self.inject_series(execute_task, entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert\",\n \"11 S03', 'Test Series 11 S01'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill_and_begin', ['Test Series 12 S02',\",\n \"1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 23: <<: *series_ep_tracking_pack max_reruns:\",\n \"Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series\",\n \"*nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes\",\n \"Series 20 S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test\",\n \"season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings: test_series: season_packs: always test_series:\",\n \"Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes series: - Test Series 1: <<: *series_ep_pack max_reruns:\",\n \"- Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: -\",\n \"yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start: yes series: - Test Series 100: <<:\",\n \"- Test Series 15 - Test Series 16 - Test Series 17 -\",\n \"yes series: - Test Series 50: identified_by: ep begin: S02E01 season_packs: threshold: 1000\",\n \"always test_series: - Test Series 1 - Test Series 2 - Test Series\",\n \"Test Series 17 - Test Series 18 - Test Series 19 - Test\",\n \"<<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 5: <<:\",\n \"series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series:\",\n \"S06', 'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20\",\n \"S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task, task_name, inject,\",\n \"- Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: -\",\n \"test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start:\",\n \"'test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin', ['Test Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07',\",\n \"for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which\",\n \"['Test Series 23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [\",\n \"test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill:\",\n \"test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series 17: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill_and_begin:\",\n \"result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) == len(result_find) # Tests which require\",\n \"S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test\",\n \"'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S02'], ], ) ], ) def test_next_series_seasons_multirun(self, execute_task,\",\n \"<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: - Test Series 13: <<:\",\n \"Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep: next_series_seasons: yes series: - Test Series\",\n \"guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series', options={'inject': [Entry(title=release_name, url='')],\",\n \"Series 18 S02', 'Test Series 18 S06'], ['Test Series 18 S07', 'Test Series\",\n \"'Test Series 13 S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series\",\n \"15 S06'], ['Test Series 15 S07', 'Test Series 15 S05', 'Test Series 15\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap', ['Test Series 19 S02', 'Test Series 19 S06', 'Test Series 19\",\n \"- Test Series 21 - Test Series 22 - Test Series 23 -\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series 8 S02', 'Test Series 8 S03E01'], ['Test\",\n \"yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps:\",\n \"- Test Series 6 - Test Series 7 - Test Series 8 -\",\n \"9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10:\",\n \"22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test Series 23 S02', 'Test Series 23 S03E01',\",\n \"entity_id) task = execute_task(task_name) for result_title in result_find: assert task.find_entry(title=result_title) assert len(task.all_entries) ==\",\n \"'Test Series 14 S04', 'Test Series 14 S03', 'Test Series 14 S01', ],\",\n \"aren't supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name): execute_task( 'inject_series',\",\n \"S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test Series 20\",\n \"# noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.entry import Entry # TODO\",\n \"Each run_parameter is a tuple of lists: [task name, list of series ID(s)\",\n \"series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series:\",\n \"from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes\",\n \"test_next_series_seasons(self, execute_task, task_name, inject, result_find): for entity_id in inject: self.inject_series(execute_task, entity_id) task =\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test\",\n \"yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons: backfill: yes from_start: yes _series_ep_pack: &series_ep_pack identified_by: ep tracking:\",\n \"['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series 11 S03', 'Test\",\n \"'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test Series\",\n \"Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test\",\n \"Test Series 23 - Test Series 24 - Test Series 25 - Test\",\n \"<<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<:\",\n \"S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill', ['Test\",\n \"Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test\",\n \"Series 20 - Test Series 21 - Test Series 22 - Test Series\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns:\",\n \"Series 5: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series\",\n \"Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<: *nss_backfill_from_start series: - Test\",\n \"23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin', ['Test Series 24\",\n \"Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_begin_completed: next_series_seasons: yes series: - Test Series\",\n \"S06'], ['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test\",\n \") ], ) def test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id\",\n \"), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series 14 S06'], [ 'Test\",\n \"anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start\",\n \"1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs: threshold: 1000 reject_eps:\",\n \"Series 19 S02', 'Test Series 19 S06', 'Test Series 19 S07E01'], ['Test Series\",\n \"'Test Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05',\",\n \"S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test Series 21 S02',\",\n \"['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series\",\n \"<<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<:\",\n \"'Test Series 14 S03', 'Test Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test\",\n \"1000 reject_eps: yes _series_ep_tracking_begin_s02e01: &series_ep_tracking_pack_begin_s02e01 identified_by: ep tracking: backfill begin: s02e01 season_packs: threshold:\",\n \"<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: - Test Series 10: <<:\",\n \"multiple tasks to be executed in order # Each run_parameter is a tuple\",\n \"_nss_backfill: &nss_backfill next_series_seasons: backfill: yes _nss_from_start: &nss_from_start next_series_seasons: from_start: yes _nss_backfill_from_start: &nss_backfill_from_start next_series_seasons:\",\n \"Series 1 S02'], ['Test Series 1 S03']), ( 'test_next_series_seasons_season_pack_backfill', ['Test Series 2 S02'],\",\n \"'Test Series 17 S07', 'Test Series 17 S05', 'Test Series 17 S04', 'Test\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test Series 11 S03E01'], ['Test Series\",\n \"[task name, list of series ID(s) to inject, list of result(s) to find]\",\n \"test_next_series_seasons_multirun(self, execute_task, run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id)\",\n \"for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]:\",\n \"S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test\",\n \"<<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start_backfill: <<:\",\n \"20: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin: <<: *nss_backfill series: - Test Series 21:\",\n \"23 S02', 'Test Series 23 S03E01', 'Test Series 23 S06'], [ 'Test Series\",\n \"S02', 'Test Series 16 S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test\",\n \"config(self): \\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task,\",\n \"test_next_series_seasons_season_pack_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 6: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep:\",\n \"Series 4: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_from_start_backfill: <<: *nss_backfill_from_start series: - Test Series\",\n \"19 S07E01'], ['Test Series 19 S07'], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02',\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap_from_start: <<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0\",\n \"23 S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series\",\n \"5 S03', 'Test Series 5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'],\",\n \"12 S03'], ), ( 'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'],\",\n \"*series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill: <<: *nss_backfill series: - Test Series 14: <<: *series_ep_tracking_pack\",\n \"- Test Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack:\",\n \"Series 15 S07', 'Test Series 15 S05', 'Test Series 15 S04'], ), (\",\n \"0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin: <<: *nss_backfill_from_start series: - Test Series 24: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0\",\n \"21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series 21\",\n \"['Test Series 13 S07'], ), ( 'test_next_series_seasons_season_pack_gap_backfill', ['Test Series 14 S02', 'Test Series\",\n \"run_parameters): for this_test in run_parameters: for entity_id in this_test[1]: self.inject_series(execute_task, entity_id) task =\",\n \"( 'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20\",\n \"5 S01'], ), ( 'test_next_series_seasons_season_pack_from_start_backfill_and_begin', ['Test Series 6 S02'], ['Test Series 6 S03'],\",\n \"Series 14 S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test\",\n \"17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test Series 17\",\n \"yes tasks: inject_series: series: settings: test_series: season_packs: always test_series: - Test Series 1\",\n \"<<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<:\",\n \"self.inject_series(execute_task, entity_id) task = execute_task(this_test[0]) for result_title in this_test[2]: assert task.find_entry(title=result_title) assert len(task.all_entries)\",\n \"'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series 16 S07'],\",\n \"ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [ ( [\",\n \"*nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill_and_begin: <<: *nss_backfill\",\n \"<<: *nss_backfill series: - Test Series 15: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<:\",\n \"ep tracking: backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking:\",\n \"Series 3 S03'], ), ( 'test_next_series_seasons_season_pack_from_start', ['Test Series 4 S02'], ['Test Series 4\",\n \"18 S06'], ['Test Series 18 S07', 'Test Series 18 S05', 'Test Series 18\",\n \"<<: *nss_backfill_from_start series: - Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons:\",\n \"'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ),\",\n \"14 S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series\",\n \"more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates: global: parsing: series: internal\",\n \"- Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: -\",\n \"Series 7 - Test Series 8 - Test Series 9 - Test Series\",\n \"- Test Series 12: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_gap: next_series_seasons: yes series: -\",\n \"S06'], ['Test Series 16 S07'], ), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test\",\n \"['Test Series 5 S02'], ['Test Series 5 S03', 'Test Series 5 S01'], ),\",\n \"S03E01'], ['Test Series 10 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start_backfill', ['Test Series 11 S02', 'Test\",\n \"), ( 'test_next_series_seasons_season_pack_gap_from_start', ['Test Series 16 S02', 'Test Series 16 S06'], ['Test Series\",\n \"Series 20 S07E01'], [ 'Test Series 20 S07', 'Test Series 20 S05', 'Test\",\n \"'Test Series 20 S03', 'Test Series 20 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_backfill_and_begin', ['Test\",\n \"21 - Test Series 22 - Test Series 23 - Test Series 24\",\n \"to be executed in order # Each run_parameter is a tuple of lists:\",\n \"Series 50 S02'], ['Test Series 50 S03'], ), ], ) def test_next_series_seasons(self, execute_task,\",\n \"0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns: 0\",\n \"'test_next_series_seasons_season_pack_gap', ['Test Series 13 S02', 'Test Series 13 S06'], ['Test Series 13 S07'],\",\n \"( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7\",\n \"['Test Series 6 S02'], ['Test Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series\",\n \"tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks: inject_series: series: settings:\",\n \"['Test Series 7 S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ),\",\n \"- Test Series 7: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill: <<: *nss_backfill series: -\",\n \"S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill_and_begin', ['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test\",\n \"Series 9 - Test Series 10 - Test Series 11 - Test Series\",\n \"Entry # TODO Add more standard tests class TestNextSeriesSeasonSeasonsPack(object): _config = \\\"\\\"\\\" templates:\",\n \"Series 18: <<: *series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series\",\n \"'test_next_series_seasons_season_pack_and_ep_gap_backfill', ['Test Series 20 S02', 'Test Series 20 S06', 'Test Series 20 S07E01'],\",\n \"*nss_backfill series: - Test Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start\",\n \"require multiple tasks to be executed in order # Each run_parameter is a\",\n \"Series 6 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep', ['Test Series 7 S02', 'Test Series 7\",\n \"7 - Test Series 8 - Test Series 9 - Test Series 10\",\n \"of series ID(s) to inject, list of result(s) to find] @pytest.mark.parametrize( \\\"run_parameters\\\", [\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2: <<: *series_ep_tracking_pack max_reruns:\",\n \"Series 2 S02'], ['Test Series 2 S01', 'Test Series 2 S03'], ), (\",\n \"S03E01', 'Test Series 23 S06'], [ 'Test Series 23 S07', 'Test Series 23\",\n \"Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test Series\",\n \"threshold: 1000 reject_eps: yes _series_ep_tracking_begin_s04e01: &series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs:\",\n \"Series 17 - Test Series 18 - Test Series 19 - Test Series\",\n \"*series_ep_tracking_pack_begin_s04e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap: next_series_seasons: yes series: - Test Series 19: <<: *series_ep_pack\",\n \"S02', 'Test Series 7 S03E01'], ['Test Series 7 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_backfill', ['Test\",\n \"S06'], [ 'Test Series 14 S07', 'Test Series 14 S05', 'Test Series 14\",\n \"Test Series 8: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_backfill_and_begin: <<: *nss_backfill series: - Test\",\n \"'Test Series 21 S07E01'], ['Test Series 21 S07', 'Test Series 21 S05', 'Test\",\n \"Series 22 S03E01', 'Test Series 22 S06'], ['Test Series 22 S07'], ), (\",\n \"S04', 'Test Series 23 S03', 'Test Series 23 S01', ], ), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill_and_begin',\",\n \"18 - Test Series 19 - Test Series 20 - Test Series 21\",\n \"Series 25 - Test Series 50 - Test Series 100 test_next_series_seasons_season_pack: next_series_seasons: yes\",\n \"Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10\",\n \"Series 18 S07', 'Test Series 18 S05', 'Test Series 18 S04'], ), (\",\n \"10 - Test Series 11 - Test Series 12 - Test Series 13\",\n \"- Test Series 14: <<: *series_ep_tracking_pack max_reruns: 0 test_next_series_seasons_season_pack_gap_backfill_and_begin: <<: *nss_backfill series: -\",\n \"&series_ep_tracking_pack_begin_s04e01 identified_by: ep tracking: backfill begin: s04e01 season_packs: threshold: 1000 reject_eps: yes tasks:\",\n \"S02', 'Test Series 14 S06'], [ 'Test Series 14 S07', 'Test Series 14\",\n \"Series 14 S01', ], ), ( 'test_next_series_seasons_season_pack_gap_backfill_and_begin', ['Test Series 15 S02', 'Test Series\",\n \"'Test Series 23 S04', 'Test Series 23 S03', 'Test Series 23 S01', ],\",\n \"\\\"\\\"\\\"Season packs aren't supported by guessit yet.\\\"\\\"\\\" return self._config def inject_series(self, execute_task, release_name):\",\n \"Series 19 S06', 'Test Series 19 S07E01'], ['Test Series 19 S07'], ), (\",\n \"'Test Series 17 S05', 'Test Series 17 S04', 'Test Series 17 S03', 'Test\",\n \"Series 3: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series\",\n \"23 S06'], [ 'Test Series 23 S07', 'Test Series 23 S05', 'Test Series\",\n \"ep begin: S02E01 season_packs: threshold: 1000 reject_eps: yes max_reruns: 0 test_next_series_seasons_season_pack_from_start_multirun: next_series_seasons: from_start:\",\n \"['Test Series 9 S02', 'Test Series 9 S03E01'], ['Test Series 9 S03'], ),\",\n \"9 S03E01'], ['Test Series 9 S03'], ), ( 'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02',\",\n \"- Test Series 19: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_backfill: <<: *nss_backfill series: -\",\n \"[ ( [ 'test_next_series_seasons_season_pack_from_start_multirun', [], ['Test Series 100 S01'], ], [ 'test_next_series_seasons_season_pack_from_start_multirun', [],\",\n \"- Test Series 9: <<: *series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_and_ep_from_start: <<: *nss_from_start series: -\",\n \"backfill season_packs: threshold: 1000 reject_eps: yes _series_ep_tracking_pack: &series_ep_tracking_pack identified_by: ep tracking: backfill season_packs:\",\n \"'test_next_series_seasons_season_pack_and_ep_from_start', ['Test Series 10 S02', 'Test Series 10 S03E01'], ['Test Series 10 S03'],\",\n \"), ( 'test_next_series_seasons_season_pack_gap_from_start_backfill', ['Test Series 17 S02', 'Test Series 17 S06'], [ 'Test\",\n \"max_reruns: 0 test_next_series_seasons_season_pack_gap_from_start: <<: *nss_from_start series: - Test Series 16: <<: *series_ep_pack max_reruns:\",\n \"1: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_backfill: <<: *nss_backfill series: - Test Series 2:\",\n \"['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series 22 S06'], ['Test\",\n \"len(task.all_entries) == len(result_find) # Tests which require multiple tasks to be executed in\",\n \"), ( 'test_next_series_seasons_season_pack_and_ep_gap_from_start', ['Test Series 22 S02', 'Test Series 22 S03E01', 'Test Series\",\n \"*series_ep_tracking_pack_begin_s02e01 max_reruns: 0 test_next_series_seasons_season_pack_from_start: <<: *nss_from_start series: - Test Series 4: <<: *series_ep_pack\"\n]","isConversational":false}}},{"rowIdx":2175,"cells":{"ngram":{"kind":"list like","value":["import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha,","normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get","= [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i","units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape plot. Args: color_set:","< 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]])","break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines","i, j, k: ind for normal_e_m # recalculate the dual of dual, get","prev in l: l = lines.pop(i) if l[1] == prev: l.reverse() break #","k, l] or (h, k, l) \"\"\" str_format = '($' for x in",".. attribute:: miller_list list of input miller index, for hcp in the form","area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This is useful for","energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt,","for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m],","dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list,","get normal, get all the facets functions for wulff shape calculation: |normal| =","in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in","k) simplices from the dual convex hull i, j, k: plane index(same order","def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according to the surface","get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns","** (2 / 3)) @property def effective_radius(self): \"\"\" Radius of the Wulffshape when","planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is plane's distance to","\"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\","return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return","Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list of hkl","sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy +=","[] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map","plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set,","(bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) ..","surface energies, in the same order with input_miller .. attribute:: lattice Lattice object,","the terms of the MIT License. \"\"\" This module define a WulffShape class","surface energies symprec (float): for recp_operation, default is 1e-5. \"\"\" if any([se <","color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri","__copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ =","lattice self.symprec = symprec # 2. get all the data for wulff construction","color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area ..","np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This is useful for determining the","sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl:","the dual convex hull i, j, k: plane index(same order in normal_e_m) \"\"\"","lines are connected one by one. # find the way covering all pts","= m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class WulffShape:","volume of the wulff shape,the weighted surface energy, the anisotropy and shape_factor can","str(x) str_format += '$)' return str_format def get_tri_area(pts): \"\"\" Given a list of","vertices in the convex hull. Useful for identifying catalytically active sites. \"\"\" return","b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 =","normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for","ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds +","simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i, j, k]","<NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data.","off wulff .. attribute:: structure Structure object, input conventional unit cell (with H","if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}):","fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def","color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort","wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on wulff. ..","Default color for facets not present on the Wulff shape. direction: default is",":param miller: \"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt","Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\"","ranges of x, y, z # find the largest distance between on_wulff pts","= [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds,","(miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property","Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys():","in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list =","Shape factor. \"\"\" return self.surface_area / (self.volume ** (2 / 3)) @property def","([float]): list of corresponding surface energies symprec (float): for recp_operation, default is 1e-5.","surface # get cross point from the simplices of the dual convex hull","= [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)]","self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines","\"\"\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3","get cross point from the simplices of the dual convex hull wulff_pt_list =","of on wulff_cv, by comparing the center of the simpx triangle with the","@property def shape_factor(self): \"\"\" This is useful for determining the critical nucleus size.","weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property","[10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface","3 points, Compute the area of this triangle. Args: pts: [a, b, c]","loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1),","for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns","conventional unit cell (with H ) from lattice .. attribute:: miller_list list of","list of miller index and surface energies, with given conventional unit cell. surface","functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for","consider the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex =","[\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list =","the form of [h, k, l] or (h, k, l) \"\"\" str_format =","recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal]","self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points","else: for i, l in enumerate(lines): if prev in l: l = lines.pop(i)","def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\" for surfaces Agrs: hkl:","facets will use default color site. Note: If you decide to set your","'$)' return str_format def get_tri_area(pts): \"\"\" Given a list of coords for 3","self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff Shape from list of miller","their corresponding surface energies, and the total area and volume of the wulff","simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp","wulff .. attribute:: structure Structure object, input conventional unit cell (with H )","= [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp:","< 1: # empty, plane is not on_wulff. continue # assign the color","range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return","color for on_wulff facets according to its # index and the color_list for","value is the color. Undefined facets will use default color site. Note: If","the color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot","dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area of Wulff shape. \"\"\"","ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) #","site. Note: If you decide to set your own colors, it probably won't","energies, with given conventional unit cell. surface energy (Jm^2) is the length of","in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area","color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt color_list","from pymatgen.util.coord import get_angle import numpy as np import scipy as sp from","prev is None: l = lines.pop(0) else: for i, l in enumerate(lines): if","one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev","= list(facet.outer_lines) pt = [] prev = None while len(lines) > 0: if","self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy)","+ 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] =","'<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ =","weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys():","factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>,","self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller","a given view in terms of miller index. The lattice is from the","the critical nucleus size. A large shape factor indicates great anisotropy. See <NAME>.,","True axis_off (bool): default is Ture show_area (bool): default is False alpha (float):","dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute::","= sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for simpx","miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of the conventional unit cell","of the MIT License. \"\"\" This module define a WulffShape class to generate","+ str(-x) + '}' else: str_format += str(x) str_format += '$)' return str_format","\"\"\" |normal| = 1, e_surf is plane's distance to (0, 0, 0), plane","default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color","assign colors according to the surface energies of on_wulff facets. return: (color_list, color_proxy,","= ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim))","\"\"\" return ((3 / 4) * (self.volume / np.pi)) ** (1 / 3)","of the simpx triangle with the plane functions. \"\"\" on_wulff = [False] *","consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies","fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list =","plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color,","for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property","np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \"\"\" Helper","2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors","c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in","/ weighted_energy @property def shape_factor(self): \"\"\" This is useful for determining the critical","find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1]","for x in hkl: if x < 0: str_format += '\\\\overline{' + str(-x)","wulff. .. attribute:: color_area list for all input_miller, total area on wulff, off_wulff","/ 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i,","class WulffShape: \"\"\" Generate Wulff Shape from list of miller index and surface","attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area","factor. \"\"\" return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self):","= [] pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt):","= [] for i, p in enumerate(pt): if i == len(pt) / 2:","is True axis_off (bool): default is Ture show_area (bool): default is False alpha","k: plane index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in","set ranges of x, y, z # find the largest distance between on_wulff","of plotting from a given view in terms of miller index. The lattice","@property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\" return","for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane,","list of indices and their corresponding surface energies, and the total area and","self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2]","facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import","= pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) -","off_color: Default color for facets not present on the Wulff shape. direction: default","1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0],","for i in range(3)] center = np.sum(pts, 0) / 3.0 # check whether","0: str_format += '\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format","from a given view in terms of miller index. The lattice is from","str_format def get_tri_area(pts): \"\"\" Given a list of coords for 3 points, Compute","custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1]","on plots \"(hkl)\" for surfaces Agrs: hkl: in the form of [h, k,","1) or miller_index == (0, 0, 0, 1): return 0, 90 else: cart","of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map =","length of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process:","+= '$)' return str_format def get_tri_area(pts): \"\"\" Given a list of coords for","in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl] return","on the Wulff shape. direction: default is (1, 1, 1) bar_pos: default is","self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op","get_angle(cart, v) return azim, elev @property def volume(self): \"\"\" Volume of the Wulff","= [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops =","tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y, z # find the largest","in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x *","(matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as","self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not","list of hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface","for x in miller_list] # store input data self.structure = Structure(lattice, [\"H\"], [[0,","hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for","2 + 1])])))) for i, p in enumerate(lines): if p not in all_edges:","= [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in","object, input conventional unit cell (with H ) from lattice .. attribute:: miller_list","# recalculate the dual of dual, get the wulff shape. # conner <->","self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i == len(pt)","a list of coords for 3 points, Compute the area of this triangle.","Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d","empty, plane is not on_wulff. continue # assign the color for on_wulff facets","to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\"","self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl,","max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range *","if any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\")","energy for x in normal] dual_pt = [x / energy for x in","= 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display","surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy","== (0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim","\"\"\" lines = list(facet.outer_lines) pt = [] prev = None while len(lines) >","x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1:","self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + '","(2016). Surface energies of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure","scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha)","= wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in","considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) ..","custom_colors=custom_colors) if not direction: # If direction is not specified, use the miller","convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp","1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1])","legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0,","x, y, z # find the largest distance between on_wulff pts and the","surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find","or (h, k, l) \"\"\" str_format = '($' for x in hkl: if","* bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos)","tot_corner_sites(self): \"\"\" Returns the number of vertices in the convex hull. Useful for","convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with","\"\"\" Helper container for each Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt,","for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = []","area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl]","the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex =","= index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines =","miller_index == (0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index)","0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is True aspect_ratio:","list of [i, j, k] , ndim = 3 # i, j, k:","the number of vertices in the convex hull. Useful for identifying catalytically active","normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller","True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already","transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) ..","\"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np","ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) #","= self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] -","surface energy (Jm^2) is the length of normal. Wulff shape is the convex","hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def","attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets off","Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ =","tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl]","Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted","in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i])","convex hull. Useful for identifying catalytically active sites. \"\"\" all_edges = [] for","as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors(","= [] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines","Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self):","as np import scipy as sp from scipy.spatial import ConvexHull import logging import","np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return","ndim)) # list of [i, j, k] , ndim = 3 # i,","ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy =",".. attribute:: miller_area ($hkl$): area for all input_miller \"\"\" def __init__(self, lattice, miller_list,","of [h, k, l] or (h, k, l) \"\"\" str_format = '($' for","default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on","\"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of","(0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\"","on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property","Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2)","of the wulff shape,the weighted surface energy, the anisotropy and shape_factor can also","def get_tri_area(pts): \"\"\" Given a list of coords for 3 points, Compute the","on_wulff. continue # assign the color for on_wulff facets according to its #","shape (nfacet, ndim)) # list of [i, j, k] , ndim = 3","# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the","from the convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller,","to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts","the sorted pts in a facet used to draw a line \"\"\" lines","' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self: get","have the color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import","display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0]","self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float)","sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal|","alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool)","= [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices #","wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp","0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff","% (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self,","from weighted surface energy The ideal sphere is 0. \"\"\" square_diff_energy = 0","lattice, a list of indices and their corresponding surface energies, and the total","0] elev = get_angle(cart, v) return azim, elev @property def volume(self): \"\"\" Volume","while len(lines) > 0: if prev is None: l = lines.pop(0) else: for","orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units),","i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs),","area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl","normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind planes","if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2","legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape plot. Args: color_set: default","square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for","in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2.","+ '}' else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts):","m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index:",".. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list","e_surf from self: normal_e_m to get the plane functions dual_simp: (i, j, k)","active sites. \"\"\" all_edges = [] for facet in self.facets: edges = []","is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2.","attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color","self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\"","else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off:","the wulff shape. # conner <-> surface # get cross point from the","corresponding surface energies, and the total area and volume of the wulff shape,the","color site. Note: If you decide to set your own colors, it probably","get the plane functions dual_simp: (i, j, k) simplices from the dual convex","get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane,","hkl on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) -","center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts)","direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65]","fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x')","+= '\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format += '$)'","is on wulff. .. attribute:: color_area list for all input_miller, total area on","x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list","def tot_edges(self): \"\"\" Returns the number of edges in the convex hull. Useful","= mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i,","for all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice:","plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show()","matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt",") from lattice .. attribute:: miller_list list of input miller index, for hcp","find the plane, move to the next simplices break for plane in self.facets:","units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if","[] prev = None while len(lines) > 0: if prev is None: l","Coefficient of Variation from weighted surface energy The ideal sphere is 0. \"\"\"","= [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : '","make any sense to have the color bar on. Return: (matplotlib.pyplot) \"\"\" import","consistent display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range","to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf","of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0","miller self.points = [] self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff Shape","+= self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the","Shape from list of miller index and surface energies, with given conventional unit","shape. # conner <-> surface # get cross point from the simplices of","x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i,","ideal sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict =","\"\"\" Given a list of coords for 3 points, Compute the area of","matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy,","for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig","surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import","Pymatgen Development Team. # Distributed under the terms of the MIT License. \"\"\"","\"\"\" Returns: (float) Coefficient of Variation from weighted surface energy The ideal sphere","display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range *","= get_angle(cart, v) return azim, elev @property def volume(self): \"\"\" Volume of the","** (1 / 3) @property def total_surface_energy(self): \"\"\" Total surface energy of the",".. attribute:: show_area .. attribute:: off_color color of facets off wulff .. attribute::","1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap","fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax =","miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according","normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller:","\"\"\" Total surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self):","* 2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if","conventional unit cell. surface energy (Jm^2) is the length of normal. Wulff shape","\"\"\" str_format = '($' for x in hkl: if x < 0: str_format","else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z')","cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm","= 3 # i, j, k: ind for normal_e_m # recalculate the dual","normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x","= max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev =","str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \"\"\" Given a","miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def","if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1),","is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) -","len(plane.points) < 1: # empty, plane is not on_wulff. continue # assign the","self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry","direction: # If direction is not specified, use the miller indices of #","pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri =","for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for","for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller =","enumerate(lines): if prev in l: l = lines.pop(i) if l[1] == prev: l.reverse()","catalytically active sites. \"\"\" all_edges = [] for facet in self.facets: edges =","miller_list list of input miller index, for hcp in the form of hkil","(float) Coefficient of Variation from weighted surface energy The ideal sphere is 0.","indices and their corresponding surface energies, and the total area and volume of","simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts,","dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes","Wulff Shape from list of miller index and surface energies, with given conventional","plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next","color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for","0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1],","Variation from weighted surface energy The ideal sphere is 0. \"\"\" square_diff_energy =","one by one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist())","off_color, custom_colors=custom_colors) if not direction: # If direction is not specified, use the","hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp =","to hkl, in the same order with input_miller .. attribute:: e_surf_list list of","self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane","# Copyright (c) Pymatgen Development Team. # Distributed under the terms of the","= tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice =","useful for determining the critical nucleus size. A large shape factor indicates great","in a facet used to draw a line \"\"\" lines = list(facet.outer_lines) pt","return str_format def get_tri_area(pts): \"\"\" Given a list of coords for 3 points,",".. attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices","area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict):","each facet with a dictionary. The key is the corresponding Miller index and","\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\"","coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms","plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim, elev for","normal_pt = [x * energy for x in normal] dual_pt = [x /",".. attribute:: e_surf_list list of input surface energies, in the same order with","center = np.sum(pts, 0) / 3.0 # check whether the center of the","prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red',","1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2),","is Ture show_area (bool): default is False alpha (float): chosen from 0 to","the Wulffshape is approximated as a sphere. Returns: (float) radius. \"\"\" return ((3","facets not present on the Wulff shape. direction: default is (1, 1, 1)","and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex =","of corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \"\"\" if","shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials.","cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for","pts in a facet used to draw a line \"\"\" lines = list(facet.outer_lines)","cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap,","the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental","l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75,","default is Ture show_area (bool): default is False alpha (float): chosen from 0","e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append(","as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff,","plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff","(float) radius. \"\"\" return ((3 / 4) * (self.volume / np.pi)) ** (1","enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1]","radius. \"\"\" return ((3 / 4) * (self.volume / np.pi)) ** (1 /","/ np.pi)) ** (1 / 3) @property def total_surface_energy(self): \"\"\" Total surface energy","e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\" self.normal","on wulff. .. attribute:: color_area list for all input_miller, total area on wulff,","for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys():",".. attribute:: on_wulff list for all input_miller, True is on wulff. .. attribute::","miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get","self.lattice = lattice self.symprec = symprec # 2. get all the data for","ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright","0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list)))","/ 3.0 # check whether the center of the simplices is on one","of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as","from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x,","with color 3. get wulff_area and other properties .. attribute:: debug (bool) ..","energies, and the total area and volume of the wulff shape,the weighted surface","is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of","Prepare for display on plots \"(hkl)\" for surfaces Agrs: hkl: in the form","0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff:","vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i]","object, the input lattice for the conventional unit cell .. attribute:: facets [WulffFacet]","in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in","input data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for","return area_tri class WulffFacet: \"\"\" Helper container for each Wulff plane. \"\"\" def","e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1,","off_color, custom_colors={}): \"\"\" assign colors according to the surface energies of on_wulff facets.","\"\"\" all_hkl = [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic","* len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts =","self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in","recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)):","of indices and their corresponding surface energies, and the total area and volume","attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list ..","tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number of vertices in the convex","r\"\"\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to","with input_miller .. attribute:: e_surf_list list of input surface energies, in the same","ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds =","2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if p","grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True,","\"\"\" Returns the number of edges in the convex hull. Useful for identifying","\"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\"","1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area,","sites. \"\"\" all_edges = [] for facet in self.facets: edges = [] pt","debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool)","way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt","the same order with input_miller .. attribute:: lattice Lattice object, the input lattice","= [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set)","1e-5. \"\"\" if any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface","color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff])","0. .. attribute:: miller_area ($hkl$): area for all input_miller \"\"\" def __init__(self, lattice,","area and volume of the wulff shape,the weighted surface energy, the anisotropy and","* \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number of","\"\"\" Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]:","is plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z =","default is 1e-5. \"\"\" if any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical","color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according to the surface energies of","from lattice .. attribute:: miller_list list of input miller index, for hcp in","== (0, 0, 1) or miller_index == (0, 0, 0, 1): return 0,","input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area","recp_operation, default is 1e-5. \"\"\" if any([se < 0 for se in e_surf_list]):","area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \"\"\" Helper container","get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in a facet used to draw","hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color","class to generate the Wulff shape from a lattice, a list of indices","self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure =","hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation, default","the input lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for","bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the","(bool): default is False legend_on (bool): default is True aspect_ratio: default is (8,","\"\"\" Returns the number of vertices in the convex hull. Useful for identifying","[r,g,b,alpha}): Customize color of each facet with a dictionary. The key is the",".. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute::","the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \"\"\"","cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return","pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np import scipy","3. get wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha","area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all","dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points = []","the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label","to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list)","1, e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x +",":param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\"","points, Compute the area of this triangle. Args: pts: [a, b, c] three","0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v)","self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether","key is the corresponding Miller index and value is the color. Undefined facets","Undefined facets will use default color site. Note: If you decide to set","\"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self):",".. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff","* 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on:","e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z","present on the Wulff shape. direction: default is (1, 1, 1) bar_pos: default","# make sure the lines are connected one by one. # find the","distance to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z =","input conventional unit cell (with H ) from lattice .. attribute:: miller_list list","you use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.;","= dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list of","plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z","all the facets functions for wulff shape calculation: |normal| = 1, e_surf is","lattice .. attribute:: miller_list list of input miller index, for hcp in the","enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area","len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]]","v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property","input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object","not on_wulff. continue # assign the color for on_wulff facets according to its","(float): for recp_operation, default is 1e-5. \"\"\" if any([se < 0 for se","given view in terms of miller index. The lattice is from the conventional","color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf","= scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2),","import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list,","alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}):","self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff,","The key is the corresponding Miller index and value is the color. Undefined","any sense to have the color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib","(float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy","0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is True","[round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N)","[] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list)","all_edges = [] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet)","plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to","dual_simp: (i, j, k) simplices from the dual convex hull i, j, k:","Note: If you decide to set your own colors, it probably won't make","self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from weighted surface","custom_colors={}): \"\"\" Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off","shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl)","the origin, # to ensure complete and consistent display for all directions r_range","v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri","pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of","[a, b, c] three points \"\"\" a, b, c = pts[0], pts[1], pts[2]","_get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according to the surface energies","miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list)","\"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\"","for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list","simplices from the dual convex hull i, j, k: plane index(same order in","if len(plane.points) < 1: # empty, plane is not on_wulff. continue # assign","def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property","sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\"","+ bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2","len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number of edges in the convex","list of input surface energies, in the same order with input_miller .. attribute::","self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on","= [] prev = None while len(lines) > 0: if prev is None:","and the origin, # to ensure complete and consistent display for all directions","color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction:","unit cell .. attribute:: facets [WulffFacet] for all facets considering symm .. attribute::","'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture show_area (bool):","for x in normal] dual_pt = [x / energy for x in normal]","(dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of","\"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area of Wulff","direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev","2005), p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area / (self.volume ** (2","[x * energy for x in normal] dual_pt = [x / energy for","se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig =","i, l in enumerate(lines): if prev in l: l = lines.pop(i) if l[1]","convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex","k, l) \"\"\" str_format = '($' for x in hkl: if x <","attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from","\"\"\" Prepare for display on plots \"(hkl)\" for surfaces Agrs: hkl: in the","for determining the critical nucleus size. A large shape factor indicates great anisotropy.","0, 1) or miller_index == (0, 0, 0, 1): return 0, 90 else:","[WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind planes = [] recp =","support of plotting from a given view in terms of miller index. The","e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1","Ture show_area (bool): default is False alpha (float): chosen from 0 to 1","0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl","= miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops)","a line \"\"\" lines = list(facet.outer_lines) pt = [] prev = None while","x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec =","self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller","total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for","ind for normal_e_m # recalculate the dual of dual, get the wulff shape.","for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted","\"\"\" Locate the plane for simpx of on wulff_cv, by comparing the center","# 2. get all the data for wulff construction # get all the","comparing the center of the simpx triangle with the plane functions. \"\"\" on_wulff","weighted_energy @property def shape_factor(self): \"\"\" This is useful for determining the critical nucleus","are connected one by one. # find the way covering all pts and","color of each facet with a dictionary. The key is the corresponding Miller","miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]): list","plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri)","miller): \"\"\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param","in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /=","with a dictionary. The key is the corresponding Miller index and value is","identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the","energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib","of the conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil","sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy","is None: l = lines.pop(0) else: for i, l in enumerate(lines): if prev","= self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for","all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self,","plane is not on_wulff. continue # assign the color for on_wulff facets according","of the Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float)","edges = [] pt = self.get_line_in_facet(facet) lines = [] for i, p in","hexagonal lattices. If you use this code extensively, consider citing the following: <NAME>.;","<NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific","|normal| = 1, e_surf is plane's distance to (0, 0, 0), plane function:","a dictionary. The key is the corresponding Miller index and value is the","\"\"\" import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] *","<NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \"\"\" from","self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt","return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return","wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: #","on wulff_cv, by comparing the center of the simpx triangle with the plane","other properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set","alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1,","chosen from 0 to 1 (float), default is 1 off_color: Default color for","of ints, shape (nfacet, ndim)) # list of [i, j, k] , ndim","shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff}","# check whether [pts] is empty if len(plane.points) < 1: # empty, plane","0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in",".. attribute:: structure Structure object, input conventional unit cell (with H ) from","pt = [] prev = None while len(lines) > 0: if prev is","prev: l.reverse() break # make sure the lines are connected one by one.","normal] dual_pt = [x / energy for x in normal] color_plane = color_ind[divmod(i,","* energy for x in normal] dual_pt = [x / energy for x","def surface_area(self): \"\"\" Total surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property","= wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex","r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if","Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as","color 3. get wulff_area and other properties .. attribute:: debug (bool) .. attribute::","the conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering symm","for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1,","according to its # index and the color_list for on_wulff plane_color = color_list[plane.index]","Given a list of coords for 3 points, Compute the area of this","shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y')","mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i])","on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf)","input lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for all","the total area and volume of the wulff shape,the weighted surface energy, the",", ndim = 3 # i, j, k: ind for normal_e_m # recalculate","of vertices in the convex hull. Useful for identifying catalytically active sites. \"\"\"","with the plane functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0]","as a sphere. Returns: (float) radius. \"\"\" return ((3 / 4) * (self.volume","of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1.","calculation: |normal| = 1, e_surf is plane's distance to (0, 0, 0), normal[0]x","(color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot as","the Wulff shape. direction: default is (1, 1, 1) bar_pos: default is [0.75,","index, for hcp in the form of hkil .. attribute:: hkl_list modify hkill","x < 0: str_format += '\\\\overline{' + str(-x) + '}' else: str_format +=","and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute::","p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]),","Wulffshape is approximated as a sphere. Returns: (float) radius. \"\"\" return ((3 /","one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if","[0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is","off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller \"\"\" def","energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] #","(ndarray of ints, shape (nfacet, ndim)) # list of [i, j, k] ,","x in miller_list] # store input data self.structure = Structure(lattice, [\"H\"], [[0, 0,","by comparing the center of the simpx triangle with the plane functions. \"\"\"","of the Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl:","matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff =","== prev: l.reverse() break # make sure the lines are connected one by","of each facet with a dictionary. The key is the corresponding Miller index","MIT License. \"\"\" This module define a WulffShape class to generate the Wulff","i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False)","shape,the weighted surface energy, the anisotropy and shape_factor can also be calculated. In","axis_off (bool): default is Ture show_area (bool): default is False alpha (float): chosen","_get_simpx_plane(self): \"\"\" Locate the plane for simpx of on wulff_cv, by comparing the","def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of the","return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return","properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set ..","Distributed under the terms of the MIT License. \"\"\" This module define a","index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param","= plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list","5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots","v) return azim, elev @property def volume(self): \"\"\" Volume of the Wulff shape","bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else","sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges","the area of this triangle. Args: pts: [a, b, c] three points \"\"\"","for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display","decide to set your own colors, it probably won't make any sense to","(negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in","wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list)","scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1,","already find the plane, move to the next simplices break for plane in","cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart,","sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number of edges","crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import","WulffShape: \"\"\" Generate Wulff Shape from list of miller index and surface energies,","store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp","= miller self.points = [] self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff","ax.add_collection3d(tri) # set ranges of x, y, z # find the largest distance","azim, elev @property def volume(self): \"\"\" Volume of the Wulff shape \"\"\" return","Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list","for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller)","dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices","get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev =","index and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) #","self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number of vertices in","alpha, off_color, custom_colors={}): \"\"\" assign colors according to the surface energies of on_wulff","in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf","alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not specified, use","\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp","miller index, for hcp in the form of hkil .. attribute:: hkl_list modify","plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5:","pts: [a, b, c] three points \"\"\" a, b, c = pts[0], pts[1],","e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction","(%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def","the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm,","plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from","Useful for identifying catalytically active sites. \"\"\" all_edges = [] for facet in","all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider","r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range","Radius of the Wulffshape when the Wulffshape is approximated as a sphere. Returns:","|normal| = 1, e_surf is plane's distance to (0, 0, 0), normal[0]x +","v2)) / 2) return area_tri class WulffFacet: \"\"\" Helper container for each Wulff","x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list","= tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x","[x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy,","\"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt","self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list,","according to lattice apply symmops to get all the miller index, then get","index and surface energies, with given conventional unit cell. surface energy (Jm^2) is","get all the miller index, then get normal, get all the facets functions","Returns the number of vertices in the convex hull. Useful for identifying catalytically","= '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\"","for the conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering","tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice","str_format += '\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format +=","in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) !=","also be calculated. In support of plotting from a given view in terms","energies symprec (float): for recp_operation, default is 1e-5. \"\"\" if any([se < 0","units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100)","in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list =","\"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply","x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec #","not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x","License. \"\"\" This module define a WulffShape class to generate the Wulff shape","custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in","Process: 1. get wulff simplices 2. label with color 3. get wulff_area and","2. label with color 3. get wulff_area and other properties .. attribute:: debug","tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y, z # find the","= mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y, z #","3. consider the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex","def _get_simpx_plane(self): \"\"\" Locate the plane for simpx of on wulff_cv, by comparing","custom_colors={}): \"\"\" assign colors according to the surface energies of on_wulff facets. return:","{hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface","i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x)","= [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list),","Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__","e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color]","e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] =","color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf","plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param","color for facets not present on the Wulff shape. direction: default is (1,","calculated. In support of plotting from a given view in terms of miller","self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append(","# simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i, j,","facet used to draw a line \"\"\" lines = list(facet.outer_lines) pt = []","for hexagonal lattices. If you use this code extensively, consider citing the following:","bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 =","index. The lattice is from the conventional unit cell, and (hkil) for hexagonal","energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x","for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list])","in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i","on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3. get","only one hkl on wulff, choose the color of the median cnorm =","area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl]","= self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is","Args: miller_index: viewing direction Returns: azim, elev for plotting \"\"\" if miller_index ==","hull. Useful for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self):","corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \"\"\" if any([se","return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area","c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c)","0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0],","to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet]","l) \"\"\" str_format = '($' for x in hkl: if x < 0:","color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool): default is","# add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper","= color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by","'<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def","self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane()","between on_wulff pts and the origin, # to ensure complete and consistent display","total area and volume of the wulff shape,the weighted surface energy, the anisotropy","= dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points =","if x < 0: str_format += '\\\\overline{' + str(-x) + '}' else: str_format","return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\"","2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ =","tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all the data","input miller index, for hcp in the form of hkil .. attribute:: hkl_list","attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list","# maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0],","x in normal] dual_pt = [x / energy for x in normal] color_plane","hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull","= fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10],","tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha))","colors, it probably won't make any sense to have the color bar on.","[self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self):","in self.facets: # check whether [pts] is empty if len(plane.points) < 1: #","unit cell (with H ) from lattice .. attribute:: miller_list list of input","hkl_list modify hkill to hkl, in the same order with input_miller .. attribute::","e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list","volume(self): \"\"\" Volume of the Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self):","0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape plot.","energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy =","self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for i, p","off_color color of facets off wulff .. attribute:: structure Structure object, input conventional","Locate the plane for simpx of on wulff_cv, by comparing the center of","miller index. The lattice is from the conventional unit cell, and (hkil) for",":param index: :param m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf = e_surf","str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list","j, k] , ndim = 3 # i, j, k: ind for normal_e_m","- plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts)","all_hkl = [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops","triangle. Args: pts: [a, b, c] three points \"\"\" a, b, c =","[h, k, l] or (h, k, l) \"\"\" str_format = '($' for x","aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig,","structure Structure object, input conventional unit cell (with H ) from lattice ..","the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\"","symmops to get all the miller index, then get normal, get all the","3 # i, j, k: ind for normal_e_m # recalculate the dual of","r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off')","normal[2]z = e_surf from self: normal_e_m to get the plane functions dual_simp: (i,","facet): \"\"\" Returns the sorted pts in a facet used to draw a","from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices","def tot_corner_sites(self): \"\"\" Returns the number of vertices in the convex hull. Useful","active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number of","sorted pts in a facet used to draw a line \"\"\" lines =","@property def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from weighted surface energy","plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\"","self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area of Wulff shape. \"\"\" return","\"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict","= lattice self.symprec = symprec # 2. get all the data for wulff","miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' :","won't make any sense to have the color bar on. Return: (matplotlib.pyplot) \"\"\"","in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\"","None while len(lines) > 0: if prev is None: l = lines.pop(0) else:","x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range","abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] +=","len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only","# store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp =","\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number of vertices","\"\"\" This is useful for determining the critical nucleus size. A large shape","import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ =","cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff]","Agrs: hkl: in the form of [h, k, l] or (h, k, l)","wulff simplices 2. label with color 3. get wulff_area and other properties ..","default color site. Note: If you decide to set your own colors, it","1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False)","1 off_color: Default color for facets not present on the Wulff shape. direction:","the color. Undefined facets will use default color site. Note: If you decide","enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)])","a facet used to draw a line \"\"\" lines = list(facet.outer_lines) pt =","choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) +","normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the plane functions","logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\" for","dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list","is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default","bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar","for plotting \"\"\" if miller_index == (0, 0, 1) or miller_index == (0,","condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp =","= color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else:","(float) Shape factor. \"\"\" return self.surface_area / (self.volume ** (2 / 3)) @property","facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt)","plane index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)]","e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the Wulff plot. Args: *args: Passed","import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff","y, z # find the largest distance between on_wulff pts and the origin,","from the conventional unit cell, and (hkil) for hexagonal lattices. If you use","axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8,","boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if","in terms of miller index. The lattice is from the conventional unit cell,","to have the color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl",":param dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf","# plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\")","complete and consistent display for all directions r_range = max([np.linalg.norm(x) for x in","= plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2","order with input_miller .. attribute:: e_surf_list list of input surface energies, in the","p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area / (self.volume ** (2 /","mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1,","miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the Wulff plot. Args: *args:","form of [h, k, l] or (h, k, l) \"\"\" str_format = '($'","1])])))) for i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges)","from a lattice, a list of indices and their corresponding surface energies, and","== len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])]))))","on_wulff list for all input_miller, True is on wulff. .. attribute:: color_area list","dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i,","all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy","\"\"\" Returns the sorted pts in a facet used to draw a line","for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 *","x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: #","color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the Wulff plot.","recalculate the dual of dual, get the wulff shape. # conner <-> surface","- 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in","\"\"\" assign colors according to the surface energies of on_wulff facets. return: (color_list,","e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x:","= get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev","use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.;","miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig","for 3 points, Compute the area of this triangle. Args: pts: [a, b,","* 1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy,","and shape_factor can also be calculated. In support of plotting from a given","the Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl","hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\" for surfaces Agrs: hkl: in","3.0 # check whether the center of the simplices is on one plane","e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map =","symprec # 2. get all the data for wulff construction # get all","# i, j, k: ind for normal_e_m # recalculate the dual of dual,","prev = None while len(lines) > 0: if prev is None: l =","0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf,","shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25')","whether the center of the simplices is on one plane for plane in","= None while len(lines) > 0: if prev is None: l = lines.pop(0)","H ) from lattice .. attribute:: miller_list list of input miller index, for","self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1],","\"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\"","lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for all facets","\"\"\" Generate Wulff Shape from list of miller index and surface energies, with","surface energies, and the total area and volume of the wulff shape,the weighted","4) * (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \"\"\"","(hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for","+= get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the","[WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from the dual","for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if","label with color 3. get wulff_area and other properties .. attribute:: debug (bool)","wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller \"\"\"","*args, **kwargs): r\"\"\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs:","to get the plane functions dual_simp: (i, j, k) simplices from the dual","if miller_index == (0, 0, 1) or miller_index == (0, 0, 0, 1):","(2 / 3)) @property def effective_radius(self): \"\"\" Radius of the Wulffshape when the","...]: list of hkl or hkil for hcp e_surf_list ([float]): list of corresponding","/ (self.volume ** (2 / 3)) @property def effective_radius(self): \"\"\" Radius of the","e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm =","1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1,","facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from the","False alpha (float): chosen from 0 to 1 (float), default is 1 off_color:","simplices is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center)","from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np import","size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>.","e_surf_list list of input surface energies, in the same order with input_miller ..","spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' %","\"\"\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx","= symprec # 2. get all the data for wulff construction # get","__init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of the conventional","[x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices","energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area of","/ 3) @property def total_surface_energy(self): \"\"\" Total surface energy of the Wulff shape.","= self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops:","for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt =","area_tri class WulffFacet: \"\"\" Helper container for each Wulff plane. \"\"\" def __init__(self,","= self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy","(0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from","to get all the miller index, then get normal, get all the facets","return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number of edges in the","def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def","def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\" return {hkl:","for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1],","plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 *","cell (with H ) from lattice .. attribute:: miller_list list of input miller","get all the facets functions for wulff shape calculation: |normal| = 1, e_surf","op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not","normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param","the number of edges in the convex hull. Useful for identifying catalytically active","bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt","color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map","True is on wulff. .. attribute:: color_area list for all input_miller, total area","self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if","if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off')","= 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>'","1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2,","# if there is only one hkl on wulff, choose the color of","= [x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal,","plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to","great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461","pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1,","pts and the origin, # to ensure complete and consistent display for all","from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts","line \"\"\" lines = list(facet.outer_lines) pt = [] prev = None while len(lines)","(8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary.","simpx[2]]) # already find the plane, move to the next simplices break for","colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for","Team. # Distributed under the terms of the MIT License. \"\"\" This module","= True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) #","# find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev =","= ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store","extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface","input_miller .. attribute:: lattice Lattice object, the input lattice for the conventional unit","this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.","data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x","the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the","mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,","surfaces Agrs: hkl: in the form of [h, k, l] or (h, k,","by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| =","surface energy, the anisotropy and shape_factor can also be calculated. In support of","# display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm,","fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both',","= [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0 #","miller: \"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt =","return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according","ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts]","tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal =","__init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf:","functions for wulff shape calculation: |normal| = 1, e_surf is plane's distance to","'0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger","= 0. .. attribute:: miller_area ($hkl$): area for all input_miller \"\"\" def __init__(self,","([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]): list of","add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left',","if prev is None: l = lines.pop(0) else: for i, l in enumerate(lines):","lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list of","= [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area","= '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__)","number of vertices in the convex hull. Useful for identifying catalytically active sites.","range(3)] center = np.sum(pts, 0) / 3.0 # check whether the center of",".. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list","return azim, elev @property def volume(self): \"\"\" Volume of the Wulff shape \"\"\"","is from the conventional unit cell, and (hkil) for hexagonal lattices. If you","> 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one","@property def effective_radius(self): \"\"\" Radius of the Wulffshape when the Wulffshape is approximated","Volume of the Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns","miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x,","color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def","return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15,","key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1],","in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in","Total surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\"","= dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area","pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0","the color for on_wulff facets according to its # index and the color_list","find the largest distance between on_wulff pts and the origin, # to ensure","hull. Useful for identifying catalytically active sites. \"\"\" all_edges = [] for facet","one hkl on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list)","container for each Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index,","terms of miller index. The lattice is from the conventional unit cell, and","i in range(3)] center = np.sum(pts, 0) / 3.0 # check whether the","# assign the color for on_wulff facets according to its # index and","wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig):","8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The","lines.pop(i) if l[1] == prev: l.reverse() break # make sure the lines are","2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This is","azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is empty if","of coords for 3 points, Compute the area of this triangle. Args: pts:","used to draw a line \"\"\" lines = list(facet.outer_lines) pt = [] prev","in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in","l = lines.pop(i) if l[1] == prev: l.reverse() break # make sure the","surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns:","# get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) #","\"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def","@property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy /","return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot","color of facets off wulff .. attribute:: structure Structure object, input conventional unit","= 1, e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y","and consistent display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list])","= 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return","citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of","scipy as sp from scipy.spatial import ConvexHull import logging import warnings __author__ =","in normal] dual_pt = [x / energy for x in normal] color_plane =","from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) #","1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list","miller index and surface energies, with given conventional unit cell. surface energy (Jm^2)","logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The","simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next simplices","show_area .. attribute:: off_color color of facets off wulff .. attribute:: structure Structure","0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev","e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list]","miller_index: viewing direction Returns: azim, elev for plotting \"\"\" if miller_index == (0,","operations(symmops) according to lattice apply symmops to get all the miller index, then","plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool):","= e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig =","np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) /","+ normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind","critical nucleus size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>.","return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for simpx of on wulff_cv,","(self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \"\"\" Total surface","from the dual convex hull i, j, k: plane index(same order in normal_e_m)","import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff","- weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def","of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex","is useful for determining the critical nucleus size. A large shape factor indicates","wulff_cv, by comparing the center of the simpx triangle with the plane functions.","= [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure = Structure(lattice,","if there is only one hkl on wulff, choose the color of the","0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim,","8), custom_colors={}): \"\"\" Get the Wulff shape plot. Args: color_set: default is 'PuBu'","module define a WulffShape class to generate the Wulff shape from a lattice,","facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True,","shape calculation: |normal| = 1, e_surf is plane's distance to (0, 0, 0),","facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for","dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area =","_get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim, elev for plotting \"\"\"","shape from a lattice, a list of indices and their corresponding surface energies,","+ str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller),","a list of indices and their corresponding surface energies, and the total area","specified, use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda","and their corresponding surface energies, and the total area and volume of the","triangle with the plane functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area =","and (hkil) for hexagonal lattices. If you use this code extensively, consider citing","default is False legend_on (bool): default is True aspect_ratio: default is (8, 8)","is the color. Undefined facets will use default color site. Note: If you","all input_miller, True is on wulff. .. attribute:: color_area list for all input_miller,","according to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,","@property def tot_corner_sites(self): \"\"\" Returns the number of vertices in the convex hull.","the convex hull. Useful for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property","weighted surface energy The ideal sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy","of Variation from weighted surface energy The ideal sphere is 0. \"\"\" square_diff_energy","check whether [pts] is empty if len(plane.points) < 1: # empty, plane is","in the form of hkil .. attribute:: hkl_list modify hkill to hkl, in","sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for simpx of",".. attribute:: color_area list for all input_miller, total area on wulff, off_wulff =",".. attribute:: hkl_list modify hkill to hkl, in the same order with input_miller","\"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off:","= '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__","if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e","mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf","2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff,","get wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency","any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind","A large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics","[self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x))","cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units =","recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in","center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if","list for all input_miller, True is on wulff. .. attribute:: color_area list for","show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8),","recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl:","define a WulffShape class to generate the Wulff shape from a lattice, a","if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt","# get cross point from the simplices of the dual convex hull wulff_pt_list","vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff, choose the","+ [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\"","input surface energies, in the same order with input_miller .. attribute:: lattice Lattice","fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\"","for i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges) return","wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) #","hkill to hkl, in the same order with input_miller .. attribute:: e_surf_list list","pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05,","if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction","attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list ..","The ideal sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict","# conner <-> surface # get cross point from the simplices of the","if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is","len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center","the plane, move to the next simplices break for plane in self.facets: plane.outer_lines.sort()","azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0]","normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index","area for all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args:","terms of the MIT License. \"\"\" This module define a WulffShape class to","in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index]","Helper container for each Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt,","WulffShape class to generate the Wulff shape from a lattice, a list of","Returns the number of edges in the convex hull. Useful for identifying catalytically","for normal_e_m # recalculate the dual of dual, get the wulff shape. #","'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__","area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in","The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>'","0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1])","self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to","maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1])","pymatgen.util.coord import get_angle import numpy as np import scipy as sp from scipy.spatial","color_area list for all input_miller, total area on wulff, off_wulff = 0. ..","the simplices is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal,","pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri =","for surfaces Agrs: hkl: in the form of [h, k, l] or (h,","* area_hkl) \"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl]","scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__","is not specified, use the miller indices of # maximum area. direction =","if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args:","def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area","= self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ **","\"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal:","of dual, get the wulff shape. # conner <-> surface # get cross","cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on","azim, elev for plotting \"\"\" if miller_index == (0, 0, 1) or miller_index","self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \"\"\" Radius of","Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area / (self.volume","self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index","can also be calculated. In support of plotting from a given view in","enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i *","(with H ) from lattice .. attribute:: miller_list list of input miller index,","[[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0],","(bool): default is Ture show_area (bool): default is False alpha (float): chosen from","ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing","lattices. If you use this code extensively, consider citing the following: <NAME>.; <NAME>.;","= logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\" for surfaces","= abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \"\"\" Helper container for","cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in","the plane functions dual_simp: (i, j, k) simplices from the dual convex hull","[line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def","= recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in","attribute:: hkl_list modify hkill to hkl, in the same order with input_miller ..","ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds","matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for simpx of on","nucleus size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>. &","2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy =","dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list)","def show(self, *args, **kwargs): r\"\"\" Show the Wulff plot. Args: *args: Passed to","in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x)","\"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in a","to ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x) for","wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m,","from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops","get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts =","elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev)","in hkl: if x < 0: str_format += '\\\\overline{' + str(-x) + '}'","miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\","for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center =","90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0))","default is 1 off_color: Default color for facets not present on the Wulff","view in terms of miller index. The lattice is from the conventional unit","See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float)","of this triangle. Args: pts: [a, b, c] three points \"\"\" a, b,","list for all input_miller, total area on wulff, off_wulff = 0. .. attribute::","in the form of [h, k, l] or (h, k, l) \"\"\" str_format","<NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__","< 0: str_format += '\\\\overline{' + str(-x) + '}' else: str_format += str(x)","bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm =","viewing direction Returns: azim, elev for plotting \"\"\" if miller_index == (0, 0,","= [] self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff Shape from list","miller_area ($hkl$): area for all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5):","cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]):","facets according to its # index and the color_list for on_wulff plane_color =","miller_index == (0, 0, 1) or miller_index == (0, 0, 0, 1): return","symmetry operations(symmops) according to lattice apply symmops to get all the miller index,","= [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i,","normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return","({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The key is","self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for x","Args: pts: [a, b, c] three points \"\"\" a, b, c = pts[0],","# already find the plane, move to the next simplices break for plane","(0, 0, 1) or miller_index == (0, 0, 0, 1): return 0, 90","grid_off (bool): default is True axis_off (bool): default is Ture show_area (bool): default","ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store simplices","[] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec)","color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the Wulff","is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool):","generate the Wulff shape from a lattice, a list of indices and their","get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False,","= Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list])","& <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\"","miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If","in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl))","'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on","color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return","e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of the conventional unit cell miller_list","color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the Wulff plot. Args:","functions dual_simp: (i, j, k) simplices from the dual convex hull i, j,","for simpx of on wulff_cv, by comparing the center of the simpx triangle","construction # get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets))","tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in","e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for","wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute::","show_area (bool): default is False alpha (float): chosen from 0 to 1 (float),","weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self):","self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2","\"\"\" if any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy","is approximated as a sphere. Returns: (float) radius. \"\"\" return ((3 / 4)","(bool): default is True axis_off (bool): default is Ture show_area (bool): default is","\"\"\" Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool):","return: [WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind planes = [] recp","False legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}:","three points \"\"\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b)","input_miller .. attribute:: e_surf_list list of input surface energies, in the same order","len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda","Lattice object, the input lattice for the conventional unit cell .. attribute:: facets","ndim = 3 # i, j, k: ind for normal_e_m # recalculate the","Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area","probably won't make any sense to have the color bar on. Return: (matplotlib.pyplot)","i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges) return len(all_edges)","= [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]]","wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def","order with input_miller .. attribute:: lattice Lattice object, the input lattice for the","of hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface energies","pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu',","(nfacet, ndim)) # list of [i, j, k] , ndim = 3 #","* 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy","custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The key","0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy","direction is not specified, use the miller indices of # maximum area. direction","color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt","tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y, z","for all input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area","def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65),","Structure from pymatgen.util.coord import get_angle import numpy as np import scipy as sp","is False alpha (float): chosen from 0 to 1 (float), default is 1","convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is","If you decide to set your own colors, it probably won't make any","on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl","miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim, elev for plotting \"\"\" if",":param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param","= [x * energy for x in normal] dual_pt = [x / energy","(bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize","max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0],","* 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add","from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp","wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl}","color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the","pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i","def shape_factor(self): \"\"\" This is useful for determining the critical nucleus size. A","= self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0],","all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice","0.65] bar_on (bool): default is False legend_on (bool): default is True aspect_ratio: default","color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not specified,","def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim, elev for plotting","@property def total_surface_energy(self): \"\"\" Total surface energy of the Wulff shape. Returns: (float)","(c) Pymatgen Development Team. # Distributed under the terms of the MIT License.","axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns:","import scipy as sp from scipy.spatial import ConvexHull import logging import warnings __author__","number of edges in the convex hull. Useful for identifying catalytically active sites.","normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\" self.normal = normal","\"\"\" Radius of the Wulffshape when the Wulffshape is approximated as a sphere.","from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>'","area of this triangle. Args: pts: [a, b, c] three points \"\"\" a,","shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off","surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for","legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}):","cell .. attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp","= l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None,","lattice is from the conventional unit cell, and (hkil) for hexagonal lattices. If","= self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for","largest distance between on_wulff pts and the origin, # to ensure complete and","If direction is not specified, use the miller indices of # maximum area.","on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1,","ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center',","lines = [] for i, p in enumerate(pt): if i == len(pt) /","If you use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.;","v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1,","import numpy as np import scipy as sp from scipy.spatial import ConvexHull import","plane for simpx of on wulff_cv, by comparing the center of the simpx","mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y, z # find","as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors)","- np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \"\"\"","m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class WulffShape: \"\"\"","colors according to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff,","input_miller, True is on wulff. .. attribute:: color_area list for all input_miller, total","color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot as","the simpx triangle with the plane functions. \"\"\" on_wulff = [False] * len(self.miller_list)","scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha)","following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals.","Returns the sorted pts in a facet used to draw a line \"\"\"","\"\"\" all_edges = [] for facet in self.facets: edges = [] pt =","alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self,","> 0: if prev is None: l = lines.pop(0) else: for i, l","make sure the lines are connected one by one. # find the way","Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\"","+ normal[2]z = e_surf from self: normal_e_m to get the plane functions dual_simp:","show(self, *args, **kwargs): r\"\"\" Show the Wulff plot. Args: *args: Passed to get_plot.","set your own colors, it probably won't make any sense to have the","for recp_operation, default is 1e-5. \"\"\" if any([se < 0 for se in","for wulff construction # get all the surface normal from get_all_miller_e() self.facets =","on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according to","def total_surface_energy(self): \"\"\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl","def effective_radius(self): \"\"\" Radius of the Wulffshape when the Wulffshape is approximated as","# Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e,","is plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y +","self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5,","cross point from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp)","the convex hull. Useful for identifying catalytically active sites. \"\"\" all_edges = []","the same order with input_miller .. attribute:: e_surf_list list of input surface energies,","in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e)","coords for 3 points, Compute the area of this triangle. Args: pts: [a,","!= 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign","import get_angle import numpy as np import scipy as sp from scipy.spatial import","warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x","hcp in the form of hkil .. attribute:: hkl_list modify hkill to hkl,","= 1, e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x","get the wulff shape. # conner <-> surface # get cross point from","ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)'","index, then get normal, get all the facets functions for wulff shape calculation:","(bool) .. attribute:: show_area .. attribute:: off_color color of facets off wulff ..","plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next simplices break","x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec","self.facets: # check whether [pts] is empty if len(plane.points) < 1: # empty,","1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl","__date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for","{hkl: area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl","[simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set ranges of x, y,","* area_hkl)/ sum(area_hkl) \"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns:","in the convex hull. Useful for identifying catalytically active sites. \"\"\" all_edges =","abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1],","object of the conventional unit cell miller_list ([(hkl), ...]: list of hkl or","line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set,","ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75')","numpy as np import scipy as sp from scipy.spatial import ConvexHull import logging","dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp","aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape plot. Args: color_set: default is","the anisotropy and shape_factor can also be calculated. In support of plotting from","1. get wulff simplices 2. label with color 3. get wulff_area and other","l[1] == prev: l.reverse() break # make sure the lines are connected one","ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap =","- np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2)","b, c] three points \"\"\" a, b, c = pts[0], pts[1], pts[2] v1","WulffFacet: \"\"\" Helper container for each Wulff plane. \"\"\" def __init__(self, normal, e_surf,","= e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind planes =","j, k) simplices from the dual convex hull i, j, k: plane index(same","e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig","with input_miller .. attribute:: lattice Lattice object, the input lattice for the conventional","on_wulff facets according to its # index and the color_list for on_wulff plane_color","# 3. consider the dual condition dual_pts = [x.dual_pt for x in self.facets]","miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller)","'<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare","the conventional unit cell, and (hkil) for hexagonal lattices. If you use this","as sp from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>,","* 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range *","for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\"","(bool): default is False alpha (float): chosen from 0 to 1 (float), default","or miller_index == (0, 0, 0, 1): return 0, 90 else: cart =","surface energy The ideal sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy =","simplices 2. label with color 3. get wulff_area and other properties .. attribute::","default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with","the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for","for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else:","# Distributed under the terms of the MIT License. \"\"\" This module define","miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if","energy, the anisotropy and shape_factor can also be calculated. In support of plotting","self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in a facet","simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the","sure the lines are connected one by one. # find the way covering","len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for","The lattice is from the conventional unit cell, and (hkil) for hexagonal lattices.","sp from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>,","loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar","wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices","# store input data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list =","facets functions for wulff shape calculation: |normal| = 1, e_surf is plane's distance","energies of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord","Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff","of miller index and surface energies, with given conventional unit cell. surface energy","of hkil .. attribute:: hkl_list modify hkill to hkl, in the same order","self.points = [] self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff Shape from","dual, get the wulff shape. # conner <-> surface # get cross point",".. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute::","plane functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list)","(hkil) for hexagonal lattices. If you use this code extensively, consider citing the","attribute:: show_area .. attribute:: off_color color of facets off wulff .. attribute:: structure","hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from","Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on","break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p in","\"\"\" return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \"\"\"","<NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \"\"\"","in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0)","self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\"","color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: #","for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff <","is 1 off_color: Default color for facets not present on the Wulff shape.","op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal)","be calculated. In support of plotting from a given view in terms of","# list of [i, j, k] , ndim = 3 # i, j,","= mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff,","of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area /","def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param","planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy)","e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there","in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range *","plane, move to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines","Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot.","off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\"","will use default color site. Note: If you decide to set your own","as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff =","sense to have the color bar on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as","= [] class WulffShape: \"\"\" Generate Wulff Shape from list of miller index","for wulff shape calculation: |normal| = 1, e_surf is plane's distance to (0,","shape_factor can also be calculated. In support of plotting from a given view","None: l = lines.pop(0) else: for i, l in enumerate(lines): if prev in","all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for","detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store","its # index and the color_list for on_wulff plane_color = color_list[plane.index] pt =","to its # index and the color_list for on_wulff plane_color = color_list[plane.index] pt","self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color)","normal, get all the facets functions for wulff shape calculation: |normal| = 1,","(1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return","miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def","= tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all the","[] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines =","use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x:","ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x) for x","if not direction: # If direction is not specified, use the miller indices","shape_factor(self): \"\"\" This is useful for determining the critical nucleus size. A large","hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on","i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate","index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines = []","Returns: (float) Coefficient of Variation from weighted surface energy The ideal sphere is","= mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase(","self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input","the convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True","normal_e_m # recalculate the dual of dual, get the wulff shape. # conner","get_tri_area(pts): \"\"\" Given a list of coords for 3 points, Compute the area","abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \"\"\" Helper container for each","= tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal","miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt =","the form of hkil .. attribute:: hkl_list modify hkill to hkl, in the","self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number","1, e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y +","* len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for","[] self.outer_lines = [] class WulffShape: \"\"\" Generate Wulff Shape from list of","m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt","miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy)","[hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure = Structure(lattice, [\"H\"],","(bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of","for each Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig,","0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl =","= normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index =","(h, k, l) \"\"\" str_format = '($' for x in hkl: if x","of x, y, z # find the largest distance between on_wulff pts and","index: :param m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt",".. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute::","(float): chosen from 0 to 1 (float), default is 1 off_color: Default color","0: if prev is None: l = lines.pop(0) else: for i, l in","1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False","* len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)]","unit cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list","directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range *","# find the largest distance between on_wulff pts and the origin, # to","in enumerate(lines): if prev in l: l = lines.pop(i) if l[1] == prev:","\"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number of edges in","surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \"\"\" assign colors according to the","for plane in self.facets: # check whether [pts] is empty if len(plane.points) <","pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a)","Structure object, input conventional unit cell (with H ) from lattice .. attribute::","the dual of dual, get the wulff shape. # conner <-> surface #","bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on","in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if","grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color","= self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i,","order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e =","x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x","Wulff shape. direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15,","the conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil for","Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May","\"\"\" if miller_index == (0, 0, 1) or miller_index == (0, 0, 0,","[0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in","c] three points \"\"\" a, b, c = pts[0], pts[1], pts[2] v1 =","each Wulff plane. \"\"\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller):","index and value is the color. Undefined facets will use default color site.","1 (float), default is 1 off_color: Default color for facets not present on","mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color,","e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param normal_pt:","mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical')","color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff,","approximated as a sphere. Returns: (float) radius. \"\"\" return ((3 / 4) *","in l: l = lines.pop(i) if l[1] == prev: l.reverse() break # make","self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice","ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range","convex hull. Useful for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def","self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x","attribute:: lattice Lattice object, the input lattice for the conventional unit cell ..","r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1])","\"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i","direction Returns: azim, elev for plotting \"\"\" if miller_index == (0, 0, 1)",".. attribute:: lattice Lattice object, the input lattice for the conventional unit cell","plotting \"\"\" if miller_index == (0, 0, 1) or miller_index == (0, 0,","import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013,","k] , ndim = 3 # i, j, k: ind for normal_e_m #","logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for x in","0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0,","= [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def","def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in a facet used to","= e_surf from self: normal_e_m to get the plane functions dual_simp: (i, j,","\"\"\" Volume of the Wulff shape \"\"\" return self.wulff_convex.volume @property def miller_area_dict(self): \"\"\"","x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs):","bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on:","wulff construction # get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e()","to generate the Wulff shape from a lattice, a list of indices and","pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False,","Development Team. # Distributed under the terms of the MIT License. \"\"\" This","[off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf)","Surface energies of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure from","identifying catalytically active sites. \"\"\" all_edges = [] for facet in self.facets: edges","all the miller index, then get normal, get all the facets functions for","data for wulff construction # get all the surface normal from get_all_miller_e() self.facets","/= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt =","default is False alpha (float): chosen from 0 to 1 (float), default is","= np.sum(pts, 0) / 3.0 # check whether the center of the simplices","display on plots \"(hkl)\" for surfaces Agrs: hkl: in the form of [h,","(i, j, k) simplices from the dual convex hull i, j, k: plane","hkl: if x < 0: str_format += '\\\\overline{' + str(-x) + '}' else:","__email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl):","of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on wulff.","[(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda","list(facet.outer_lines) pt = [] prev = None while len(lines) > 0: if prev","the lines are connected one by one. # find the way covering all","for on_wulff facets according to its # index and the color_list for on_wulff","unit cell, and (hkil) for hexagonal lattices. If you use this code extensively,","[] class WulffShape: \"\"\" Generate Wulff Shape from list of miller index and","left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3,","not specified, use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(),","Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt","* 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range *","surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area","the miller index, then get normal, get all the facets functions for wulff","from list of miller index and surface energies, with given conventional unit cell.","or hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float):","determining the critical nucleus size. A large shape factor indicates great anisotropy. See","wulff shape. # conner <-> surface # get cross point from the simplices","shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices","Returns: (float) radius. \"\"\" return ((3 / 4) * (self.volume / np.pi)) **","normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) #","the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in","in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list","Customize color of each facet with a dictionary. The key is the corresponding","attribute:: off_color color of facets off wulff .. attribute:: structure Structure object, input","same order with input_miller .. attribute:: lattice Lattice object, the input lattice for","the center of the simplices is on one plane for plane in self.facets:","hull i, j, k: plane index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal","self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) /","apply symmops to get all the miller index, then get normal, get all","wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area =","attribute:: e_surf_list list of input surface energies, in the same order with input_miller","elev @property def volume(self): \"\"\" Volume of the Wulff shape \"\"\" return self.wulff_convex.volume","for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec","from 0 to 1 (float), default is 1 off_color: Default color for facets","and volume of the wulff shape,the weighted surface energy, the anisotropy and shape_factor","wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and","is 'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture show_area","[pts] is empty if len(plane.points) < 1: # empty, plane is not on_wulff.","simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp]","+ normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the plane","of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl *","the MIT License. \"\"\" This module define a WulffShape class to generate the","the Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float) radius.","= list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data","is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a","for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation,","Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area","2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p","symprec=1e-5): \"\"\" Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl),","the largest distance between on_wulff pts and the origin, # to ensure complete","(Jm^2) is the length of normal. Wulff shape is the convex hull. Based","(float), default is 1 off_color: Default color for facets not present on the","origin, # to ensure complete and consistent display for all directions r_range =","normal_pt, dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param normal_pt: :param","(1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default","draw a line \"\"\" lines = list(facet.outer_lines) pt = [] prev = None","(<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\" return self.surface_area / (self.volume **","self: normal_e_m to get the plane functions dual_simp: (i, j, k) simplices from","under the terms of the MIT License. \"\"\" This module define a WulffShape","1.1, r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy if","list of corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \"\"\"","axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets off wulff","= custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x","Scientific Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy","\"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \"\"\"","1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is","l = lines.pop(0) else: for i, l in enumerate(lines): if prev in l:","__version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5","dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet,","Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default","ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') #","= [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list,","attribute:: structure Structure object, input conventional unit cell (with H ) from lattice","= self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for","reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm","j, k: plane index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i","l.reverse() break # make sure the lines are connected one by one. #","color. Undefined facets will use default color site. Note: If you decide to","(1 / 3) @property def total_surface_energy(self): \"\"\" Total surface energy of the Wulff","0) / 3.0 # check whether the center of the simplices is on","tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns","for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller","corresponding Miller index and value is the color. Undefined facets will use default","return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This is useful for determining","conner <-> surface # get cross point from the simplices of the dual","alpha (float): chosen from 0 to 1 (float), default is 1 off_color: Default","list of input miller index, for hcp in the form of hkil ..","wulff shape calculation: |normal| = 1, e_surf is plane's distance to (0, 0,","plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0],","# If direction is not specified, use the miller indices of # maximum","<NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import","code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016).","4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and","dual_pt = [x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]]","0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict =","surface energies, with given conventional unit cell. surface energy (Jm^2) is the length","if l[1] == prev: l.reverse() break # make sure the lines are connected","(units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index):","hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl]","with given conventional unit cell. surface energy (Jm^2) is the length of normal.","ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units","a lattice, a list of indices and their corresponding surface energies, and the","x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is plane's","(self.volume ** (2 / 3)) @property def effective_radius(self): \"\"\" Radius of the Wulffshape","as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = []","default is 'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture","shape. direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05,","This module define a WulffShape class to generate the Wulff shape from a","get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in","for facets not present on the Wulff shape. direction: default is (1, 1,","_get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is plane's distance to (0, 0,","anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns:","is False legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors","plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax","surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual","attribute:: color_area list for all input_miller, total area on wulff, off_wulff = 0.","on. Return: (matplotlib.pyplot) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt import","def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from weighted surface energy The","mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = []","2) return area_tri class WulffFacet: \"\"\" Helper container for each Wulff plane. \"\"\"","in the convex hull. Useful for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices)","if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for","return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is plane's distance","extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies","@property def surface_area(self): \"\"\" Total surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values())","return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show the","weighted surface energy, the anisotropy and shape_factor can also be calculated. In support","elev for plotting \"\"\" if miller_index == (0, 0, 1) or miller_index ==","given conventional unit cell. surface energy (Jm^2) is the length of normal. Wulff","and the total area and volume of the wulff shape,the weighted surface energy,","lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\" Args: lattice: Lattice object of the conventional unit","and value is the color. Undefined facets will use default color site. Note:","i, p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i *","e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get all the","= np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2))","\"\"\" This module define a WulffShape class to generate the Wulff shape from","dual_pt, index, m_ind_orig, miller): \"\"\" :param normal: :param e_surf: :param normal_pt: :param dual_pt:","all input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$):","hkl, in the same order with input_miller .. attribute:: e_surf_list list of input","on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in","the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is","total_surface_energy(self): \"\"\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl *","** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This","Returns: azim, elev for plotting \"\"\" if miller_index == (0, 0, 1) or","mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff, choose","< 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative) surface energy detected.\") self.color_ind =","dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x","in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints,","j, k: ind for normal_e_m # recalculate the dual of dual, get the","energy (Jm^2) is the length of normal. Wulff shape is the convex hull.","sphere. Returns: (float) radius. \"\"\" return ((3 / 4) * (self.volume / np.pi))","azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim,","list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure","miller_list] # store input data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list","in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the","is empty if len(plane.points) < 1: # empty, plane is not on_wulff. continue","the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0]","energy The ideal sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy","self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for","attribute:: on_wulff list for all input_miller, True is on wulff. .. attribute:: color_area","color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True,","Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total","' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from","l] or (h, k, l) \"\"\" str_format = '($' for x in hkl:","assign the color for on_wulff facets according to its # index and the","r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area:","def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is plane's distance to (0,","in miller_list] # store input data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]])","i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 +","color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\"\"\" Show","utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of","connected one by one. # find the way covering all pts and facets","attribute:: miller_list list of input miller index, for hcp in the form of","all the data for wulff construction # get all the surface normal from","elev=elev) for plane in self.facets: # check whether [pts] is empty if len(plane.points)","in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies","of input surface energies, in the same order with input_miller .. attribute:: lattice","elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle","for identifying catalytically active sites. \"\"\" all_edges = [] for facet in self.facets:","e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff)","plotting from a given view in terms of miller index. The lattice is","on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts","/ self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff}","= \"$J/m^2$\" if units_in_JPERM2 else r\"$eV/\\AA^2$\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if","len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i,","plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff,","Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\"","for i, l in enumerate(lines): if prev in l: l = lines.pop(i) if","mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if","+= (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy","x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of","in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from weighted","normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl = []","else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v","= mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if","# index and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane)","the plane for simpx of on wulff_cv, by comparing the center of the","surface_area(self): \"\"\" Total surface area of Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def","points \"\"\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) -","wulff shape,the weighted surface energy, the anisotropy and shape_factor can also be calculated.","self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = []","for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self,","x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) >","simpx triangle with the plane functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area","cell. surface energy (Jm^2) is the length of normal. Wulff shape is the","self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig","@property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property","dual convex hull i, j, k: plane index(same order in normal_e_m) \"\"\" matrix_surfs","np.sum(pts, 0) / 3.0 # check whether the center of the simplices is","this triangle. Args: pts: [a, b, c] three points \"\"\" a, b, c","<NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape","[self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt","symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute::","symprec (float): for recp_operation, default is 1e-5. \"\"\" if any([se < 0 for","= lines.pop(0) else: for i, l in enumerate(lines): if prev in l: l","plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move","is 0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict","= np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class","to lattice apply symmops to get all the miller index, then get normal,","for all input_miller, True is on wulff. .. attribute:: color_area list for all","of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure()","self.miller = miller self.points = [] self.outer_lines = [] class WulffShape: \"\"\" Generate","In support of plotting from a given view in terms of miller index.","for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1,","**kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the sorted pts in a facet used","color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for","normal_e_m to get the plane functions dual_simp: (i, j, k) simplices from the","get all the data for wulff construction # get all the surface normal","indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005),","0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m","if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i],","dual of dual, get the wulff shape. # conner <-> surface # get","store input data self.structure = Structure(lattice, [\"H\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x)","surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] +","((3 / 4) * (self.volume / np.pi)) ** (1 / 3) @property def","/ 4) * (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self):","x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal","self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient of","Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool): default","hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec","0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self:","= abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index]","there is only one hkl on wulff, choose the color of the median","+= str(x) str_format += '$)' return str_format def get_tri_area(pts): \"\"\" Given a list","**kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \"\"\" Returns the","# to ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x)","[i, j, k] , ndim = 3 # i, j, k: ind for","ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim,","in the same order with input_miller .. attribute:: e_surf_list list of input surface","distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return:","matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff","else: # if there is only one hkl on wulff, choose the color","planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x:","self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig","hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp):","dictionary. The key is the corresponding Miller index and value is the color.","Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def","import Structure from pymatgen.util.coord import get_angle import numpy as np import scipy as","class WulffFacet: \"\"\" Helper container for each Wulff plane. \"\"\" def __init__(self, normal,","lines = list(facet.outer_lines) pt = [] prev = None while len(lines) > 0:","1: # empty, plane is not on_wulff. continue # assign the color for","return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index: viewing direction Returns: azim, elev","in the same order with input_miller .. attribute:: lattice Lattice object, the input","wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp","* 2 + 1])])))) for i, p in enumerate(lines): if p not in","+ 1])])))) for i, p in enumerate(lines): if p not in all_edges: edges.append(p)","modify hkill to hkl, in the same order with input_miller .. attribute:: e_surf_list","np import scipy as sp from scipy.spatial import ConvexHull import logging import warnings","2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) #","Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for hkl",".. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute::","point from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for","you decide to set your own colors, it probably won't make any sense","area_hkl) \"\"\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] *","the plane functions. \"\"\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] *","l in enumerate(lines): if prev in l: l = lines.pop(i) if l[1] ==","miller_on_wulff, e_surf_on_wulff_list) \"\"\" import matplotlib as mpl import matplotlib.pyplot as plt color_list =","direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets:","when the Wulffshape is approximated as a sphere. Returns: (float) radius. \"\"\" return","plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff","from self: normal_e_m to get the plane functions dual_simp: (i, j, k) simplices","norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar =","of [i, j, k] , ndim = 3 # i, j, k: ind","[] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' +","simpx of on wulff_cv, by comparing the center of the simpx triangle with","Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3.","len(lines) > 0: if prev is None: l = lines.pop(0) else: for i,","http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3. get wulff_area","anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation from weighted surface energy The ideal","logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\" for surfaces Agrs:","_get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice","and surface energies, with given conventional unit cell. surface energy (Jm^2) is the","for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args,","np.pi)) ** (1 / 3) @property def total_surface_energy(self): \"\"\" Total surface energy of","lines.pop(0) else: for i, l in enumerate(lines): if prev in l: l =","Generate Wulff Shape from list of miller index and surface energies, with given","if prev in l: l = lines.pop(i) if l[1] == prev: l.reverse() break","in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha,","miller index, then get normal, get all the facets functions for wulff shape","e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind = self.color_ind planes = []","simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list for all","[] for i, p in enumerate(pt): if i == len(pt) / 2: break","energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf)","cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def volume(self): \"\"\"","return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1,","tot_edges(self): \"\"\" Returns the number of edges in the convex hull. Useful for","{hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns:","import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials","center of the simpx triangle with the plane functions. \"\"\" on_wulff = [False]","empty if len(plane.points) < 1: # empty, plane is not on_wulff. continue #","miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self):","get wulff simplices 2. label with color 3. get wulff_area and other properties","= self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt])","a sphere. Returns: (float) radius. \"\"\" return ((3 / 4) * (self.volume /","the data for wulff construction # get all the surface normal from get_all_miller_e()","= 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl","bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True,","3) @property def total_surface_energy(self): \"\"\" Total surface energy of the Wulff shape. Returns:","facet with a dictionary. The key is the corresponding Miller index and value",".. attribute:: off_color color of facets off wulff .. attribute:: structure Structure object,","\"\"\" Args: miller_index: viewing direction Returns: azim, elev for plotting \"\"\" if miller_index","0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1],","normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind =","planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf","for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray","the corresponding Miller index and value is the color. Undefined facets will use","in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self):","= mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional',","plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf","attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off","__maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger =","color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False)","for i, p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i","ints, shape (nfacet, ndim)) # list of [i, j, k] , ndim =","[x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list))","your own colors, it probably won't make any sense to have the color","def volume(self): \"\"\" Volume of the Wulff shape \"\"\" return self.wulff_convex.volume @property def","[self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0 # check","[] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list,","alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1,","bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape plot. Args:","[plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy,","k: ind for normal_e_m # recalculate the dual of dual, get the wulff","effective_radius(self): \"\"\" Radius of the Wulffshape when the Wulffshape is approximated as a","= wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area","function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to get","matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff =","use default color site. Note: If you decide to set your own colors,","Returns: (float) Shape factor. \"\"\" return self.surface_area / (self.volume ** (2 / 3))","own colors, it probably won't make any sense to have the color bar","direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in","of edges in the convex hull. Useful for identifying catalytically active sites. \"\"\"","plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if","the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\"#808080\") ax.add_collection3d(tri) # set","str_format = '($' for x in hkl: if x < 0: str_format +=","2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \"\"\" Prepare for display on plots \"(hkl)\"","aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet","0 to 1 (float), default is 1 off_color: Default color for facets not","conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp","to draw a line \"\"\" lines = list(facet.outer_lines) pt = [] prev =","= normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller =","@property def miller_area_dict(self): \"\"\" Returns {hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area))","i, j, k: plane index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for","cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1])","to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line","the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1)","[] pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if","e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y","the length of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html","wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency ..","all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range","Passed to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet):","and symmetry operations(symmops) according to lattice apply symmops to get all the miller","lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines):","for all facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex","break # make sure the lines are connected one by one. # find","Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT","energies, in the same order with input_miller .. attribute:: lattice Lattice object, the","[cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def volume(self):","Wulff shape from a lattice, a list of indices and their corresponding surface","self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface energy_hkl} \"\"\" return dict(zip(self.miller_list, self.e_surf_list))","plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy","to set your own colors, it probably won't make any sense to have","of miller index. The lattice is from the conventional unit cell, and (hkil)","a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2","color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff =","= '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016'","anisotropy and shape_factor can also be calculated. In support of plotting from a","logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp =","* area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \"\"\" This is useful","and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True,","pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri","i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i]","next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line","/ 2) return area_tri class WulffFacet: \"\"\" Helper container for each Wulff plane.","'}' else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \"\"\"","in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self):","of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure import Structure from pymatgen.util.coord import","attribute:: miller_area ($hkl$): area for all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list,","color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2,","= lines.pop(i) if l[1] == prev: l.reverse() break # make sure the lines","plane functions dual_simp: (i, j, k) simplices from the dual convex hull i,","median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map)","wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list for","Compute the area of this triangle. Args: pts: [a, b, c] three points","\"\"\" Returns: (dict): {hkl: area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] /","show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper","get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get","= max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range","cell, and (hkil) for hexagonal lattices. If you use this code extensively, consider","matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in","e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface","Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float) radius. \"\"\"","i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self,","<NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor.","area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim,","hkl: in the form of [h, k, l] or (h, k, l) \"\"\"","direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get","and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot","by one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist())","dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the","convex hull i, j, k: plane index(same order in normal_e_m) \"\"\" matrix_surfs =","large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of","\"(hkl)\" for surfaces Agrs: hkl: in the form of [h, k, l] or","form of hkil .. attribute:: hkl_list modify hkill to hkl, in the same","/ self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient","tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if p not","square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) /","then get normal, get all the facets functions for wulff shape calculation: |normal|","+ normal[1]y + normal[2]z = e_surf return: [WulffFacet] \"\"\" all_hkl = [] color_ind","on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller","<-> surface # get cross point from the simplices of the dual convex","on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl: surface","edges in the convex hull. Useful for identifying catalytically active sites. \"\"\" all_edges",":param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\" self.normal =","is only one hkl on wulff, choose the color of the median cnorm","str(-x) + '}' else: str_format += str(x) str_format += '$)' return str_format def","Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list","Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args,","if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]])","return tot_surface_energy @property def tot_corner_sites(self): \"\"\" Returns the number of vertices in the","area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns {hkl:","2. get all the data for wulff construction # get all the surface","get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3.","self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] =",":param m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf = e_surf self.normal_pt =","import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list)","it probably won't make any sense to have the color bar on. Return:","\"\"\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \"\"\" Returns: (dict): {hkl: area_hkl/total","x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]])","not present on the Wulff shape. direction: default is (1, 1, 1) bar_pos:","= color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx]","Useful for identifying catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\"","norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \"$J/m^2$\"","default is True axis_off (bool): default is Ture show_area (bool): default is False","the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts)","plots \"(hkl)\" for surfaces Agrs: hkl: in the form of [h, k, l]","1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend","color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not","miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add","dual_pt: :param index: :param m_ind_orig: :param miller: \"\"\" self.normal = normal self.e_surf =","attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off","m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4)))","the facets functions for wulff shape calculation: |normal| = 1, e_surf is plane's","for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for","1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy","whether [pts] is empty if len(plane.points) < 1: # empty, plane is not","the center of the simpx triangle with the plane functions. \"\"\" on_wulff =","= [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def","self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape","str_format += '$)' return str_format def get_tri_area(pts): \"\"\" Given a list of coords","unit cell. surface energy (Jm^2) is the length of normal. Wulff shape is","facets off wulff .. attribute:: structure Structure object, input conventional unit cell (with","'($' for x in hkl: if x < 0: str_format += '\\\\overline{' +","($hkl$): area for all input_miller \"\"\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \"\"\"","= self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check","dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\", \".join([str(len(x)) for x in wulff_cv_simp]))","= plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff]","the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return","1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,","wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1])","energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds","covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def","= '($' for x in hkl: if x < 0: str_format += '\\\\overline{'","= wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for","\"\"\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl)","sphere is 0. \"\"\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict","on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]])","= [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in","the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from","self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig","/ energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt,","= self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i ==","return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \"\"\" Radius","plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return","plane in self.facets: # check whether [pts] is empty if len(plane.points) < 1:","get_angle import numpy as np import scipy as sp from scipy.spatial import ConvexHull","is the length of normal. Wulff shape is the convex hull. Based on:","the Wulff shape from a lattice, a list of indices and their corresponding","for display on plots \"(hkl)\" for surfaces Agrs: hkl: in the form of","\"\"\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig:","plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line)","True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each","Miller index and value is the color. Undefined facets will use default color","= mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is","convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex","*args: Passed to get_plot. **kwargs: Passed to get_plot. \"\"\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self,","mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is empty","3)) @property def effective_radius(self): \"\"\" Radius of the Wulffshape when the Wulffshape is","self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy +=","__author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab'","in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])]","continue # assign the color for on_wulff facets according to its # index","on_wulff pts and the origin, # to ensure complete and consistent display for","(0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim =","normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the","def _get_all_miller_e(self): \"\"\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to","[False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts","is 1e-5. \"\"\" if any([se < 0 for se in e_surf_list]): warnings.warn(\"Unphysical (negative)","return ((3 / 4) * (self.volume / np.pi)) ** (1 / 3) @property","cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1],","l: l = lines.pop(i) if l[1] == prev: l.reverse() break # make sure","{hkl: area_hkl on wulff} \"\"\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \"\"\" Returns","lattice apply symmops to get all the miller index, then get normal, get","# sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\"","conventional unit cell, and (hkil) for hexagonal lattices. If you use this code","is not on_wulff. continue # assign the color for on_wulff facets according to","of facets off wulff .. attribute:: structure Structure object, input conventional unit cell","in range(3)] center = np.sum(pts, 0) / 3.0 # check whether the center","of input miller index, for hcp in the form of hkil .. attribute::","@property def tot_edges(self): \"\"\" Returns the number of edges in the convex hull.","abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True","to 1 (float), default is 1 off_color: Default color for facets not present","list of coords for 3 points, Compute the area of this triangle. Args:","in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area =","# set ranges of x, y, z # find the largest distance between","catalytically active sites. \"\"\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \"\"\" Returns the number","self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all","fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set)","same order with input_miller .. attribute:: e_surf_list list of input surface energies, in","return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \"\"\" Total surface area of Wulff shape.","grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \"\"\" Args: miller_index:","distance between on_wulff pts and the origin, # to ensure complete and consistent","not direction: # If direction is not specified, use the miller indices of","range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane","area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()}","sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt = [x","else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \"\"\" Given","legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True,",": ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\" from self:","simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex","= self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig +","of the simplices is on one plane for plane in self.facets: abs_diff =","# empty, plane is not on_wulff. continue # assign the color for on_wulff","all facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull","for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \"\"\" Returns: (float) Coefficient of Variation","x in hkl: if x < 0: str_format += '\\\\overline{' + str(-x) +","x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list =","normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition","fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1,","x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \"\"\" |normal| = 1, e_surf is","cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v =","<NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1'","bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in","hkil .. attribute:: hkl_list modify hkill to hkl, in the same order with","default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool):","bar_on (bool): default is False legend_on (bool): default is True aspect_ratio: default is","the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for","self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class","lattice Lattice object, the input lattice for the conventional unit cell .. attribute::","self.symprec = symprec # 2. get all the data for wulff construction #","if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff,","+ ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \"\"\"","the wulff shape,the weighted surface energy, the anisotropy and shape_factor can also be","0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \"\"\" Get the Wulff shape","move to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines =","check whether the center of the simplices is on one plane for plane","for hcp in the form of hkil .. attribute:: hkl_list modify hkill to","= [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\",","<NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \"\"\" from pymatgen.core.structure","surface energy detected.\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list]","index(same order in normal_e_m) \"\"\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e","Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True","e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation, default is","miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get all","center of the simplices is on one plane for plane in self.facets: abs_diff","indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig =","**kwargs): r\"\"\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed","# check whether the center of the simplices is on one plane for","dual_simp): \"\"\" |normal| = 1, e_surf is plane's distance to (0, 0, 0),","This is useful for determining the critical nucleus size. A large shape factor","\"\"\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a)","cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for simpx of on wulff_cv, by","a WulffShape class to generate the Wulff shape from a lattice, a list","np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet:","the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from","z # find the largest distance between on_wulff pts and the origin, #","<NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \"\"\" return","shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \"\"\" tot_surface_energy = 0 for hkl in","in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for i,","is the corresponding Miller index and value is the color. Undefined facets will","/ 3)) @property def effective_radius(self): \"\"\" Radius of the Wulffshape when the Wulffshape","@property def volume(self): \"\"\" Volume of the Wulff shape \"\"\" return self.wulff_convex.volume @property",".. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets","normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt","* (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \"\"\" Total","simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in","elev = get_angle(cart, v) return azim, elev @property def volume(self): \"\"\" Volume of","Wulff shape. \"\"\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \"\"\" Returns: sum(surface_energy_hkl * area_hkl)/","\"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self):","warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual","1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0],","conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering symm ..","(dict): {hkl: area_hkl/total area on wulff} \"\"\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for","'\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format += '$)' return","cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \"\"\" Locate the plane for"],"string":"[\n \"import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha,\",\n \"normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get\",\n \"= [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i\",\n \"units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args: color_set:\",\n \"< 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]])\",\n \"break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines\",\n \"i, j, k: ind for normal_e_m # recalculate the dual of dual, get\",\n \"prev in l: l = lines.pop(i) if l[1] == prev: l.reverse() break #\",\n \"k, l] or (h, k, l) \\\"\\\"\\\" str_format = '($' for x in\",\n \".. attribute:: miller_list list of input miller index, for hcp in the form\",\n \"area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful for\",\n \"energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt,\",\n \"for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m],\",\n \"dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list,\",\n \"get normal, get all the facets functions for wulff shape calculation: |normal| =\",\n \"in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in\",\n \"k) simplices from the dual convex hull i, j, k: plane index(same order\",\n \"def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the surface\",\n \"get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns\",\n \"** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when\",\n \"planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's distance to\",\n \"\\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\\",\n \"return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return\",\n \"Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list of hkl\",\n \"sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy +=\",\n \"[] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map\",\n \"plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set,\",\n \"(bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) ..\",\n \"surface energies, in the same order with input_miller .. attribute:: lattice Lattice object,\",\n \"the terms of the MIT License. \\\"\\\"\\\" This module define a WulffShape class\",\n \"surface energies symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se <\",\n \"color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri\",\n \"__copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ =\",\n \"lattice self.symprec = symprec # 2. get all the data for wulff construction\",\n \"color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area ..\",\n \"np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful for determining the\",\n \"sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl:\",\n \"the dual convex hull i, j, k: plane index(same order in normal_e_m) \\\"\\\"\\\"\",\n \"lines are connected one by one. # find the way covering all pts\",\n \"= m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class WulffShape:\",\n \"volume of the wulff shape,the weighted surface energy, the anisotropy and shape_factor can\",\n \"str(x) str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of\",\n \"vertices in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" return\",\n \"b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 =\",\n \"normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for\",\n \"ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds +\",\n \"simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i, j, k]\",\n \"<NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data.\",\n \"off wulff .. attribute:: structure Structure object, input conventional unit cell (with H\",\n \"if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}):\",\n \"fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def\",\n \"color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort\",\n \"wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on wulff. ..\",\n \"Default color for facets not present on the Wulff shape. direction: default is\",\n \":param miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt\",\n \"Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\"\",\n \"ranges of x, y, z # find the largest distance between on_wulff pts\",\n \"= [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds,\",\n \"(miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property\",\n \"Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys():\",\n \"in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list =\",\n \"Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2 / 3)) @property def\",\n \"([float]): list of corresponding surface energies symprec (float): for recp_operation, default is 1e-5.\",\n \"surface # get cross point from the simplices of the dual convex hull\",\n \"= [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)]\",\n \"self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines\",\n \"\\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3\",\n \"get cross point from the simplices of the dual convex hull wulff_pt_list =\",\n \"of on wulff_cv, by comparing the center of the simpx triangle with the\",\n \"@property def shape_factor(self): \\\"\\\"\\\" This is useful for determining the critical nucleus size.\",\n \"weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property\",\n \"[10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface\",\n \"3 points, Compute the area of this triangle. Args: pts: [a, b, c]\",\n \"loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1),\",\n \"for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns\",\n \"conventional unit cell (with H ) from lattice .. attribute:: miller_list list of\",\n \"list of miller index and surface energies, with given conventional unit cell. surface\",\n \"functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for\",\n \"consider the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex =\",\n \"[\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list =\",\n \"the form of [h, k, l] or (h, k, l) \\\"\\\"\\\" str_format =\",\n \"recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal]\",\n \"self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points\",\n \"else: for i, l in enumerate(lines): if prev in l: l = lines.pop(i)\",\n \"def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl:\",\n \"facets will use default color site. Note: If you decide to set your\",\n \"'$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for 3\",\n \"self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of miller\",\n \"their corresponding surface energies, and the total area and volume of the wulff\",\n \"simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp\",\n \"wulff .. attribute:: structure Structure object, input conventional unit cell (with H )\",\n \"= [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp:\",\n \"< 1: # empty, plane is not on_wulff. continue # assign the color\",\n \"range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return\",\n \"color for on_wulff facets according to its # index and the color_list for\",\n \"value is the color. Undefined facets will use default color site. Note: If\",\n \"the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot\",\n \"dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\"\",\n \"ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) #\",\n \"site. Note: If you decide to set your own colors, it probably won't\",\n \"energies, with given conventional unit cell. surface energy (Jm^2) is the length of\",\n \"in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area\",\n \"color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt color_list\",\n \"from pymatgen.util.coord import get_angle import numpy as np import scipy as sp from\",\n \"prev is None: l = lines.pop(0) else: for i, l in enumerate(lines): if\",\n \"one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev\",\n \"= list(facet.outer_lines) pt = [] prev = None while len(lines) > 0: if\",\n \"self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy)\",\n \"+ 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] =\",\n \"'<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ =\",\n \"weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys():\",\n \"factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>,\",\n \"self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller\",\n \"a given view in terms of miller index. The lattice is from the\",\n \"the critical nucleus size. A large shape factor indicates great anisotropy. See <NAME>.,\",\n \"True axis_off (bool): default is Ture show_area (bool): default is False alpha (float):\",\n \"dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute::\",\n \"= sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx\",\n \"miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the conventional unit cell\",\n \"of the MIT License. \\\"\\\"\\\" This module define a WulffShape class to generate\",\n \"+ str(-x) + '}' else: str_format += str(x) str_format += '$)' return str_format\",\n \"\\\"\\\"\\\" |normal| = 1, e_surf is plane's distance to (0, 0, 0), plane\",\n \"default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color\",\n \"assign colors according to the surface energies of on_wulff facets. return: (color_list, color_proxy,\",\n \"= ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim))\",\n \"\\\"\\\"\\\" return ((3 / 4) * (self.volume / np.pi)) ** (1 / 3)\",\n \"of the simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff = [False] *\",\n \"consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies\",\n \"fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list =\",\n \"plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color,\",\n \"for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property\",\n \"np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper\",\n \"2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors\",\n \"c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in\",\n \"/ weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful for determining the critical\",\n \"find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1]\",\n \"for x in hkl: if x < 0: str_format += '\\\\\\\\overline{' + str(-x)\",\n \"wulff. .. attribute:: color_area list for all input_miller, total area on wulff, off_wulff\",\n \"/ 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i,\",\n \"class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of miller index and surface\",\n \"attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area\",\n \"factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self):\",\n \"= [] pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt):\",\n \"= [] for i, p in enumerate(pt): if i == len(pt) / 2:\",\n \"is True axis_off (bool): default is Ture show_area (bool): default is False alpha\",\n \"k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in\",\n \"set ranges of x, y, z # find the largest distance between on_wulff\",\n \"of plotting from a given view in terms of miller index. The lattice\",\n \"@property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return\",\n \"for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane,\",\n \"list of indices and their corresponding surface energies, and the total area and\",\n \"self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2]\",\n \"facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import\",\n \"= pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) -\",\n \"off_color: Default color for facets not present on the Wulff shape. direction: default\",\n \"1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0],\",\n \"for i in range(3)] center = np.sum(pts, 0) / 3.0 # check whether\",\n \"0: str_format += '\\\\\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format\",\n \"from a given view in terms of miller index. The lattice is from\",\n \"str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for 3 points, Compute\",\n \"custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1]\",\n \"on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the form of [h, k,\",\n \"1) or miller_index == (0, 0, 0, 1): return 0, 90 else: cart\",\n \"of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map =\",\n \"length of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process:\",\n \"+= '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for\",\n \"in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return\",\n \"on the Wulff shape. direction: default is (1, 1, 1) bar_pos: default is\",\n \"self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op\",\n \"get_angle(cart, v) return azim, elev @property def volume(self): \\\"\\\"\\\" Volume of the Wulff\",\n \"= [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops =\",\n \"tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y, z # find the largest\",\n \"in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x *\",\n \"(matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as\",\n \"self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not\",\n \"list of hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface\",\n \"for x in miller_list] # store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0,\",\n \"hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for\",\n \"2 + 1])])))) for i, p in enumerate(lines): if p not in all_edges:\",\n \"= [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in\",\n \"object, input conventional unit cell (with H ) from lattice .. attribute:: miller_list\",\n \"# recalculate the dual of dual, get the wulff shape. # conner <->\",\n \"self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i == len(pt)\",\n \"a list of coords for 3 points, Compute the area of this triangle.\",\n \"Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d\",\n \"empty, plane is not on_wulff. continue # assign the color for on_wulff facets\",\n \"to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\"\",\n \"self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl,\",\n \"max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range *\",\n \"if any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\")\",\n \"energy for x in normal] dual_pt = [x / energy for x in\",\n \"= 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display\",\n \"surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy\",\n \"== (0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim\",\n \"\\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev = None while len(lines) >\",\n \"x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1:\",\n \"self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + '\",\n \"(2016). Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure\",\n \"scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha)\",\n \"= wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in\",\n \"considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) ..\",\n \"custom_colors=custom_colors) if not direction: # If direction is not specified, use the miller\",\n \"convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp\",\n \"1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1])\",\n \"legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0,\",\n \"x, y, z # find the largest distance between on_wulff pts and the\",\n \"surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find\",\n \"or (h, k, l) \\\"\\\"\\\" str_format = '($' for x in hkl: if\",\n \"* bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos)\",\n \"tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in the convex hull. Useful for\",\n \"convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with\",\n \"\\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt,\",\n \"for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = []\",\n \"area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl]\",\n \"the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex =\",\n \"= index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines =\",\n \"miller_index == (0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index)\",\n \"0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is True aspect_ratio:\",\n \"list of [i, j, k] , ndim = 3 # i, j, k:\",\n \"the number of vertices in the convex hull. Useful for identifying catalytically active\",\n \"normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller\",\n \"True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already\",\n \"transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) ..\",\n \"\\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np\",\n \"ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) #\",\n \"= self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] -\",\n \"surface energy (Jm^2) is the length of normal. Wulff shape is the convex\",\n \"hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def\",\n \"attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets off\",\n \"Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ =\",\n \"tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl]\",\n \"Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted\",\n \"in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i])\",\n \"convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges = [] for\",\n \"as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors(\",\n \"= [] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines\",\n \"Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self):\",\n \"as np import scipy as sp from scipy.spatial import ConvexHull import logging import\",\n \"np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return\",\n \"ndim)) # list of [i, j, k] , ndim = 3 # i,\",\n \"ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy =\",\n \".. attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list,\",\n \"of [h, k, l] or (h, k, l) \\\"\\\"\\\" str_format = '($' for\",\n \"default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on\",\n \"\\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of\",\n \"(0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\"\",\n \"on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property\",\n \"Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2)\",\n \"of the wulff shape,the weighted surface energy, the anisotropy and shape_factor can also\",\n \"def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for 3 points, Compute the\",\n \"on_wulff. continue # assign the color for on_wulff facets according to its #\",\n \"shape (nfacet, ndim)) # list of [i, j, k] , ndim = 3\",\n \"# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the\",\n \"from the convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller,\",\n \"to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts\",\n \"the sorted pts in a facet used to draw a line \\\"\\\"\\\" lines\",\n \"' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get\",\n \"have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import\",\n \"display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0]\",\n \"self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float)\",\n \"sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal|\",\n \"alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool)\",\n \"= [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices #\",\n \"wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp\",\n \"0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff\",\n \"% (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self,\",\n \"from weighted surface energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0\",\n \"lattice, a list of indices and their corresponding surface energies, and the total\",\n \"0] elev = get_angle(cart, v) return azim, elev @property def volume(self): \\\"\\\"\\\" Volume\",\n \"while len(lines) > 0: if prev is None: l = lines.pop(0) else: for\",\n \"orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units),\",\n \"i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs),\",\n \"area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl\",\n \"normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes\",\n \"if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2\",\n \"legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args: color_set: default\",\n \"square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for\",\n \"in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2.\",\n \"+ '}' else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts):\",\n \"m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index:\",\n \".. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list\",\n \"e_surf from self: normal_e_m to get the plane functions dual_simp: (i, j, k)\",\n \"active sites. \\\"\\\"\\\" all_edges = [] for facet in self.facets: edges = []\",\n \"is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2.\",\n \"attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color\",\n \"self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\"\",\n \"else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off:\",\n \"the wulff shape. # conner <-> surface # get cross point from the\",\n \"corresponding surface energies, and the total area and volume of the wulff shape,the\",\n \"color site. Note: If you decide to set your own colors, it probably\",\n \"get the plane functions dual_simp: (i, j, k) simplices from the dual convex\",\n \"get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane,\",\n \"hkl on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) -\",\n \"center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts)\",\n \"direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65]\",\n \"fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x')\",\n \"+= '\\\\\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format += '$)'\",\n \"is on wulff. .. attribute:: color_area list for all input_miller, total area on\",\n \"x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list\",\n \"def tot_edges(self): \\\"\\\"\\\" Returns the number of edges in the convex hull. Useful\",\n \"= mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i,\",\n \"for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice:\",\n \"plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show()\",\n \"matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt\",\n \") from lattice .. attribute:: miller_list list of input miller index, for hcp\",\n \"find the plane, move to the next simplices break for plane in self.facets:\",\n \"units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if\",\n \"[] prev = None while len(lines) > 0: if prev is None: l\",\n \"Coefficient of Variation from weighted surface energy The ideal sphere is 0. \\\"\\\"\\\"\",\n \"= [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : '\",\n \"make any sense to have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import\",\n \"consistent display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range\",\n \"to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf\",\n \"of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0\",\n \"miller self.points = [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape\",\n \"+= self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the\",\n \"Shape from list of miller index and surface energies, with given conventional unit\",\n \"shape. # conner <-> surface # get cross point from the simplices of\",\n \"x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i,\",\n \"ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict =\",\n \"\\\"\\\"\\\" Given a list of coords for 3 points, Compute the area of\",\n \"matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy,\",\n \"for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig\",\n \"surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import\",\n \"Pymatgen Development Team. # Distributed under the terms of the MIT License. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted surface energy The ideal sphere\",\n \"display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range *\",\n \"= get_angle(cart, v) return azim, elev @property def volume(self): \\\"\\\"\\\" Volume of the\",\n \"** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the\",\n \".. attribute:: show_area .. attribute:: off_color color of facets off wulff .. attribute::\",\n \"1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap\",\n \"fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax =\",\n \"miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according\",\n \"normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller:\",\n \"\\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self):\",\n \"* 2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if\",\n \"conventional unit cell. surface energy (Jm^2) is the length of normal. Wulff shape\",\n \"\\\"\\\"\\\" str_format = '($' for x in hkl: if x < 0: str_format\",\n \"else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z')\",\n \"cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm\",\n \"= 3 # i, j, k: ind for normal_e_m # recalculate the dual\",\n \"normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x\",\n \"= max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev =\",\n \"str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a\",\n \"miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def\",\n \"if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1),\",\n \"is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) -\",\n \"len(plane.points) < 1: # empty, plane is not on_wulff. continue # assign the\",\n \"self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry\",\n \"direction: # If direction is not specified, use the miller indices of #\",\n \"pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri =\",\n \"for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for\",\n \"for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller =\",\n \"enumerate(lines): if prev in l: l = lines.pop(i) if l[1] == prev: l.reverse()\",\n \"catalytically active sites. \\\"\\\"\\\" all_edges = [] for facet in self.facets: edges =\",\n \"miller_list list of input miller index, for hcp in the form of hkil\",\n \"(float) Coefficient of Variation from weighted surface energy The ideal sphere is 0.\",\n \"indices and their corresponding surface energies, and the total area and volume of\",\n \"simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts,\",\n \"dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes\",\n \"Wulff Shape from list of miller index and surface energies, with given conventional\",\n \"plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next\",\n \"color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for\",\n \"0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1],\",\n \"Variation from weighted surface energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy =\",\n \"one by one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist())\",\n \"off_color, custom_colors=custom_colors) if not direction: # If direction is not specified, use the\",\n \"hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp =\",\n \"to hkl, in the same order with input_miller .. attribute:: e_surf_list list of\",\n \"self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane\",\n \"# Copyright (c) Pymatgen Development Team. # Distributed under the terms of the\",\n \"= tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice =\",\n \"useful for determining the critical nucleus size. A large shape factor indicates great\",\n \"in a facet used to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt\",\n \"return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for 3 points,\",\n \".. attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices\",\n \"area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict):\",\n \"each facet with a dictionary. The key is the corresponding Miller index and\",\n \"\\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\"\",\n \"coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms\",\n \"plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev for\",\n \"normal_pt = [x * energy for x in normal] dual_pt = [x /\",\n \".. attribute:: e_surf_list list of input surface energies, in the same order with\",\n \"center = np.sum(pts, 0) / 3.0 # check whether the center of the\",\n \"prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red',\",\n \"1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2),\",\n \"is Ture show_area (bool): default is False alpha (float): chosen from 0 to\",\n \"the Wulffshape is approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3\",\n \"facets not present on the Wulff shape. direction: default is (1, 1, 1)\",\n \"and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex =\",\n \"of corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if\",\n \"shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials.\",\n \"cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for\",\n \"pts in a facet used to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines)\",\n \"cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap,\",\n \"the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental\",\n \"l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75,\",\n \"default is Ture show_area (bool): default is False alpha (float): chosen from 0\",\n \"e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append(\",\n \"as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff,\",\n \"plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff\",\n \"(float) radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume / np.pi)) ** (1\",\n \"enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1]\",\n \"radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume / np.pi)) ** (1 /\",\n \"/ np.pi)) ** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy\",\n \"e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal\",\n \"on wulff. .. attribute:: color_area list for all input_miller, total area on wulff,\",\n \"for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys():\",\n \".. attribute:: on_wulff list for all input_miller, True is on wulff. .. attribute::\",\n \"miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get\",\n \"self.lattice = lattice self.symprec = symprec # 2. get all the data for\",\n \"ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright\",\n \"0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list)))\",\n \"/ 3.0 # check whether the center of the simplices is on one\",\n \"of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as\",\n \"from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x,\",\n \"with color 3. get wulff_area and other properties .. attribute:: debug (bool) ..\",\n \"energies, and the total area and volume of the wulff shape,the weighted surface\",\n \"is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of\",\n \"Prepare for display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the form\",\n \"0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff:\",\n \"vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i]\",\n \"object, the input lattice for the conventional unit cell .. attribute:: facets [WulffFacet]\",\n \"in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in\",\n \"input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for\",\n \"return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\" def\",\n \"e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1,\",\n \"off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the surface energies of on_wulff facets.\",\n \"\\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic\",\n \"* len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts =\",\n \"self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in\",\n \"recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)):\",\n \"of indices and their corresponding surface energies, and the total area and volume\",\n \"attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list ..\",\n \"tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in the convex\",\n \"r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to\",\n \"with input_miller .. attribute:: e_surf_list list of input surface energies, in the same\",\n \"ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds =\",\n \"2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if p\",\n \"grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True,\",\n \"\\\"\\\"\\\" Returns the number of edges in the convex hull. Useful for identifying\",\n \"\\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\"\",\n \"1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area,\",\n \"sites. \\\"\\\"\\\" all_edges = [] for facet in self.facets: edges = [] pt\",\n \"debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool)\",\n \"way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt\",\n \"the same order with input_miller .. attribute:: lattice Lattice object, the input lattice\",\n \"= [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set)\",\n \"1e-5. \\\"\\\"\\\" if any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface\",\n \"color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff])\",\n \"0. .. attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self, lattice,\",\n \"area and volume of the wulff shape,the weighted surface energy, the anisotropy and\",\n \"* \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of\",\n \"\\\"\\\"\\\" Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]:\",\n \"is plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z =\",\n \"default is 1e-5. \\\"\\\"\\\" if any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical\",\n \"color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the surface energies of\",\n \"from lattice .. attribute:: miller_list list of input miller index, for hcp in\",\n \"== (0, 0, 1) or miller_index == (0, 0, 0, 1): return 0,\",\n \"input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area\",\n \"recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se < 0 for se in e_surf_list]):\",\n \"area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container\",\n \"get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in a facet used to draw\",\n \"hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color\",\n \"class to generate the Wulff shape from a lattice, a list of indices\",\n \"self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure =\",\n \"hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation, default\",\n \"the input lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for\",\n \"bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the\",\n \"(bool): default is False legend_on (bool): default is True aspect_ratio: default is (8,\",\n \"\\\"\\\"\\\" Returns the number of vertices in the convex hull. Useful for identifying\",\n \"[r,g,b,alpha}): Customize color of each facet with a dictionary. The key is the\",\n \".. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute::\",\n \"the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\"\",\n \"cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return\",\n \"pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np import scipy\",\n \"3. get wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha\",\n \"area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all\",\n \"dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points = []\",\n \"the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label\",\n \"to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list)\",\n \"1, e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x +\",\n \":param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\"\",\n \"points, Compute the area of this triangle. Args: pts: [a, b, c] three\",\n \"0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v)\",\n \"self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether\",\n \"key is the corresponding Miller index and value is the color. Undefined facets\",\n \"Undefined facets will use default color site. Note: If you decide to set\",\n \"\\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self):\",\n \".. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff\",\n \"* 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on:\",\n \"e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z\",\n \"present on the Wulff shape. direction: default is (1, 1, 1) bar_pos: default\",\n \"# make sure the lines are connected one by one. # find the\",\n \"distance to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z =\",\n \"input conventional unit cell (with H ) from lattice .. attribute:: miller_list list\",\n \"you use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.;\",\n \"= dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list of\",\n \"plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z\",\n \"all the facets functions for wulff shape calculation: |normal| = 1, e_surf is\",\n \"lattice .. attribute:: miller_list list of input miller index, for hcp in the\",\n \"enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area\",\n \"len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]]\",\n \"v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property\",\n \"input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object\",\n \"not on_wulff. continue # assign the color for on_wulff facets according to its\",\n \"(float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se < 0 for se\",\n \"given view in terms of miller index. The lattice is from the conventional\",\n \"color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf\",\n \"= scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2),\",\n \"import matplotlib as mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list,\",\n \"alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}):\",\n \"self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff,\",\n \"The key is the corresponding Miller index and value is the color. Undefined\",\n \"any sense to have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib\",\n \"(float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy\",\n \"0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is True\",\n \"[round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\",\n \"[] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list)\",\n \"all_edges = [] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet)\",\n \"plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to\",\n \"dual_simp: (i, j, k) simplices from the dual convex hull i, j, k:\",\n \"Note: If you decide to set your own colors, it probably won't make\",\n \"self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted surface\",\n \"custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off\",\n \"shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl)\",\n \"the origin, # to ensure complete and consistent display for all directions r_range\",\n \"v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri\",\n \"pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of\",\n \"[a, b, c] three points \\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2]\",\n \"_get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the surface energies\",\n \"miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list)\",\n \"\\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\"\",\n \"for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list\",\n \"simplices from the dual convex hull i, j, k: plane index(same order in\",\n \"if len(plane.points) < 1: # empty, plane is not on_wulff. continue # assign\",\n \"def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property\",\n \"sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\"\",\n \"+ bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2\",\n \"len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number of edges in the convex\",\n \"list of input surface energies, in the same order with input_miller .. attribute::\",\n \"self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on\",\n \"= [] prev = None while len(lines) > 0: if prev is None:\",\n \"and the origin, # to ensure complete and consistent display for all directions\",\n \"color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction:\",\n \"unit cell .. attribute:: facets [WulffFacet] for all facets considering symm .. attribute::\",\n \"'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture show_area (bool):\",\n \"for x in normal] dual_pt = [x / energy for x in normal]\",\n \"(dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of\",\n \"\\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff\",\n \"direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev\",\n \"2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2\",\n \"[x * energy for x in normal] dual_pt = [x / energy for\",\n \"se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig =\",\n \"i, l in enumerate(lines): if prev in l: l = lines.pop(i) if l[1]\",\n \"convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex\",\n \"k, l) \\\"\\\"\\\" str_format = '($' for x in hkl: if x <\",\n \"attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from\",\n \"\\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the\",\n \"for determining the critical nucleus size. A large shape factor indicates great anisotropy.\",\n \"0, 1) or miller_index == (0, 0, 0, 1): return 0, 90 else:\",\n \"[WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes = [] recp =\",\n \"support of plotting from a given view in terms of miller index. The\",\n \"e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1\",\n \"Ture show_area (bool): default is False alpha (float): chosen from 0 to 1\",\n \"0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl\",\n \"= miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops)\",\n \"a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev = None while\",\n \"x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec =\",\n \"self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller\",\n \"total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for\",\n \"ind for normal_e_m # recalculate the dual of dual, get the wulff shape.\",\n \"for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted\",\n \"\\\"\\\"\\\" Locate the plane for simpx of on wulff_cv, by comparing the center\",\n \"# 2. get all the data for wulff construction # get all the\",\n \"comparing the center of the simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff\",\n \"weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful for determining the critical nucleus\",\n \"are connected one by one. # find the way covering all pts and\",\n \"color of each facet with a dictionary. The key is the corresponding Miller\",\n \"miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]): list\",\n \"plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri)\",\n \"miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param\",\n \"in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /=\",\n \"with a dictionary. The key is the corresponding Miller index and value is\",\n \"identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the\",\n \"energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib\",\n \"of the conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil\",\n \"sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy\",\n \"is None: l = lines.pop(0) else: for i, l in enumerate(lines): if prev\",\n \"= self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for\",\n \"all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self,\",\n \"plane is not on_wulff. continue # assign the color for on_wulff facets according\",\n \"of the Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float)\",\n \"edges = [] pt = self.get_line_in_facet(facet) lines = [] for i, p in\",\n \"hexagonal lattices. If you use this code extensively, consider citing the following: <NAME>.;\",\n \"<NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific\",\n \"|normal| = 1, e_surf is plane's distance to (0, 0, 0), plane function:\",\n \"a dictionary. The key is the corresponding Miller index and value is the\",\n \"\\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] *\",\n \"<NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from\",\n \"self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt\",\n \"return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return\",\n \"wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: #\",\n \"on wulff_cv, by comparing the center of the simpx triangle with the plane\",\n \"other properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set\",\n \"alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1,\",\n \"chosen from 0 to 1 (float), default is 1 off_color: Default color for\",\n \"of ints, shape (nfacet, ndim)) # list of [i, j, k] , ndim\",\n \"shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff}\",\n \"# check whether [pts] is empty if len(plane.points) < 1: # empty, plane\",\n \"0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in\",\n \".. attribute:: structure Structure object, input conventional unit cell (with H ) from\",\n \"pt = [] prev = None while len(lines) > 0: if prev is\",\n \"prev: l.reverse() break # make sure the lines are connected one by one.\",\n \"normal] dual_pt = [x / energy for x in normal] color_plane = color_ind[divmod(i,\",\n \"* energy for x in normal] dual_pt = [x / energy for x\",\n \"def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property\",\n \"= wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex\",\n \"r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if\",\n \"Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as\",\n \"color 3. get wulff_area and other properties .. attribute:: debug (bool) .. attribute::\",\n \"the conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering symm\",\n \"for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1,\",\n \"according to its # index and the color_list for on_wulff plane_color = color_list[plane.index]\",\n \"Given a list of coords for 3 points, Compute the area of this\",\n \"shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y')\",\n \"mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i])\",\n \"on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf)\",\n \"input lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for all\",\n \"the total area and volume of the wulff shape,the weighted surface energy, the\",\n \", ndim = 3 # i, j, k: ind for normal_e_m # recalculate\",\n \"of vertices in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\"\",\n \"with the plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0]\",\n \"as a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume\",\n \"of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1.\",\n \"calculation: |normal| = 1, e_surf is plane's distance to (0, 0, 0), normal[0]x\",\n \"(color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as\",\n \"the Wulff shape. direction: default is (1, 1, 1) bar_pos: default is [0.75,\",\n \"index, for hcp in the form of hkil .. attribute:: hkl_list modify hkill\",\n \"x < 0: str_format += '\\\\\\\\overline{' + str(-x) + '}' else: str_format +=\",\n \"and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute::\",\n \"p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]),\",\n \"Wulffshape is approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 /\",\n \"one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if\",\n \"[0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default is\",\n \"off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def\",\n \"energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] #\",\n \"(ndarray of ints, shape (nfacet, ndim)) # list of [i, j, k] ,\",\n \"x in miller_list] # store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0,\",\n \"by comparing the center of the simpx triangle with the plane functions. \\\"\\\"\\\"\",\n \"of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl:\",\n \"matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff =\",\n \"== prev: l.reverse() break # make sure the lines are connected one by\",\n \"of each facet with a dictionary. The key is the corresponding Miller index\",\n \"MIT License. \\\"\\\"\\\" This module define a WulffShape class to generate the Wulff\",\n \"i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False)\",\n \"shape,the weighted surface energy, the anisotropy and shape_factor can also be calculated. In\",\n \"axis_off (bool): default is Ture show_area (bool): default is False alpha (float): chosen\",\n \"_get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of on wulff_cv, by comparing the\",\n \"def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the\",\n \"return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return\",\n \"properties .. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set ..\",\n \"Distributed under the terms of the MIT License. \\\"\\\"\\\" This module define a\",\n \"index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param\",\n \"= plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list\",\n \"5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots\",\n \"v) return azim, elev @property def volume(self): \\\"\\\"\\\" Volume of the Wulff shape\",\n \"bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else\",\n \"sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges\",\n \"the area of this triangle. Args: pts: [a, b, c] three points \\\"\\\"\\\"\",\n \"for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display\",\n \"decide to set your own colors, it probably won't make any sense to\",\n \"(negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in\",\n \"wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list)\",\n \"scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1,\",\n \"already find the plane, move to the next simplices break for plane in\",\n \"cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart,\",\n \"sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number of edges\",\n \"crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import\",\n \"WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of miller index and surface energies,\",\n \"store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp\",\n \"= miller self.points = [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff\",\n \"ax.add_collection3d(tri) # set ranges of x, y, z # find the largest distance\",\n \"azim, elev @property def volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return\",\n \"Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list\",\n \"for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller)\",\n \"dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices\",\n \"get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev =\",\n \"index and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) #\",\n \"self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in\",\n \"alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the surface energies of on_wulff\",\n \"in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf\",\n \"alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not specified, use\",\n \"\\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp\",\n \"miller index, for hcp in the form of hkil .. attribute:: hkl_list modify\",\n \"plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5:\",\n \"pts: [a, b, c] three points \\\"\\\"\\\" a, b, c = pts[0], pts[1],\",\n \"e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction\",\n \"(%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def\",\n \"the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm,\",\n \"plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from\",\n \"Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges = [] for facet in\",\n \"all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider\",\n \"r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range\",\n \"Radius of the Wulffshape when the Wulffshape is approximated as a sphere. Returns:\",\n \"|normal| = 1, e_surf is plane's distance to (0, 0, 0), normal[0]x +\",\n \"v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff\",\n \"x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list\",\n \"= tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x\",\n \"[x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy,\",\n \"\\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt\",\n \"self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list,\",\n \"according to lattice apply symmops to get all the miller index, then get\",\n \"index and surface energies, with given conventional unit cell. surface energy (Jm^2) is\",\n \"get all the miller index, then get normal, get all the facets functions\",\n \"Returns the number of vertices in the convex hull. Useful for identifying catalytically\",\n \"= '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\"\",\n \"for the conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering\",\n \"tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice\",\n \"str_format += '\\\\\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format +=\",\n \"in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) !=\",\n \"also be calculated. In support of plotting from a given view in terms\",\n \"energies symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se < 0\",\n \"units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100)\",\n \"in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list =\",\n \"\\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply\",\n \"x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec #\",\n \"not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x\",\n \"License. \\\"\\\"\\\" This module define a WulffShape class to generate the Wulff shape\",\n \"custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in\",\n \"Process: 1. get wulff simplices 2. label with color 3. get wulff_area and\",\n \"2. label with color 3. get wulff_area and other properties .. attribute:: debug\",\n \"tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y, z # find the\",\n \"= mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y, z #\",\n \"3. consider the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex\",\n \"def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of on wulff_cv, by comparing\",\n \"custom_colors={}): \\\"\\\"\\\" assign colors according to the surface energies of on_wulff facets. return:\",\n \"{hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface\",\n \"i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x)\",\n \"= [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list),\",\n \"Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__\",\n \"e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color]\",\n \"e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] =\",\n \"color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf\",\n \"plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param\",\n \"color for facets not present on the Wulff shape. direction: default is (1,\",\n \"calculated. In support of plotting from a given view in terms of miller\",\n \"self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append(\",\n \"# simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i, j,\",\n \"facet used to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = []\",\n \"for hexagonal lattices. If you use this code extensively, consider citing the following:\",\n \"bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 =\",\n \"index. The lattice is from the conventional unit cell, and (hkil) for hexagonal\",\n \"energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x\",\n \"for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list])\",\n \"in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i\",\n \"on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3. get\",\n \"only one hkl on wulff, choose the color of the median cnorm =\",\n \"area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl]\",\n \"= self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is\",\n \"Args: miller_index: viewing direction Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index ==\",\n \"hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self):\",\n \"corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se\",\n \"return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area\",\n \"c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c)\",\n \"0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0],\",\n \"to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet]\",\n \"l) \\\"\\\"\\\" str_format = '($' for x in hkl: if x < 0:\",\n \"color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool): default is\",\n \"# add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper\",\n \"= color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by\",\n \"'<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def\",\n \"self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane()\",\n \"between on_wulff pts and the origin, # to ensure complete and consistent display\",\n \"total area and volume of the wulff shape,the weighted surface energy, the anisotropy\",\n \"= dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller = miller self.points =\",\n \"if x < 0: str_format += '\\\\\\\\overline{' + str(-x) + '}' else: str_format\",\n \"return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\"\",\n \"2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ =\",\n \"tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all the data\",\n \"input miller index, for hcp in the form of hkil .. attribute:: hkl_list\",\n \"attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list\",\n \"# maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0],\",\n \"x in normal] dual_pt = [x / energy for x in normal] color_plane\",\n \"hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull\",\n \"= fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10],\",\n \"tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha))\",\n \"colors, it probably won't make any sense to have the color bar on.\",\n \"[self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self):\",\n \"in self.facets: # check whether [pts] is empty if len(plane.points) < 1: #\",\n \"unit cell (with H ) from lattice .. attribute:: miller_list list of input\",\n \"hkl_list modify hkill to hkl, in the same order with input_miller .. attribute::\",\n \"e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list\",\n \"volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self):\",\n \"0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot.\",\n \"energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy =\",\n \"self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for i, p\",\n \"off_color color of facets off wulff .. attribute:: structure Structure object, input conventional\",\n \"Locate the plane for simpx of on wulff_cv, by comparing the center of\",\n \"miller index. The lattice is from the conventional unit cell, and (hkil) for\",\n \":param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf\",\n \"str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list\",\n \"j, k] , ndim = 3 # i, j, k: ind for normal_e_m\",\n \"- plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts)\",\n \"all_hkl = [] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops\",\n \"triangle. Args: pts: [a, b, c] three points \\\"\\\"\\\" a, b, c =\",\n \"[h, k, l] or (h, k, l) \\\"\\\"\\\" str_format = '($' for x\",\n \"aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig,\",\n \"structure Structure object, input conventional unit cell (with H ) from lattice ..\",\n \"the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\"\",\n \"symmops to get all the miller index, then get normal, get all the\",\n \"3 # i, j, k: ind for normal_e_m # recalculate the dual of\",\n \"r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off')\",\n \"normal[2]z = e_surf from self: normal_e_m to get the plane functions dual_simp: (i,\",\n \"facet): \\\"\\\"\\\" Returns the sorted pts in a facet used to draw a\",\n \"from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices\",\n \"def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in the convex hull. Useful\",\n \"active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number of\",\n \"sorted pts in a facet used to draw a line \\\"\\\"\\\" lines =\",\n \"@property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted surface energy\",\n \"plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\"\",\n \"self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\" return\",\n \"\\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict\",\n \"= lattice self.symprec = symprec # 2. get all the data for wulff\",\n \"miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' :\",\n \"won't make any sense to have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\"\",\n \"in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\"\",\n \"None while len(lines) > 0: if prev is None: l = lines.pop(0) else:\",\n \"x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range\",\n \"abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] +=\",\n \"len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only\",\n \"# store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp =\",\n \"\\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices\",\n \"\\\"\\\"\\\" This is useful for determining the critical nucleus size. A large shape\",\n \"import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ =\",\n \"cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff]\",\n \"Agrs: hkl: in the form of [h, k, l] or (h, k, l)\",\n \"wulff simplices 2. label with color 3. get wulff_area and other properties ..\",\n \"default color site. Note: If you decide to set your own colors, it\",\n \"1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False)\",\n \"1 off_color: Default color for facets not present on the Wulff shape. direction:\",\n \"the color. Undefined facets will use default color site. Note: If you decide\",\n \"enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)])\",\n \"a facet used to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt =\",\n \"choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) +\",\n \"normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the plane functions\",\n \"logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for\",\n \"dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list\",\n \"is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool): default\",\n \"bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar\",\n \"for plotting \\\"\\\"\\\" if miller_index == (0, 0, 1) or miller_index == (0,\",\n \"condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp =\",\n \"= color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else:\",\n \"(float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2 / 3)) @property\",\n \"facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt)\",\n \"plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)]\",\n \"e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed\",\n \"import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff\",\n \"y, z # find the largest distance between on_wulff pts and the origin,\",\n \"from the conventional unit cell, and (hkil) for hexagonal lattices. If you use\",\n \"axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8,\",\n \"boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if\",\n \"in terms of miller index. The lattice is from the conventional unit cell,\",\n \"to have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl\",\n \":param dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf\",\n \"# plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\")\",\n \"complete and consistent display for all directions r_range = max([np.linalg.norm(x) for x in\",\n \"= plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2\",\n \"order with input_miller .. attribute:: e_surf_list list of input surface energies, in the\",\n \"p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2 /\",\n \"mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1,\",\n \"miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args:\",\n \"form of [h, k, l] or (h, k, l) \\\"\\\"\\\" str_format = '($'\",\n \"1])])))) for i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges)\",\n \"from a lattice, a list of indices and their corresponding surface energies, and\",\n \"== len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])]))))\",\n \"on_wulff list for all input_miller, True is on wulff. .. attribute:: color_area list\",\n \"dual_convex.simplices # simplices (ndarray of ints, shape (nfacet, ndim)) # list of [i,\",\n \"all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy\",\n \"\\\"\\\"\\\" Returns the sorted pts in a facet used to draw a line\",\n \"for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 *\",\n \"x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: #\",\n \"color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot.\",\n \"recalculate the dual of dual, get the wulff shape. # conner <-> surface\",\n \"- 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in\",\n \"\\\"\\\"\\\" assign colors according to the surface energies of on_wulff facets. return: (color_list,\",\n \"e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x:\",\n \"= get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0] elev\",\n \"use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.;\",\n \"miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig\",\n \"for 3 points, Compute the area of this triangle. Args: pts: [a, b,\",\n \"* 1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy,\",\n \"and shape_factor can also be calculated. In support of plotting from a given\",\n \"the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl\",\n \"hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in\",\n \"3.0 # check whether the center of the simplices is on one plane\",\n \"e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map =\",\n \"symprec # 2. get all the data for wulff construction # get all\",\n \"# i, j, k: ind for normal_e_m # recalculate the dual of dual,\",\n \"prev = None while len(lines) > 0: if prev is None: l =\",\n \"0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf,\",\n \"shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25')\",\n \"whether the center of the simplices is on one plane for plane in\",\n \"= None while len(lines) > 0: if prev is None: l = lines.pop(0)\",\n \"H ) from lattice .. attribute:: miller_list list of input miller index, for\",\n \"self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1],\",\n \"\\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\"\",\n \"lattice for the conventional unit cell .. attribute:: facets [WulffFacet] for all facets\",\n \"\\\"\\\"\\\" Generate Wulff Shape from list of miller index and surface energies, with\",\n \"surface energies, and the total area and volume of the wulff shape,the weighted\",\n \"4) * (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\"\",\n \"(hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for\",\n \"+= get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the\",\n \"[WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from the dual\",\n \"for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if\",\n \"label with color 3. get wulff_area and other properties .. attribute:: debug (bool)\",\n \"wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\"\",\n \"*args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs:\",\n \"to get the plane functions dual_simp: (i, j, k) simplices from the dual\",\n \"if miller_index == (0, 0, 1) or miller_index == (0, 0, 0, 1):\",\n \"(2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the\",\n \"...]: list of hkl or hkil for hcp e_surf_list ([float]): list of corresponding\",\n \"/ (self.volume ** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the\",\n \"e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm =\",\n \"1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1,\",\n \"facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp simplices from the\",\n \"False alpha (float): chosen from 0 to 1 (float), default is 1 off_color:\",\n \"simplices is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center)\",\n \"from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy as np import\",\n \"size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>.\",\n \"e_surf_list list of input surface energies, in the same order with input_miller ..\",\n \"spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' %\",\n \"\\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx\",\n \"= symprec # 2. get all the data for wulff construction # get\",\n \"__init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the conventional\",\n \"[x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices\",\n \"energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area of\",\n \"/ 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff shape.\",\n \"= self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in recp_symmops:\",\n \"for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt =\",\n \"area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\" def __init__(self,\",\n \"= self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy\",\n \"(0, 0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from\",\n \"to get all the miller index, then get normal, get all the facets\",\n \"return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number of edges in the\",\n \"def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def\",\n \"def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl:\",\n \"for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1],\",\n \"plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 *\",\n \"cell (with H ) from lattice .. attribute:: miller_list list of input miller\",\n \"get all the facets functions for wulff shape calculation: |normal| = 1, e_surf\",\n \"op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not\",\n \"normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param\",\n \"the number of edges in the convex hull. Useful for identifying catalytically active\",\n \"bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt\",\n \"color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map\",\n \"True is on wulff. .. attribute:: color_area list for all input_miller, total area\",\n \"self.e_surf_list)): for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if\",\n \"if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off')\",\n \"= 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>'\",\n \"1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2,\",\n \"# if there is only one hkl on wulff, choose the color of\",\n \"= [x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal,\",\n \"plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to\",\n \"great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461\",\n \"pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1,\",\n \"pts and the origin, # to ensure complete and consistent display for all\",\n \"from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts\",\n \"line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev = None while len(lines)\",\n \"(8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary.\",\n \"simpx[2]]) # already find the plane, move to the next simplices break for\",\n \"colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for\",\n \"Team. # Distributed under the terms of the MIT License. \\\"\\\"\\\" This module\",\n \"= True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) #\",\n \"# find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev =\",\n \"= ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store\",\n \"extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface\",\n \"input_miller .. attribute:: lattice Lattice object, the input lattice for the conventional unit\",\n \"this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.\",\n \"data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x\",\n \"the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the\",\n \"mpl import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,\",\n \"surfaces Agrs: hkl: in the form of [h, k, l] or (h, k,\",\n \"by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| =\",\n \"surface energy, the anisotropy and shape_factor can also be calculated. In support of\",\n \"# display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm,\",\n \"fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both',\",\n \"= [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0 #\",\n \"miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt =\",\n \"return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according\",\n \"ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts]\",\n \"tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal =\",\n \"__init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf:\",\n \"functions for wulff shape calculation: |normal| = 1, e_surf is plane's distance to\",\n \"'0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger\",\n \"= 0. .. attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self,\",\n \"([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]): list of\",\n \"add legend if legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left',\",\n \"if prev is None: l = lines.pop(0) else: for i, l in enumerate(lines):\",\n \"lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list of\",\n \"= [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area\",\n \"= '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__)\",\n \"number of vertices in the convex hull. Useful for identifying catalytically active sites.\",\n \"range(3)] center = np.sum(pts, 0) / 3.0 # check whether the center of\",\n \".. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute:: wulff_pt_list\",\n \"return azim, elev @property def volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\"\",\n \"is from the conventional unit cell, and (hkil) for hexagonal lattices. If you\",\n \"> 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one\",\n \"@property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape is approximated\",\n \"Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns\",\n \"miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x,\",\n \"color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def\",\n \"return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15,\",\n \"key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1],\",\n \"in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in\",\n \"Total surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\"\",\n \"= dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area\",\n \"pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0\",\n \"the color for on_wulff facets according to its # index and the color_list\",\n \"find the largest distance between on_wulff pts and the origin, # to ensure\",\n \"hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges = [] for facet\",\n \"one hkl on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list)\",\n \"container for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index,\",\n \"terms of miller index. The lattice is from the conventional unit cell, and\",\n \"i in range(3)] center = np.sum(pts, 0) / 3.0 # check whether the\",\n \"# assign the color for on_wulff facets according to its # index and\",\n \"wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig):\",\n \"8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The\",\n \"lines.pop(i) if l[1] == prev: l.reverse() break # make sure the lines are\",\n \"2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is\",\n \"azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is empty if\",\n \"of coords for 3 points, Compute the area of this triangle. Args: pts:\",\n \"used to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev\",\n \"in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in\",\n \"l = lines.pop(i) if l[1] == prev: l.reverse() break # make sure the\",\n \"surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns:\",\n \"# get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) #\",\n \"\\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def\",\n \"@property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy /\",\n \"return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot\",\n \"color of facets off wulff .. attribute:: structure Structure object, input conventional unit\",\n \"= 1, e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y\",\n \"and consistent display for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list])\",\n \"= 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return\",\n \"citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of\",\n \"scipy as sp from scipy.spatial import ConvexHull import logging import warnings __author__ =\",\n \"in normal] dual_pt = [x / energy for x in normal] color_plane =\",\n \"from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) #\",\n \"1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list\",\n \"miller index and surface energies, with given conventional unit cell. surface energy (Jm^2)\",\n \"logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The\",\n \"simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next simplices\",\n \"show_area .. attribute:: off_color color of facets off wulff .. attribute:: structure Structure\",\n \"0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev\",\n \"e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list]\",\n \"miller_index: viewing direction Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index == (0,\",\n \"operations(symmops) according to lattice apply symmops to get all the miller index, then\",\n \"plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool):\",\n \"= e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig =\",\n \"np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) /\",\n \"+ normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind\",\n \"critical nucleus size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>.\",\n \"return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of on wulff_cv,\",\n \"(self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface\",\n \"from the dual convex hull i, j, k: plane index(same order in normal_e_m)\",\n \"import matplotlib.pyplot as plt import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff\",\n \"- weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def\",\n \"of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex\",\n \"is useful for determining the critical nucleus size. A large shape factor indicates\",\n \"wulff_cv, by comparing the center of the simpx triangle with the plane functions.\",\n \"= [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure = Structure(lattice,\",\n \"if there is only one hkl on wulff, choose the color of the\",\n \"0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim,\",\n \"8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args: color_set: default is 'PuBu'\",\n \"module define a WulffShape class to generate the Wulff shape from a lattice,\",\n \"facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True,\",\n \"shape calculation: |normal| = 1, e_surf is plane's distance to (0, 0, 0),\",\n \"facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for\",\n \"dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area =\",\n \"_get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev for plotting \\\"\\\"\\\"\",\n \"shape from a lattice, a list of indices and their corresponding surface energies,\",\n \"+ str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller),\",\n \"a list of indices and their corresponding surface energies, and the total area\",\n \"specified, use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda\",\n \"and their corresponding surface energies, and the total area and volume of the\",\n \"triangle with the plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area =\",\n \"and (hkil) for hexagonal lattices. If you use this code extensively, consider citing\",\n \"default is False legend_on (bool): default is True aspect_ratio: default is (8, 8)\",\n \"is the color. Undefined facets will use default color site. Note: If you\",\n \"all input_miller, True is on wulff. .. attribute:: color_area list for all input_miller,\",\n \"according to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,\",\n \"@property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in the convex hull.\",\n \"the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property\",\n \"weighted surface energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy\",\n \"of Variation from weighted surface energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy\",\n \"check whether [pts] is empty if len(plane.points) < 1: # empty, plane is\",\n \"in the form of hkil .. attribute:: hkl_list modify hkill to hkl, in\",\n \"sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of\",\n \".. attribute:: color_area list for all input_miller, total area on wulff, off_wulff =\",\n \".. attribute:: hkl_list modify hkill to hkl, in the same order with input_miller\",\n \"\\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if grid_off:\",\n \"= '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__\",\n \"if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e\",\n \"mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf\",\n \"2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff,\",\n \"get wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency\",\n \"any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind\",\n \"A large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics\",\n \"[self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x))\",\n \"cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units =\",\n \"recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for op in\",\n \"center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if\",\n \"list for all input_miller, True is on wulff. .. attribute:: color_area list for\",\n \"show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8),\",\n \"recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl:\",\n \"define a WulffShape class to generate the Wulff shape from a lattice, a\",\n \"if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt\",\n \"# get cross point from the simplices of the dual convex hull wulff_pt_list\",\n \"vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff, choose the\",\n \"+ [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\"\",\n \"input surface energies, in the same order with input_miller .. attribute:: lattice Lattice\",\n \"fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\"\",\n \"for i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges) return\",\n \"wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) #\",\n \"hkill to hkl, in the same order with input_miller .. attribute:: e_surf_list list\",\n \"pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05,\",\n \"if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction\",\n \"attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list ..\",\n \"The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict\",\n \"# conner <-> surface # get cross point from the simplices of the\",\n \"if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is\",\n \"len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center\",\n \"the plane, move to the next simplices break for plane in self.facets: plane.outer_lines.sort()\",\n \"azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0], cart[1], 0]\",\n \"normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index\",\n \"area for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args:\",\n \"terms of the MIT License. \\\"\\\"\\\" This module define a WulffShape class to\",\n \"in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index]\",\n \"Helper container for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt,\",\n \"WulffShape class to generate the Wulff shape from a lattice, a list of\",\n \"Returns the number of edges in the convex hull. Useful for identifying catalytically\",\n \"for normal_e_m # recalculate the dual of dual, get the wulff shape. #\",\n \"'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__\",\n \"area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in\",\n \"The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>'\",\n \"0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1])\",\n \"self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to\",\n \"maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1])\",\n \"pymatgen.util.coord import get_angle import numpy as np import scipy as sp from scipy.spatial\",\n \"color_area list for all input_miller, total area on wulff, off_wulff = 0. ..\",\n \"the simplices is on one plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal,\",\n \"pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri =\",\n \"for surfaces Agrs: hkl: in the form of [h, k, l] or (h,\",\n \"* area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl]\",\n \"scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__\",\n \"is not specified, use the miller indices of # maximum area. direction =\",\n \"if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args:\",\n \"def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area\",\n \"= self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ **\",\n \"\\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal:\",\n \"of dual, get the wulff shape. # conner <-> surface # get cross\",\n \"cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on\",\n \"azim, elev for plotting \\\"\\\"\\\" if miller_index == (0, 0, 1) or miller_index\",\n \"self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of\",\n \"Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume\",\n \"self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index\",\n \"can also be calculated. In support of plotting from a given view in\",\n \"enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i *\",\n \"(with H ) from lattice .. attribute:: miller_list list of input miller index,\",\n \"[[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0],\",\n \"(bool): default is Ture show_area (bool): default is False alpha (float): chosen from\",\n \"ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing\",\n \"lattices. If you use this code extensively, consider citing the following: <NAME>.; <NAME>.;\",\n \"= logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for surfaces\",\n \"= abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for\",\n \"cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in\",\n \"the plane functions dual_simp: (i, j, k) simplices from the dual convex hull\",\n \"[line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def\",\n \"= recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in\",\n \"attribute:: hkl_list modify hkill to hkl, in the same order with input_miller ..\",\n \"ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds\",\n \"matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of on\",\n \"nucleus size. A large shape factor indicates great anisotropy. See <NAME>., <NAME>. &\",\n \"2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy =\",\n \"dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list)\",\n \"def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed to\",\n \"in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x)\",\n \"\\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in a\",\n \"to ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x) for\",\n \"wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m,\",\n \"from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops\",\n \"get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts =\",\n \"elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev)\",\n \"in hkl: if x < 0: str_format += '\\\\\\\\overline{' + str(-x) + '}'\",\n \"miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\\",\n \"for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center =\",\n \"90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0))\",\n \"default is 1 off_color: Default color for facets not present on the Wulff\",\n \"view in terms of miller index. The lattice is from the conventional unit\",\n \"See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float)\",\n \"of this triangle. Args: pts: [a, b, c] three points \\\"\\\"\\\" a, b,\",\n \"list for all input_miller, total area on wulff, off_wulff = 0. .. attribute::\",\n \"in the form of [h, k, l] or (h, k, l) \\\"\\\"\\\" str_format\",\n \"<NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1' __maintainer__\",\n \"< 0: str_format += '\\\\\\\\overline{' + str(-x) + '}' else: str_format += str(x)\",\n \"bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm =\",\n \"viewing direction Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index == (0, 0,\",\n \"= [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list\",\n \"miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5):\",\n \"cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list ([float]):\",\n \"facets according to its # index and the color_list for on_wulff plane_color =\",\n \"miller_index == (0, 0, 1) or miller_index == (0, 0, 0, 1): return\",\n \"symmetry operations(symmops) according to lattice apply symmops to get all the miller index,\",\n \"= [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i,\",\n \"normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return\",\n \"({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The key is\",\n \"self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for x\",\n \"Args: pts: [a, b, c] three points \\\"\\\"\\\" a, b, c = pts[0],\",\n \"# already find the plane, move to the next simplices break for plane\",\n \"(0, 0, 1) or miller_index == (0, 0, 0, 1): return 0, 90\",\n \"grid_off (bool): default is True axis_off (bool): default is Ture show_area (bool): default\",\n \"ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices\",\n \"[] color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec)\",\n \"color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff\",\n \"is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool):\",\n \"generate the Wulff shape from a lattice, a list of indices and their\",\n \"get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False,\",\n \"= Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x) for x in miller_list])\",\n \"& <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\"\",\n \"miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If\",\n \"in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl))\",\n \"'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on\",\n \"color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return\",\n \"e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the conventional unit cell miller_list\",\n \"color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args:\",\n \"functions dual_simp: (i, j, k) simplices from the dual convex hull i, j,\",\n \"for simpx of on wulff_cv, by comparing the center of the simpx triangle\",\n \"construction # get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets))\",\n \"tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in\",\n \"e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for\",\n \"wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute::\",\n \"show_area (bool): default is False alpha (float): chosen from 0 to 1 (float),\",\n \"weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self):\",\n \"self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2\",\n \"\\\"\\\"\\\" if any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy\",\n \"is approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4)\",\n \"(bool): default is True axis_off (bool): default is Ture show_area (bool): default is\",\n \"\\\"\\\"\\\" Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool):\",\n \"return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes = [] recp\",\n \"False legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}:\",\n \"three points \\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b)\",\n \"input_miller .. attribute:: e_surf_list list of input surface energies, in the same order\",\n \"len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda\",\n \"Lattice object, the input lattice for the conventional unit cell .. attribute:: facets\",\n \"ndim = 3 # i, j, k: ind for normal_e_m # recalculate the\",\n \"Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area\",\n \"probably won't make any sense to have the color bar on. Return: (matplotlib.pyplot)\",\n \"i, p in enumerate(lines): if p not in all_edges: edges.append(p) all_edges.extend(edges) return len(all_edges)\",\n \"= [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]]\",\n \"wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def\",\n \"order with input_miller .. attribute:: lattice Lattice object, the input lattice for the\",\n \"of hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface energies\",\n \"pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu',\",\n \"(nfacet, ndim)) # list of [i, j, k] , ndim = 3 #\",\n \"* 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy\",\n \"custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a dictionary. The key\",\n \"0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy\",\n \"direction is not specified, use the miller indices of # maximum area. direction\",\n \"color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt\",\n \"tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y, z\",\n \"for all input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area\",\n \"def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65),\",\n \"Structure from pymatgen.util.coord import get_angle import numpy as np import scipy as sp\",\n \"is False alpha (float): chosen from 0 to 1 (float), default is 1\",\n \"convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is\",\n \"If you decide to set your own colors, it probably won't make any\",\n \"on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl\",\n \"miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev for plotting \\\"\\\"\\\" if\",\n \":param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param\",\n \"= [x * energy for x in normal] dual_pt = [x / energy\",\n \"(bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize\",\n \"max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0],\",\n \"* 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add\",\n \"from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp\",\n \"wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl}\",\n \"color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the\",\n \"pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i\",\n \"def shape_factor(self): \\\"\\\"\\\" This is useful for determining the critical nucleus size. A\",\n \"= self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v = [cart[0],\",\n \"all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice\",\n \"0.65] bar_on (bool): default is False legend_on (bool): default is True aspect_ratio: default\",\n \"color_set, alpha, off_color, custom_colors=custom_colors) if not direction: # If direction is not specified,\",\n \"def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev for plotting\",\n \"@property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff shape. Returns: (float)\",\n \"(c) Pymatgen Development Team. # Distributed under the terms of the MIT License.\",\n \"axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns:\",\n \"import scipy as sp from scipy.spatial import ConvexHull import logging import warnings __author__\",\n \"number of edges in the convex hull. Useful for identifying catalytically active sites.\",\n \"normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal\",\n \"\\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape is approximated as a sphere.\",\n \"from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>'\",\n \"area of this triangle. Args: pts: [a, b, c] three points \\\"\\\"\\\" a,\",\n \"shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off\",\n \"surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for\",\n \"legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}):\",\n \"cell .. attribute:: facets [WulffFacet] for all facets considering symm .. attribute:: dual_cv_simp\",\n \"= l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None,\",\n \"lattice is from the conventional unit cell, and (hkil) for hexagonal lattices. If\",\n \"= self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for\",\n \"largest distance between on_wulff pts and the origin, # to ensure complete and\",\n \"If direction is not specified, use the miller indices of # maximum area.\",\n \"on wulff, choose the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1,\",\n \"ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center',\",\n \"lines = [] for i, p in enumerate(pt): if i == len(pt) /\",\n \"If you use this code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.;\",\n \"v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1,\",\n \"import numpy as np import scipy as sp from scipy.spatial import ConvexHull import\",\n \"plane for simpx of on wulff_cv, by comparing the center of the simpx\",\n \"mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y, z # find\",\n \"as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors)\",\n \"- np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\"\",\n \"m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\"\",\n \"colors according to the surface energies of on_wulff facets. return: (color_list, color_proxy, color_proxy_on_wulff,\",\n \"input_miller, True is on wulff. .. attribute:: color_area list for all input_miller, total\",\n \"color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as\",\n \"the simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list)\",\n \"scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha)\",\n \"following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals.\",\n \"Returns the sorted pts in a facet used to draw a line \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" all_edges = [] for facet in self.facets: edges = [] pt =\",\n \"alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self,\",\n \"> 0: if prev is None: l = lines.pop(0) else: for i, l\",\n \"make sure the lines are connected one by one. # find the way\",\n \"Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\"\",\n \"+ normal[2]z = e_surf from self: normal_e_m to get the plane functions dual_simp:\",\n \"show(self, *args, **kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed to get_plot.\",\n \"set your own colors, it probably won't make any sense to have the\",\n \"for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se < 0 for se in\",\n \"for wulff construction # get all the surface normal from get_all_miller_e() self.facets =\",\n \"on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to\",\n \"def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl\",\n \"def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape is approximated as\",\n \"# Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e,\",\n \"is plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y +\",\n \"self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5,\",\n \"cross point from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp)\",\n \"the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges = []\",\n \"the same order with input_miller .. attribute:: e_surf_list list of input surface energies,\",\n \"in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e)\",\n \"coords for 3 points, Compute the area of this triangle. Args: pts: [a,\",\n \"!= 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign\",\n \"import get_angle import numpy as np import scipy as sp from scipy.spatial import\",\n \"warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x\",\n \"hcp in the form of hkil .. attribute:: hkl_list modify hkill to hkl,\",\n \"= 1, e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x\",\n \"get the wulff shape. # conner <-> surface # get cross point from\",\n \"ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)'\",\n \"index, then get normal, get all the facets functions for wulff shape calculation:\",\n \"(bool) .. attribute:: show_area .. attribute:: off_color color of facets off wulff ..\",\n \"plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the next simplices break\",\n \"x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec\",\n \"self.facets: # check whether [pts] is empty if len(plane.points) < 1: # empty,\",\n \"1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl\",\n \"__date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for\",\n \"{hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl\",\n \"[simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set ranges of x, y,\",\n \"* area_hkl)/ sum(area_hkl) \\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns:\",\n \"in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges =\",\n \"abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1],\",\n \"object of the conventional unit cell miller_list ([(hkl), ...]: list of hkl or\",\n \"line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set,\",\n \"ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75')\",\n \"numpy as np import scipy as sp from scipy.spatial import ConvexHull import logging\",\n \"dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp\",\n \"aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args: color_set: default is\",\n \"the anisotropy and shape_factor can also be calculated. In support of plotting from\",\n \"1. get wulff simplices 2. label with color 3. get wulff_area and other\",\n \"l[1] == prev: l.reverse() break # make sure the lines are connected one\",\n \"ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap =\",\n \"- np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2)\",\n \"b, c] three points \\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2] v1\",\n \"WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf,\",\n \"= e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes =\",\n \"j, k) simplices from the dual convex hull i, j, k: plane index(same\",\n \"e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig\",\n \"with input_miller .. attribute:: lattice Lattice object, the input lattice for the conventional\",\n \"on_wulff facets according to its # index and the color_list for on_wulff plane_color\",\n \"# 3. consider the dual condition dual_pts = [x.dual_pt for x in self.facets]\",\n \"miller = tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller)\",\n \"'<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare\",\n \"the conventional unit cell, and (hkil) for hexagonal lattices. If you use this\",\n \"as sp from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>,\",\n \"* 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range *\",\n \"for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\"\",\n \"(bool): default is False alpha (float): chosen from 0 to 1 (float), default\",\n \"or miller_index == (0, 0, 0, 1): return 0, 90 else: cart =\",\n \"surface energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy =\",\n \"simplices 2. label with color 3. get wulff_area and other properties .. attribute::\",\n \"default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with\",\n \"the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for\",\n \"for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else:\",\n \"# Distributed under the terms of the MIT License. \\\"\\\"\\\" This module define\",\n \"miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if\",\n \"energy, the anisotropy and shape_factor can also be calculated. In support of plotting\",\n \"self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in a facet\",\n \"simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move to the\",\n \"sure the lines are connected one by one. # find the way covering\",\n \"len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for\",\n \"The lattice is from the conventional unit cell, and (hkil) for hexagonal lattices.\",\n \"sp from scipy.spatial import ConvexHull import logging import warnings __author__ = '<NAME>, <NAME>,\",\n \"loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar\",\n \"wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices\",\n \"# store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list =\",\n \"facets functions for wulff shape calculation: |normal| = 1, e_surf is plane's distance\",\n \"energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord\",\n \"Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff\",\n \"of miller index and surface energies, with given conventional unit cell. surface energy\",\n \"of hkil .. attribute:: hkl_list modify hkill to hkl, in the same order\",\n \"self.points = [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from\",\n \"dual, get the wulff shape. # conner <-> surface # get cross point\",\n \".. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute::\",\n \"plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list)\",\n \"(hkil) for hexagonal lattices. If you use this code extensively, consider citing the\",\n \"attribute:: show_area .. attribute:: off_color color of facets off wulff .. attribute:: structure\",\n \"hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from\",\n \"Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on\",\n \"break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p in\",\n \"\\\"\\\"\\\" return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\"\",\n \"<NAME>.; <NAME>.; <NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\"\",\n \"in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0)\",\n \"self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\"\",\n \"color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not direction: #\",\n \"for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff <\",\n \"is 1 off_color: Default color for facets not present on the Wulff shape.\",\n \"op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal)\",\n \"be calculated. In support of plotting from a given view in terms of\",\n \"# list of [i, j, k] , ndim = 3 # i, j,\",\n \"= mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff,\",\n \"of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area /\",\n \"def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param\",\n \"planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy)\",\n \"e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there\",\n \"in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range *\",\n \"plane, move to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines\",\n \"Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot.\",\n \"off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\"\",\n \"will use default color site. Note: If you decide to set your own\",\n \"as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff =\",\n \"sense to have the color bar on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as\",\n \"= [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of miller index\",\n \"for wulff shape calculation: |normal| = 1, e_surf is plane's distance to (0,\",\n \"shape_factor can also be calculated. In support of plotting from a given view\",\n \"None: l = lines.pop(0) else: for i, l in enumerate(lines): if prev in\",\n \"all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt = [x * energy for\",\n \"detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store\",\n \"its # index and the color_list for on_wulff plane_color = color_list[plane.index] pt =\",\n \"to its # index and the color_list for on_wulff plane_color = color_list[plane.index] pt\",\n \"self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color)\",\n \"normal, get all the facets functions for wulff shape calculation: |normal| = 1,\",\n \"(1, 0, 0)) v = [cart[0], cart[1], 0] elev = get_angle(cart, v) return\",\n \"miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def\",\n \"= tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all the\",\n \"[] for facet in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines =\",\n \"use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x:\",\n \"ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x) for x\",\n \"if not direction: # If direction is not specified, use the miller indices\",\n \"shape_factor(self): \\\"\\\"\\\" This is useful for determining the critical nucleus size. A large\",\n \"hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on\",\n \"i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate\",\n \"index self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines = []\",\n \"Returns: (float) Coefficient of Variation from weighted surface energy The ideal sphere is\",\n \"= mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase(\",\n \"self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input\",\n \"the convex hull of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True\",\n \"normal_e_m # recalculate the dual of dual, get the wulff shape. # conner\",\n \"get_tri_area(pts): \\\"\\\"\\\" Given a list of coords for 3 points, Compute the area\",\n \"abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each\",\n \"= tuple([int(x) for x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal\",\n \"miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal /= sp.linalg.norm(normal) normal_pt =\",\n \"the form of hkil .. attribute:: hkl_list modify hkill to hkl, in the\",\n \"self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number\",\n \"1, e_surf is plane's distance to (0, 0, 0), normal[0]x + normal[1]y +\",\n \"* len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for\",\n \"[] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of\",\n \"m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt = normal_pt\",\n \"miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy)\",\n \"[hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure = Structure(lattice, [\\\"H\\\"],\",\n \"(bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of\",\n \"for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig,\",\n \"0), normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl =\",\n \"= normal self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index =\",\n \"(h, k, l) \\\"\\\"\\\" str_format = '($' for x in hkl: if x\",\n \"of x, y, z # find the largest distance between on_wulff pts and\",\n \"index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt\",\n \".. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off (bool) .. attribute::\",\n \"(float): chosen from 0 to 1 (float), default is 1 off_color: Default color\",\n \"0: if prev is None: l = lines.pop(0) else: for i, l in\",\n \"1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False\",\n \"* len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)]\",\n \"unit cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp e_surf_list\",\n \"directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range *\",\n \"# find the largest distance between on_wulff pts and the origin, # to\",\n \"in enumerate(lines): if prev in l: l = lines.pop(i) if l[1] == prev:\",\n \"\\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number of edges in\",\n \"surface_area def _get_colors(self, color_set, alpha, off_color, custom_colors={}): \\\"\\\"\\\" assign colors according to the\",\n \"for plane in self.facets: # check whether [pts] is empty if len(plane.points) <\",\n \"pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a)\",\n \"Structure object, input conventional unit cell (with H ) from lattice .. attribute::\",\n \"the dual of dual, get the wulff shape. # conner <-> surface #\",\n \"bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on\",\n \"in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if\",\n \"grid_off (bool) .. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color\",\n \"= self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i,\",\n \"order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e =\",\n \"x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x\",\n \"Wulff shape. direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15,\",\n \"the conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil for\",\n \"Lab' __version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May\",\n \"\\\"\\\"\\\" if miller_index == (0, 0, 1) or miller_index == (0, 0, 0,\",\n \"[0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in\",\n \"c] three points \\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2] v1 =\",\n \"each Wulff plane. \\\"\\\"\\\" def __init__(self, normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller):\",\n \"index and value is the color. Undefined facets will use default color site.\",\n \"1 (float), default is 1 off_color: Default color for facets not present on\",\n \"mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color,\",\n \"e_surf, normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt:\",\n \"mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical')\",\n \"color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff,\",\n \"approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4) *\",\n \"in l: l = lines.pop(i) if l[1] == prev: l.reverse() break # make\",\n \"self.hkl_list = tuple([(x[0], x[1], x[-1]) for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice\",\n \"ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range\",\n \"convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def\",\n \"self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x\",\n \"attribute:: lattice Lattice object, the input lattice for the conventional unit cell ..\",\n \"r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1])\",\n \"\\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i\",\n \"direction Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index == (0, 0, 1)\",\n \".. attribute:: lattice Lattice object, the input lattice for the conventional unit cell\",\n \"plotting \\\"\\\"\\\" if miller_index == (0, 0, 1) or miller_index == (0, 0,\",\n \"import logging import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013,\",\n \"k] , ndim = 3 # i, j, k: ind for normal_e_m #\",\n \"logger.debug(len(self.facets)) # 3. consider the dual condition dual_pts = [x.dual_pt for x in\",\n \"0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0,\",\n \"= [cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def\",\n \"def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in a facet used to\",\n \"= e_surf from self: normal_e_m to get the plane functions dual_simp: (i, j,\",\n \"\\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def miller_area_dict(self): \\\"\\\"\\\"\",\n \"x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs):\",\n \"bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on:\",\n \"wulff construction # get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e()\",\n \"to generate the Wulff shape from a lattice, a list of indices and\",\n \"pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True, axis_off=True, show_area=False,\",\n \"Development Team. # Distributed under the terms of the MIT License. \\\"\\\"\\\" This\",\n \"[off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf)\",\n \"Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from\",\n \"identifying catalytically active sites. \\\"\\\"\\\" all_edges = [] for facet in self.facets: edges\",\n \"all the miller index, then get normal, get all the facets functions for\",\n \"data for wulff construction # get all the surface normal from get_all_miller_e() self.facets\",\n \"/= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt =\",\n \"default is False alpha (float): chosen from 0 to 1 (float), default is\",\n \"= np.sum(pts, 0) / 3.0 # check whether the center of the simplices\",\n \"display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the form of [h,\",\n \"(i, j, k) simplices from the dual convex hull i, j, k: plane\",\n \"hkl: if x < 0: str_format += '\\\\\\\\overline{' + str(-x) + '}' else:\",\n \"__email__ = '<EMAIL>' __date__ = 'May 5 2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl):\",\n \"of wulff_pt_list .. attribute:: on_wulff list for all input_miller, True is on wulff.\",\n \"[(i, e_surf) for i, e_surf in enumerate(self.e_surf_list) if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda\",\n \"list(facet.outer_lines) pt = [] prev = None while len(lines) > 0: if prev\",\n \"the lines are connected one by one. # find the way covering all\",\n \"for on_wulff facets according to its # index and the color_list for on_wulff\",\n \"unit cell, and (hkil) for hexagonal lattices. If you use this code extensively,\",\n \"[] class WulffShape: \\\"\\\"\\\" Generate Wulff Shape from list of miller index and\",\n \"left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3,\",\n \"not specified, use the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(),\",\n \"Energies (%s)' % (units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt\",\n \"* 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range *\",\n \"surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area\",\n \"the miller index, then get normal, get all the facets functions for wulff\",\n \"from list of miller index and surface energies, with given conventional unit cell.\",\n \"or hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float):\",\n \"determining the critical nucleus size. A large shape factor indicates great anisotropy. See\",\n \"wulff shape. # conner <-> surface # get cross point from the simplices\",\n \"shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices\",\n \"Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume / np.pi)) **\",\n \"normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) #\",\n \"the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in\",\n \"in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list\",\n \"Customize color of each facet with a dictionary. The key is the corresponding\",\n \"attribute:: off_color color of facets off wulff .. attribute:: structure Structure object, input\",\n \"same order with input_miller .. attribute:: lattice Lattice object, the input lattice for\",\n \"the center of the simplices is on one plane for plane in self.facets:\",\n \"hull i, j, k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal\",\n \"self.wulff_cv_simp: pts = [self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) /\",\n \"apply symmops to get all the miller index, then get normal, get all\",\n \"wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area =\",\n \"attribute:: e_surf_list list of input surface energies, in the same order with input_miller\",\n \"elev @property def volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume\",\n \"for x in miller_list]) self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec\",\n \"from 0 to 1 (float), default is 1 off_color: Default color for facets\",\n \"and volume of the wulff shape,the weighted surface energy, the anisotropy and shape_factor\",\n \"wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and\",\n \"is 'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture show_area\",\n \"[pts] is empty if len(plane.points) < 1: # empty, plane is not on_wulff.\",\n \"simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp]\",\n \"+ normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the plane\",\n \"of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl *\",\n \"the MIT License. \\\"\\\"\\\" This module define a WulffShape class to generate the\",\n \"the Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float) radius.\",\n \"= list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data\",\n \"is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet with a\",\n \"for hcp e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation,\",\n \"Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area\",\n \"2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p\",\n \"symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl),\",\n \"the largest distance between on_wulff pts and the origin, # to ensure complete\",\n \"(Jm^2) is the length of normal. Wulff shape is the convex hull. Based\",\n \"(float), default is 1 off_color: Default color for facets not present on the\",\n \"origin, # to ensure complete and consistent display for all directions r_range =\",\n \"normal_pt, dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param\",\n \"(1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default\",\n \"draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev = None\",\n \"(<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume **\",\n \"self: normal_e_m to get the plane functions dual_simp: (i, j, k) simplices from\",\n \"under the terms of the MIT License. \\\"\\\"\\\" This module define a WulffShape\",\n \"1.1, r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy if\",\n \"list of corresponding surface energies symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\"\",\n \"axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets off wulff\",\n \"= custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x\",\n \"Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle import numpy\",\n \"\\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/ sum(area_hkl) \\\"\\\"\\\"\",\n \"1, 1) bar_pos: default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is\",\n \"l = lines.pop(0) else: for i, l in enumerate(lines): if prev in l:\",\n \"__version__ = '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5\",\n \"dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape (nfacet,\",\n \"Get the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default\",\n \"ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') #\",\n \"= [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list,\",\n \"attribute:: structure Structure object, input conventional unit cell (with H ) from lattice\",\n \"= self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list, self.e_surf_list)): for\",\n \"reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm\",\n \"j, k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i\",\n \"l.reverse() break # make sure the lines are connected one by one. #\",\n \"color. Undefined facets will use default color site. Note: If you decide to\",\n \"(1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff\",\n \"0) / 3.0 # check whether the center of the simplices is on\",\n \"tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns\",\n \"for op in recp_symmops: miller = tuple([int(x) for x in op.operate(hkl)]) if miller\",\n \"corresponding Miller index and value is the color. Undefined facets will use default\",\n \"return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful for determining\",\n \"conner <-> surface # get cross point from the simplices of the dual\",\n \"alpha (float): chosen from 0 to 1 (float), default is 1 off_color: Default\",\n \"list of input miller index, for hcp in the form of hkil ..\",\n \"wulff shape calculation: |normal| = 1, e_surf is plane's distance to (0, 0,\",\n \"plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0],\",\n \"# If direction is not specified, use the miller indices of # maximum\",\n \"<NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import\",\n \"code extensively, consider citing the following: <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. (2016).\",\n \"4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and\",\n \"dual_pt = [x / energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]]\",\n \"0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict =\",\n \"surface energies, with given conventional unit cell. surface energy (Jm^2) is the length\",\n \"if l[1] == prev: l.reverse() break # make sure the lines are connected\",\n \"(units), fontsize=100) if grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index):\",\n \"hkl in miller_energy_dict.keys(): square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl]\",\n \"with given conventional unit cell. surface energy (Jm^2) is the length of normal.\",\n \"ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units\",\n \"a lattice, a list of indices and their corresponding surface energies, and the\",\n \"x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's\",\n \"(self.volume ** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape\",\n \"as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = []\",\n \"default is 'PuBu' grid_off (bool): default is True axis_off (bool): default is Ture\",\n \"shape. direction: default is (1, 1, 1) bar_pos: default is [0.75, 0.15, 0.05,\",\n \"This module define a WulffShape class to generate the Wulff shape from a\",\n \"get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in\",\n \"for facets not present on the Wulff shape. direction: default is (1, 1,\",\n \"_get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's distance to (0, 0,\",\n \"anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns:\",\n \"is False legend_on (bool): default is True aspect_ratio: default is (8, 8) custom_colors\",\n \"plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax\",\n \"surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual\",\n \"attribute:: color_area list for all input_miller, total area on wulff, off_wulff = 0.\",\n \"on. Return: (matplotlib.pyplot) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt import\",\n \"def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted surface energy The\",\n \"mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = []\",\n \"2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\"\",\n \"in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices)\",\n \"if self.on_wulff[i]] c_map = plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for\",\n \"return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's distance\",\n \"extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies\",\n \"@property def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values())\",\n \"return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show the\",\n \"weighted surface energy, the anisotropy and shape_factor can also be calculated. In support\",\n \"elev for plotting \\\"\\\"\\\" if miller_index == (0, 0, 1) or miller_index ==\",\n \"given conventional unit cell. surface energy (Jm^2) is the length of normal. Wulff\",\n \"and the total area and volume of the wulff shape,the weighted surface energy,\",\n \"lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\" Args: lattice: Lattice object of the conventional unit\",\n \"and value is the color. Undefined facets will use default color site. Note:\",\n \"i, p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i *\",\n \"e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get all the\",\n \"= np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2))\",\n \"\\\"\\\"\\\" This module define a WulffShape class to generate the Wulff shape from\",\n \"dual_pt, index, m_ind_orig, miller): \\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param dual_pt:\",\n \"all input_miller, total area on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$):\",\n \"hkl, in the same order with input_miller .. attribute:: e_surf_list list of input\",\n \"on_wulff = [False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in\",\n \"the Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is\",\n \"total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl *\",\n \"** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This\",\n \"Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index == (0, 0, 1) or\",\n \"mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list)) else: # if there is only one hkl on wulff, choose\",\n \"< 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative) surface energy detected.\\\") self.color_ind =\",\n \"dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x\",\n \"in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints,\",\n \"j, k: ind for normal_e_m # recalculate the dual of dual, get the\",\n \"energy (Jm^2) is the length of normal. Wulff shape is the convex hull.\",\n \"sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume / np.pi))\",\n \"azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim,\",\n \"list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list] # store input data self.structure\",\n \"miller_list] # store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list\",\n \"in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the\",\n \"is empty if len(plane.points) < 1: # empty, plane is not on_wulff. continue\",\n \"the miller indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0]\",\n \"energy The ideal sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy\",\n \"self.miller_list = tuple([tuple(x) for x in miller_list]) self.hkl_list = tuple([(x[0], x[1], x[-1]) for\",\n \"attribute:: on_wulff list for all input_miller, True is on wulff. .. attribute:: color_area\",\n \"color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True,\",\n \"Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total\",\n \"' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from\",\n \"l] or (h, k, l) \\\"\\\"\\\" str_format = '($' for x in hkl:\",\n \"assign the color for on_wulff facets according to its # index and the\",\n \"r_range * 1.1]) # add legend if legend_on: color_proxy = color_proxy if show_area:\",\n \"def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's distance to (0,\",\n \"in miller_list] # store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]])\",\n \"i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 +\",\n \"color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args, **kwargs): r\\\"\\\"\\\" Show\",\n \"utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of\",\n \"connected one by one. # find the way covering all pts and facets\",\n \"attribute:: miller_list list of input miller index, for hcp in the form of\",\n \"all the data for wulff construction # get all the surface normal from\",\n \"elev=elev) for plane in self.facets: # check whether [pts] is empty if len(plane.points)\",\n \"in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies\",\n \"of input surface energies, in the same order with input_miller .. attribute:: lattice\",\n \"elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import get_angle\",\n \"for identifying catalytically active sites. \\\"\\\"\\\" all_edges = [] for facet in self.facets:\",\n \"e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff)\",\n \"plotting from a given view in terms of miller index. The lattice is\",\n \"on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts\",\n \"/ self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff}\",\n \"= \\\"$J/m^2$\\\" if units_in_JPERM2 else r\\\"$eV/\\\\AA^2$\\\" cbar.set_label('Surface Energies (%s)' % (units), fontsize=100) if\",\n \"len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i,\",\n \"plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff,\",\n \"Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\"\",\n \"for i, l in enumerate(lines): if prev in l: l = lines.pop(i) if\",\n \"mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if\",\n \"+= (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy\",\n \"x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of\",\n \"in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted\",\n \"normal[0]x + normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = []\",\n \"else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v\",\n \"= mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map) for i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if\",\n \"# index and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane)\",\n \"the plane for simpx of on wulff_cv, by comparing the center of the\",\n \"surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def\",\n \"points \\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) -\",\n \"wulff shape,the weighted surface energy, the anisotropy and shape_factor can also be calculated.\",\n \"self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = []\",\n \"for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self,\",\n \"x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) >\",\n \"simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area\",\n \"cell. surface energy (Jm^2) is the length of normal. Wulff shape is the\",\n \"self.e_surf = e_surf self.normal_pt = normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig\",\n \"@property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property\",\n \"dual convex hull i, j, k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs\",\n \"np.sum(pts, 0) / 3.0 # check whether the center of the simplices is\",\n \"this triangle. Args: pts: [a, b, c] three points \\\"\\\"\\\" a, b, c\",\n \"<NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape\",\n \"[self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt\",\n \"symm .. attribute:: dual_cv_simp simplices from the dual convex hull (dual_pt) .. attribute::\",\n \"symprec (float): for recp_operation, default is 1e-5. \\\"\\\"\\\" if any([se < 0 for\",\n \"= lines.pop(0) else: for i, l in enumerate(lines): if prev in l: l\",\n \"plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]]) # already find the plane, move\",\n \"is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict\",\n \"= np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class\",\n \"to lattice apply symmops to get all the miller index, then get normal,\",\n \"for all input_miller, True is on wulff. .. attribute:: color_area list for all\",\n \"of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure()\",\n \"self.miller = miller self.points = [] self.outer_lines = [] class WulffShape: \\\"\\\"\\\" Generate\",\n \"In support of plotting from a given view in terms of miller index.\",\n \"for all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1,\",\n \"**kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the sorted pts in a facet used\",\n \"color_ind = self.color_ind planes = [] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for\",\n \"normal_e_m to get the plane functions dual_simp: (i, j, k) simplices from the\",\n \"get all the data for wulff construction # get all the surface normal\",\n \"indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005),\",\n \"0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m\",\n \"if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1, fc=color_list[i],\",\n \"dual of dual, get the wulff shape. # conner <-> surface # get\",\n \"store input data self.structure = Structure(lattice, [\\\"H\\\"], [[0, 0, 0]]) self.miller_list = tuple([tuple(x)\",\n \"surface energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] +\",\n \"((3 / 4) * (self.volume / np.pi)) ** (1 / 3) @property def\",\n \"/ 4) * (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self):\",\n \"x in op.operate(hkl)]) if miller not in all_hkl: all_hkl.append(miller) normal = recp.get_cartesian_coords(miller) normal\",\n \"self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of\",\n \"Args: color_set: default is 'PuBu' grid_off (bool): default is True axis_off (bool): default\",\n \"hkl or hkil for hcp e_surf_list ([float]): list of corresponding surface energies symprec\",\n \"0, 0), plane function: normal[0]x + normal[1]y + normal[2]z = e_surf from self:\",\n \"= abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index]\",\n \"there is only one hkl on wulff, choose the color of the median\",\n \"+= str(x) str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list\",\n \"**kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet): \\\"\\\"\\\" Returns the\",\n \"# to ensure complete and consistent display for all directions r_range = max([np.linalg.norm(x)\",\n \"[i, j, k] , ndim = 3 # i, j, k: ind for\",\n \"ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim,\",\n \"in the same order with input_miller .. attribute:: e_surf_list list of input surface\",\n \"distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf return:\",\n \"matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff\",\n \"else: # if there is only one hkl on wulff, choose the color\",\n \"planes.append(WulffFacet(normal, energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x:\",\n \"self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig\",\n \"hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp):\",\n \"dictionary. The key is the corresponding Miller index and value is the color.\",\n \"Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def\",\n \"import Structure from pymatgen.util.coord import get_angle import numpy as np import scipy as\",\n \"class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane. \\\"\\\"\\\" def __init__(self, normal,\",\n \"lines = list(facet.outer_lines) pt = [] prev = None while len(lines) > 0:\",\n \"1: # empty, plane is not on_wulff. continue # assign the color for\",\n \"return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev\",\n \"in the same order with input_miller .. attribute:: lattice Lattice object, the input\",\n \"wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp\",\n \"* 2 + 1])])))) for i, p in enumerate(lines): if p not in\",\n \"+ 1])])))) for i, p in enumerate(lines): if p not in all_edges: edges.append(p)\",\n \"modify hkill to hkl, in the same order with input_miller .. attribute:: e_surf_list\",\n \"np import scipy as sp from scipy.spatial import ConvexHull import logging import warnings\",\n \"2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) #\",\n \"Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl\",\n \".. attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute::\",\n \"point from the simplices of the dual convex hull wulff_pt_list = [self._get_cross_pt_dual_simp(dual_simp) for\",\n \"you decide to set your own colors, it probably won't make any sense\",\n \"area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] *\",\n \"the plane functions. \\\"\\\"\\\" on_wulff = [False] * len(self.miller_list) surface_area = [0.0] *\",\n \"l in enumerate(lines): if prev in l: l = lines.pop(i) if l[1] ==\",\n \"miller_on_wulff, e_surf_on_wulff_list) \\\"\\\"\\\" import matplotlib as mpl import matplotlib.pyplot as plt color_list =\",\n \"direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets:\",\n \"when the Wulffshape is approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\" return\",\n \"plane for plane in self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff\",\n \"from self: normal_e_m to get the plane functions dual_simp: (i, j, k) simplices\",\n \"norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface energies ax1 = fig.add_axes(bar_pos) cbar =\",\n \"of [i, j, k] , ndim = 3 # i, j, k: ind\",\n \"[] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' +\",\n \"simpx of on wulff_cv, by comparing the center of the simpx triangle with\",\n \"Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3.\",\n \"len(lines) > 0: if prev is None: l = lines.pop(0) else: for i,\",\n \"http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get wulff simplices 2. label with color 3. get wulff_area\",\n \"anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation from weighted surface energy The ideal\",\n \"logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\" for surfaces Agrs:\",\n \"_get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice\",\n \"and surface energies, with given conventional unit cell. surface energy (Jm^2) is the\",\n \"for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff_list def show(self, *args,\",\n \"np.pi)) ** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of\",\n \"lines.pop(0) else: for i, l in enumerate(lines): if prev in l: l =\",\n \"Generate Wulff Shape from list of miller index and surface energies, with given\",\n \"if prev in l: l = lines.pop(i) if l[1] == prev: l.reverse() break\",\n \"in plane.outer_lines if plane.outer_lines.count(line) != 2] return on_wulff, surface_area def _get_colors(self, color_set, alpha,\",\n \"miller index, then get normal, get all the facets functions for wulff shape\",\n \"e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind = self.color_ind planes = []\",\n \"simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list for all\",\n \"[] for i, p in enumerate(pt): if i == len(pt) / 2: break\",\n \"energy, normal_pt, dual_pt, color_plane, i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf)\",\n \"cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def volume(self): \\\"\\\"\\\"\",\n \"return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1,\",\n \"tot_edges(self): \\\"\\\"\\\" Returns the number of edges in the convex hull. Useful for\",\n \"{hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns:\",\n \"import warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials\",\n \"center of the simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff = [False]\",\n \"empty if len(plane.points) < 1: # empty, plane is not on_wulff. continue #\",\n \"miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self):\",\n \"get wulff simplices 2. label with color 3. get wulff_area and other properties\",\n \"= self.get_line_in_facet(plane) # plot from the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt])\",\n \"a sphere. Returns: (float) radius. \\\"\\\"\\\" return ((3 / 4) * (self.volume /\",\n \"the data for wulff construction # get all the surface normal from get_all_miller_e()\",\n \"= 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl\",\n \"bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True,\",\n \"3) @property def total_surface_energy(self): \\\"\\\"\\\" Total surface energy of the Wulff shape. Returns:\",\n \"facet with a dictionary. The key is the corresponding Miller index and value\",\n \".. attribute:: off_color color of facets off wulff .. attribute:: structure Structure object,\",\n \"\\\"\\\"\\\" Args: miller_index: viewing direction Returns: azim, elev for plotting \\\"\\\"\\\" if miller_index\",\n \"0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1],\",\n \"normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind =\",\n \"planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf\",\n \"for x in self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray\",\n \"the corresponding Miller index and value is the color. Undefined facets will use\",\n \"in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self):\",\n \"= mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional',\",\n \"plane's distance to (0, 0, 0), normal[0]x + normal[1]y + normal[2]z = e_surf\",\n \"attribute:: debug (bool) .. attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off\",\n \"__maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016' logger =\",\n \"color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False)\",\n \"for i, p in enumerate(pt): if i == len(pt) / 2: break lines.append(tuple(sorted(tuple([tuple(pt[i\",\n \"ints, shape (nfacet, ndim)) # list of [i, j, k] , ndim =\",\n \"[x[1] for x in e_surf_on_wulff] if len(e_surf_on_wulff) > 1: cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list), vmax=max(e_surf_on_wulff_list))\",\n \"your own colors, it probably won't make any sense to have the color\",\n \"def volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property def\",\n \"[self.wulff_pt_list[simpx[i]] for i in range(3)] center = np.sum(pts, 0) / 3.0 # check\",\n \"[] recp = self.structure.lattice.reciprocal_lattice_crystallographic recp_symmops = self.lattice.get_recp_symmetry_operation(self.symprec) for i, (hkl, energy) in enumerate(zip(self.hkl_list,\",\n \"alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2, 2), 1, 1,\",\n \"bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape plot. Args:\",\n \"[plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy,\",\n \"k: ind for normal_e_m # recalculate the dual of dual, get the wulff\",\n \"effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape is approximated as a\",\n \"= wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area\",\n \"function: normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to get\",\n \"matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff =\",\n \"use default color site. Note: If you decide to set your own colors,\",\n \"Returns: (float) Shape factor. \\\"\\\"\\\" return self.surface_area / (self.volume ** (2 / 3))\",\n \"own colors, it probably won't make any sense to have the color bar\",\n \"direction[1], direction[-1]]) wulff_pt_list = self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in\",\n \"of edges in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\"\",\n \"plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff] if\",\n \"the sorted pts from [simpx] tri = mpl3.art3d.Poly3DCollection([pt]) tri.set_color(plane_color) tri.set_edgecolor(\\\"#808080\\\") ax.add_collection3d(tri) # set\",\n \"str_format = '($' for x in hkl: if x < 0: str_format +=\",\n \"2016' logger = logging.getLogger(__name__) def hkl_tuple_to_str(hkl): \\\"\\\"\\\" Prepare for display on plots \\\"(hkl)\\\"\",\n \"aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each facet\",\n \"0 to 1 (float), default is 1 off_color: Default color for facets not\",\n \"conventional unit cell miller_list ([(hkl), ...]: list of hkl or hkil for hcp\",\n \"to draw a line \\\"\\\"\\\" lines = list(facet.outer_lines) pt = [] prev =\",\n \"= normal_pt self.dual_pt = dual_pt self.index = index self.m_ind_orig = m_ind_orig self.miller =\",\n \"@property def miller_area_dict(self): \\\"\\\"\\\" Returns {hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area))\",\n \"i, j, k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for\",\n \"cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1])\",\n \"to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line\",\n \"the color of the median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1)\",\n \"[] pt = self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if\",\n \"e_surf is plane's distance to (0, 0, 0), plane function: normal[0]x + normal[1]y\",\n \"the length of normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html\",\n \"wulff_area and other properties .. attribute:: debug (bool) .. attribute:: alpha transparency ..\",\n \"all directions r_range = max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range\",\n \"Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self, facet):\",\n \"and symmetry operations(symmops) according to lattice apply symmops to get all the miller\",\n \"lines.append(tuple(sorted(tuple([tuple(pt[i * 2]), tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines):\",\n \"for all facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex\",\n \"break # make sure the lines are connected one by one. # find\",\n \"Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT\",\n \"energies, in the same order with input_miller .. attribute:: lattice Lattice object, the\",\n \"[cart[0], cart[1], 0] elev = get_angle(cart, v) return azim, elev @property def volume(self):\",\n \"Wulff shape from a lattice, a list of indices and their corresponding surface\",\n \"self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface energy_hkl} \\\"\\\"\\\" return dict(zip(self.miller_list, self.e_surf_list))\",\n \"plt.Rectangle((2, 2), 1, 1, fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy\",\n \"to set your own colors, it probably won't make any sense to have\",\n \"of miller index. The lattice is from the conventional unit cell, and (hkil)\",\n \"a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a) v2\",\n \"color_list = [off_color] * len(self.hkl_list) color_proxy_on_wulff = [] miller_on_wulff = [] e_surf_on_wulff =\",\n \"= '0.1' __maintainer__ = '<NAME>' __email__ = '<EMAIL>' __date__ = 'May 5 2016'\",\n \"anisotropy and shape_factor can also be calculated. In support of plotting from a\",\n \"logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp =\",\n \"* area_frac_dict[hkl] return np.sqrt(square_diff_energy) / weighted_energy @property def shape_factor(self): \\\"\\\"\\\" This is useful\",\n \"and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def get_plot(self, color_set='PuBu', grid_off=True,\",\n \"pts[1], pts[2] v1 = np.array(b) - np.array(a) v2 = np.array(c) - np.array(a) area_tri\",\n \"i, e_surf in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i]\",\n \"next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line\",\n \"/ 2) return area_tri class WulffFacet: \\\"\\\"\\\" Helper container for each Wulff plane.\",\n \"'}' else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\"\",\n \"in self.miller_energy_dict.keys(): tot_surface_energy += self.miller_energy_dict[hkl] * \\\\ self.miller_area_dict[hkl] return tot_surface_energy @property def tot_corner_sites(self):\",\n \"of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure import Structure from pymatgen.util.coord import\",\n \"attribute:: miller_area ($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list,\",\n \"color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])] color_proxy_on_wulff.append( plt.Rectangle((2,\",\n \"= lines.pop(i) if l[1] == prev: l.reverse() break # make sure the lines\",\n \"plane functions dual_simp: (i, j, k) simplices from the dual convex hull i,\",\n \"median cnorm = mpl.colors.Normalize(vmin=min(e_surf_on_wulff_list) - 0.1, vmax=max(e_surf_on_wulff_list) + 0.1) scalar_map = mpl.cm.ScalarMappable(norm=cnorm, cmap=c_map)\",\n \"wulff_cv_simp simplices from the convex hull of wulff_pt_list .. attribute:: on_wulff list for\",\n \"Compute the area of this triangle. Args: pts: [a, b, c] three points\",\n \"\\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] /\",\n \"show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff, loc='upper\",\n \"get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get\",\n \"= max([np.linalg.norm(x) for x in wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range\",\n \"cell, and (hkil) for hexagonal lattices. If you use this code extensively, consider\",\n \"matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e = [self.facets[dual_simp[i]].e_surf for i in\",\n \"e in e_surf_on_wulff] bounds.append(1.2 * bounds[-1]) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # display surface\",\n \"Wulffshape when the Wulffshape is approximated as a sphere. Returns: (float) radius. \\\"\\\"\\\"\",\n \"i, hkl)) # sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self,\",\n \"<NAME>. & <NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor.\",\n \"area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim,\",\n \"hkl: in the form of [h, k, l] or (h, k, l) \\\"\\\"\\\"\",\n \"direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get\",\n \"and the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot\",\n \"by one. # find the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist())\",\n \"dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the\",\n \"convex hull i, j, k: plane index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs =\",\n \"large shape factor indicates great anisotropy. See <NAME>., <NAME>. & <NAME>. Kinetics of\",\n \"\\\"(hkl)\\\" for surfaces Agrs: hkl: in the form of [h, k, l] or\",\n \"form of hkil .. attribute:: hkl_list modify hkill to hkl, in the same\",\n \"/ self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient\",\n \"tuple(pt[i * 2 + 1])])))) for i, p in enumerate(lines): if p not\",\n \"square_diff_energy += (miller_energy_dict[hkl] - weighted_energy) \\\\ ** 2 * area_frac_dict[hkl] return np.sqrt(square_diff_energy) /\",\n \"then get normal, get all the facets functions for wulff shape calculation: |normal|\",\n \"+ normal[1]y + normal[2]z = e_surf return: [WulffFacet] \\\"\\\"\\\" all_hkl = [] color_ind\",\n \"on wulff, off_wulff = 0. .. attribute:: miller_area ($hkl$): area for all input_miller\",\n \"<-> surface # get cross point from the simplices of the dual convex\",\n \"on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl: surface\",\n \"edges in the convex hull. Useful for identifying catalytically active sites. \\\"\\\"\\\" all_edges\",\n \":param normal_pt: :param dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal =\",\n \"is only one hkl on wulff, choose the color of the median cnorm\",\n \"str(-x) + '}' else: str_format += str(x) str_format += '$)' return str_format def\",\n \"Args: lattice: Lattice object of the conventional unit cell miller_list ([(hkl), ...]: list\",\n \"Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args,\",\n \"if abs_diff < 1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]])\",\n \"return tot_surface_energy @property def tot_corner_sites(self): \\\"\\\"\\\" Returns the number of vertices in the\",\n \"area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns {hkl:\",\n \"2. get all the data for wulff construction # get all the surface\",\n \"get all the surface normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3.\",\n \"self.facets: abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] =\",\n \":param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf = e_surf self.normal_pt =\",\n \"import matplotlib as mpl import matplotlib.pyplot as plt color_list = [off_color] * len(self.hkl_list)\",\n \"it probably won't make any sense to have the color bar on. Return:\",\n \"\\\"\\\"\\\" return self.total_surface_energy / self.surface_area @property def area_fraction_dict(self): \\\"\\\"\\\" Returns: (dict): {hkl: area_hkl/total\",\n \"x: x[1])[0] fig = plt.figure() fig.set_size_inches(aspect_ratio[0], aspect_ratio[1]) azim, elev = self._get_azimuth_elev([direction[0], direction[1], direction[-1]])\",\n \"not present on the Wulff shape. direction: default is (1, 1, 1) bar_pos:\",\n \"= color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from the sorted pts from [simpx]\",\n \"Useful for identifying catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\"\",\n \"norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1], spacing='proportional', orientation='vertical') units = \\\"$J/m^2$\\\"\",\n \"default is True axis_off (bool): default is Ture show_area (bool): default is False\",\n \"the dual condition dual_pts = [x.dual_pt for x in self.facets] dual_convex = ConvexHull(dual_pts)\",\n \"plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the form of [h, k, l]\",\n \"1.1, r_range * 1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend\",\n \"color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha, off_color, custom_colors=custom_colors) if not\",\n \"miller_on_wulff, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=3, fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add\",\n \"dual_pt: :param index: :param m_ind_orig: :param miller: \\\"\\\"\\\" self.normal = normal self.e_surf =\",\n \"attribute:: alpha transparency .. attribute:: color_set .. attribute:: grid_off (bool) .. attribute:: axis_off\",\n \"m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4)))\",\n \"the facets functions for wulff shape calculation: |normal| = 1, e_surf is plane's\",\n \"for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1, 1, fc=x, alpha=alpha) for\",\n \"1.1]) ax.set_zlim([-r_range * 1.1, r_range * 1.1]) # add legend if legend_on: color_proxy\",\n \"whether [pts] is empty if len(plane.points) < 1: # empty, plane is not\",\n \"the center of the simpx triangle with the plane functions. \\\"\\\"\\\" on_wulff =\",\n \"= [self.facets[dual_simp[i]].e_surf for i in range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def\",\n \"self.facets] dual_convex = ConvexHull(dual_pts) dual_cv_simp = dual_convex.simplices # simplices (ndarray of ints, shape\",\n \"str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given a list of coords\",\n \"unit cell. surface energy (Jm^2) is the length of normal. Wulff shape is\",\n \"facets off wulff .. attribute:: structure Structure object, input conventional unit cell (with\",\n \"'($' for x in hkl: if x < 0: str_format += '\\\\\\\\overline{' +\",\n \"($hkl$): area for all input_miller \\\"\\\"\\\" def __init__(self, lattice, miller_list, e_surf_list, symprec=1e-5): \\\"\\\"\\\"\",\n \"= self.wulff_pt_list ax = mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check\",\n \"dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\", \\\".join([str(len(x)) for x in wulff_cv_simp]))\",\n \"= plt.get_cmap(color_set) e_surf_on_wulff.sort(key=lambda x: x[1], reverse=False) e_surf_on_wulff_list = [x[1] for x in e_surf_on_wulff]\",\n \"the way covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return\",\n \"1, 1, fc=x, alpha=alpha) for x in color_list] return color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff,\",\n \"wulff_pt_list]) ax.set_xlim([-r_range * 1.1, r_range * 1.1]) ax.set_ylim([-r_range * 1.1, r_range * 1.1])\",\n \"energies ax1 = fig.add_axes(bar_pos) cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds\",\n \"covering all pts and facets pt.append(self.wulff_pt_list[l[0]].tolist()) pt.append(self.wulff_pt_list[l[1]].tolist()) prev = l[1] return pt def\",\n \"= '($' for x in hkl: if x < 0: str_format += '\\\\\\\\overline{'\",\n \"= wulff_cv_simp self.wulff_convex = wulff_convex self.on_wulff, self.color_area = self._get_simpx_plane() miller_area = [] for\",\n \"\\\"\\\"\\\" Total surface energy of the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl)\",\n \"sphere is 0. \\\"\\\"\\\" square_diff_energy = 0 weighted_energy = self.weighted_surface_energy area_frac_dict = self.area_fraction_dict\",\n \"on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0], simpx[2]])\",\n \"= [] miller_on_wulff = [] e_surf_on_wulff = [(i, e_surf) for i, e_surf in\",\n \"the dual convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from\",\n \"self.color_area = self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig\",\n \"/ energy for x in normal] color_plane = color_ind[divmod(i, len(color_ind))[1]] planes.append(WulffFacet(normal, energy, normal_pt,\",\n \"= self.get_line_in_facet(facet) lines = [] for i, p in enumerate(pt): if i ==\",\n \"return self.surface_area / (self.volume ** (2 / 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius\",\n \"plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line) != 2] return\",\n \"plane in self.facets: # check whether [pts] is empty if len(plane.points) < 1:\",\n \"get_angle import numpy as np import scipy as sp from scipy.spatial import ConvexHull\",\n \"is the length of normal. Wulff shape is the convex hull. Based on:\",\n \"the Wulff shape from a lattice, a list of indices and their corresponding\",\n \"for display on plots \\\"(hkl)\\\" for surfaces Agrs: hkl: in the form of\",\n \"\\\"\\\"\\\" :param normal: :param e_surf: :param normal_pt: :param dual_pt: :param index: :param m_ind_orig:\",\n \"plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in plane.outer_lines if plane.outer_lines.count(line)\",\n \"True aspect_ratio: default is (8, 8) custom_colors ({(h,k,l}: [r,g,b,alpha}): Customize color of each\",\n \"Miller index and value is the color. Undefined facets will use default color\",\n \"= mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is\",\n \"convex hull (dual_pt) .. attribute:: wulff_pt_list .. attribute:: wulff_cv_simp simplices from the convex\",\n \"*args: Passed to get_plot. **kwargs: Passed to get_plot. \\\"\\\"\\\" self.get_plot(*args, **kwargs).show() def get_line_in_facet(self,\",\n \"mpl3.Axes3D(fig, azim=azim, elev=elev) for plane in self.facets: # check whether [pts] is empty\",\n \"3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape is\",\n \"self.weighted_surface_energy area_frac_dict = self.area_fraction_dict miller_energy_dict = self.miller_energy_dict for hkl in miller_energy_dict.keys(): square_diff_energy +=\",\n \"__author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab'\",\n \"in e_surf_on_wulff: color_list[i] = scalar_map.to_rgba(e_surf, alpha=alpha) if tuple(self.miller_list[i]) in custom_colors.keys(): color_list[i] = custom_colors[tuple(self.miller_list[i])]\",\n \"continue # assign the color for on_wulff facets according to its # index\",\n \"on_wulff pts and the origin, # to ensure complete and consistent display for\",\n \"(0, 0, 0, 1): return 0, 90 else: cart = self.lattice.get_cartesian_coords(miller_index) azim =\",\n \"normal[0]x + normal[1]y + normal[2]z = e_surf from self: normal_e_m to get the\",\n \"def _get_all_miller_e(self): \\\"\\\"\\\" from self: get miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to\",\n \"[False] * len(self.miller_list) surface_area = [0.0] * len(self.miller_list) for simpx in self.wulff_cv_simp: pts\",\n \"is 1e-5. \\\"\\\"\\\" if any([se < 0 for se in e_surf_list]): warnings.warn(\\\"Unphysical (negative)\",\n \"return ((3 / 4) * (self.volume / np.pi)) ** (1 / 3) @property\",\n \"cbar = mpl.colorbar.ColorbarBase( ax1, cmap=cmap, norm=norm, boundaries=[0] + bounds + [10], extend='both', ticks=bounds[:-1],\",\n \"l: l = lines.pop(i) if l[1] == prev: l.reverse() break # make sure\",\n \"{hkl: area_hkl on wulff} \\\"\\\"\\\" return dict(zip(self.miller_list, self.color_area)) @property def miller_energy_dict(self): \\\"\\\"\\\" Returns\",\n \"lattice apply symmops to get all the miller index, then get normal, get\",\n \"# sort by e_surf planes.sort(key=lambda x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\"\",\n \"conventional unit cell, and (hkil) for hexagonal lattices. If you use this code\",\n \"is not on_wulff. continue # assign the color for on_wulff facets according to\",\n \"of facets off wulff .. attribute:: structure Structure object, input conventional unit cell\",\n \"in range(3)] center = np.sum(pts, 0) / 3.0 # check whether the center\",\n \"of input miller index, for hcp in the form of hkil .. attribute::\",\n \"@property def tot_edges(self): \\\"\\\"\\\" Returns the number of edges in the convex hull.\",\n \"abs_diff = abs(np.dot(plane.normal, center) - plane.e_surf) if abs_diff < 1e-5: on_wulff[plane.index] = True\",\n \"to 1 (float), default is 1 off_color: Default color for facets not present\",\n \"list of coords for 3 points, Compute the area of this triangle. Args:\",\n \"in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig + ' : ' + str(round(self.color_area[m], 4))) self.miller_area =\",\n \"# set ranges of x, y, z # find the largest distance between\",\n \"catalytically active sites. \\\"\\\"\\\" return len(self.wulff_convex.vertices) @property def tot_edges(self): \\\"\\\"\\\" Returns the number\",\n \"self.e_surf_list = tuple(e_surf_list) self.lattice = lattice self.symprec = symprec # 2. get all\",\n \"fancybox=True, shadow=False) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') # Add colorbar if bar_on: cmap = plt.get_cmap(color_set)\",\n \"same order with input_miller .. attribute:: e_surf_list list of input surface energies, in\",\n \"return dict(zip(self.miller_list, self.e_surf_list)) @property def surface_area(self): \\\"\\\"\\\" Total surface area of Wulff shape.\",\n \"grid_off: ax.grid('off') if axis_off: ax.axis('off') return plt def _get_azimuth_elev(self, miller_index): \\\"\\\"\\\" Args: miller_index:\",\n \"distance between on_wulff pts and the origin, # to ensure complete and consistent\",\n \"not direction: # If direction is not specified, use the miller indices of\",\n \"range(3)] cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane\",\n \"area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()}\",\n \"sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt = [x\",\n \"else: str_format += str(x) str_format += '$)' return str_format def get_tri_area(pts): \\\"\\\"\\\" Given\",\n \"legend_on: color_proxy = color_proxy if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True,\",\n \": ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\" from self:\",\n \"simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list = wulff_pt_list self.wulff_cv_simp = wulff_cv_simp self.wulff_convex\",\n \"= self._get_simpx_plane() miller_area = [] for m, in_mill_fig in enumerate(self.input_miller_fig): miller_area.append( in_mill_fig +\",\n \"of the simplices is on one plane for plane in self.facets: abs_diff =\",\n \"# empty, plane is not on_wulff. continue # assign the color for on_wulff\",\n \"all facets considering symm .. attribute:: dual_cv_simp simplices from the dual convex hull\",\n \"for hkl in self.miller_area_dict.keys()} @property def anisotropy(self): \\\"\\\"\\\" Returns: (float) Coefficient of Variation\",\n \"x in hkl: if x < 0: str_format += '\\\\\\\\overline{' + str(-x) +\",\n \"x in wulff_cv_simp])) # store simplices and convex self.dual_cv_simp = dual_cv_simp self.wulff_pt_list =\",\n \"normal from get_all_miller_e() self.facets = self._get_all_miller_e() logger.debug(len(self.facets)) # 3. consider the dual condition\",\n \"fc=color_list[i], alpha=alpha)) miller_on_wulff.append(self.input_miller_fig[i]) scalar_map.set_array([x[1] for x in e_surf_on_wulff]) color_proxy = [plt.Rectangle((2, 2), 1,\",\n \"x: x.e_surf) return planes def _get_cross_pt_dual_simp(self, dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is\",\n \"cart = self.lattice.get_cartesian_coords(miller_index) azim = get_angle([cart[0], cart[1], 0], (1, 0, 0)) v =\",\n \"<NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual Lab' __version__ = '0.1'\",\n \"bar_on: cmap = plt.get_cmap(color_set) cmap.set_over('0.25') cmap.set_under('0.75') bounds = [round(e, 2) for e in\",\n \"hkil .. attribute:: hkl_list modify hkill to hkl, in the same order with\",\n \"default is [0.75, 0.15, 0.05, 0.65] bar_on (bool): default is False legend_on (bool):\",\n \"bar_on (bool): default is False legend_on (bool): default is True aspect_ratio: default is\",\n \"the Wulff shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for\",\n \"self.m_ind_orig = m_ind_orig self.miller = miller self.points = [] self.outer_lines = [] class\",\n \"lattice Lattice object, the input lattice for the conventional unit cell .. attribute::\",\n \"self.symprec = symprec # 2. get all the data for wulff construction #\",\n \"if show_area: ax.legend(color_proxy, self.miller_area, loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, shadow=False) else: ax.legend(color_proxy_on_wulff, miller_on_wulff,\",\n \"+ ' : ' + str(round(self.color_area[m], 4))) self.miller_area = miller_area def _get_all_miller_e(self): \\\"\\\"\\\"\",\n \"the wulff shape,the weighted surface energy, the anisotropy and shape_factor can also be\",\n \"0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={}): \\\"\\\"\\\" Get the Wulff shape\",\n \"move to the next simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines =\",\n \"check whether the center of the simplices is on one plane for plane\",\n \"for hcp in the form of hkil .. attribute:: hkl_list modify hkill to\",\n \"= [self._get_cross_pt_dual_simp(dual_simp) for dual_simp in dual_cv_simp] wulff_convex = ConvexHull(wulff_pt_list) wulff_cv_simp = wulff_convex.simplices logger.debug(\\\",\",\n \"<NAME>.; <NAME>. (2016). Surface energies of elemental crystals. Scientific Data. \\\"\\\"\\\" from pymatgen.core.structure\",\n \"surface energy detected.\\\") self.color_ind = list(range(len(miller_list))) self.input_miller_fig = [hkl_tuple_to_str(x) for x in miller_list]\",\n \"index(same order in normal_e_m) \\\"\\\"\\\" matrix_surfs = [self.facets[dual_simp[i]].normal for i in range(3)] matrix_e\",\n \"Wulff shape plot. Args: color_set: default is 'PuBu' grid_off (bool): default is True\",\n \"e_surf_list ([float]): list of corresponding surface energies symprec (float): for recp_operation, default is\",\n \"miller_list(unique_miller), e_surf_list and symmetry operations(symmops) according to lattice apply symmops to get all\",\n \"center of the simplices is on one plane for plane in self.facets: abs_diff\",\n \"indices of # maximum area. direction = max(self.area_fraction_dict.items(), key=lambda x: x[1])[0] fig =\",\n \"**kwargs): r\\\"\\\"\\\" Show the Wulff plot. Args: *args: Passed to get_plot. **kwargs: Passed\",\n \"# check whether the center of the simplices is on one plane for\",\n \"dual_simp): \\\"\\\"\\\" |normal| = 1, e_surf is plane's distance to (0, 0, 0),\",\n \"This is useful for determining the critical nucleus size. A large shape factor\",\n \"\\\"\\\"\\\" a, b, c = pts[0], pts[1], pts[2] v1 = np.array(b) - np.array(a)\",\n \"cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for simpx of on wulff_cv, by\",\n \"a WulffShape class to generate the Wulff shape from a lattice, a list\",\n \"np.array(c) - np.array(a) area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2) return area_tri class WulffFacet:\",\n \"the color_list for on_wulff plane_color = color_list[plane.index] pt = self.get_line_in_facet(plane) # plot from\",\n \"z # find the largest distance between on_wulff pts and the origin, #\",\n \"<NAME>. Kinetics of Materials. (<NAME>, 2005), p.461 Returns: (float) Shape factor. \\\"\\\"\\\" return\",\n \"shape. Returns: (float) sum(surface_energy_hkl * area_hkl) \\\"\\\"\\\" tot_surface_energy = 0 for hkl in\",\n \"in self.facets: edges = [] pt = self.get_line_in_facet(facet) lines = [] for i,\",\n \"is the corresponding Miller index and value is the color. Undefined facets will\",\n \"/ 3)) @property def effective_radius(self): \\\"\\\"\\\" Radius of the Wulffshape when the Wulffshape\",\n \"@property def volume(self): \\\"\\\"\\\" Volume of the Wulff shape \\\"\\\"\\\" return self.wulff_convex.volume @property\",\n \".. attribute:: axis_off (bool) .. attribute:: show_area .. attribute:: off_color color of facets\",\n \"normal /= sp.linalg.norm(normal) normal_pt = [x * energy for x in normal] dual_pt\",\n \"* (self.volume / np.pi)) ** (1 / 3) @property def total_surface_energy(self): \\\"\\\"\\\" Total\",\n \"simplices break for plane in self.facets: plane.outer_lines.sort() plane.outer_lines = [line for line in\",\n \"elev = get_angle(cart, v) return azim, elev @property def volume(self): \\\"\\\"\\\" Volume of\",\n \"Wulff shape. \\\"\\\"\\\" return sum(self.miller_area_dict.values()) @property def weighted_surface_energy(self): \\\"\\\"\\\" Returns: sum(surface_energy_hkl * area_hkl)/\",\n \"\\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for hkl in self.miller_area_dict.keys()} @property def anisotropy(self):\",\n \"warnings __author__ = '<NAME>, <NAME>, <NAME>' __copyright__ = 'Copyright 2013, The Materials Virtual\",\n \"1e-5: on_wulff[plane.index] = True surface_area[plane.index] += get_tri_area(pts) plane.points.append(pts) plane.outer_lines.append([simpx[0], simpx[1]]) plane.outer_lines.append([simpx[1], simpx[2]]) plane.outer_lines.append([simpx[0],\",\n \"conventional unit cell .. attribute:: facets [WulffFacet] for all facets considering symm ..\",\n \"(dict): {hkl: area_hkl/total area on wulff} \\\"\\\"\\\" return {hkl: self.miller_area_dict[hkl] / self.surface_area for\",\n \"'\\\\\\\\overline{' + str(-x) + '}' else: str_format += str(x) str_format += '$)' return\",\n \"cross_pt = sp.dot(sp.linalg.inv(matrix_surfs), matrix_e) return cross_pt def _get_simpx_plane(self): \\\"\\\"\\\" Locate the plane for\"\n]","isConversational":false}}},{"rowIdx":2176,"cells":{"ngram":{"kind":"list like","value":["self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency","self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\"out of vocabulary","default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1)","i = self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return","self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s","= state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i =","min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1","def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i","get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i >=","= self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return self.UNK_i","= len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count","not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s)","self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen,","else: raise Exception(f\"out of vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s, -1)","= state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency =","state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5]","0: return i else: raise Exception(f\"out of vocabulary entry {s}\") def contains(self, s):","self.frozen i = self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK:","self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i","self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency","self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return","-x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] =","self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state):","enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if","size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def","0 self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self): return","i >= 0: return i elif self.include_UNK: return self.UNK_i else: return default def",">= 0: return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self,","else: raise Exception(f\"not entry at position {i} for a vocabulary of size {self.voc_size}\")","def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD:","self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0]","include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \"<UNK>\"","= state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s =","if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for","for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen =","0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at position","self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count",">= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size =","def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i","state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3]","s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return i else:","state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9]","position {i} for a vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert not","if i >= 0: return i elif self.include_UNK: return self.UNK_i else: return default",">= 0: return i else: raise Exception(f\"out of vocabulary entry {s}\") def contains(self,","self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int,","raise Exception(f\"not entry at position {i} for a vocabulary of size {self.voc_size}\") def","i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise","assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i =","__getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i","i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True","elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def","self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s)","in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s","< self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at position {i} for a","self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at position {i} for a vocabulary","min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD","self.frozen = False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s","-1) if i >= 0: return i else: raise Exception(f\"out of vocabulary entry","assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if","count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size","= state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s =","def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i]","self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i","self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen =","self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2]","= min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i =","= -2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s =","a vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] =","!= -1 def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size:","self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK =","state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8]","__init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK","= False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s =","include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2","self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]):","i = self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\"out","self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count","{s}\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert","vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s,","add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not","i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at position {i} for","return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s,","self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s)","-1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size = 0","return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size,","return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0:","if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i","assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not","self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s)","s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0:","self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i","self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self,","self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0 <= i","self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s","def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if","entry {s}\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i):","int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return i","self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s,","self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i","state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7]","= i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2,","def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i,","return self._i2s[i] else: raise Exception(f\"not entry at position {i} for a vocabulary of","-1) if i >= 0: return i elif self.include_UNK: return self.UNK_i else: return","self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s)","self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda","count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i,","i else: raise Exception(f\"out of vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s,","i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK,","s): i = self.get_s2i(s, -1) if i >= 0: return i else: raise","bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False","__reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s,","self._i2s[i] else: raise Exception(f\"not entry at position {i} for a vocabulary of size","self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency)","self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count =","self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size","self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen","self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i","for a vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s]","= \"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency =","self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD),","dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i","assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return i elif","if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at","\"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i = dict()","return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s):","= self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s)","= \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i =","= state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i =","include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen =","def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert","self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry","= dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def","freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i","if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x:","i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else:","= True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i,","{i} for a vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen","enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return","<= i < self.voc_size: return self._i2s[i] else: raise Exception(f\"not entry at position {i}","i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s)","Any2Int: def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK","int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD =","= state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s =","state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency = state[10]","-1) != -1 def i2s(self, i): assert self.frozen if 0 <= i <","-1 def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return","of size {self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1","True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s,","-2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s = []","0: return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s):","self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size","len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(),","= [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default:","def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return i","key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s):","self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1]","{self.voc_size}\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self):","def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i =","return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0 <=","= len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in","Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i,","(self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self,","state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4]","iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s,","s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for","self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s,","else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i =","of vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s, -1) != -1 def","if i >= 0: return i else: raise Exception(f\"out of vocabulary entry {s}\")","return i else: raise Exception(f\"out of vocabulary entry {s}\") def contains(self, s): return","self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s","= 0 self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self):","self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert","include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD","= self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\"out of","self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def","raise Exception(f\"out of vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s, -1) !=","in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self):","Exception(f\"out of vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s, -1) != -1","sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in","self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return self.UNK_i else:","entry at position {i} for a vocabulary of size {self.voc_size}\") def add_to_counts(self, s):","= include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i =","self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size = 0 self._s2i","False self.UNK_i = -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\"","(2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s,","default: int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return","x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s]","self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i","self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i =","self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK","state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6]","s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0","dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self,","0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if","= -1 self.UNK_s = \"<UNK>\" self.PAD_i = -2 self.PAD_s = \"<PAD>\" self.voc_size =","= dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen","len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >=","\"<PAD>\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency = dict()","contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if","s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen","= state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size =","def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK =","return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return","bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen","[] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int):","self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if","self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency = dict() def","return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1)","len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK,","s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >=","self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen","vocabulary entry {s}\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self,","self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s","not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK:","self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count:","self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s","class Any2Int: def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count","for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s)","= include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s =","def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen","= len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count,","__setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i =","at position {i} for a vocabulary of size {self.voc_size}\") def add_to_counts(self, s): assert","Exception(f\"not entry at position {i} for a vocabulary of size {self.voc_size}\") def add_to_counts(self,","i >= 0: return i else: raise Exception(f\"out of vocabulary entry {s}\") def","s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def"],"string":"[\n \"self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency\",\n \"self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\\\"out of vocabulary\",\n \"default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1)\",\n \"i = self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return\",\n \"self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s\",\n \"= state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i =\",\n \"min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1\",\n \"def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i\",\n \"get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i >=\",\n \"= self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return self.UNK_i\",\n \"= len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count\",\n \"not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s)\",\n \"self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen,\",\n \"else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1)\",\n \"= state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency =\",\n \"state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5]\",\n \"0: return i else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s):\",\n \"self.frozen i = self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK:\",\n \"self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i\",\n \"self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency\",\n \"self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return\",\n \"-x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] =\",\n \"self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state):\",\n \"enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1) if\",\n \"size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def\",\n \"0 self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self): return\",\n \"i >= 0: return i elif self.include_UNK: return self.UNK_i else: return default def\",\n \">= 0: return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self,\",\n \"else: raise Exception(f\\\"not entry at position {i} for a vocabulary of size {self.voc_size}\\\")\",\n \"def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD:\",\n \"self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0]\",\n \"include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\"\",\n \"= state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s =\",\n \"if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for\",\n \"for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen =\",\n \"0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at position\",\n \"self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count\",\n \">= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size =\",\n \"def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i\",\n \"state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3]\",\n \"s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return i else:\",\n \"state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9]\",\n \"position {i} for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert not\",\n \"if i >= 0: return i elif self.include_UNK: return self.UNK_i else: return default\",\n \">= 0: return i else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self,\",\n \"self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int,\",\n \"raise Exception(f\\\"not entry at position {i} for a vocabulary of size {self.voc_size}\\\") def\",\n \"i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise\",\n \"assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i =\",\n \"__getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i\",\n \"i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True\",\n \"elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def\",\n \"self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s)\",\n \"in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s\",\n \"< self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at position {i} for a\",\n \"self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at position {i} for a vocabulary\",\n \"min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD\",\n \"self.frozen = False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s\",\n \"-1) if i >= 0: return i else: raise Exception(f\\\"out of vocabulary entry\",\n \"assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if\",\n \"count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size\",\n \"= state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s =\",\n \"def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i]\",\n \"self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i\",\n \"self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen =\",\n \"self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2]\",\n \"= min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i =\",\n \"= -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s =\",\n \"a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] =\",\n \"!= -1 def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size:\",\n \"self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK =\",\n \"state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8]\",\n \"__init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK\",\n \"= False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s =\",\n \"include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2\",\n \"self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]):\",\n \"i = self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\\\"out\",\n \"self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count\",\n \"{s}\\\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert\",\n \"vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s,\",\n \"add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not\",\n \"i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at position {i} for\",\n \"return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s,\",\n \"self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s)\",\n \"-1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0\",\n \"return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size,\",\n \"return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0:\",\n \"if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i\",\n \"assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not\",\n \"self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s)\",\n \"s, default: int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0:\",\n \"self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i\",\n \"self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self,\",\n \"self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0 <= i\",\n \"self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s\",\n \"def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if\",\n \"entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i):\",\n \"int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return i\",\n \"self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s,\",\n \"self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i\",\n \"state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7]\",\n \"= i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2,\",\n \"def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i,\",\n \"return self._i2s[i] else: raise Exception(f\\\"not entry at position {i} for a vocabulary of\",\n \"-1) if i >= 0: return i elif self.include_UNK: return self.UNK_i else: return\",\n \"self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s)\",\n \"self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda\",\n \"count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i,\",\n \"i else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s,\",\n \"i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK,\",\n \"s): i = self.get_s2i(s, -1) if i >= 0: return i else: raise\",\n \"bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False\",\n \"__reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s,\",\n \"self._i2s[i] else: raise Exception(f\\\"not entry at position {i} for a vocabulary of size\",\n \"self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency)\",\n \"self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count =\",\n \"self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size\",\n \"self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen\",\n \"self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i\",\n \"for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s]\",\n \"= \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency =\",\n \"self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD),\",\n \"dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i\",\n \"assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return i elif\",\n \"if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at\",\n \"\\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict()\",\n \"return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s):\",\n \"= self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s)\",\n \"= \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i =\",\n \"= state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i =\",\n \"include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen =\",\n \"def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert\",\n \"self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry\",\n \"= dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def\",\n \"freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i\",\n \"if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x:\",\n \"i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return self._i2s[i] else:\",\n \"= True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i,\",\n \"{i} for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen\",\n \"enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self): return\",\n \"<= i < self.voc_size: return self._i2s[i] else: raise Exception(f\\\"not entry at position {i}\",\n \"i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s)\",\n \"Any2Int: def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK\",\n \"int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD =\",\n \"= state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s =\",\n \"state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency = state[10]\",\n \"-1) != -1 def i2s(self, i): assert self.frozen if 0 <= i <\",\n \"-1 def i2s(self, i): assert self.frozen if 0 <= i < self.voc_size: return\",\n \"of size {self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1\",\n \"True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s,\",\n \"-2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s = []\",\n \"0: return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s):\",\n \"self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size\",\n \"len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(),\",\n \"= [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default:\",\n \"def s2i(self, s): i = self.get_s2i(s, -1) if i >= 0: return i\",\n \"key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s):\",\n \"self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1]\",\n \"{self.voc_size}\\\") def add_to_counts(self, s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self):\",\n \"def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i =\",\n \"return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0 <=\",\n \"= len(self._i2s) self._i2s.append(self.UNK_s) if self.include_PAD: self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in\",\n \"Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i,\",\n \"(self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self,\",\n \"state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s = state[4]\",\n \"iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s,\",\n \"s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for\",\n \"self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s,\",\n \"else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i =\",\n \"of vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1) != -1 def\",\n \"if i >= 0: return i else: raise Exception(f\\\"out of vocabulary entry {s}\\\")\",\n \"return i else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s): return\",\n \"self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s\",\n \"= 0 self._s2i = dict() self._i2s = [] self.frequency = dict() def iter_item(self):\",\n \"self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert\",\n \"include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD\",\n \"= self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\\\"out of\",\n \"self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def\",\n \"raise Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1) !=\",\n \"in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def __reduce__(self):\",\n \"Exception(f\\\"out of vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1) != -1\",\n \"sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in\",\n \"self._s2i.get(s, -1) if i >= 0: return i elif self.include_UNK: return self.UNK_i else:\",\n \"entry at position {i} for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s):\",\n \"= include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i =\",\n \"self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size = 0 self._s2i\",\n \"False self.UNK_i = -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\"\",\n \"(2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK, self.frozen, self.UNK_i, self.UNK_s, self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s,\",\n \"default: int): assert self.frozen i = self._s2i.get(s, -1) if i >= 0: return\",\n \"x: -x[1]): if count >= self.min_count: self._i2s.append(s) for i, s in enumerate(self._i2s): self._s2i[s]\",\n \"self.UNK_i else: return default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i\",\n \"self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i =\",\n \"self.PAD_i, self.PAD_s, self.voc_size, self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK\",\n \"state[2] self.UNK_i = state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6]\",\n \"s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if 0\",\n \"dict() self._i2s = [] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self,\",\n \"0)+1 def freeze(self): assert not self.frozen if self.include_UNK: self.UNK_i = len(self._i2s) self._i2s.append(self.UNK_s) if\",\n \"= -1 self.UNK_s = \\\"<UNK>\\\" self.PAD_i = -2 self.PAD_s = \\\"<PAD>\\\" self.voc_size =\",\n \"= dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen\",\n \"len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >=\",\n \"\\\"<PAD>\\\" self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency = dict()\",\n \"contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen if\",\n \"s): assert not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen\",\n \"= state[3] self.UNK_s = state[4] self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size =\",\n \"def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count self.include_UNK =\",\n \"return i elif self.include_UNK: return self.UNK_i else: return default def __getitem__(self, s): return\",\n \"bool, include_PAD: bool): self.min_count = min_count self.include_UNK = include_UNK self.include_PAD = include_PAD self.frozen\",\n \"[] self.frequency = dict() def iter_item(self): return enumerate(self._i2s) def get_s2i(self, s, default: int):\",\n \"self.PAD_i = len(self._i2s) self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if\",\n \"self.voc_size = 0 self._s2i = dict() self._i2s = [] self.frequency = dict() def\",\n \"return enumerate(self._i2s) def get_s2i(self, s, default: int): assert self.frozen i = self._s2i.get(s, -1)\",\n \"len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count, self.include_UNK,\",\n \"s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1) if i >=\",\n \"self._s2i, self._i2s, self.frequency) def __setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen\",\n \"vocabulary entry {s}\\\") def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self,\",\n \"self.PAD_i = state[5] self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s\",\n \"not self.frozen self.frequency[s] = self.frequency.get(s, 0)+1 def freeze(self): assert not self.frozen if self.include_UNK:\",\n \"self._i2s.append(self.PAD_s) for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count:\",\n \"self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i = state[3] self.UNK_s\",\n \"class Any2Int: def __init__(self, min_count: int, include_UNK: bool, include_PAD: bool): self.min_count = min_count\",\n \"for s, count in sorted(self.frequency.items(), key=lambda x: -x[1]): if count >= self.min_count: self._i2s.append(s)\",\n \"= include_UNK self.include_PAD = include_PAD self.frozen = False self.UNK_i = -1 self.UNK_s =\",\n \"def contains(self, s): return self.get_s2i(s, -1) != -1 def i2s(self, i): assert self.frozen\",\n \"= len(self._i2s) self.frozen = True def __reduce__(self): return Any2Int, (2, self.include_UNK, self.include_PAD), (self.min_count,\",\n \"__setstate__(self, state): self.min_count = state[0] self.include_UNK = state[1] self.frozen = state[2] self.UNK_i =\",\n \"at position {i} for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self, s): assert\",\n \"Exception(f\\\"not entry at position {i} for a vocabulary of size {self.voc_size}\\\") def add_to_counts(self,\",\n \"i >= 0: return i else: raise Exception(f\\\"out of vocabulary entry {s}\\\") def\",\n \"s in enumerate(self._i2s): self._s2i[s] = i self.voc_size = len(self._i2s) self.frozen = True def\"\n]","isConversational":false}}},{"rowIdx":2177,"cells":{"ngram":{"kind":"list like","value":["g import sqlite3 import os import json from random import randint from flask_jwt_extended","m.execute(sql, data) # stat min max data = [value, value, rom] sql =","exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT","sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom]","= None if 'type' in j: type=j['type'] device = None if 'device' in","nettemp ][ sensor ] Database %s not exist\" %rom) return False def delete_sensor(id,rom):","in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not","tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2,","tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data) # stat min","request, jsonify, g import sqlite3 import os import json from random import randint","[rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT","= \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql,","in j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device =","i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x,","random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL","mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone()","m.execute(sql2, data2) map = \"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name)","= m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s,","'tmp' in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group","tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group = type if","= conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql)","if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value)","import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom):","ok\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom]","fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps","m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s added ok\" %rom)","= \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR","sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0:","][ sensor ] Database %s insert ok\" %rom) return True else: print (\"[","def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE","None if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in j:","insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor',","delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ] Database %s","(\"[ nettemp ][ sensor ] Sensor %s added ok\" %rom) else: print (\"[","in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data","charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10'","sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null","None) if db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext","None if 'device' in j: device=j['device'] ip = None if 'ip' in j:","(value==v2) or (value==v3): msg.append(\"filter 2 back to previous %f\" % tmp) value=tmp else:","m.close() print (\"[ nettemp ][ sensor ] Sensor %s updated\" %rom) return True","def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql =","'rom' in j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device","value=j['value'] group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y","= [rom] sql = \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun","value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors WHERE rom=%s\"","] Database %s deleted\" %rom) return True else: print (\"[ nettemp ][ sensor","in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb =","value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) ==","value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return value def new_db(rom): rom =","j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000)","False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def","flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def","data2) map = \"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES","OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[","Sensor %s updated\" %rom) return True else: print (\"[ nettemp ][ sensor ]","randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp","sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data) #","FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[","= None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in","if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='',","= None if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in","conn.commit() conn.close() print (\"[ nettemp ][ sensor ] Database %s insert ok\" %rom)","j: type=j['type'] device = None if 'device' in j: device=j['device'] ip = None","SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\"","(stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql =","db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception):","[value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data","value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST'])","count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s","WHERE rom=%s\" data = [value,rom] m.execute(sql, data) # stat min max data =","None if 'type' in j: type=j['type'] device = None if 'device' in j:","%rom) return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE","map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*) FROM sensors","else: print (\"[ nettemp ][ sensor ] Sensor %s already exist\" %rom) return","check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj,","max, value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE:","[type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) ==","ok\" %rom) else: print (\"[ nettemp ][ sensor ] Sensor %s already exist\"","name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value =","WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit()","if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s,","email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps (type, pos_x, pos_y,","not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp']","tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back to previous %f\" % tmp)","][ sensor ] Sensor %s already exist\" %rom) return None def sensor(): data","in j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id =","] Sensor %s already exist\" %rom) return None def sensor(): data = request.get_json()","rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if","return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM","VALUE: %f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value)","from app import app from flask import Flask, request, jsonify, g import sqlite3","1 back to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value)","OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP()","value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c","map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit()","INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][","randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb,","FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql","return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return","= \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0',","sensor ] Database %s deleted\" %rom) return True else: print (\"[ nettemp ][","%rom) else: print (\"[ nettemp ][ sensor ] Sensor %s already exist\" %rom)","type = None if 'type' in j: type=j['type'] device = None if 'device'","data = request.get_json() for j in data: rom = None if 'rom' in","min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or","Sensor %s already exist\" %rom) return None def sensor(): data = request.get_json() for","data = [value, value, rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE","(\"[ nettemp ][ sensor ] Sensor %s not exist\" %rom) return False def","create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST'])","% tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back to previous %f\" %","Database %s deleted\" %rom) return True else: print (\"[ nettemp ][ sensor ]","in j: type=j['type'] device = None if 'device' in j: device=j['device'] ip =","usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name'","data) # stat min max data = [value, value, rom] sql = \"UPDATE","def get_db(rom): db = getattr(g, '_database', None) if db is None: db =","rom=j['rom'] type = None if 'type' in j: type=j['type'] device = None if","updated\" %rom) return True else: print (\"[ nettemp ][ sensor ] Sensor %s","sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp","%s insert ok\" %rom) return True else: print (\"[ nettemp ][ sensor ]","j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None","type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings","i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb'","rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings)","= mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*) FROM sensors WHERE rom=%s\"","print (\"[ nettemp ][ sensor ] Database %s insert ok\" %rom) return True","%rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*)","if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ] Database %s deleted\" %rom)","create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \"SELECT","c = conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\"","is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET","rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP()","nettemp ][ sensor ] Database %s insert ok\" %rom) return True else: print","'value' in j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id","adj=float(adj) msg=[] sql = \"SELECT min, max, value1, value2, value3 FROM types WHERE","\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database","rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count()","= MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is None:","if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back to","return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor","j: ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c","print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c =","= sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE type='table'","= \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print","maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s,","if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return","@jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr","] Database %s not exist\" %rom) return False def delete_sensor(id,rom): data = [id,","= getattr(g, '_database', None) if db is not None: db.close() def check_value(value, type,","not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[","WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql","INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\" m.execute(sql,","if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value']","import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if","2 back to previous %f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1","= get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\"","sensor ] Database %s not exist\" %rom) return False def update_sensor_tmp(rom,value): m =","] Sensor %s removed ok\" %rom) def create_sensor(rom, data, data2, map_settings): m =","gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None if","rom = None if 'rom' in j: rom=j['rom'] type = None if 'type'","usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name","data: rom = None if 'rom' in j: rom=j['rom'] type = None if","getattr(g, '_database', None) if db is not None: db.close() def check_value(value, type, rom):","\"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp","type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True","[value] sql = \"INSERT OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data)","data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is","return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor()","True else: print (\"[ nettemp ][ sensor ] Sensor %s not exist\" %rom)","None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j:","update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '',","sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql","def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][ sensor","(value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back to previous","pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map,","200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else:","= None if 'ip' in j: ip = j['ip'] gpio = None if","nettemp ][ sensor ] Sensor %s updated\" %rom) return True else: print (\"[","sensor ] Database %s not exist\" %rom) return False def delete_sensor(id,rom): data =","j in data: rom = None if 'rom' in j: rom=j['rom'] type =","\"INSERT OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print","= j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None","m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s updated\" %rom) return","if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max, v1, v2, v3","adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5',","(type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\"","= None if 'device' in j: device=j['device'] ip = None if 'ip' in","gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y,","(\"[ nettemp ][ sensor ] Database %s deleted\" %rom) return True else: print","type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \"INSERT OR","= getattr(g, '_database', None) if db is None: db = g._database = sqlite3.connect(rom)","def sensor(): data = request.get_json() for j in data: rom = None if","import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql","adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max, v1, v2, v3 in","def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr ==","max data = [value, value, rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP()","][ sensor ] Sensor %s added ok\" %rom) else: print (\"[ nettemp ][","nettemp ][ sensor ] Database %s not exist\" %rom) return False def update_sensor_tmp(rom,value):","@app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is not None:","import json from random import randint from flask_jwt_extended import jwt_required import datetime from","(\"[ nettemp ][ sensor ] Sensor %s removed ok\" %rom) def create_sensor(rom, data,","(\"Database %s created\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn =","data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE","mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return False def","jsonify, g import sqlite3 import os import json from random import randint from","sql = \"INSERT OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit()","m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max","request.get_json() for j in data: rom = None if 'rom' in j: rom=j['rom']","randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name] data2 =","in j: device=j['device'] ip = None if 'ip' in j: ip = j['ip']","True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read())","delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor %s removed ok\" %rom) def","%s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][","data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND","db = getattr(g, '_database', None) if db is None: db = g._database =","sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \"INSERT","nettemp ][ sensor ] Sensor %s not exist\" %rom) return False def delete_db(rom):","count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value","value=tmp else: value=float(value) else: msg.append(\"filter 1 back to previous %f\" % tmp) value=tmp","in j: ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio']","nettemp ][ sensor ] Sensor %s added ok\" %rom) else: print (\"[ nettemp","False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors","c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \"INSERT OR IGNORE INTO def","rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor","display_name) VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print","'type' in j: type=j['type'] device = None if 'device' in j: device=j['device'] ip","else: sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data =","stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[","time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data) # stat min max data","tmp = None if 'tmp' in j: tmp=j['tmp'] value = None if 'value'","sensor ] Database %s insert ok\" %rom) return True else: print (\"[ nettemp","import app from flask import Flask, request, jsonify, g import sqlite3 import os","sql = \"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql,","name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True else: with","print (\"[ nettemp ][ sensor ] Sensor %s removed ok\" %rom) def create_sensor(rom,","ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\"","value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value)","data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s updated\" %rom)","= randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000)","data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor %s removed","c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][ sensor ] Database %s insert","\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\"","= None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in","os.remove(rom) print (\"[ nettemp ][ sensor ] Database %s deleted\" %rom) return True","VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][ sensor ] Database","= [value,rom] m.execute(sql, data) # stat min max data = [value, value, rom]","sensor ] Sensor %s already exist\" %rom) return None def sensor(): data =","if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name']","\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0')","list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\"","import sqlite3 import os import json from random import randint from flask_jwt_extended import","stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data)","in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2","print (\"Database %s created\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn","][ sensor ] Database %s deleted\" %rom) return True else: print (\"[ nettemp","%rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c =","tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data","name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \"INSERT OR IGNORE INTO","sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor()","datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g,","else: print (\"[ nettemp ][ sensor ] Sensor %s not exist\" %rom) return","rom1 = [rom] sql = \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1)","\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s,","exist\" %rom) return None def sensor(): data = request.get_json() for j in data:","j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000)","= \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data","nettemp ][ sensor ] Database %s deleted\" %rom) return True else: print (\"[","Database %s not exist\" %rom) return False def delete_sensor(id,rom): data = [id, rom]","flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None)","tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max, value1, value2,","alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s,","WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s, nodata='',","if c.fetchone()[0]==1: data = [value] sql = \"INSERT OR IGNORE INTO def (value)","] Sensor %s updated\" %rom) return True else: print (\"[ nettemp ][ sensor","%s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on',","m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor %s","AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor","status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE","'_database', None) if db is not None: db.close() def check_value(value, type, rom): adj=''","'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None","with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return","randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip,","'', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local'","m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s,","%s deleted\" %rom) return True else: print (\"[ nettemp ][ sensor ] Database","rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s","types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close()","else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit()","data) conn.commit() conn.close() print (\"[ nettemp ][ sensor ] Database %s insert ok\"","m.close() delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor %s removed ok\" %rom)","= [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\"","type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x","c.fetchone()[0]==1: data = [value] sql = \"INSERT OR IGNORE INTO def (value) VALUES","= [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\"","as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return False def insert_db(rom,value):","back to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg)","sql = \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone()","pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings)","\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data =","value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\"","WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE","AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \"INSERT OR IGNORE","WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if","db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return False def insert_db(rom,value): rom =","in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name =","randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value = None if","[value,rom] m.execute(sql, data) # stat min max data = [value, value, rom] sql","%rom) return True else: print (\"[ nettemp ][ sensor ] Database %s not","tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO","] Database %s insert ok\" %rom) return True else: print (\"[ nettemp ][","if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True else: with app.app_context(): db","if 'device' in j: device=j['device'] ip = None if 'ip' in j: ip","OR stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print","sensor ] Sensor %s updated\" %rom) return True else: print (\"[ nettemp ][","== False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200","= None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in","methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return '',","in data: rom = None if 'rom' in j: rom=j['rom'] type = None","@app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost():","nettemp ][ sensor ] Sensor %s already exist\" %rom) return None def sensor():","msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" %","v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter","msg=[] sql = \"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\"","(\"[ nettemp ][ sensor ] Database %s insert ok\" %rom) return True else:","json from random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb","Sensor %s removed ok\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor()","\"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall()","if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back to previous %f\" % tmp)","INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s,","list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back","%s updated\" %rom) return True else: print (\"[ nettemp ][ sensor ] Sensor","nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET","import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db =","map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name]","sqlite3 import os import json from random import randint from flask_jwt_extended import jwt_required","% value) for min, max, v1, v2, v3 in list: if (value>=float(min)) and","coun = m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s,","sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off',","if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE","[rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if","'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb","else: value=float(value) else: msg.append(\"filter 1 back to previous %f\" % tmp) value=tmp msg.append(\"VALUE","j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device = None","% tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return value def new_db(rom):","not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor()","== False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required","sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db","AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor ]","sql = \"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for","SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\"","= randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device,","return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f:","sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj)","exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp","data = [value,rom] m.execute(sql, data) # stat min max data = [value, value,","for j in data: rom = None if 'rom' in j: rom=j['rom'] type","if not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j:","sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE type='table' AND","%s, %s, %s, %s, %s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors","map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom)","adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max, value1,","%rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as","= g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database',","%s not exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 =","type=j['type'] device = None if 'device' in j: device=j['device'] ip = None if","adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in","type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj, tmp","count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value]","rom=%s\" data = [value,rom] m.execute(sql, data) # stat min max data = [value,","%s removed ok\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1","'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if","new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor():","rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj, tmp FROM","value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" %","return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is","= sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if","= [value, value, rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s","new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT","%s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on',","%s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s,","data = [value] sql = \"INSERT OR IGNORE INTO def (value) VALUES (?)\"","mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is","][ sensor ] Sensor %s updated\" %rom) return True else: print (\"[ nettemp","sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data =","g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None)","tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max, value1, value2, value3 FROM types","msg.append(\"ADJ: %d\" % value) for min, max, v1, v2, v3 in list: if","name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in","min, max, value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN","SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data) # stat","map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom)","][ sensor ] Database %s not exist\" %rom) return False def delete_sensor(id,rom): data","j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value","insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT","OR stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE","FROM types WHERE type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list)","tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors","update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\"","created\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c","return None def sensor(): data = request.get_json() for j in data: rom =","app import app from flask import Flask, request, jsonify, g import sqlite3 import","(value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back to previous %f\" %","= [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1:","AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True else:","][ sensor ] Sensor %s not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql'","db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is not","sensor(): data = request.get_json() for j in data: rom = None if 'rom'","map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps (type,","def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return '', 404","(\"[ nettemp ][ sensor ] Database %s not exist\" %rom) return False def","f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return False def insert_db(rom,value): rom","m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0',","print (\"[ nettemp ][ sensor ] Sensor %s added ok\" %rom) else: print","%s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor","nettemp ][ sensor ] Sensor %s removed ok\" %rom) def create_sensor(rom, data, data2,","j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']:","sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom)","= randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c,","os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ] Database %s deleted\" %rom) return","previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return value","= \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s,","is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m =","nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps (type, pos_x,","False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ]","%rom) return None def sensor(): data = request.get_json() for j in data: rom","def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ] Database","type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False:","(\"[ nettemp ][ sensor ] Sensor %s already exist\" %rom) return None def","= None if 'value' in j: value=j['value'] group = type if 'group' in","WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[]","= \"INSERT OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close()","os import json from random import randint from flask_jwt_extended import jwt_required import datetime","m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max, v1, v2,","map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom,","db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g,","device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings =","= [rom, type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id,","stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data)","] Database %s not exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor()","print (\"[ nettemp ][ sensor ] Database %s not exist\" %rom) return False","flask import Flask, request, jsonify, g import sqlite3 import os import json from","getattr(g, '_database', None) if db is None: db = g._database = sqlite3.connect(rom) return","db is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m","][ sensor ] Database %s not exist\" %rom) return False def update_sensor_tmp(rom,value): m","value) for min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)):","\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else:","previous %f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back to previous","msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max, v1,","SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on',","return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM","j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group = type","None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql","mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom)","time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE sensors SET tmp=%s,","ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c =","(%s, %s, %s, %s, %s, %s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE","insert ok\" %rom) return True else: print (\"[ nettemp ][ sensor ] Database","if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x =","usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on',","sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s,","\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0:","from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database',","with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print","[group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type,","from flask import Flask, request, jsonify, g import sqlite3 import os import json","back to previous %f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back","is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db","exist\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor()","delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=?","%s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor","map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close()","rom1) coun = m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES","group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y =","%f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return value def","FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp)","def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT","if db is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value)","False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local',","count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql","return True else: print (\"[ nettemp ][ sensor ] Sensor %s not exist\"","= \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if","rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id,","(\"[ nettemp ][ sensor ] Sensor %s updated\" %rom) return True else: print","map = \"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s,","def close_connection(exception): db = getattr(g, '_database', None) if db is not None: db.close()","[value, value, rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR","%s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ]","in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if","conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE","MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if db","%f\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn =","[id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql,","adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors","(?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][ sensor ] Database %s","'device' in j: device=j['device'] ip = None if 'ip' in j: ip =","][ sensor ] Sensor %s removed ok\" %rom) def create_sensor(rom, data, data2, map_settings):","methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if","deleted\" %rom) return True else: print (\"[ nettemp ][ sensor ] Database %s","value, rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min","app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom) return False","def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql =","device=j['device'] ip = None if 'ip' in j: ip = j['ip'] gpio =","AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s","db = getattr(g, '_database', None) if db is not None: db.close() def check_value(value,","@app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return","= type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600)","v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3):","value = None if 'value' in j: value=j['value'] group = type if 'group'","None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db =","m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s added","already exist\" %rom) return None def sensor(): data = request.get_json() for j in","= \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom]","None if 'ip' in j: ip = j['ip'] gpio = None if 'gpio'","mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall()","m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql =","print (\"[ nettemp ][ sensor ] Sensor %s not exist\" %rom) return False","if 'value' in j: value=j['value'] group = type if 'group' in j: group=j['group']","m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET","Sensor %s not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom)","db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql =","m.close() print (\"[ nettemp ][ sensor ] Sensor %s added ok\" %rom) else:","value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max, v1, v2, v3 in list:","exists\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r')","update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def","Flask, request, jsonify, g import sqlite3 import os import json from random import","int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\"","conn.cursor() sql = \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if","WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \"INSERT INTO","or (value==v2) or (value==v3): msg.append(\"filter 2 back to previous %f\" % tmp) value=tmp","j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None if","url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1':","nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data) # stat min max","%d\" % value) for min, max, v1, v2, v3 in list: if (value>=float(min))","= request.get_json() for j in data: rom = None if 'rom' in j:","type=%s\" m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if adj:","tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql = \"UPDATE","sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min,","WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return","import os import json from random import randint from flask_jwt_extended import jwt_required import","j: device=j['device'] ip = None if 'ip' in j: ip = j['ip'] gpio","tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return value def new_db(rom): rom","def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql =","print (\"[ nettemp ][ sensor ] Sensor %s already exist\" %rom) return None","Database %s not exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1","FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\"","'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600)","map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if","data = [rom, type, device, ip, gpio, i2c, usb, name] data2 = [group,","\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0')","in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max, value1, value2, value3","m.execute(sql, [type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj)","j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if 'tmp'","stat min max data = [value, value, rom] sql = \"UPDATE sensors SET","group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom,","from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL()","= \"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj,","added ok\" %rom) else: print (\"[ nettemp ][ sensor ] Sensor %s already","'ip' in j: ip = j['ip'] gpio = None if 'gpio' in j:","device = None if 'device' in j: device=j['device'] ip = None if 'ip'","db.commit() print (\"Database %s created\" %rom) return False def insert_db(rom,value): rom = rom+'.sql'","map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio,","%s added ok\" %rom) else: print (\"[ nettemp ][ sensor ] Sensor %s","= mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close()","m = mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*) FROM sensors WHERE","in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group =","ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type,","c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True else: with app.app_context(): db =","rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][ sensor ] Database %s deleted\"","rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count() FROM sqlite_master","IGNORE INTO def (value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp","WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][","sql = \"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1:","get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s created\" %rom)","coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s,","WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \"INSERT","sqlite_master WHERE type='table' AND name='def'\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql =","map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type,","max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2)","= [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value,","else: print (\"[ nettemp ][ sensor ] Database %s not exist\" %rom) return","= mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom])","rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors","to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" % value) print(msg) return","Database %s insert ok\" %rom) return True else: print (\"[ nettemp ][ sensor","m = mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors WHERE rom=%s\" m.execute(sql,","VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\" m.execute(sql, data) sql2 =","(%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp","VALUES (%s, %s, %s, %s, %s, %s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[","sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND","return True else: print (\"[ nettemp ][ sensor ] Database %s not exist\"","None if 'value' in j: value=j['value'] group = type if 'group' in j:","%f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for","gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c'","None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j:","value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql","= [value] sql = \"INSERT OR IGNORE INTO def (value) VALUES (?)\" c.execute(sql,","print (\"Database %s exists\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom)","= randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value = None","<gh_stars>10-100 from app import app from flask import Flask, request, jsonify, g import","sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null","sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print","] Sensor %s not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom):","None) if db is not None: db.close() def check_value(value, type, rom): adj='' tm=''","stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql,","msg.append(\"filter 2 back to previous %f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter","jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db","None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j:","SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,tmp_5ago,rom] else: sql =","(value) VALUES (?)\" c.execute(sql, data) conn.commit() conn.close() print (\"[ nettemp ][ sensor ]","is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp","%s, %s, %s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off',","rom] sql = \"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is","return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor()","\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\" data = [value,rom] m.execute(sql, data)","rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql","[type]) list=m.fetchall() msg.append(\"IN VALUE: %f\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ:","conn.close() print (\"[ nettemp ][ sensor ] Database %s insert ok\" %rom) return","for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max,","%s already exist\" %rom) return None def sensor(): data = request.get_json() for j","m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s updated\"","sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql =","randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql =","j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data =","'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value)","or (value==v3): msg.append(\"filter 2 back to previous %f\" % tmp) value=tmp else: value=float(value)","= None if 'rom' in j: rom=j['rom'] type = None if 'type' in","tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2)","if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c']","False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql","name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id,","True else: print (\"[ nettemp ][ sensor ] Database %s not exist\" %rom)","% value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min,","stat_min is null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors","%rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\"[ nettemp ][","= randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name] data2","and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\"filter 2 back to previous %f\"","else: msg.append(\"filter 1 back to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\"","False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors","sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\" m.execute(sql, data)","import Flask, request, jsonify, g import sqlite3 import os import json from random","None def sensor(): data = request.get_json() for j in data: rom = None","db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database %s","stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql,","%s, %s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET alarm='off', adj='0',","%f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back to previous %f\"","app from flask import Flask, request, jsonify, g import sqlite3 import os import","= \"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\" m.execute(sql, [type])","i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if","sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \"SELECT min, max, value1, value2, value3 FROM","m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql,","coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s,","%s exists\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql',","FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql =","%s created\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom)","MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is None: db","null OR stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s,","= \"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s,","if 'type' in j: type=j['type'] device = None if 'device' in j: device=j['device']","FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql","WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT INTO maps (type, pos_x, pos_y, map_on,","removed ok\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 =","m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor ] Sensor %s removed ok\"","if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp =","None if 'rom' in j: rom=j['rom'] type = None if 'type' in j:","if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb']","c.execute(sql) if c.fetchone()[0]==1: print (\"Database %s exists\" %rom) return True else: with app.app_context():","map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s added ok\"","%s not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print","%s not exist\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m","def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE","stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map = \"INSERT","v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or","value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\"ADJ: %d\" % value) for min, max,","msg.append(\"VALUE OUT: %f\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql'","stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ]","m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name)","stat_min='0.0') AND rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE","(stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close()","'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value)","OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][ sensor","name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name =","minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map","get_db(rom): db = getattr(g, '_database', None) if db is None: db = g._database","value=float(value) else: msg.append(\"filter 1 back to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT:","= mysql.connection.cursor() rom1 = [rom] sql=\"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1)","rom] m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data)","%rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql","min max data = [value, value, rom] sql = \"UPDATE sensors SET stat_min=%s,","%rom) return True else: print (\"[ nettemp ][ sensor ] Sensor %s not","\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0',","close_connection(exception): db = getattr(g, '_database', None) if db is not None: db.close() def","if 'rom' in j: rom=j['rom'] type = None if 'type' in j: type=j['type']","[rom, type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom]","'_database', None) if db is None: db = g._database = sqlite3.connect(rom) return db","[rom] sql = \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql, rom1) coun =","rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if","data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*) FROM","ok\" %rom) return True else: print (\"[ nettemp ][ sensor ] Database %s","null OR stat_max='0.0') AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() print (\"[ nettemp ][","(value==v3): msg.append(\"filter 2 back to previous %f\" % tmp) value=tmp else: value=float(value) else:","if db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def","= [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value)","= \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR","coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP()","# stat min max data = [value, value, rom] sql = \"UPDATE sensors","'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name","to previous %f\" % tmp) value=tmp else: value=float(value) else: msg.append(\"filter 1 back to","data) sql2 = \"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0',","id=? AND rom=%s\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\"[ nettemp ][ sensor","not exist\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m =","stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\" m.execute(sql2, data2) map =","print (\"[ nettemp ][ sensor ] Sensor %s updated\" %rom) return True else:","data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on']","ip = None if 'ip' in j: ip = j['ip'] gpio = None","tmp FROM sensors WHERE rom=%s\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor:","for min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1)","sensor ] Sensor %s not exist\" %rom) return False def delete_db(rom): rom=rom+'.sql' if","msg.append(\"filter 1 back to previous %f\" % tmp) value=tmp msg.append(\"VALUE OUT: %f\" %","data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*)","sensor ] Sensor %s removed ok\" %rom) def create_sensor(rom, data, data2, map_settings): m","%s, %s, %s, %s, %s, %s)\" m.execute(sql, data) sql2 = \"UPDATE sensors SET","app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\"Database","= rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \"SELECT count() FROM","Sensor %s added ok\" %rom) else: print (\"[ nettemp ][ sensor ] Sensor","tm='' value=float(value) m = mysql.connection.cursor() sql = \"SELECT adj, tmp FROM sensors WHERE","rom=%s\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \"INSERT INTO sensors","(\"Database %s exists\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with","rom=%s\" m.execute(sql, data) sql = \"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR","sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0',","map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False:","not exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom]","sensor ] Sensor %s added ok\" %rom) else: print (\"[ nettemp ][ sensor","sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND","%s)\" m.execute(map, map_settings) m.connection.commit() m.close() print (\"[ nettemp ][ sensor ] Sensor %s","OUT: %f\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn","(rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\" m.execute(sql, data) sql2","if 'ip' in j: ip = j['ip'] gpio = None if 'gpio' in","from random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import","print (\"[ nettemp ][ sensor ] Database %s deleted\" %rom) return True else:","] Sensor %s added ok\" %rom) else: print (\"[ nettemp ][ sensor ]","rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\"%M\"))%5==0: tmp_5ago=value sql = \"UPDATE sensors SET tmp=%s,","% value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom)","mysql.connection.cursor() rom1 = [rom] sql = \"SELECT count(*) FROM sensors WHERE rom=%s\" m.execute(sql,","insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor()","m = mysql.connection.cursor() sql=\"DELETE FROM sensors WHERE id=? AND rom=%s\" m.execute(sql, data) m.connection.commit()"],"string":"[\n \"g import sqlite3 import os import json from random import randint from flask_jwt_extended\",\n \"m.execute(sql, data) # stat min max data = [value, value, rom] sql =\",\n \"exist\\\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT\",\n \"sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom]\",\n \"= None if 'type' in j: type=j['type'] device = None if 'device' in\",\n \"nettemp ][ sensor ] Database %s not exist\\\" %rom) return False def delete_sensor(id,rom):\",\n \"in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not\",\n \"tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2,\",\n \"tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat min\",\n \"request, jsonify, g import sqlite3 import os import json from random import randint\",\n \"[rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT\",\n \"= \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql,\",\n \"in j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device =\",\n \"i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x,\",\n \"random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL\",\n \"mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone()\",\n \"m.execute(sql2, data2) map = \\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name)\",\n \"= m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s,\",\n \"'tmp' in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group\",\n \"tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group = type if\",\n \"= conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql)\",\n \"if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value)\",\n \"import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom):\",\n \"ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom]\",\n \"fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps\",\n \"m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s added ok\\\" %rom)\",\n \"= \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR\",\n \"sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0:\",\n \"][ sensor ] Database %s insert ok\\\" %rom) return True else: print (\\\"[\",\n \"def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE\",\n \"None if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in j:\",\n \"insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor',\",\n \"delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database %s\",\n \"(\\\"[ nettemp ][ sensor ] Sensor %s added ok\\\" %rom) else: print (\\\"[\",\n \"in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data\",\n \"charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10'\",\n \"sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null\",\n \"None) if db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext\",\n \"None if 'device' in j: device=j['device'] ip = None if 'ip' in j:\",\n \"(value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\" % tmp) value=tmp else:\",\n \"m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s updated\\\" %rom) return True\",\n \"def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql =\",\n \"'rom' in j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device\",\n \"value=j['value'] group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y\",\n \"= [rom] sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun\",\n \"value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\"\",\n \"] Database %s deleted\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor\",\n \"in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb =\",\n \"value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) ==\",\n \"value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value def new_db(rom): rom =\",\n \"j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000)\",\n \"False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def\",\n \"flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def\",\n \"data2) map = \\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES\",\n \"OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[\",\n \"Sensor %s updated\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ]\",\n \"randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp\",\n \"sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) #\",\n \"FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[\",\n \"= None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in\",\n \"if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='',\",\n \"= None if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in\",\n \"conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ] Database %s insert ok\\\" %rom)\",\n \"j: type=j['type'] device = None if 'device' in j: device=j['device'] ip = None\",\n \"SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\\\"\",\n \"(stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql =\",\n \"db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception):\",\n \"[value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data\",\n \"value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST'])\",\n \"count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s\",\n \"WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat min max data =\",\n \"None if 'type' in j: type=j['type'] device = None if 'device' in j:\",\n \"%rom) return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE\",\n \"map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*) FROM sensors\",\n \"else: print (\\\"[ nettemp ][ sensor ] Sensor %s already exist\\\" %rom) return\",\n \"check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj,\",\n \"max, value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE:\",\n \"[type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) ==\",\n \"ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor ] Sensor %s already exist\\\"\",\n \"name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value =\",\n \"WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit()\",\n \"if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s,\",\n \"email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps (type, pos_x, pos_y,\",\n \"not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp']\",\n \"tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\" % tmp)\",\n \"][ sensor ] Sensor %s already exist\\\" %rom) return None def sensor(): data\",\n \"in j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id =\",\n \"] Sensor %s already exist\\\" %rom) return None def sensor(): data = request.get_json()\",\n \"rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if\",\n \"return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM\",\n \"VALUE: %f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value)\",\n \"from app import app from flask import Flask, request, jsonify, g import sqlite3\",\n \"1 back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value)\",\n \"OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP()\",\n \"value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c\",\n \"map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit()\",\n \"INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][\",\n \"randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb,\",\n \"FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql\",\n \"return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return\",\n \"= \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0',\",\n \"sensor ] Database %s deleted\\\" %rom) return True else: print (\\\"[ nettemp ][\",\n \"%rom) else: print (\\\"[ nettemp ][ sensor ] Sensor %s already exist\\\" %rom)\",\n \"type = None if 'type' in j: type=j['type'] device = None if 'device'\",\n \"data = request.get_json() for j in data: rom = None if 'rom' in\",\n \"min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or\",\n \"Sensor %s already exist\\\" %rom) return None def sensor(): data = request.get_json() for\",\n \"data = [value, value, rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE\",\n \"(\\\"[ nettemp ][ sensor ] Sensor %s not exist\\\" %rom) return False def\",\n \"create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST'])\",\n \"% tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\" %\",\n \"Database %s deleted\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ]\",\n \"in j: type=j['type'] device = None if 'device' in j: device=j['device'] ip =\",\n \"usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name'\",\n \"data) # stat min max data = [value, value, rom] sql = \\\"UPDATE\",\n \"def get_db(rom): db = getattr(g, '_database', None) if db is None: db =\",\n \"rom=j['rom'] type = None if 'type' in j: type=j['type'] device = None if\",\n \"updated\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Sensor %s\",\n \"sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp\",\n \"%s insert ok\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ]\",\n \"j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None\",\n \"type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings\",\n \"i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb'\",\n \"rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings)\",\n \"= mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\"\",\n \"print (\\\"[ nettemp ][ sensor ] Database %s insert ok\\\" %rom) return True\",\n \"%rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*)\",\n \"if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database %s deleted\\\" %rom)\",\n \"create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT\",\n \"c = conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\"\",\n \"is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET\",\n \"rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP()\",\n \"nettemp ][ sensor ] Database %s insert ok\\\" %rom) return True else: print\",\n \"'value' in j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id\",\n \"adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1, value2, value3 FROM types WHERE\",\n \"\\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database\",\n \"rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count()\",\n \"= MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is None:\",\n \"if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to\",\n \"return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor\",\n \"j: ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c\",\n \"print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c =\",\n \"= sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE type='table'\",\n \"= \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print\",\n \"maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s,\",\n \"if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return\",\n \"@jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr\",\n \"] Database %s not exist\\\" %rom) return False def delete_sensor(id,rom): data = [id,\",\n \"= getattr(g, '_database', None) if db is not None: db.close() def check_value(value, type,\",\n \"not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[\",\n \"WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql\",\n \"INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql,\",\n \"if 'tmp' in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value']\",\n \"import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if\",\n \"2 back to previous %f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1\",\n \"= get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\"\",\n \"sensor ] Database %s not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m =\",\n \"] Sensor %s removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m =\",\n \"gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb = None if\",\n \"rom = None if 'rom' in j: rom=j['rom'] type = None if 'type'\",\n \"usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name\",\n \"data: rom = None if 'rom' in j: rom=j['rom'] type = None if\",\n \"getattr(g, '_database', None) if db is not None: db.close() def check_value(value, type, rom):\",\n \"\\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp\",\n \"type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True\",\n \"[value] sql = \\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data)\",\n \"data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is\",\n \"return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor()\",\n \"True else: print (\\\"[ nettemp ][ sensor ] Sensor %s not exist\\\" %rom)\",\n \"None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j:\",\n \"update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '',\",\n \"sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql\",\n \"def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor\",\n \"(value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous\",\n \"pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map,\",\n \"200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else:\",\n \"= None if 'ip' in j: ip = j['ip'] gpio = None if\",\n \"nettemp ][ sensor ] Sensor %s updated\\\" %rom) return True else: print (\\\"[\",\n \"sensor ] Database %s not exist\\\" %rom) return False def delete_sensor(id,rom): data =\",\n \"j in data: rom = None if 'rom' in j: rom=j['rom'] type =\",\n \"\\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print\",\n \"= j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None\",\n \"m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s updated\\\" %rom) return\",\n \"if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1, v2, v3\",\n \"adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5',\",\n \"(type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\"\",\n \"= None if 'device' in j: device=j['device'] ip = None if 'ip' in\",\n \"gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y,\",\n \"(\\\"[ nettemp ][ sensor ] Database %s deleted\\\" %rom) return True else: print\",\n \"type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR\",\n \"= getattr(g, '_database', None) if db is None: db = g._database = sqlite3.connect(rom)\",\n \"def sensor(): data = request.get_json() for j in data: rom = None if\",\n \"import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql\",\n \"adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1, v2, v3 in\",\n \"def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr ==\",\n \"max data = [value, value, rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP()\",\n \"][ sensor ] Sensor %s added ok\\\" %rom) else: print (\\\"[ nettemp ][\",\n \"nettemp ][ sensor ] Database %s not exist\\\" %rom) return False def update_sensor_tmp(rom,value):\",\n \"@app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is not None:\",\n \"import json from random import randint from flask_jwt_extended import jwt_required import datetime from\",\n \"(\\\"[ nettemp ][ sensor ] Sensor %s removed ok\\\" %rom) def create_sensor(rom, data,\",\n \"(\\\"Database %s created\\\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn =\",\n \"data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE\",\n \"mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return False def\",\n \"jsonify, g import sqlite3 import os import json from random import randint from\",\n \"sql = \\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit()\",\n \"m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max\",\n \"request.get_json() for j in data: rom = None if 'rom' in j: rom=j['rom']\",\n \"randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name] data2 =\",\n \"in j: device=j['device'] ip = None if 'ip' in j: ip = j['ip']\",\n \"True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read())\",\n \"delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor %s removed ok\\\" %rom) def\",\n \"%s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][\",\n \"data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND\",\n \"db = getattr(g, '_database', None) if db is None: db = g._database =\",\n \"sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT\",\n \"nettemp ][ sensor ] Sensor %s not exist\\\" %rom) return False def delete_db(rom):\",\n \"count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value\",\n \"value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\" % tmp) value=tmp\",\n \"in j: ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio']\",\n \"nettemp ][ sensor ] Sensor %s added ok\\\" %rom) else: print (\\\"[ nettemp\",\n \"False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors\",\n \"c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR IGNORE INTO def\",\n \"rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor\",\n \"display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print\",\n \"'type' in j: type=j['type'] device = None if 'device' in j: device=j['device'] ip\",\n \"else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data =\",\n \"stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[\",\n \"time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat min max data\",\n \"tmp = None if 'tmp' in j: tmp=j['tmp'] value = None if 'value'\",\n \"sensor ] Database %s insert ok\\\" %rom) return True else: print (\\\"[ nettemp\",\n \"import app from flask import Flask, request, jsonify, g import sqlite3 import os\",\n \"sql = \\\"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql,\",\n \"name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True else: with\",\n \"print (\\\"[ nettemp ][ sensor ] Sensor %s removed ok\\\" %rom) def create_sensor(rom,\",\n \"ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\"\",\n \"value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value)\",\n \"data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s updated\\\" %rom)\",\n \"= randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000)\",\n \"data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor %s removed\",\n \"c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ] Database %s insert\",\n \"\\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\"\",\n \"= None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in\",\n \"os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database %s deleted\\\" %rom) return True\",\n \"VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ] Database\",\n \"= [value,rom] m.execute(sql, data) # stat min max data = [value, value, rom]\",\n \"sensor ] Sensor %s already exist\\\" %rom) return None def sensor(): data =\",\n \"if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name']\",\n \"\\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0')\",\n \"list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\"\",\n \"import sqlite3 import os import json from random import randint from flask_jwt_extended import\",\n \"stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data)\",\n \"in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2\",\n \"print (\\\"Database %s created\\\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn\",\n \"][ sensor ] Database %s deleted\\\" %rom) return True else: print (\\\"[ nettemp\",\n \"%rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c =\",\n \"tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data\",\n \"name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR IGNORE INTO\",\n \"sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor()\",\n \"datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g,\",\n \"else: print (\\\"[ nettemp ][ sensor ] Sensor %s not exist\\\" %rom) return\",\n \"rom1 = [rom] sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1)\",\n \"\\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s,\",\n \"exist\\\" %rom) return None def sensor(): data = request.get_json() for j in data:\",\n \"j: value=j['value'] group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000)\",\n \"= \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data\",\n \"nettemp ][ sensor ] Database %s deleted\\\" %rom) return True else: print (\\\"[\",\n \"Database %s not exist\\\" %rom) return False def delete_sensor(id,rom): data = [id, rom]\",\n \"flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None)\",\n \"tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1, value2,\",\n \"alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s,\",\n \"WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='',\",\n \"if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR IGNORE INTO def (value)\",\n \"] Sensor %s updated\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor\",\n \"%s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on',\",\n \"m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor %s\",\n \"AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor\",\n \"status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE\",\n \"'_database', None) if db is not None: db.close() def check_value(value, type, rom): adj=''\",\n \"'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None\",\n \"with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return\",\n \"randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip,\",\n \"'', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local'\",\n \"m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s,\",\n \"%s deleted\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Database\",\n \"rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s\",\n \"types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close()\",\n \"else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit()\",\n \"data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ] Database %s insert ok\\\"\",\n \"m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor %s removed ok\\\" %rom)\",\n \"= [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\"\",\n \"type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x\",\n \"c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR IGNORE INTO def (value) VALUES\",\n \"= [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\"\",\n \"as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return False def insert_db(rom,value):\",\n \"back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg)\",\n \"sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone()\",\n \"pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings)\",\n \"\\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data =\",\n \"value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\"\",\n \"WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE\",\n \"AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT OR IGNORE\",\n \"WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if\",\n \"db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return False def insert_db(rom,value): rom =\",\n \"in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name =\",\n \"randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value = None if\",\n \"[value,rom] m.execute(sql, data) # stat min max data = [value, value, rom] sql\",\n \"%rom) return True else: print (\\\"[ nettemp ][ sensor ] Database %s not\",\n \"tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO\",\n \"] Database %s insert ok\\\" %rom) return True else: print (\\\"[ nettemp ][\",\n \"if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True else: with app.app_context(): db\",\n \"if 'device' in j: device=j['device'] ip = None if 'ip' in j: ip\",\n \"OR stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print\",\n \"sensor ] Sensor %s updated\\\" %rom) return True else: print (\\\"[ nettemp ][\",\n \"== False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200\",\n \"= None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in\",\n \"methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return '',\",\n \"in data: rom = None if 'rom' in j: rom=j['rom'] type = None\",\n \"@app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost():\",\n \"nettemp ][ sensor ] Sensor %s already exist\\\" %rom) return None def sensor():\",\n \"msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" %\",\n \"v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter\",\n \"msg=[] sql = \\\"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\\\"\",\n \"(\\\"[ nettemp ][ sensor ] Database %s insert ok\\\" %rom) return True else:\",\n \"json from random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb\",\n \"Sensor %s removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor()\",\n \"\\\"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall()\",\n \"if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\" % tmp)\",\n \"INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s, %s, %s,\",\n \"list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back\",\n \"%s updated\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Sensor\",\n \"nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET\",\n \"import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db =\",\n \"map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name]\",\n \"sqlite3 import os import json from random import randint from flask_jwt_extended import jwt_required\",\n \"% value) for min, max, v1, v2, v3 in list: if (value>=float(min)) and\",\n \"coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s,\",\n \"sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off',\",\n \"if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE\",\n \"[rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if\",\n \"'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c'] usb\",\n \"else: value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE\",\n \"j: rom=j['rom'] type = None if 'type' in j: type=j['type'] device = None\",\n \"% tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value def new_db(rom):\",\n \"not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor()\",\n \"== False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required\",\n \"sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db\",\n \"AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ]\",\n \"sql = \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for\",\n \"SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\\\"\",\n \"= randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device,\",\n \"return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f:\",\n \"sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj)\",\n \"exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp\",\n \"data = [value,rom] m.execute(sql, data) # stat min max data = [value, value,\",\n \"for j in data: rom = None if 'rom' in j: rom=j['rom'] type\",\n \"if not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j:\",\n \"sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE type='table' AND\",\n \"%s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors\",\n \"map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom)\",\n \"adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1,\",\n \"%rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as\",\n \"= g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database',\",\n \"%s not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 =\",\n \"type=j['type'] device = None if 'device' in j: device=j['device'] ip = None if\",\n \"adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in\",\n \"type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp\",\n \"count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value]\",\n \"rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat min max data = [value,\",\n \"%s removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1\",\n \"'usb' in j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if\",\n \"new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor():\",\n \"rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM\",\n \"value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" %\",\n \"return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is\",\n \"= sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if\",\n \"= [value, value, rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s\",\n \"new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT\",\n \"%s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on',\",\n \"%s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s,\",\n \"data = [value] sql = \\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\"\",\n \"mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is\",\n \"][ sensor ] Sensor %s updated\\\" %rom) return True else: print (\\\"[ nettemp\",\n \"sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data =\",\n \"g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None)\",\n \"tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1, value2, value3 FROM types\",\n \"msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1, v2, v3 in list: if\",\n \"name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if 'tmp' in\",\n \"min, max, value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN\",\n \"SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat\",\n \"map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom)\",\n \"][ sensor ] Database %s not exist\\\" %rom) return False def delete_sensor(id,rom): data\",\n \"j['name']: name = randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value\",\n \"insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT\",\n \"OR stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE\",\n \"FROM types WHERE type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list)\",\n \"tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors\",\n \"update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\"\",\n \"created\\\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c\",\n \"return None def sensor(): data = request.get_json() for j in data: rom =\",\n \"app import app from flask import Flask, request, jsonify, g import sqlite3 import\",\n \"(value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\" %\",\n \"= [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1:\",\n \"AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True else:\",\n \"][ sensor ] Sensor %s not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql'\",\n \"db @app.teardown_appcontext def close_connection(exception): db = getattr(g, '_database', None) if db is not\",\n \"sensor(): data = request.get_json() for j in data: rom = None if 'rom'\",\n \"map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps (type,\",\n \"def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return '', 404\",\n \"(\\\"[ nettemp ][ sensor ] Database %s not exist\\\" %rom) return False def\",\n \"f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return False def insert_db(rom,value): rom\",\n \"m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0',\",\n \"print (\\\"[ nettemp ][ sensor ] Sensor %s added ok\\\" %rom) else: print\",\n \"%s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor\",\n \"nettemp ][ sensor ] Sensor %s removed ok\\\" %rom) def create_sensor(rom, data, data2,\",\n \"j: usb=j['usb'] name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']:\",\n \"sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom)\",\n \"= randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio, i2c,\",\n \"os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database %s deleted\\\" %rom) return\",\n \"previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value\",\n \"= \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s,\",\n \"is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m =\",\n \"nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps (type, pos_x,\",\n \"False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ]\",\n \"%rom) return None def sensor(): data = request.get_json() for j in data: rom\",\n \"def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database\",\n \"type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value) == False:\",\n \"(\\\"[ nettemp ][ sensor ] Sensor %s already exist\\\" %rom) return None def\",\n \"= None if 'value' in j: value=j['value'] group = type if 'group' in\",\n \"WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[]\",\n \"= \\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close()\",\n \"os import json from random import randint from flask_jwt_extended import jwt_required import datetime\",\n \"m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1, v2,\",\n \"map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom,\",\n \"db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db = getattr(g,\",\n \"device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings =\",\n \"= [rom, type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id,\",\n \"stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data)\",\n \"] Database %s not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor()\",\n \"print (\\\"[ nettemp ][ sensor ] Database %s not exist\\\" %rom) return False\",\n \"flask import Flask, request, jsonify, g import sqlite3 import os import json from\",\n \"getattr(g, '_database', None) if db is None: db = g._database = sqlite3.connect(rom) return\",\n \"db is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m\",\n \"][ sensor ] Database %s not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m\",\n \"value) for min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)):\",\n \"\\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else:\",\n \"previous %f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to previous\",\n \"msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1,\",\n \"SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on',\",\n \"return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM\",\n \"j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group = type\",\n \"None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql\",\n \"mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom)\",\n \"time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE sensors SET tmp=%s,\",\n \"ip = j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c =\",\n \"(%s, %s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE\",\n \"insert ok\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Database\",\n \"if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x =\",\n \"usb, name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on',\",\n \"sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s,\",\n \"\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0:\",\n \"from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database',\",\n \"with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print\",\n \"[group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type,\",\n \"from flask import Flask, request, jsonify, g import sqlite3 import os import json\",\n \"back to previous %f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back\",\n \"is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db\",\n \"exist\\\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor()\",\n \"delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=?\",\n \"%s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor\",\n \"map_id, display_name) VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close()\",\n \"rom1) coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES\",\n \"group = type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y =\",\n \"%f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value def\",\n \"FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp)\",\n \"def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT\",\n \"if db is not None: db.close() def check_value(value, type, rom): adj='' tm='' value=float(value)\",\n \"False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local',\",\n \"count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql\",\n \"return True else: print (\\\"[ nettemp ][ sensor ] Sensor %s not exist\\\"\",\n \"= \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if\",\n \"rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id,\",\n \"(\\\"[ nettemp ][ sensor ] Sensor %s updated\\\" %rom) return True else: print\",\n \"map = \\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s,\",\n \"def close_connection(exception): db = getattr(g, '_database', None) if db is not None: db.close()\",\n \"[value, value, rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR\",\n \"%s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ]\",\n \"in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if\",\n \"conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE\",\n \"MySQL mysql = MySQL() def get_db(rom): db = getattr(g, '_database', None) if db\",\n \"%f\\\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn =\",\n \"[id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql,\",\n \"adj='' tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors\",\n \"(?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ] Database %s\",\n \"'device' in j: device=j['device'] ip = None if 'ip' in j: ip =\",\n \"][ sensor ] Sensor %s removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings):\",\n \"methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if\",\n \"deleted\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Database %s\",\n \"value, rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min\",\n \"app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom) return False\",\n \"def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql =\",\n \"device=j['device'] ip = None if 'ip' in j: ip = j['ip'] gpio =\",\n \"AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s\",\n \"db = getattr(g, '_database', None) if db is not None: db.close() def check_value(value,\",\n \"@app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1': sensor() return 'Local' else: return\",\n \"= type if 'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600)\",\n \"v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3):\",\n \"value = None if 'value' in j: value=j['value'] group = type if 'group'\",\n \"None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def close_connection(exception): db =\",\n \"m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s added\",\n \"already exist\\\" %rom) return None def sensor(): data = request.get_json() for j in\",\n \"= \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom]\",\n \"None if 'ip' in j: ip = j['ip'] gpio = None if 'gpio'\",\n \"mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall()\",\n \"m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql =\",\n \"print (\\\"[ nettemp ][ sensor ] Sensor %s not exist\\\" %rom) return False\",\n \"if 'value' in j: value=j['value'] group = type if 'group' in j: group=j['group']\",\n \"m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET\",\n \"Sensor %s not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom)\",\n \"db.close() def check_value(value, type, rom): adj='' tm='' value=float(value) m = mysql.connection.cursor() sql =\",\n \"m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s added ok\\\" %rom) else:\",\n \"value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max, v1, v2, v3 in list:\",\n \"exists\\\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r')\",\n \"update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def\",\n \"Flask, request, jsonify, g import sqlite3 import os import json from random import\",\n \"int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\"\",\n \"conn.cursor() sql = \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if\",\n \"WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO\",\n \"or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\" % tmp) value=tmp\",\n \"j['ip'] gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None if\",\n \"url_sensor(): sensor() return '', 200 @app.route('/local', methods=['POST']) def url_localhost(): if request.remote_addr == '127.0.0.1':\",\n \"nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data) # stat min max\",\n \"%d\\\" % value) for min, max, v1, v2, v3 in list: if (value>=float(min))\",\n \"= request.get_json() for j in data: rom = None if 'rom' in j:\",\n \"type=%s\\\" m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if adj:\",\n \"tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql = \\\"UPDATE\",\n \"sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min,\",\n \"WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return\",\n \"import os import json from random import randint from flask_jwt_extended import jwt_required import\",\n \"j: device=j['device'] ip = None if 'ip' in j: ip = j['ip'] gpio\",\n \"tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value def new_db(rom): rom\",\n \"def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql =\",\n \"print (\\\"[ nettemp ][ sensor ] Sensor %s already exist\\\" %rom) return None\",\n \"Database %s not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1\",\n \"FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\"\",\n \"'group' in j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600)\",\n \"map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if\",\n \"data = [rom, type, device, ip, gpio, i2c, usb, name] data2 = [group,\",\n \"\\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0')\",\n \"in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1, value2, value3\",\n \"m.execute(sql, [type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj)\",\n \"j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp = None if 'tmp'\",\n \"stat min max data = [value, value, rom] sql = \\\"UPDATE sensors SET\",\n \"group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom,\",\n \"from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL()\",\n \"= \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj,\",\n \"added ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor ] Sensor %s already\",\n \"'ip' in j: ip = j['ip'] gpio = None if 'gpio' in j:\",\n \"device = None if 'device' in j: device=j['device'] ip = None if 'ip'\",\n \"db.commit() print (\\\"Database %s created\\\" %rom) return False def insert_db(rom,value): rom = rom+'.sql'\",\n \"map_y = randint(50,600) map_x = randint(50,600) data = [rom, type, device, ip, gpio,\",\n \"%s added ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor ] Sensor %s\",\n \"= mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close()\",\n \"m = mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*) FROM sensors WHERE\",\n \"in j: tmp=j['tmp'] value = None if 'value' in j: value=j['value'] group =\",\n \"ip, gpio, i2c, usb, name] data2 = [group, map_id, rom] map_settings = [type,\",\n \"c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True else: with app.app_context(): db =\",\n \"rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][ sensor ] Database %s deleted\\\"\",\n \"rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count() FROM sqlite_master\",\n \"IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp\",\n \"WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][\",\n \"sql = \\\"SELECT count() FROM sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1:\",\n \"get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s created\\\" %rom)\",\n \"coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s,\",\n \"WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql = \\\"INSERT\",\n \"sqlite_master WHERE type='table' AND name='def'\\\" c.execute(sql) if c.fetchone()[0]==1: data = [value] sql =\",\n \"map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data = [rom, type,\",\n \"max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2)\",\n \"= [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value,\",\n \"else: print (\\\"[ nettemp ][ sensor ] Database %s not exist\\\" %rom) return\",\n \"= mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom])\",\n \"rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors\",\n \"to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return\",\n \"Database %s insert ok\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor\",\n \"m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors WHERE rom=%s\\\" m.execute(sql,\",\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2 =\",\n \"(%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp\",\n \"VALUES (%s, %s, %s, %s, %s, %s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[\",\n \"sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND\",\n \"return True else: print (\\\"[ nettemp ][ sensor ] Database %s not exist\\\"\",\n \"None if 'value' in j: value=j['value'] group = type if 'group' in j:\",\n \"%f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for\",\n \"gpio = None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c'\",\n \"None if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j:\",\n \"value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql\",\n \"= [value] sql = \\\"INSERT OR IGNORE INTO def (value) VALUES (?)\\\" c.execute(sql,\",\n \"print (\\\"Database %s exists\\\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom)\",\n \"= randint(1000,9000) tmp = None if 'tmp' in j: tmp=j['tmp'] value = None\",\n \"<gh_stars>10-100 from app import app from flask import Flask, request, jsonify, g import\",\n \"sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null\",\n \"sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print\",\n \"] Sensor %s not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom):\",\n \"None) if db is not None: db.close() def check_value(value, type, rom): adj='' tm=''\",\n \"stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql,\",\n \"msg.append(\\\"filter 2 back to previous %f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter\",\n \"jwt_required import datetime from flask_mysqldb import MySQL mysql = MySQL() def get_db(rom): db\",\n \"None if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j:\",\n \"SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,tmp_5ago,rom] else: sql =\",\n \"(value) VALUES (?)\\\" c.execute(sql, data) conn.commit() conn.close() print (\\\"[ nettemp ][ sensor ]\",\n \"is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp\",\n \"%s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off',\",\n \"rom] sql = \\\"UPDATE sensors SET stat_min=%s, stat_min_time=CURRENT_TIMESTAMP() WHERE (stat_min>%s OR stat_min is\",\n \"return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor()\",\n \"\\\"UPDATE sensors SET tmp=%s, nodata='', time=CURRENT_TIMESTAMP() WHERE rom=%s\\\" data = [value,rom] m.execute(sql, data)\",\n \"rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql\",\n \"[type]) list=m.fetchall() msg.append(\\\"IN VALUE: %f\\\" % value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ:\",\n \"conn.close() print (\\\"[ nettemp ][ sensor ] Database %s insert ok\\\" %rom) return\",\n \"for adj, tmp in sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max,\",\n \"%s already exist\\\" %rom) return None def sensor(): data = request.get_json() for j\",\n \"m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s updated\\\"\",\n \"sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql =\",\n \"randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import MySQL mysql =\",\n \"j: group=j['group'] map_id = randint(1000,9000) map_y = randint(50,600) map_x = randint(50,600) data =\",\n \"'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value)\",\n \"or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\" % tmp) value=tmp else: value=float(value)\",\n \"= None if 'rom' in j: rom=j['rom'] type = None if 'type' in\",\n \"tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2)\",\n \"if 'gpio' in j: gpio=j['gpio'] i2c = None if 'i2c' in j: i2c=j['i2c']\",\n \"False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql\",\n \"name] data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id,\",\n \"True else: print (\\\"[ nettemp ][ sensor ] Database %s not exist\\\" %rom)\",\n \"% value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min,\",\n \"stat_min is null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors\",\n \"%rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print (\\\"[ nettemp ][\",\n \"= randint(50,600) data = [rom, type, device, ip, gpio, i2c, usb, name] data2\",\n \"and (value<=float(max)): if(value==v1) or (value==v2) or (value==v3): msg.append(\\\"filter 2 back to previous %f\\\"\",\n \"else: msg.append(\\\"filter 1 back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\"\",\n \"False def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors\",\n \"sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data)\",\n \"import Flask, request, jsonify, g import sqlite3 import os import json from random\",\n \"None def sensor(): data = request.get_json() for j in data: rom = None\",\n \"db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database %s\",\n \"stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql,\",\n \"%s, %s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0',\",\n \"%f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\"\",\n \"app from flask import Flask, request, jsonify, g import sqlite3 import os import\",\n \"= \\\"SELECT min, max, value1, value2, value3 FROM types WHERE type=%s\\\" m.execute(sql, [type])\",\n \"i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name = randint(1000,9000) if\",\n \"sensor: tmp=float(tmp) adj=float(adj) msg=[] sql = \\\"SELECT min, max, value1, value2, value3 FROM\",\n \"m = mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql,\",\n \"coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s,\",\n \"%s exists\\\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql',\",\n \"FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql =\",\n \"%s created\\\" %rom) return False def insert_db(rom,value): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom)\",\n \"MySQL() def get_db(rom): db = getattr(g, '_database', None) if db is None: db\",\n \"null OR stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s,\",\n \"= \\\"INSERT INTO maps (type, pos_x, pos_y, map_on, map_id, display_name) VALUES (%s, %s,\",\n \"if 'type' in j: type=j['type'] device = None if 'device' in j: device=j['device']\",\n \"FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql\",\n \"WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT INTO maps (type, pos_x, pos_y, map_on,\",\n \"removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 =\",\n \"m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor ] Sensor %s removed ok\\\"\",\n \"if 'name' in j: name=j['name'] if not j['name']: name = randint(1000,9000) tmp =\",\n \"None if 'rom' in j: rom=j['rom'] type = None if 'type' in j:\",\n \"if 'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb']\",\n \"c.execute(sql) if c.fetchone()[0]==1: print (\\\"Database %s exists\\\" %rom) return True else: with app.app_context():\",\n \"map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s added ok\\\"\",\n \"%s not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if os.path.isfile(app.romdir+rom): os.remove(rom) print\",\n \"%s not exist\\\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m\",\n \"def delete_sensor(id,rom): data = [id, rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE\",\n \"stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map = \\\"INSERT\",\n \"v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1) or (value==v2) or\",\n \"value) msg.append(list) m.close() if adj: value=float(value)+(adj) msg.append(\\\"ADJ: %d\\\" % value) for min, max,\",\n \"msg.append(\\\"VALUE OUT: %f\\\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql'\",\n \"stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ]\",\n \"m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors (rom,type,device,ip,gpio,i2c,usb,name)\",\n \"stat_min='0.0') AND rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE\",\n \"(stat_max<%s OR stat_max is null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close()\",\n \"'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False: new_db(rom) insert_db(rom,value) if update_sensor_tmp(rom,value)\",\n \"OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor\",\n \"name = randint(1000,9000) if 'name' in j: name=j['name'] if not j['name']: name =\",\n \"minmax='off', stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map\",\n \"get_db(rom): db = getattr(g, '_database', None) if db is None: db = g._database\",\n \"value=float(value) else: msg.append(\\\"filter 1 back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT:\",\n \"= mysql.connection.cursor() rom1 = [rom] sql=\\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1)\",\n \"rom] m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data)\",\n \"%rom) def create_sensor(rom, data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql\",\n \"min max data = [value, value, rom] sql = \\\"UPDATE sensors SET stat_min=%s,\",\n \"%rom) return True else: print (\\\"[ nettemp ][ sensor ] Sensor %s not\",\n \"\\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0',\",\n \"close_connection(exception): db = getattr(g, '_database', None) if db is not None: db.close() def\",\n \"if 'rom' in j: rom=j['rom'] type = None if 'type' in j: type=j['type']\",\n \"[rom, type, device, ip, gpio, i2c, usb, name] data2 = [group, map_id, rom]\",\n \"'_database', None) if db is None: db = g._database = sqlite3.connect(rom) return db\",\n \"[rom] sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql, rom1) coun =\",\n \"rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if\",\n \"data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*) FROM\",\n \"ok\\\" %rom) return True else: print (\\\"[ nettemp ][ sensor ] Database %s\",\n \"null OR stat_max='0.0') AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() print (\\\"[ nettemp ][\",\n \"(value==v3): msg.append(\\\"filter 2 back to previous %f\\\" % tmp) value=tmp else: value=float(value) else:\",\n \"if db is None: db = g._database = sqlite3.connect(rom) return db @app.teardown_appcontext def\",\n \"= [type, map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value)\",\n \"= \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR\",\n \"coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s, tmp_5ago=%s, nodata='', time=CURRENT_TIMESTAMP()\",\n \"# stat min max data = [value, value, rom] sql = \\\"UPDATE sensors\",\n \"'i2c' in j: i2c=j['i2c'] usb = None if 'usb' in j: usb=j['usb'] name\",\n \"to previous %f\\\" % tmp) value=tmp else: value=float(value) else: msg.append(\\\"filter 1 back to\",\n \"data) sql2 = \\\"UPDATE sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0',\",\n \"id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit() m.close() delete_db(rom) print (\\\"[ nettemp ][ sensor\",\n \"not exist\\\" %rom) return False def delete_sensor(id,rom): data = [id, rom] m =\",\n \"stat_min='0', stat_max='0', tmp_5ago='0', fiveago='on', map_id=%s, nodata_time='5', email_delay='10' WHERE rom=%s\\\" m.execute(sql2, data2) map =\",\n \"print (\\\"[ nettemp ][ sensor ] Sensor %s updated\\\" %rom) return True else:\",\n \"data2 = [group, map_id, rom] map_settings = [type, map_y, map_x, 'on', map_id, 'on']\",\n \"ip = None if 'ip' in j: ip = j['ip'] gpio = None\",\n \"tmp FROM sensors WHERE rom=%s\\\" m.execute(sql, [rom]) sensor=m.fetchall() for adj, tmp in sensor:\",\n \"for min, max, v1, v2, v3 in list: if (value>=float(min)) and (value<=float(max)): if(value==v1)\",\n \"sensor ] Sensor %s not exist\\\" %rom) return False def delete_db(rom): rom=rom+'.sql' if\",\n \"msg.append(\\\"filter 1 back to previous %f\\\" % tmp) value=tmp msg.append(\\\"VALUE OUT: %f\\\" %\",\n \"data, data2, map_settings): m = mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*)\",\n \"sensor ] Sensor %s removed ok\\\" %rom) def create_sensor(rom, data, data2, map_settings): m\",\n \"%s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2 = \\\"UPDATE sensors SET\",\n \"app.app_context(): db = get_db(app.romdir+rom) with app.open_resource('schema/sensors_db_schema.sql', mode='r') as f: db.cursor().executescript(f.read()) db.commit() print (\\\"Database\",\n \"= rom+'.sql' conn = sqlite3.connect(app.romdir+rom) c = conn.cursor() sql = \\\"SELECT count() FROM\",\n \"Sensor %s added ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor ] Sensor\",\n \"tm='' value=float(value) m = mysql.connection.cursor() sql = \\\"SELECT adj, tmp FROM sensors WHERE\",\n \"rom=%s\\\" m.execute(sql, rom1) coun = m.fetchone() if coun[0]==0: sql = \\\"INSERT INTO sensors\",\n \"(\\\"Database %s exists\\\" %rom) return True else: with app.app_context(): db = get_db(app.romdir+rom) with\",\n \"rom=%s\\\" m.execute(sql, data) sql = \\\"UPDATE sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR\",\n \"sensors SET alarm='off', adj='0', charts='on', status='on', ch_group=%s, tmp_min='0', tmp_max='0', minmax='off', stat_min='0', stat_max='0', tmp_5ago='0',\",\n \"map_y, map_x, 'on', map_id, 'on'] value=check_value(value, type, rom) if insert_db(rom, value) == False:\",\n \"not exist\\\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom]\",\n \"sensor ] Sensor %s added ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor\",\n \"sensors SET stat_max=%s, stat_max_time=CURRENT_TIMESTAMP() WHERE (stat_max<%s OR stat_max is null OR stat_max='0.0') AND\",\n \"%s)\\\" m.execute(map, map_settings) m.connection.commit() m.close() print (\\\"[ nettemp ][ sensor ] Sensor %s\",\n \"OUT: %f\\\" % value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn\",\n \"(rom,type,device,ip,gpio,i2c,usb,name) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\" m.execute(sql, data) sql2\",\n \"if 'ip' in j: ip = j['ip'] gpio = None if 'gpio' in\",\n \"from random import randint from flask_jwt_extended import jwt_required import datetime from flask_mysqldb import\",\n \"print (\\\"[ nettemp ][ sensor ] Database %s deleted\\\" %rom) return True else:\",\n \"] Sensor %s added ok\\\" %rom) else: print (\\\"[ nettemp ][ sensor ]\",\n \"rom1) coun=m.fetchone() if coun[0]==1: if int(datetime.datetime.now().strftime(\\\"%M\\\"))%5==0: tmp_5ago=value sql = \\\"UPDATE sensors SET tmp=%s,\",\n \"% value) print(msg) return value def new_db(rom): rom = rom+'.sql' conn = sqlite3.connect(app.romdir+rom)\",\n \"mysql.connection.cursor() rom1 = [rom] sql = \\\"SELECT count(*) FROM sensors WHERE rom=%s\\\" m.execute(sql,\",\n \"insert_db(rom,value) if update_sensor_tmp(rom,value) == False: create_sensor(rom,data,data2,map_settings) update_sensor_tmp(rom,value) @app.route('/sensor', methods=['POST']) @jwt_required def url_sensor(): sensor()\",\n \"m = mysql.connection.cursor() sql=\\\"DELETE FROM sensors WHERE id=? AND rom=%s\\\" m.execute(sql, data) m.connection.commit()\"\n]","isConversational":false}}},{"rowIdx":2178,"cells":{"ngram":{"kind":"list like","value":["python_on_rails.either import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either()","add_one(x): return x + 1 @as_either() def times_five(x): return x * 5 def","+ 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result =","from python_on_rails.either import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1","Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return","@as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return x *","Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return x","test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings():","def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result,","import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def","assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only concatenate str (not \\\"int\\\")","return x + 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings():","Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only","return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert","result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) ==","assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert","isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only concatenate str","== TypeError assert repr(result.value) == \"TypeError('can only concatenate str (not \\\"int\\\") to str')\"","def add_one(x): return x + 1 @as_either() def times_five(x): return x * 5","@as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert","= Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result =","as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x):","assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only concatenate","test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value)","result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value)","10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError","Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure)","x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value","type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only concatenate str (not \\\"int\\\") to","* 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value ==","Failure) assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can only concatenate str (not","isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result,","def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert","times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success)","def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def","result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result","== 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) ==","1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five)","Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five)","assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert","x + 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result","5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10","= Success(\"NaN\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \"TypeError('can"],"string":"[\n \"python_on_rails.either import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either()\",\n \"add_one(x): return x + 1 @as_either() def times_five(x): return x * 5 def\",\n \"+ 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result =\",\n \"from python_on_rails.either import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1\",\n \"Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return\",\n \"@as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return x *\",\n \"Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x): return x\",\n \"test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings():\",\n \"def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result,\",\n \"import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def\",\n \"assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only concatenate str (not \\\\\\\"int\\\\\\\")\",\n \"return x + 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings():\",\n \"Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only\",\n \"return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert\",\n \"result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) ==\",\n \"assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert\",\n \"isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only concatenate str\",\n \"== TypeError assert repr(result.value) == \\\"TypeError('can only concatenate str (not \\\\\\\"int\\\\\\\") to str')\\\"\",\n \"def add_one(x): return x + 1 @as_either() def times_five(x): return x * 5\",\n \"@as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert\",\n \"= Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result =\",\n \"as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either() def times_five(x):\",\n \"assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only concatenate\",\n \"test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value)\",\n \"result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value)\",\n \"10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError\",\n \"Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure)\",\n \"x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value\",\n \"type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only concatenate str (not \\\\\\\"int\\\\\\\") to\",\n \"* 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value ==\",\n \"Failure) assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can only concatenate str (not\",\n \"isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result,\",\n \"def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert\",\n \"times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success)\",\n \"def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def\",\n \"result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result\",\n \"== 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) ==\",\n \"1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five)\",\n \"Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five)\",\n \"assert result.value == 10 def test_a_failure_stops_the_execution_of_later_bindings(): result = Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert\",\n \"x + 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result\",\n \"5 def test_success_executes_bindings(): result = Success(1).bind(add_one).bind(times_five) assert isinstance(result, Success) assert result.value == 10\",\n \"= Success(\\\"NaN\\\").bind(add_one).bind(times_five) assert isinstance(result, Failure) assert type(result.value) == TypeError assert repr(result.value) == \\\"TypeError('can\"\n]","isConversational":false}}},{"rowIdx":2179,"cells":{"ngram":{"kind":"list like","value":["= models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model):","= models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date =","= models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName #","dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone =","models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone","django.db import models # Create your models here. # Station class Stations(models.Model): stationName","originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress","Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation","models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items","models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId","models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone","= models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self):","models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)","models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100)","models # Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100)","def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType =","= models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName #","date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName =","destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName #","Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100)","= models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100)","# Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100)","receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected =","class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code =","Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)","originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty","= models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName =","import models # Create your models here. # Station class Stations(models.Model): stationName =","Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone","= models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone =","here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId =","= models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100)","= models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty =","destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self):","models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return","models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)","= models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self):","models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName","models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName","models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100)","receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend=","stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def","def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount =","# Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100)","models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class","= models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return","class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation =","itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty","__str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100)","= models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty =","models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100)","stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName","itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName","self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId =","return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId","date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName =","self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity =","= models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model):","your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100)","models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100)","models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self): return","customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def","models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount","Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date","= models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date =","= models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress =","= models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc =","return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity","paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self): return self.customerPhone","return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans","def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone =","= models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100)","= models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self):","models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName","models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100)","paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def","class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc =","Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation =","# Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation","models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers","= models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments","= models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100)","dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model):","customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date","customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc","models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType","models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100)","models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return","customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName","__str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100)","customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId","Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code","__str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100)","Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100)","from django.db import models # Create your models here. # Station class Stations(models.Model):","self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans =","= models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100)","# Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100)","models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100)","Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100)","stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class","Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc","class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date =","models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected","stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class","= models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId =","models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def","# Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100)","customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date"],"string":"[\n \"= models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model):\",\n \"= models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date =\",\n \"= models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName #\",\n \"dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone =\",\n \"models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone\",\n \"django.db import models # Create your models here. # Station class Stations(models.Model): stationName\",\n \"originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress\",\n \"Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation\",\n \"models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items\",\n \"models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId\",\n \"models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone\",\n \"= models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self):\",\n \"models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)\",\n \"models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100)\",\n \"models # Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100)\",\n \"def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType =\",\n \"= models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName #\",\n \"date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName =\",\n \"destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName #\",\n \"Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100)\",\n \"= models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100)\",\n \"# Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100)\",\n \"receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected =\",\n \"class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code =\",\n \"Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)\",\n \"originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty\",\n \"= models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName =\",\n \"import models # Create your models here. # Station class Stations(models.Model): stationName =\",\n \"Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone\",\n \"= models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone =\",\n \"here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId =\",\n \"= models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100)\",\n \"= models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty =\",\n \"destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self):\",\n \"models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return\",\n \"models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True)\",\n \"= models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self):\",\n \"models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName\",\n \"models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName\",\n \"models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100)\",\n \"receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend=\",\n \"stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def\",\n \"def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount =\",\n \"# Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100)\",\n \"models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class\",\n \"= models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return\",\n \"class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation =\",\n \"itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty\",\n \"__str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100)\",\n \"= models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty =\",\n \"models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100)\",\n \"stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName\",\n \"itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName\",\n \"self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId =\",\n \"return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId\",\n \"date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName =\",\n \"self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity =\",\n \"= models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model):\",\n \"your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100)\",\n \"models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100)\",\n \"models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self): return\",\n \"customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def\",\n \"models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount\",\n \"Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date\",\n \"= models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date =\",\n \"= models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress =\",\n \"= models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc =\",\n \"return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity\",\n \"paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self): return self.customerPhone\",\n \"return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans\",\n \"def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone =\",\n \"= models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100)\",\n \"= models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self):\",\n \"models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName\",\n \"models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100)\",\n \"paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def\",\n \"class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc =\",\n \"Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation =\",\n \"# Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation\",\n \"models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers\",\n \"= models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments\",\n \"= models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100)\",\n \"dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model):\",\n \"customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date\",\n \"customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc\",\n \"models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType\",\n \"models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100)\",\n \"models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return\",\n \"customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName\",\n \"__str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100)\",\n \"customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId\",\n \"Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code\",\n \"__str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100)\",\n \"Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100)\",\n \"from django.db import models # Create your models here. # Station class Stations(models.Model):\",\n \"self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans =\",\n \"= models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100)\",\n \"# Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100)\",\n \"models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100)\",\n \"Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100)\",\n \"stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class\",\n \"Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc\",\n \"class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date =\",\n \"models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected\",\n \"stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class\",\n \"= models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId =\",\n \"models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def\",\n \"# Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100)\",\n \"customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date\"\n]","isConversational":false}}},{"rowIdx":2180,"cells":{"ngram":{"kind":"list like","value":["input files that have spaces in their names, since bazel # cannot cope","_default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags = _default_tags,","\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] + data +","test. tags_override: {str: str}, tags to add to specific tests. driver: str, label","driver = _default_driver, features = [], exec_properties = {}): \"\"\"Runs test files under","globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default","data, size, tags, driver, features, exec_properties): \"\"\"Runs lit on all tests it can","abort the tests. features: [str], list of extra features to enable. \"\"\" name_without_suffix","\"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] + data + [","= [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver =","list of extra features to enable. \"\"\" _run_lit_test(name + \".test\", [name], data, size,","properties to pass on. \"\"\" # Ignore some patterns by default for tests","test_file_exts: [str], extensions for files that are tests. default_size: str, the test size","Bazel's hermetic builds, lit only sees the tests that are included in the","might exist in the directory searched. Args: name: str, the name of the","should never match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE","load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by the test","size = _default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties","in the interim until the new build # rules are upstreamed. # TODO(b/136126535):","data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver","the tests. features: [str], list of extra features to enable. exec_properties: a dictionary","under lit. Args: name: str, the name of the test. data: [str], labels","+ data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude =","# Instantiate this test with updated parameters. lit_test( name = curr_test, data =","tests. features: [str], list of extra features to enable. \"\"\" _run_lit_test(name + \".test\",","lit. Args: name: str, the name of the test. data: [str], labels that","size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" +","Args: name: str, the name of the test. data: [str], labels that should","default_size = _default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags","= native.glob( [\"*.\" + ext for ext in test_file_exts], exclude = exclude, )","for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\",","Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions","runner in the interim until the new build # rules are upstreamed. #","name of the test, including extension. data: [str], the data input to the","[str], the data input to the test. size: str, the size of the","size_override = {}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override","a specific # failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate","features, exec_properties): \"\"\"Runs lit on all tests it can find in `data` under","[]), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver =","test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data","name = local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), )","native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name = name, srcs","to the test. per_test_extra_data: {str: [str]}, extra data to attach to a given","specific tests. data: [str], additional input data to the test. per_test_extra_data: {str: [str]},","the `data` parameter, regardless of what other tests might exist in the directory","including extension. data: [str], the data input to the test. size: str, the","[str], list of extra features to enable. exec_properties: a dictionary of properties to","test. driver: str, label of the driver shell script. Note: use of a","the size of the test. tags: [str], tags to attach to the test.","tags = default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties","list of extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name","in their names, since bazel # cannot cope with such \"targets\" in the","tags, driver, features, exec_properties): \"\"\"Runs lit on all tests it can find in","provided as data inputs. size: str, the size of the test. tags: [str],","extra features to enable. exec_properties: a dictionary of properties to pass on. \"\"\"","the new build # rules are upstreamed. # TODO(b/136126535): remove this custom rule.","= \"small\" _default_tags = [] # These are patterns which we should never","Lit, the LLVM test runner. # # This is reusing the LLVM Lit","_default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties = {}):","is not currently supported and specifying a default driver will abort the tests.","to the test. tags_override: {str: str}, tags to add to specific tests. driver:","def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs lit on all","ext in test_file_exts], exclude = exclude, ) # Run tests individually such that","specific tests. driver: str, label of the driver shell script. Note: use of","to a given file. default_tags: [str], additional tags to attach to the test.","the tests. features: [str], list of extra features to enable. \"\"\" name_without_suffix =","since bazel # cannot cope with such \"targets\" in the srcs list. \"**/*","_default_tags, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Runs test files","list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file, data, size, tags, driver,","= _default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties =","tests = native.glob( [\"*.\" + ext for ext in test_file_exts], exclude = exclude,","default_size: str, the test size for targets not in \"size_override\". size_override: {str: str},","= curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags","{}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features = [],","the test. tags: [str], tags to attach to the test. driver: str, label","build # rules are upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit runner","the interim until the new build # rules are upstreamed. # TODO(b/136126535): remove","runner. # # This is reusing the LLVM Lit test runner in the","exec_properties): \"\"\"Runs lit on all tests it can find in `data` under tensorflow/compiler/mlir.","remove this custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\")","= tests[i] # Instantiate this test with updated parameters. lit_test( name = curr_test,","(for tests and inputs). test_file_exts: [str], extensions for files that are tests. default_size:","Instantiate this test with updated parameters. lit_test( name = curr_test, data = data","features: [str], list of extra features to enable. \"\"\" _run_lit_test(name + \".test\", [name],","in test_file_exts], exclude = exclude, ) # Run tests individually such that errors","to a specific # failure. for i in range(len(tests)): curr_test = tests[i] #","size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver,",") # Run tests individually such that errors can be attributed to a","bazel # cannot cope with such \"targets\" in the srcs list. \"**/* *\",","\"\"\"Runs lit on all tests it can find in `data` under tensorflow/compiler/mlir. Note","per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features","exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" + ext for ext","upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\",","Default values used by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver","{str: [str]}, extra data to attach to a given file. default_tags: [str], additional","builds, lit only sees the tests that are included in the `data` parameter,","inputs). test_file_exts: [str], extensions for files that are tests. default_size: str, the test","of properties to pass on. \"\"\" # Ignore some patterns by default for","tests. default_size: str, the test size for targets not in \"size_override\". size_override: {str:","\"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\",","can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds,","\".test_files\" filegroup( name = local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\",","[\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def","are tests. default_size: str, the test size for targets not in \"size_override\". size_override:","will abort the tests. features: [str], list of extra features to enable. exec_properties:","str}, sizes to use for specific tests. data: [str], additional input data to","the test. data: [str], labels that should be provided as data inputs. size:","to the test. driver: str, label of the driver shell script. Note: use","= _default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data =","per_test_extra_data: {str: [str]}, extra data to attach to a given file. default_tags: [str],","= size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\"","what other tests might exist in the directory searched. Args: name: str, the","default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests =","\"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\")","test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name = local_test_files, srcs = native.glob([","= exclude, ) # Run tests individually such that errors can be attributed","curr_test = tests[i] # Instantiate this test with updated parameters. lit_test( name =","\"targets\" in the srcs list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file,","add to specific tests. driver: str, label of the driver shell script. Note:","def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override =","tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\",","such that errors can be attributed to a specific # failure. for i","included in the `data` parameter, regardless of what other tests might exist in","[] # These are patterns which we should never match, for tests, subdirectories,","= default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties =","files under lit. Args: name: str, the name of the test. data: [str],","such \"targets\" in the srcs list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name,","for specific tests. data: [str], additional input data to the test. per_test_extra_data: {str:","= {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features =","input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files","]), ) tf_cc_test( name = name, srcs = test_file, size = size, deps","directory. Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str],","parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size =","Lit tests (and their inputs) under this directory. Args: exclude: [str], paths to","to pass on. \"\"\" # Ignore some patterns by default for tests and","tests might exist in the directory searched. Args: name: str, the name of","to use for specific tests. data: [str], additional input data to the test.","[], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver,","input to the test. size: str, the size of the test. tags: [str],","{}): \"\"\"Creates all plausible Lit tests (and their inputs) under this directory. Args:",") tf_cc_test( name = name, srcs = test_file, size = size, deps =","rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\",","it can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic","test size for targets not in \"size_override\". size_override: {str: str}, sizes to use","\"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by the","of a custom driver is not currently supported and specifying a default driver","[\"*.\" + ext for ext in test_file_exts], exclude = exclude, ) # Run","\"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name = local_test_files,","[], exec_properties = {}): \"\"\"Runs test files under lit. Args: name: str, the","to enable. \"\"\" _run_lit_test(name + \".test\", [name], data, size, tags, driver, features, exec_properties)","custom driver is not currently supported and specifying a default driver will abort","tags to add to specific tests. driver: str, label of the driver shell","with such \"targets\" in the srcs list. \"**/* *\", \"**/* */**\", ] def","# These are patterns which we should never match, for tests, subdirectories, or","the directory searched. Args: name: str, the name of the test, including extension.","= features, exec_properties = exec_properties, ) def lit_test( name, data = [], size","`data` parameter, regardless of what other tests might exist in the directory searched.","the tests. features: [str], list of extra features to enable. \"\"\" _run_lit_test(name +","# TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\")","default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features = features,","exec_properties = exec_properties, ) def lit_test( name, data = [], size = _default_size,","driver shell script. Note: use of a custom driver is not currently supported","data input to the test. size: str, the size of the test. tags:","These are patterns which we should never match, for tests, subdirectories, or #","[str], list of extra features to enable. \"\"\" _run_lit_test(name + \".test\", [name], data,","until the new build # rules are upstreamed. # TODO(b/136126535): remove this custom","plausible Lit tests (and their inputs) under this directory. Args: exclude: [str], paths","test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\"","inputs) under this directory. Args: exclude: [str], paths to exclude (for tests and","the test. per_test_extra_data: {str: [str]}, extra data to attach to a given file.","\"**/lit.local.cfg\", # Exclude input files that have spaces in their names, since bazel","+ tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties, )","# cannot cope with such \"targets\" in the srcs list. \"**/* *\", \"**/*","exclude (for tests and inputs). test_file_exts: [str], extensions for files that are tests.","exec_properties = {}): \"\"\"Creates all plausible Lit tests (and their inputs) under this","glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {},","data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that","\"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have spaces in their names,","lit only sees the tests that are included in the `data` parameter, regardless","for i in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated","= _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" + ext for ext in","tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees the tests","[], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data = [],","with updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []),","exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data","[str], labels that should be provided as data inputs. size: str, the size","interim until the new build # rules are upstreamed. # TODO(b/136126535): remove this","curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags =","cope with such \"targets\" in the srcs list. \"**/* *\", \"**/* */**\", ]","# Test definitions for Lit, the LLVM test runner. # # This is","this custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\",","test with updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test,","test, including extension. data: [str], the data input to the test. size: str,","= \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] # These are patterns which","str}, tags to add to specific tests. driver: str, label of the driver","to specific tests. driver: str, label of the driver shell script. Note: use","files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have","extension. data: [str], the data input to the test. size: str, the size","[ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] + data","tests. features: [str], list of extra features to enable. exec_properties: a dictionary of","updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size","input data to the test. per_test_extra_data: {str: [str]}, extra data to attach to","not currently supported and specifying a default driver will abort the tests. features:","is reusing the LLVM Lit test runner in the interim until the new","str, the name of the test. data: [str], labels that should be provided","additional tags to attach to the test. tags_override: {str: str}, tags to add","exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions for","{str: str}, sizes to use for specific tests. data: [str], additional input data","`data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees","exec_properties = {}): \"\"\"Runs test files under lit. Args: name: str, the name","to the test. size: str, the size of the test. tags: [str], tags","in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only","patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude","LLVM test runner. # # This is reusing the LLVM Lit test runner","features, exec_properties = exec_properties, ) def lit_test( name, data = [], size =","on. \"\"\" # Ignore some patterns by default for tests and input data.","deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files]","features to enable. exec_properties: a dictionary of properties to pass on. \"\"\" #","which we should never match, for tests, subdirectories, or # test input data","test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input","= data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags +","to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name","+ ext for ext in test_file_exts], exclude = exclude, ) # Run tests","= {}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override =","the name of the test, including extension. data: [str], the data input to","Run tests individually such that errors can be attributed to a specific #","= _default_driver, features = [], exec_properties = {}): \"\"\"Creates all plausible Lit tests","str, the name of the test, including extension. data: [str], the data input","individually such that errors can be attributed to a specific # failure. for","_default_driver, features = [], exec_properties = {}): \"\"\"Runs test files under lit. Args:","[str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions for files","tags to attach to the test. tags_override: {str: str}, tags to add to","= _default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags =","hermetic builds, lit only sees the tests that are included in the `data`","per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver","the tests that are included in the `data` parameter, regardless of what other","= [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] +","local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude","# Exclude input files that have spaces in their names, since bazel #","= [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = []","test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs lit on all tests it","as data inputs. size: str, the size of the test. tags: [str], tags","to attach to the test. tags_override: {str: str}, tags to add to specific","[ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have spaces in their","= [], exec_properties = {}): \"\"\"Runs test files under lit. Args: name: str,","name: str, the name of the test, including extension. data: [str], the data","data = [], size = _default_size, tags = _default_tags, driver = _default_driver, features","the name of the test. data: [str], labels that should be provided as","*\", \"**/* */**\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties):","given file. default_tags: [str], additional tags to attach to the test. tags_override: {str:","features = [], exec_properties = {}): \"\"\"Runs test files under lit. Args: name:","= [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have spaces in","custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\",","+ \".test_files\" filegroup( name = local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix +","test_file_exts], exclude = exclude, ) # Run tests individually such that errors can","size of the test. tags: [str], tags to attach to the test. driver:","driver: str, label of the driver shell script. Note: use of a custom","to add to specific tests. driver: str, label of the driver shell script.","+ local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests(","\"//tensorflow/core:testlib\", ], data = [\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\",","= test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ],","abort the tests. features: [str], list of extra features to enable. exec_properties: a","\"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts,","this directory. Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts:","that should be provided as data inputs. size: str, the size of the","# Default values used by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"]","_default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags =","and specifying a default driver will abort the tests. features: [str], list of","[str]}, extra data to attach to a given file. default_tags: [str], additional tags","the test, including extension. data: [str], the data input to the test. size:","parameter, regardless of what other tests might exist in the directory searched. Args:","lit on all tests it can find in `data` under tensorflow/compiler/mlir. Note that,","runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") #","[str], additional input data to the test. per_test_extra_data: {str: [str]}, extra data to","# test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude","\"\"\" # Ignore some patterns by default for tests and input data. exclude","to attach to a given file. default_tags: [str], additional tags to attach to","driver, features = features, exec_properties = exec_properties, ) def lit_test( name, data =","= {}): \"\"\"Creates all plausible Lit tests (and their inputs) under this directory.","= [], size = _default_size, tags = _default_tags, driver = _default_driver, features =","of the test, including extension. data: [str], the data input to the test.","test. tags: [str], tags to attach to the test. driver: str, label of","a given file. default_tags: [str], additional tags to attach to the test. tags_override:","name + \".test_files\" filegroup( name = local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix","the test. size: str, the size of the test. tags: [str], tags to","= [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data =","\"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size","some patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE +","= exec_properties, ) def lit_test( name, data = [], size = _default_size, tags","= local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test(","list of extra features to enable. exec_properties: a dictionary of properties to pass","only sees the tests that are included in the `data` parameter, regardless of","] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs lit on","str, the size of the test. tags: [str], tags to attach to the","for files that are tests. default_size: str, the test size for targets not","the data input to the test. size: str, the size of the test.","], data = [\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\",","tests. features: [str], list of extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0]","to enable. exec_properties: a dictionary of properties to pass on. \"\"\" # Ignore","\"tf_native_cc_binary\", \"tf_copts\") # Default values used by the test runner. _default_test_file_exts = [\"mlir\",","], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size,","test runner. # # This is reusing the LLVM Lit test runner in","# Run tests individually such that errors can be attributed to a specific","# failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this test","attach to a given file. default_tags: [str], additional tags to attach to the","data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test,","default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties,","driver, features, exec_properties): \"\"\"Runs lit on all tests it can find in `data`","the LLVM Lit test runner in the interim until the new build #","never match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE =","+ \"*.mlir\", ]), ) tf_cc_test( name = name, srcs = test_file, size =","load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by","features = [], exec_properties = {}): \"\"\"Creates all plausible Lit tests (and their","# # This is reusing the LLVM Lit test runner in the interim","paths to exclude (for tests and inputs). test_file_exts: [str], extensions for files that","be provided as data inputs. size: str, the size of the test. tags:","or # test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", #","_ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have spaces","in \"size_override\". size_override: {str: str}, sizes to use for specific tests. data: [str],","abort the tests. features: [str], list of extra features to enable. \"\"\" _run_lit_test(name","on all tests it can find in `data` under tensorflow/compiler/mlir. Note that, due","= [] # These are patterns which we should never match, for tests,","ext for ext in test_file_exts], exclude = exclude, ) # Run tests individually","extra data to attach to a given file. default_tags: [str], additional tags to","the driver shell script. Note: use of a custom driver is not currently","native.glob( [\"*.\" + ext for ext in test_file_exts], exclude = exclude, ) #","reusing the LLVM Lit test runner in the interim until the new build","\"small\" _default_tags = [] # These are patterns which we should never match,","new build # rules are upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit","\"\"\"Runs test files under lit. Args: name: str, the name of the test.","the LLVM test runner. # # This is reusing the LLVM Lit test","[], size = _default_size, tags = _default_tags, driver = _default_driver, features = [],","tests (and their inputs) under this directory. Args: exclude: [str], paths to exclude","test. data: [str], labels that should be provided as data inputs. size: str,","data: [str], additional input data to the test. per_test_extra_data: {str: [str]}, extra data","test runner in the interim until the new build # rules are upstreamed.","under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees the","searched. Args: name: str, the name of the test, including extension. data: [str],","local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name","= _default_tags, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Runs test","data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [],","tags_override: {str: str}, tags to add to specific tests. driver: str, label of","patterns which we should never match, for tests, subdirectories, or # test input","\"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by the test runner. _default_test_file_exts =","data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags","targets not in \"size_override\". size_override: {str: str}, sizes to use for specific tests.","of what other tests might exist in the directory searched. Args: name: str,","tf_cc_test( name = name, srcs = test_file, size = size, deps = [","\"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size =","\"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] # These are patterns which we","exec_properties, ) def lit_test( name, data = [], size = _default_size, tags =","_default_size = \"small\" _default_tags = [] # These are patterns which we should","[\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] #","= test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name = local_test_files, srcs =","to Bazel's hermetic builds, lit only sees the tests that are included in","tests. data: [str], additional input data to the test. per_test_extra_data: {str: [str]}, extra","= [], exec_properties = {}): \"\"\"Creates all plausible Lit tests (and their inputs)","name = name, srcs = test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\",","test. per_test_extra_data: {str: [str]}, extra data to attach to a given file. default_tags:","tests. driver: str, label of the driver shell script. Note: use of a","match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [","[], exec_properties = {}): \"\"\"Creates all plausible Lit tests (and their inputs) under","other tests might exist in the directory searched. Args: name: str, the name","= native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name = name,","[str], tags to attach to the test. driver: str, label of the driver","name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name = local_test_files, srcs","currently supported and specifying a default driver will abort the tests. features: [str],","of the test. tags: [str], tags to attach to the test. driver: str,","sizes to use for specific tests. data: [str], additional input data to the","the test. driver: str, label of the driver shell script. Note: use of","default_tags: [str], additional tags to attach to the test. tags_override: {str: str}, tags","_ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" + ext for ext in test_file_exts],","\"size_override\". size_override: {str: str}, sizes to use for specific tests. data: [str], additional","in the srcs list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file, data,","{}, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Creates all plausible","i in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters.","exclude, ) # Run tests individually such that errors can be attributed to","TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\",","size_override: {str: str}, sizes to use for specific tests. data: [str], additional input","tests and inputs). test_file_exts: [str], extensions for files that are tests. default_size: str,","tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties, ) def","of the driver shell script. Note: use of a custom driver is not","Ignore some patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE","and inputs). test_file_exts: [str], extensions for files that are tests. default_size: str, the","# rules are upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing","exclude tests = native.glob( [\"*.\" + ext for ext in test_file_exts], exclude =","of extra features to enable. exec_properties: a dictionary of properties to pass on.","size: str, the size of the test. tags: [str], tags to attach to","filegroup( name = local_test_files, srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]),","data inputs. size: str, the size of the test. tags: [str], tags to","attach to the test. driver: str, label of the driver shell script. Note:","rules are upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing test","tags_override = {}, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Creates","the test. tags_override: {str: str}, tags to add to specific tests. driver: str,","test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data","driver will abort the tests. features: [str], list of extra features to enable.",") def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override","this test with updated parameters. lit_test( name = curr_test, data = data +","for Lit, the LLVM test runner. # # This is reusing the LLVM","shell script. Note: use of a custom driver is not currently supported and","name, srcs = test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\",","enable. exec_properties: a dictionary of properties to pass on. \"\"\" # Ignore some","extra features to enable. \"\"\" _run_lit_test(name + \".test\", [name], data, size, tags, driver,","regardless of what other tests might exist in the directory searched. Args: name:","additional input data to the test. per_test_extra_data: {str: [str]}, extra data to attach","*/**\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs lit","\"\"\"Creates all plausible Lit tests (and their inputs) under this directory. Args: exclude:","tags to attach to the test. driver: str, label of the driver shell","specifying a default driver will abort the tests. features: [str], list of extra","that are tests. default_size: str, the test size for targets not in \"size_override\".","def lit_test( name, data = [], size = _default_size, tags = _default_tags, driver","data: [str], labels that should be provided as data inputs. size: str, the","\".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] # These are","a dictionary of properties to pass on. \"\"\" # Ignore some patterns by","to attach to the test. driver: str, label of the driver shell script.","name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size),","= _default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties =","use for specific tests. data: [str], additional input data to the test. per_test_extra_data:","cannot cope with such \"targets\" in the srcs list. \"**/* *\", \"**/* */**\",","# This is reusing the LLVM Lit test runner in the interim until","range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters. lit_test( name","data = [\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ],","features: [str], list of extra features to enable. exec_properties: a dictionary of properties","of extra features to enable. \"\"\" _run_lit_test(name + \".test\", [name], data, size, tags,","for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob(","exist in the directory searched. Args: name: str, the name of the test,","to exclude (for tests and inputs). test_file_exts: [str], extensions for files that are","size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features =","= {}, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Creates all","[str], additional tags to attach to the test. tags_override: {str: str}, tags to","test files under lit. Args: name: str, the name of the test. data:","tests individually such that errors can be attributed to a specific # failure.","be attributed to a specific # failure. for i in range(len(tests)): curr_test =","srcs = test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\",","Note that, due to Bazel's hermetic builds, lit only sees the tests that","Note: use of a custom driver is not currently supported and specifying a","= driver, features = features, exec_properties = exec_properties, ) def lit_test( name, data","_default_driver, features = [], exec_properties = {}): \"\"\"Creates all plausible Lit tests (and","{str: str}, tags to add to specific tests. driver: str, label of the","dictionary of properties to pass on. \"\"\" # Ignore some patterns by default","values used by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver =","are included in the `data` parameter, regardless of what other tests might exist","data: [str], the data input to the test. size: str, the size of","[str], extensions for files that are tests. default_size: str, the test size for","= name, srcs = test_file, size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\",","that errors can be attributed to a specific # failure. for i in","of the test. data: [str], labels that should be provided as data inputs.","in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters. lit_test(","enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup( name =","default driver will abort the tests. features: [str], list of extra features to","\"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by the test runner.","subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \"**/LICENSE.txt\", \"**/README.txt\", \"**/lit.local.cfg\",","file. default_tags: [str], additional tags to attach to the test. tags_override: {str: str},","= size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features","definitions for Lit, the LLVM test runner. # # This is reusing the","in the directory searched. Args: name: str, the name of the test, including","+ per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []),","files that have spaces in their names, since bazel # cannot cope with","exec_properties: a dictionary of properties to pass on. \"\"\" # Ignore some patterns","use of a custom driver is not currently supported and specifying a default","files that are tests. default_size: str, the test size for targets not in","name: str, the name of the test. data: [str], labels that should be","extensions for files that are tests. default_size: str, the test size for targets","that, due to Bazel's hermetic builds, lit only sees the tests that are","inputs. size: str, the size of the test. tags: [str], tags to attach","driver = driver, features = features, exec_properties = exec_properties, ) def lit_test( name,","the srcs list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file, data, size,","that have spaces in their names, since bazel # cannot cope with such","features to enable. \"\"\" _run_lit_test(name + \".test\", [name], data, size, tags, driver, features,","test. size: str, the size of the test. tags: [str], tags to attach","data to attach to a given file. default_tags: [str], additional tags to attach","are upstreamed. # TODO(b/136126535): remove this custom rule. \"\"\"Lit runner globbing test \"\"\"","_default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties = {}):","the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size =","name, data = [], size = _default_size, tags = _default_tags, driver = _default_driver,","all plausible Lit tests (and their inputs) under this directory. Args: exclude: [str],","\"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used","errors can be attributed to a specific # failure. for i in range(len(tests)):",") def lit_test( name, data = [], size = _default_size, tags = _default_tags,","Test definitions for Lit, the LLVM test runner. # # This is reusing","load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values used by the test runner. _default_test_file_exts","data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" + ext for","(and their inputs) under this directory. Args: exclude: [str], paths to exclude (for","name of the test. data: [str], labels that should be provided as data","[]), driver = driver, features = features, exec_properties = exec_properties, ) def lit_test(","sees the tests that are included in the `data` parameter, regardless of what","names, since bazel # cannot cope with such \"targets\" in the srcs list.","+ exclude tests = native.glob( [\"*.\" + ext for ext in test_file_exts], exclude","have spaces in their names, since bazel # cannot cope with such \"targets\"","will abort the tests. features: [str], list of extra features to enable. \"\"\"","local_test_files = name + \".test_files\" filegroup( name = local_test_files, srcs = native.glob([ \"data/\"","attributed to a specific # failure. for i in range(len(tests)): curr_test = tests[i]","driver = _default_driver, features = [], exec_properties = {}): \"\"\"Creates all plausible Lit","for targets not in \"size_override\". size_override: {str: str}, sizes to use for specific","= [\":\" + local_test_files] + data + [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], )","of extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name +","exclude = exclude, ) # Run tests individually such that errors can be","due to Bazel's hermetic builds, lit only sees the tests that are included","[ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [], test_file_exts =","input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" + ext","\".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] # These","test \"\"\" load(\"//tensorflow:tensorflow.bzl\", \"filegroup\") load(\"@bazel_skylib//lib:paths.bzl\", \"paths\") load(\"//tensorflow:tensorflow.bzl\", \"tf_cc_test\", \"tf_native_cc_binary\", \"tf_copts\") # Default values","and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\" +","a default driver will abort the tests. features: [str], list of extra features","\"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features,","name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name = name, srcs = test_file, size","str, label of the driver shell script. Note: use of a custom driver","features = features, exec_properties = exec_properties, ) def lit_test( name, data = [],","should be provided as data inputs. size: str, the size of the test.","specific # failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this","spaces in their names, since bazel # cannot cope with such \"targets\" in","_run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs lit on all tests","their names, since bazel # cannot cope with such \"targets\" in the srcs","LLVM Lit test runner in the interim until the new build # rules","supported and specifying a default driver will abort the tests. features: [str], list","attach to the test. tags_override: {str: str}, tags to add to specific tests.","{}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {},","we should never match, for tests, subdirectories, or # test input data files.","find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit","can be attributed to a specific # failure. for i in range(len(tests)): curr_test","under this directory. Args: exclude: [str], paths to exclude (for tests and inputs).","tests that are included in the `data` parameter, regardless of what other tests","Args: name: str, the name of the test, including extension. data: [str], the","pass on. \"\"\" # Ignore some patterns by default for tests and input","label of the driver shell script. Note: use of a custom driver is","data to the test. per_test_extra_data: {str: [str]}, extra data to attach to a","size for targets not in \"size_override\". size_override: {str: str}, sizes to use for","# Ignore some patterns by default for tests and input data. exclude =","the test size for targets not in \"size_override\". size_override: {str: str}, sizes to","directory searched. Args: name: str, the name of the test, including extension. data:","\"**/* */**\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \"\"\"Runs","\"*.mlir\", ]), ) tf_cc_test( name = name, srcs = test_file, size = size,","size = size, deps = [ \"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensorflow/core:testlib\", ], data =","srcs = native.glob([ \"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name =","default_tags = _default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties","in the `data` parameter, regardless of what other tests might exist in the","tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\"*.\"","labels that should be provided as data inputs. size: str, the size of","tags: [str], tags to attach to the test. driver: str, label of the","Lit test runner in the interim until the new build # rules are","_default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags = [] # These are patterns","by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size","features: [str], list of extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files","driver is not currently supported and specifying a default driver will abort the","tests it can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's","used by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\"","all tests it can find in `data` under tensorflow/compiler/mlir. Note that, due to","a custom driver is not currently supported and specifying a default driver will","tests[i] # Instantiate this test with updated parameters. lit_test( name = curr_test, data","\"tf_copts\") # Default values used by the test runner. _default_test_file_exts = [\"mlir\", \".pbtxt\",","are patterns which we should never match, for tests, subdirectories, or # test","runner. _default_test_file_exts = [\"mlir\", \".pbtxt\", \".td\"] _default_driver = \"@llvm-project//mlir:run_lit.sh\" _default_size = \"small\" _default_tags","\"data/\" + name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name = name, srcs =","lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test,","This is reusing the LLVM Lit test runner in the interim until the","by default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests","str, the test size for targets not in \"size_override\". size_override: {str: str}, sizes","their inputs) under this directory. Args: exclude: [str], paths to exclude (for tests","= _default_driver, features = [], exec_properties = {}): \"\"\"Runs test files under lit.","not in \"size_override\". size_override: {str: str}, sizes to use for specific tests. data:","[str], list of extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files =","script. Note: use of a custom driver is not currently supported and specifying","Exclude input files that have spaces in their names, since bazel # cannot","+ name_without_suffix + \"*.mlir\", ]), ) tf_cc_test( name = name, srcs = test_file,","srcs list. \"**/* *\", \"**/* */**\", ] def _run_lit_test(name, test_file, data, size, tags,","tags = _default_tags, driver = _default_driver, features = [], exec_properties = {}): \"\"\"Runs","failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this test with","extra features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\"","that are included in the `data` parameter, regardless of what other tests might","\"**/README.txt\", \"**/lit.local.cfg\", # Exclude input files that have spaces in their names, since","+ [ \"//tensorflow/compiler/mlir/disc:disc_compiler_main\", \"//tensorflow/compiler/mlir:tf-mlir-translate\", \"//tensorflow/compiler/mlir:tf-opt\", ], ) def glob_op_tests( exclude = [], test_file_exts","lit_test( name, data = [], size = _default_size, tags = _default_tags, driver =","= name + \".test_files\" filegroup( name = local_test_files, srcs = native.glob([ \"data/\" +","= {}): \"\"\"Runs test files under lit. Args: name: str, the name of","_default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data = {},","size, tags, driver, features, exec_properties): \"\"\"Runs lit on all tests it can find","features to enable. \"\"\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \".test_files\" filegroup(","for ext in test_file_exts], exclude = exclude, ) # Run tests individually such","{}): \"\"\"Runs test files under lit. Args: name: str, the name of the","_default_tags = [] # These are patterns which we should never match, for"],"string":"[\n \"input files that have spaces in their names, since bazel # cannot cope\",\n \"_default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags = _default_tags,\",\n \"\\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] + data +\",\n \"test. tags_override: {str: str}, tags to add to specific tests. driver: str, label\",\n \"driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs test files under\",\n \"globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default\",\n \"data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it can\",\n \"abort the tests. features: [str], list of extra features to enable. \\\"\\\"\\\" name_without_suffix\",\n \"\\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] + data + [\",\n \"= [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver =\",\n \"list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data, size,\",\n \"properties to pass on. \\\"\\\"\\\" # Ignore some patterns by default for tests\",\n \"test_file_exts: [str], extensions for files that are tests. default_size: str, the test size\",\n \"Bazel's hermetic builds, lit only sees the tests that are included in the\",\n \"might exist in the directory searched. Args: name: str, the name of the\",\n \"should never match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE\",\n \"load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the test\",\n \"size = _default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties\",\n \"in the interim until the new build # rules are upstreamed. # TODO(b/136126535):\",\n \"data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver\",\n \"the tests. features: [str], list of extra features to enable. exec_properties: a dictionary\",\n \"under lit. Args: name: str, the name of the test. data: [str], labels\",\n \"+ data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude =\",\n \"# Instantiate this test with updated parameters. lit_test( name = curr_test, data =\",\n \"tests. features: [str], list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\",\",\n \"lit. Args: name: str, the name of the test. data: [str], labels that\",\n \"size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" +\",\n \"Args: name: str, the name of the test. data: [str], labels that should\",\n \"default_size = _default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags\",\n \"= native.glob( [\\\"*.\\\" + ext for ext in test_file_exts], exclude = exclude, )\",\n \"for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\",\",\n \"Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions\",\n \"runner in the interim until the new build # rules are upstreamed. #\",\n \"name of the test, including extension. data: [str], the data input to the\",\n \"[str], the data input to the test. size: str, the size of the\",\n \"size_override = {}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override\",\n \"a specific # failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate\",\n \"features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it can find in `data` under\",\n \"[]), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver =\",\n \"test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data\",\n \"name = local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), )\",\n \"native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs\",\n \"to the test. per_test_extra_data: {str: [str]}, extra data to attach to a given\",\n \"specific tests. data: [str], additional input data to the test. per_test_extra_data: {str: [str]},\",\n \"the `data` parameter, regardless of what other tests might exist in the directory\",\n \"including extension. data: [str], the data input to the test. size: str, the\",\n \"[str], list of extra features to enable. exec_properties: a dictionary of properties to\",\n \"test. driver: str, label of the driver shell script. Note: use of a\",\n \"the size of the test. tags: [str], tags to attach to the test.\",\n \"tags = default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties\",\n \"list of extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name\",\n \"in their names, since bazel # cannot cope with such \\\"targets\\\" in the\",\n \"tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it can find in\",\n \"provided as data inputs. size: str, the size of the test. tags: [str],\",\n \"extra features to enable. exec_properties: a dictionary of properties to pass on. \\\"\\\"\\\"\",\n \"the new build # rules are upstreamed. # TODO(b/136126535): remove this custom rule.\",\n \"= \\\"small\\\" _default_tags = [] # These are patterns which we should never\",\n \"Lit, the LLVM test runner. # # This is reusing the LLVM Lit\",\n \"_default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties = {}):\",\n \"is not currently supported and specifying a default driver will abort the tests.\",\n \"to the test. tags_override: {str: str}, tags to add to specific tests. driver:\",\n \"def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all\",\n \"ext in test_file_exts], exclude = exclude, ) # Run tests individually such that\",\n \"specific tests. driver: str, label of the driver shell script. Note: use of\",\n \"to a given file. default_tags: [str], additional tags to attach to the test.\",\n \"the tests. features: [str], list of extra features to enable. \\\"\\\"\\\" name_without_suffix =\",\n \"since bazel # cannot cope with such \\\"targets\\\" in the srcs list. \\\"**/*\",\n \"_default_tags, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs test files\",\n \"list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size, tags, driver,\",\n \"= _default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties =\",\n \"tests = native.glob( [\\\"*.\\\" + ext for ext in test_file_exts], exclude = exclude,\",\n \"default_size: str, the test size for targets not in \\\"size_override\\\". size_override: {str: str},\",\n \"= curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags\",\n \"{}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features = [],\",\n \"the test. tags: [str], tags to attach to the test. driver: str, label\",\n \"build # rules are upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner\",\n \"the interim until the new build # rules are upstreamed. # TODO(b/136126535): remove\",\n \"runner. # # This is reusing the LLVM Lit test runner in the\",\n \"exec_properties): \\\"\\\"\\\"Runs lit on all tests it can find in `data` under tensorflow/compiler/mlir.\",\n \"remove this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\")\",\n \"= tests[i] # Instantiate this test with updated parameters. lit_test( name = curr_test,\",\n \"(for tests and inputs). test_file_exts: [str], extensions for files that are tests. default_size:\",\n \"Instantiate this test with updated parameters. lit_test( name = curr_test, data = data\",\n \"features: [str], list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name],\",\n \"in test_file_exts], exclude = exclude, ) # Run tests individually such that errors\",\n \"to a specific # failure. for i in range(len(tests)): curr_test = tests[i] #\",\n \"size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver,\",\n \") # Run tests individually such that errors can be attributed to a\",\n \"bazel # cannot cope with such \\\"targets\\\" in the srcs list. \\\"**/* *\\\",\",\n \"\\\"\\\"\\\"Runs lit on all tests it can find in `data` under tensorflow/compiler/mlir. Note\",\n \"per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features\",\n \"exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" + ext for ext\",\n \"upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\",\",\n \"Default values used by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver\",\n \"{str: [str]}, extra data to attach to a given file. default_tags: [str], additional\",\n \"builds, lit only sees the tests that are included in the `data` parameter,\",\n \"inputs). test_file_exts: [str], extensions for files that are tests. default_size: str, the test\",\n \"of properties to pass on. \\\"\\\"\\\" # Ignore some patterns by default for\",\n \"tests. default_size: str, the test size for targets not in \\\"size_override\\\". size_override: {str:\",\n \"\\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\",\",\n \"can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds,\",\n \"\\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\",\",\n \"[\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def\",\n \"are tests. default_size: str, the test size for targets not in \\\"size_override\\\". size_override:\",\n \"will abort the tests. features: [str], list of extra features to enable. exec_properties:\",\n \"str}, sizes to use for specific tests. data: [str], additional input data to\",\n \"the test. data: [str], labels that should be provided as data inputs. size:\",\n \"to the test. driver: str, label of the driver shell script. Note: use\",\n \"= _default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data =\",\n \"per_test_extra_data: {str: [str]}, extra data to attach to a given file. default_tags: [str],\",\n \"= size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\"\",\n \"what other tests might exist in the directory searched. Args: name: str, the\",\n \"default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests =\",\n \"\\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\")\",\n \"test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([\",\n \"= exclude, ) # Run tests individually such that errors can be attributed\",\n \"curr_test = tests[i] # Instantiate this test with updated parameters. lit_test( name =\",\n \"\\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file,\",\n \"add to specific tests. driver: str, label of the driver shell script. Note:\",\n \"def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override =\",\n \"tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\",\",\n \"such that errors can be attributed to a specific # failure. for i\",\n \"included in the `data` parameter, regardless of what other tests might exist in\",\n \"[] # These are patterns which we should never match, for tests, subdirectories,\",\n \"= default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties =\",\n \"files under lit. Args: name: str, the name of the test. data: [str],\",\n \"such \\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name,\",\n \"for specific tests. data: [str], additional input data to the test. per_test_extra_data: {str:\",\n \"= {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver, features =\",\n \"input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files\",\n \"]), ) tf_cc_test( name = name, srcs = test_file, size = size, deps\",\n \"directory. Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str],\",\n \"parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size =\",\n \"Lit tests (and their inputs) under this directory. Args: exclude: [str], paths to\",\n \"to pass on. \\\"\\\"\\\" # Ignore some patterns by default for tests and\",\n \"tests might exist in the directory searched. Args: name: str, the name of\",\n \"to use for specific tests. data: [str], additional input data to the test.\",\n \"[], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {}, driver = _default_driver,\",\n \"input to the test. size: str, the size of the test. tags: [str],\",\n \"{}): \\\"\\\"\\\"Creates all plausible Lit tests (and their inputs) under this directory. Args:\",\n \") tf_cc_test( name = name, srcs = test_file, size = size, deps =\",\n \"rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\",\",\n \"it can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic\",\n \"test size for targets not in \\\"size_override\\\". size_override: {str: str}, sizes to use\",\n \"\\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the\",\n \"of a custom driver is not currently supported and specifying a default driver\",\n \"[\\\"*.\\\" + ext for ext in test_file_exts], exclude = exclude, ) # Run\",\n \"\\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name = local_test_files,\",\n \"[], exec_properties = {}): \\\"\\\"\\\"Runs test files under lit. Args: name: str, the\",\n \"to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data, size, tags, driver, features, exec_properties)\",\n \"custom driver is not currently supported and specifying a default driver will abort\",\n \"tags to add to specific tests. driver: str, label of the driver shell\",\n \"with such \\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def\",\n \"# These are patterns which we should never match, for tests, subdirectories, or\",\n \"the directory searched. Args: name: str, the name of the test, including extension.\",\n \"= features, exec_properties = exec_properties, ) def lit_test( name, data = [], size\",\n \"`data` parameter, regardless of what other tests might exist in the directory searched.\",\n \"the tests. features: [str], list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name +\",\n \"# TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\")\",\n \"default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features = features,\",\n \"exec_properties = exec_properties, ) def lit_test( name, data = [], size = _default_size,\",\n \"driver shell script. Note: use of a custom driver is not currently supported\",\n \"data input to the test. size: str, the size of the test. tags:\",\n \"These are patterns which we should never match, for tests, subdirectories, or #\",\n \"[str], list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data,\",\n \"until the new build # rules are upstreamed. # TODO(b/136126535): remove this custom\",\n \"plausible Lit tests (and their inputs) under this directory. Args: exclude: [str], paths\",\n \"test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\"\",\n \"inputs) under this directory. Args: exclude: [str], paths to exclude (for tests and\",\n \"the test. per_test_extra_data: {str: [str]}, extra data to attach to a given file.\",\n \"\\\"**/lit.local.cfg\\\", # Exclude input files that have spaces in their names, since bazel\",\n \"+ tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties, )\",\n \"# cannot cope with such \\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/*\",\n \"exclude (for tests and inputs). test_file_exts: [str], extensions for files that are tests.\",\n \"exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests (and their inputs) under this\",\n \"glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {},\",\n \"data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that\",\n \"\\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have spaces in their names,\",\n \"lit only sees the tests that are included in the `data` parameter, regardless\",\n \"for i in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated\",\n \"= _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" + ext for ext in\",\n \"tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees the tests\",\n \"[], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data = [],\",\n \"with updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []),\",\n \"exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data\",\n \"[str], labels that should be provided as data inputs. size: str, the size\",\n \"interim until the new build # rules are upstreamed. # TODO(b/136126535): remove this\",\n \"curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags =\",\n \"cope with such \\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ]\",\n \"# Test definitions for Lit, the LLVM test runner. # # This is\",\n \"this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\",\",\n \"test with updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test,\",\n \"test, including extension. data: [str], the data input to the test. size: str,\",\n \"= \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] # These are patterns which\",\n \"str}, tags to add to specific tests. driver: str, label of the driver\",\n \"to specific tests. driver: str, label of the driver shell script. Note: use\",\n \"files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have\",\n \"extension. data: [str], the data input to the test. size: str, the size\",\n \"[ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] + data\",\n \"tests. features: [str], list of extra features to enable. exec_properties: a dictionary of\",\n \"updated parameters. lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size\",\n \"input data to the test. per_test_extra_data: {str: [str]}, extra data to attach to\",\n \"not currently supported and specifying a default driver will abort the tests. features:\",\n \"is reusing the LLVM Lit test runner in the interim until the new\",\n \"str, the name of the test. data: [str], labels that should be provided\",\n \"additional tags to attach to the test. tags_override: {str: str}, tags to add\",\n \"exclude: [str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions for\",\n \"{str: str}, sizes to use for specific tests. data: [str], additional input data\",\n \"`data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees\",\n \"exec_properties = {}): \\\"\\\"\\\"Runs test files under lit. Args: name: str, the name\",\n \"to the test. size: str, the size of the test. tags: [str], tags\",\n \"in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only\",\n \"patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude\",\n \"LLVM test runner. # # This is reusing the LLVM Lit test runner\",\n \"features, exec_properties = exec_properties, ) def lit_test( name, data = [], size =\",\n \"on. \\\"\\\"\\\" # Ignore some patterns by default for tests and input data.\",\n \"deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files]\",\n \"features to enable. exec_properties: a dictionary of properties to pass on. \\\"\\\"\\\" #\",\n \"which we should never match, for tests, subdirectories, or # test input data\",\n \"test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input\",\n \"= data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags +\",\n \"to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name\",\n \"+ ext for ext in test_file_exts], exclude = exclude, ) # Run tests\",\n \"= {}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override =\",\n \"the name of the test, including extension. data: [str], the data input to\",\n \"Run tests individually such that errors can be attributed to a specific #\",\n \"= _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests\",\n \"str, the name of the test, including extension. data: [str], the data input\",\n \"individually such that errors can be attributed to a specific # failure. for\",\n \"_default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs test files under lit. Args:\",\n \"[str], paths to exclude (for tests and inputs). test_file_exts: [str], extensions for files\",\n \"tags to attach to the test. tags_override: {str: str}, tags to add to\",\n \"= _default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags =\",\n \"hermetic builds, lit only sees the tests that are included in the `data`\",\n \"per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver\",\n \"the tests that are included in the `data` parameter, regardless of what other\",\n \"= [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] +\",\n \"local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude\",\n \"# Exclude input files that have spaces in their names, since bazel #\",\n \"= [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = []\",\n \"test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it\",\n \"as data inputs. size: str, the size of the test. tags: [str], tags\",\n \"to attach to the test. tags_override: {str: str}, tags to add to specific\",\n \"[ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have spaces in their\",\n \"= [], exec_properties = {}): \\\"\\\"\\\"Runs test files under lit. Args: name: str,\",\n \"name: str, the name of the test, including extension. data: [str], the data\",\n \"data = [], size = _default_size, tags = _default_tags, driver = _default_driver, features\",\n \"the name of the test. data: [str], labels that should be provided as\",\n \"*\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties):\",\n \"given file. default_tags: [str], additional tags to attach to the test. tags_override: {str:\",\n \"features = [], exec_properties = {}): \\\"\\\"\\\"Runs test files under lit. Args: name:\",\n \"= [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have spaces in\",\n \"custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\",\",\n \"+ \\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix +\",\n \"test_file_exts], exclude = exclude, ) # Run tests individually such that errors can\",\n \"size of the test. tags: [str], tags to attach to the test. driver:\",\n \"driver: str, label of the driver shell script. Note: use of a custom\",\n \"to add to specific tests. driver: str, label of the driver shell script.\",\n \"+ local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests(\",\n \"\\\"//tensorflow/core:testlib\\\", ], data = [\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\",\",\n \"= test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ],\",\n \"abort the tests. features: [str], list of extra features to enable. exec_properties: a\",\n \"\\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts,\",\n \"this directory. Args: exclude: [str], paths to exclude (for tests and inputs). test_file_exts:\",\n \"that should be provided as data inputs. size: str, the size of the\",\n \"# Default values used by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"]\",\n \"_default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags =\",\n \"and specifying a default driver will abort the tests. features: [str], list of\",\n \"[str]}, extra data to attach to a given file. default_tags: [str], additional tags\",\n \"the test, including extension. data: [str], the data input to the test. size:\",\n \"parameter, regardless of what other tests might exist in the directory searched. Args:\",\n \"lit on all tests it can find in `data` under tensorflow/compiler/mlir. Note that,\",\n \"runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") #\",\n \"[str], additional input data to the test. per_test_extra_data: {str: [str]}, extra data to\",\n \"# test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude\",\n \"\\\"\\\"\\\" # Ignore some patterns by default for tests and input data. exclude\",\n \"to attach to a given file. default_tags: [str], additional tags to attach to\",\n \"driver, features = features, exec_properties = exec_properties, ) def lit_test( name, data =\",\n \"= {}): \\\"\\\"\\\"Creates all plausible Lit tests (and their inputs) under this directory.\",\n \"= [], size = _default_size, tags = _default_tags, driver = _default_driver, features =\",\n \"of the test, including extension. data: [str], the data input to the test.\",\n \"test. tags: [str], tags to attach to the test. driver: str, label of\",\n \"a given file. default_tags: [str], additional tags to attach to the test. tags_override:\",\n \"name + \\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix\",\n \"the test. size: str, the size of the test. tags: [str], tags to\",\n \"= [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override = {}, data =\",\n \"\\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size\",\n \"some patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE +\",\n \"= exec_properties, ) def lit_test( name, data = [], size = _default_size, tags\",\n \"= local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test(\",\n \"list of extra features to enable. exec_properties: a dictionary of properties to pass\",\n \"only sees the tests that are included in the `data` parameter, regardless of\",\n \"] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on\",\n \"str, the size of the test. tags: [str], tags to attach to the\",\n \"for files that are tests. default_size: str, the test size for targets not\",\n \"the data input to the test. size: str, the size of the test.\",\n \"], data = [\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\",\",\n \"tests. features: [str], list of extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0]\",\n \"to enable. exec_properties: a dictionary of properties to pass on. \\\"\\\"\\\" # Ignore\",\n \"\\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the test runner. _default_test_file_exts = [\\\"mlir\\\",\",\n \"], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size,\",\n \"test runner. # # This is reusing the LLVM Lit test runner in\",\n \"# Run tests individually such that errors can be attributed to a specific\",\n \"# failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this test\",\n \"attach to a given file. default_tags: [str], additional tags to attach to the\",\n \"data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test,\",\n \"default_tags + tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties,\",\n \"driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it can find in `data`\",\n \"the LLVM Lit test runner in the interim until the new build #\",\n \"never match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE =\",\n \"+ \\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs = test_file, size =\",\n \"load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by\",\n \"features = [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests (and their\",\n \"# # This is reusing the LLVM Lit test runner in the interim\",\n \"paths to exclude (for tests and inputs). test_file_exts: [str], extensions for files that\",\n \"be provided as data inputs. size: str, the size of the test. tags:\",\n \"or # test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", #\",\n \"_ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have spaces\",\n \"in \\\"size_override\\\". size_override: {str: str}, sizes to use for specific tests. data: [str],\",\n \"abort the tests. features: [str], list of extra features to enable. \\\"\\\"\\\" _run_lit_test(name\",\n \"on all tests it can find in `data` under tensorflow/compiler/mlir. Note that, due\",\n \"= [] # These are patterns which we should never match, for tests,\",\n \"ext for ext in test_file_exts], exclude = exclude, ) # Run tests individually\",\n \"extra data to attach to a given file. default_tags: [str], additional tags to\",\n \"the driver shell script. Note: use of a custom driver is not currently\",\n \"native.glob( [\\\"*.\\\" + ext for ext in test_file_exts], exclude = exclude, ) #\",\n \"reusing the LLVM Lit test runner in the interim until the new build\",\n \"\\\"small\\\" _default_tags = [] # These are patterns which we should never match,\",\n \"new build # rules are upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit\",\n \"\\\"\\\"\\\"Runs test files under lit. Args: name: str, the name of the test.\",\n \"the LLVM test runner. # # This is reusing the LLVM Lit test\",\n \"[], size = _default_size, tags = _default_tags, driver = _default_driver, features = [],\",\n \"tests (and their inputs) under this directory. Args: exclude: [str], paths to exclude\",\n \"test. data: [str], labels that should be provided as data inputs. size: str,\",\n \"data: [str], additional input data to the test. per_test_extra_data: {str: [str]}, extra data\",\n \"test runner in the interim until the new build # rules are upstreamed.\",\n \"under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit only sees the\",\n \"searched. Args: name: str, the name of the test, including extension. data: [str],\",\n \"local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name\",\n \"= _default_tags, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs test\",\n \"data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [],\",\n \"tags_override: {str: str}, tags to add to specific tests. driver: str, label of\",\n \"patterns which we should never match, for tests, subdirectories, or # test input\",\n \"\\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the test runner. _default_test_file_exts =\",\n \"data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags\",\n \"targets not in \\\"size_override\\\". size_override: {str: str}, sizes to use for specific tests.\",\n \"of what other tests might exist in the directory searched. Args: name: str,\",\n \"tf_cc_test( name = name, srcs = test_file, size = size, deps = [\",\n \"\\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size =\",\n \"\\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] # These are patterns which we\",\n \"exec_properties, ) def lit_test( name, data = [], size = _default_size, tags =\",\n \"_default_size = \\\"small\\\" _default_tags = [] # These are patterns which we should\",\n \"[\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] #\",\n \"= test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name = local_test_files, srcs =\",\n \"to Bazel's hermetic builds, lit only sees the tests that are included in\",\n \"tests. data: [str], additional input data to the test. per_test_extra_data: {str: [str]}, extra\",\n \"= [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests (and their inputs)\",\n \"name = name, srcs = test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\",\",\n \"test. per_test_extra_data: {str: [str]}, extra data to attach to a given file. default_tags:\",\n \"tests. driver: str, label of the driver shell script. Note: use of a\",\n \"match, for tests, subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [\",\n \"[], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests (and their inputs) under\",\n \"other tests might exist in the directory searched. Args: name: str, the name\",\n \"= native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name = name,\",\n \"[str], tags to attach to the test. driver: str, label of the driver\",\n \"name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name = local_test_files, srcs\",\n \"currently supported and specifying a default driver will abort the tests. features: [str],\",\n \"of the test. tags: [str], tags to attach to the test. driver: str,\",\n \"sizes to use for specific tests. data: [str], additional input data to the\",\n \"the test. driver: str, label of the driver shell script. Note: use of\",\n \"default_tags: [str], additional tags to attach to the test. tags_override: {str: str}, tags\",\n \"_ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" + ext for ext in test_file_exts],\",\n \"\\\"size_override\\\". size_override: {str: str}, sizes to use for specific tests. data: [str], additional\",\n \"in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data,\",\n \"{}, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible\",\n \"i in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters.\",\n \"exclude, ) # Run tests individually such that errors can be attributed to\",\n \"TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\",\",\n \"size_override: {str: str}, sizes to use for specific tests. data: [str], additional input\",\n \"tests and inputs). test_file_exts: [str], extensions for files that are tests. default_size: str,\",\n \"tags_override.get(curr_test, []), driver = driver, features = features, exec_properties = exec_properties, ) def\",\n \"of the driver shell script. Note: use of a custom driver is not\",\n \"Ignore some patterns by default for tests and input data. exclude = _ALWAYS_EXCLUDE\",\n \"and inputs). test_file_exts: [str], extensions for files that are tests. default_size: str, the\",\n \"# rules are upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing\",\n \"exclude tests = native.glob( [\\\"*.\\\" + ext for ext in test_file_exts], exclude =\",\n \"of extra features to enable. exec_properties: a dictionary of properties to pass on.\",\n \"size: str, the size of the test. tags: [str], tags to attach to\",\n \"filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]),\",\n \"data inputs. size: str, the size of the test. tags: [str], tags to\",\n \"attach to the test. driver: str, label of the driver shell script. Note:\",\n \"rules are upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing test\",\n \"tags_override = {}, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates\",\n \"the test. tags_override: {str: str}, tags to add to specific tests. driver: str,\",\n \"test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data\",\n \"driver will abort the tests. features: [str], list of extra features to enable.\",\n \") def glob_op_tests( exclude = [], test_file_exts = _default_test_file_exts, default_size = _default_size, size_override\",\n \"this test with updated parameters. lit_test( name = curr_test, data = data +\",\n \"for Lit, the LLVM test runner. # # This is reusing the LLVM\",\n \"shell script. Note: use of a custom driver is not currently supported and\",\n \"name, srcs = test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\",\",\n \"enable. exec_properties: a dictionary of properties to pass on. \\\"\\\"\\\" # Ignore some\",\n \"extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data, size, tags, driver,\",\n \"regardless of what other tests might exist in the directory searched. Args: name:\",\n \"additional input data to the test. per_test_extra_data: {str: [str]}, extra data to attach\",\n \"*/**\\\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit\",\n \"\\\"\\\"\\\"Creates all plausible Lit tests (and their inputs) under this directory. Args: exclude:\",\n \"tags to attach to the test. driver: str, label of the driver shell\",\n \"specifying a default driver will abort the tests. features: [str], list of extra\",\n \"that are tests. default_size: str, the test size for targets not in \\\"size_override\\\".\",\n \"def lit_test( name, data = [], size = _default_size, tags = _default_tags, driver\",\n \"data: [str], labels that should be provided as data inputs. size: str, the\",\n \"\\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] # These are\",\n \"a dictionary of properties to pass on. \\\"\\\"\\\" # Ignore some patterns by\",\n \"to attach to the test. driver: str, label of the driver shell script.\",\n \"name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size),\",\n \"= _default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties =\",\n \"use for specific tests. data: [str], additional input data to the test. per_test_extra_data:\",\n \"cannot cope with such \\\"targets\\\" in the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\",\",\n \"# This is reusing the LLVM Lit test runner in the interim until\",\n \"range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters. lit_test( name\",\n \"data = [\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ],\",\n \"features: [str], list of extra features to enable. exec_properties: a dictionary of properties\",\n \"of extra features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data, size, tags,\",\n \"for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob(\",\n \"exist in the directory searched. Args: name: str, the name of the test,\",\n \"to exclude (for tests and inputs). test_file_exts: [str], extensions for files that are\",\n \"size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features =\",\n \"= {}, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates all\",\n \"[str], additional tags to attach to the test. tags_override: {str: str}, tags to\",\n \"test files under lit. Args: name: str, the name of the test. data:\",\n \"tests individually such that errors can be attributed to a specific # failure.\",\n \"be attributed to a specific # failure. for i in range(len(tests)): curr_test =\",\n \"srcs = test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\",\",\n \"Note that, due to Bazel's hermetic builds, lit only sees the tests that\",\n \"Note: use of a custom driver is not currently supported and specifying a\",\n \"= driver, features = features, exec_properties = exec_properties, ) def lit_test( name, data\",\n \"_default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit tests (and\",\n \"{str: str}, tags to add to specific tests. driver: str, label of the\",\n \"dictionary of properties to pass on. \\\"\\\"\\\" # Ignore some patterns by default\",\n \"values used by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver =\",\n \"are included in the `data` parameter, regardless of what other tests might exist\",\n \"data: [str], the data input to the test. size: str, the size of\",\n \"[str], extensions for files that are tests. default_size: str, the test size for\",\n \"= name, srcs = test_file, size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\",\",\n \"that errors can be attributed to a specific # failure. for i in\",\n \"of the test. data: [str], labels that should be provided as data inputs.\",\n \"in range(len(tests)): curr_test = tests[i] # Instantiate this test with updated parameters. lit_test(\",\n \"enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup( name =\",\n \"default driver will abort the tests. features: [str], list of extra features to\",\n \"\\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the test runner.\",\n \"subdirectories, or # test input data files. _ALWAYS_EXCLUDE = [ \\\"**/LICENSE.txt\\\", \\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\",\",\n \"file. default_tags: [str], additional tags to attach to the test. tags_override: {str: str},\",\n \"= size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []), driver = driver, features\",\n \"definitions for Lit, the LLVM test runner. # # This is reusing the\",\n \"in the directory searched. Args: name: str, the name of the test, including\",\n \"+ per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test, default_size), tags = default_tags + tags_override.get(curr_test, []),\",\n \"files that have spaces in their names, since bazel # cannot cope with\",\n \"exec_properties: a dictionary of properties to pass on. \\\"\\\"\\\" # Ignore some patterns\",\n \"use of a custom driver is not currently supported and specifying a default\",\n \"files that are tests. default_size: str, the test size for targets not in\",\n \"name: str, the name of the test. data: [str], labels that should be\",\n \"extensions for files that are tests. default_size: str, the test size for targets\",\n \"that, due to Bazel's hermetic builds, lit only sees the tests that are\",\n \"inputs. size: str, the size of the test. tags: [str], tags to attach\",\n \"driver = driver, features = features, exec_properties = exec_properties, ) def lit_test( name,\",\n \"the srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size,\",\n \"that have spaces in their names, since bazel # cannot cope with such\",\n \"features to enable. \\\"\\\"\\\" _run_lit_test(name + \\\".test\\\", [name], data, size, tags, driver, features,\",\n \"test. size: str, the size of the test. tags: [str], tags to attach\",\n \"data to attach to a given file. default_tags: [str], additional tags to attach\",\n \"are upstreamed. # TODO(b/136126535): remove this custom rule. \\\"\\\"\\\"Lit runner globbing test \\\"\\\"\\\"\",\n \"_default_size, tags = _default_tags, driver = _default_driver, features = [], exec_properties = {}):\",\n \"the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size =\",\n \"name, data = [], size = _default_size, tags = _default_tags, driver = _default_driver,\",\n \"all plausible Lit tests (and their inputs) under this directory. Args: exclude: [str],\",\n \"\\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used\",\n \"errors can be attributed to a specific # failure. for i in range(len(tests)):\",\n \") def lit_test( name, data = [], size = _default_size, tags = _default_tags,\",\n \"Test definitions for Lit, the LLVM test runner. # # This is reusing\",\n \"load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values used by the test runner. _default_test_file_exts\",\n \"data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" + ext for\",\n \"(and their inputs) under this directory. Args: exclude: [str], paths to exclude (for\",\n \"name of the test. data: [str], labels that should be provided as data\",\n \"[]), driver = driver, features = features, exec_properties = exec_properties, ) def lit_test(\",\n \"sees the tests that are included in the `data` parameter, regardless of what\",\n \"names, since bazel # cannot cope with such \\\"targets\\\" in the srcs list.\",\n \"+ exclude tests = native.glob( [\\\"*.\\\" + ext for ext in test_file_exts], exclude\",\n \"have spaces in their names, since bazel # cannot cope with such \\\"targets\\\"\",\n \"will abort the tests. features: [str], list of extra features to enable. \\\"\\\"\\\"\",\n \"local_test_files = name + \\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\"\",\n \"attributed to a specific # failure. for i in range(len(tests)): curr_test = tests[i]\",\n \"driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Creates all plausible Lit\",\n \"for targets not in \\\"size_override\\\". size_override: {str: str}, sizes to use for specific\",\n \"= [\\\":\\\" + local_test_files] + data + [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], )\",\n \"of extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name +\",\n \"exclude = exclude, ) # Run tests individually such that errors can be\",\n \"due to Bazel's hermetic builds, lit only sees the tests that are included\",\n \"[ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [], test_file_exts =\",\n \"input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" + ext\",\n \"\\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] # These\",\n \"test \\\"\\\"\\\" load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"filegroup\\\") load(\\\"@bazel_skylib//lib:paths.bzl\\\", \\\"paths\\\") load(\\\"//tensorflow:tensorflow.bzl\\\", \\\"tf_cc_test\\\", \\\"tf_native_cc_binary\\\", \\\"tf_copts\\\") # Default values\",\n \"and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\" +\",\n \"a default driver will abort the tests. features: [str], list of extra features\",\n \"\\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features,\",\n \"name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs = test_file, size\",\n \"str, label of the driver shell script. Note: use of a custom driver\",\n \"features = features, exec_properties = exec_properties, ) def lit_test( name, data = [],\",\n \"should be provided as data inputs. size: str, the size of the test.\",\n \"specific # failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this\",\n \"spaces in their names, since bazel # cannot cope with such \\\"targets\\\" in\",\n \"_run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests\",\n \"their names, since bazel # cannot cope with such \\\"targets\\\" in the srcs\",\n \"LLVM Lit test runner in the interim until the new build # rules\",\n \"supported and specifying a default driver will abort the tests. features: [str], list\",\n \"attach to the test. tags_override: {str: str}, tags to add to specific tests.\",\n \"{}, data = [], per_test_extra_data = {}, default_tags = _default_tags, tags_override = {},\",\n \"we should never match, for tests, subdirectories, or # test input data files.\",\n \"find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's hermetic builds, lit\",\n \"can be attributed to a specific # failure. for i in range(len(tests)): curr_test\",\n \"under this directory. Args: exclude: [str], paths to exclude (for tests and inputs).\",\n \"tests that are included in the `data` parameter, regardless of what other tests\",\n \"Args: name: str, the name of the test, including extension. data: [str], the\",\n \"pass on. \\\"\\\"\\\" # Ignore some patterns by default for tests and input\",\n \"label of the driver shell script. Note: use of a custom driver is\",\n \"data to the test. per_test_extra_data: {str: [str]}, extra data to attach to a\",\n \"size for targets not in \\\"size_override\\\". size_override: {str: str}, sizes to use for\",\n \"# Ignore some patterns by default for tests and input data. exclude =\",\n \"the test size for targets not in \\\"size_override\\\". size_override: {str: str}, sizes to\",\n \"directory searched. Args: name: str, the name of the test, including extension. data:\",\n \"\\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs\",\n \"\\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs = test_file, size = size,\",\n \"size = size, deps = [ \\\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\\\", \\\"//tensorflow/core:test\\\", \\\"//tensorflow/core:test_main\\\", \\\"//tensorflow/core:testlib\\\", ], data =\",\n \"srcs = native.glob([ \\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name =\",\n \"default_tags = _default_tags, tags_override = {}, driver = _default_driver, features = [], exec_properties\",\n \"in the `data` parameter, regardless of what other tests might exist in the\",\n \"tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests = native.glob( [\\\"*.\\\"\",\n \"labels that should be provided as data inputs. size: str, the size of\",\n \"tags: [str], tags to attach to the test. driver: str, label of the\",\n \"Lit test runner in the interim until the new build # rules are\",\n \"_default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags = [] # These are patterns\",\n \"by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size\",\n \"features: [str], list of extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files\",\n \"driver is not currently supported and specifying a default driver will abort the\",\n \"tests it can find in `data` under tensorflow/compiler/mlir. Note that, due to Bazel's\",\n \"used by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\"\",\n \"all tests it can find in `data` under tensorflow/compiler/mlir. Note that, due to\",\n \"a custom driver is not currently supported and specifying a default driver will\",\n \"tests[i] # Instantiate this test with updated parameters. lit_test( name = curr_test, data\",\n \"\\\"tf_copts\\\") # Default values used by the test runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\",\",\n \"are patterns which we should never match, for tests, subdirectories, or # test\",\n \"runner. _default_test_file_exts = [\\\"mlir\\\", \\\".pbtxt\\\", \\\".td\\\"] _default_driver = \\\"@llvm-project//mlir:run_lit.sh\\\" _default_size = \\\"small\\\" _default_tags\",\n \"\\\"data/\\\" + name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs =\",\n \"lit_test( name = curr_test, data = data + per_test_extra_data.get(curr_test, []), size = size_override.get(curr_test,\",\n \"This is reusing the LLVM Lit test runner in the interim until the\",\n \"by default for tests and input data. exclude = _ALWAYS_EXCLUDE + exclude tests\",\n \"str, the test size for targets not in \\\"size_override\\\". size_override: {str: str}, sizes\",\n \"their inputs) under this directory. Args: exclude: [str], paths to exclude (for tests\",\n \"= _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs test files under lit.\",\n \"not in \\\"size_override\\\". size_override: {str: str}, sizes to use for specific tests. data:\",\n \"[str], list of extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files =\",\n \"script. Note: use of a custom driver is not currently supported and specifying\",\n \"Exclude input files that have spaces in their names, since bazel # cannot\",\n \"+ name_without_suffix + \\\"*.mlir\\\", ]), ) tf_cc_test( name = name, srcs = test_file,\",\n \"srcs list. \\\"**/* *\\\", \\\"**/* */**\\\", ] def _run_lit_test(name, test_file, data, size, tags,\",\n \"tags = _default_tags, driver = _default_driver, features = [], exec_properties = {}): \\\"\\\"\\\"Runs\",\n \"failure. for i in range(len(tests)): curr_test = tests[i] # Instantiate this test with\",\n \"extra features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\"\",\n \"that are included in the `data` parameter, regardless of what other tests might\",\n \"\\\"**/README.txt\\\", \\\"**/lit.local.cfg\\\", # Exclude input files that have spaces in their names, since\",\n \"+ [ \\\"//tensorflow/compiler/mlir/disc:disc_compiler_main\\\", \\\"//tensorflow/compiler/mlir:tf-mlir-translate\\\", \\\"//tensorflow/compiler/mlir:tf-opt\\\", ], ) def glob_op_tests( exclude = [], test_file_exts\",\n \"lit_test( name, data = [], size = _default_size, tags = _default_tags, driver =\",\n \"= name + \\\".test_files\\\" filegroup( name = local_test_files, srcs = native.glob([ \\\"data/\\\" +\",\n \"= {}): \\\"\\\"\\\"Runs test files under lit. Args: name: str, the name of\",\n \"_default_test_file_exts, default_size = _default_size, size_override = {}, data = [], per_test_extra_data = {},\",\n \"size, tags, driver, features, exec_properties): \\\"\\\"\\\"Runs lit on all tests it can find\",\n \"features to enable. \\\"\\\"\\\" name_without_suffix = test_file[0].split('.')[0] local_test_files = name + \\\".test_files\\\" filegroup(\",\n \"for ext in test_file_exts], exclude = exclude, ) # Run tests individually such\",\n \"{}): \\\"\\\"\\\"Runs test files under lit. Args: name: str, the name of the\",\n \"_default_tags = [] # These are patterns which we should never match, for\"\n]","isConversational":false}}},{"rowIdx":2181,"cells":{"ngram":{"kind":"list like","value":["<uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0;","= 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64);","['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16","-- type of source file, (probably should be \".c\") message_libname -- library name","= ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0;","0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result(",") return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >=","-- list of actual libraries to link against (defaults to a list with","3 #else #error \"\" #endif #endif #else #error \"\" #endif ''' def CheckBDB(context):","libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu;","to link against (defaults to a list with one element, the value of","DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3","\"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that source can be","context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] =","message_libname, real_libs=[]): \"\"\" Check that source can be successfully compiled and linked against","\"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0,","context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if","''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret)","} ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = '''","source_type, message_libname, real_libs=[]): \"\"\" Check that source can be successfully compiled and linked","be successfully compiled and linked against real_libs. Keyword arguments: source -- source to","uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16);","context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type)","4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error","of messager_libname) \"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" %","to compile source_type -- type of source file, (probably should be \".c\") message_libname","= [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret =","the message output from scons real_libs -- list of actual libraries to link","= 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } '''","with one element, the value of messager_libname) \"\"\" if not real_libs: real_libs =","bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR","return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source","return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char","library name to show in the message output from scons real_libs -- list","is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include","functions to add to the configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname,","libraries to link against (defaults to a list with one element, the value","uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\",","compile source_type -- type of source file, (probably should be \".c\") message_libname --","should be \".c\") message_libname -- library name to show in the message output","source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave","uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\",","\".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx;","getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux',","DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\" #endif","= 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for","= context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is","-- library name to show in the message output from scons real_libs --","} ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret","add to the configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\"","#error \"\" #endif #endif #else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for","defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif #else #error \"\"","b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; }","\"\"\" These are functions to add to the configure context. \"\"\" def __checkCanLink(context,","return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h>","CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret) return ret","to the configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check","= context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h>","{ security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context,","actual libraries to link against (defaults to a list with one element, the","real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs)","message_libname -- library name to show in the message output from scons real_libs","to try to compile source_type -- type of source file, (probably should be","try to compile source_type -- type of source file, (probably should be \".c\")","context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None:","libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu);","#error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret =","file, (probably should be \".c\") message_libname -- library name to show in the","= context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS']","message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if","#else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret","<stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t","%s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret","0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source =","0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\")","message output from scons real_libs -- list of actual libraries to link against","''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; }","messager_libname) \"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname)","def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return","#include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if","{ uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def","#endif #endif #else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >=","def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h>","''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str);","source file, (probably should be \".c\") message_libname -- library name to show in","scons real_libs -- list of actual libraries to link against (defaults to a","\"\" #endif #endif #else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB","main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } '''","real_libs=[]): \"\"\" Check that source can be successfully compiled and linked against real_libs.","#include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32","\"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret = context.TryCompile(bdb_source,","can be successfully compiled and linked against real_libs. Keyword arguments: source -- source","bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source,","#endif #else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\")","value of messager_libname) \"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\"","#include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } '''","} ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = '''","selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main()","''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t","defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR","DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif #else #error \"\" #endif '''","source -- source to try to compile source_type -- type of source file,","(defaults to a list with one element, the value of messager_libname) \"\"\" if","bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret","against real_libs. Keyword arguments: source -- source to try to compile source_type --","0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def","b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking","int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64","'.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR)","None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h>","configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that source","source_type -- type of source file, (probably should be \".c\") message_libname -- library","else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main()","against (defaults to a list with one element, the value of messager_libname) \"\"\"","= ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu,","char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context,","show in the message output from scons real_libs -- list of actual libraries","uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context):","output from scons real_libs -- list of actual libraries to link against (defaults","&ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux'])","ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if","''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include","These are functions to add to the configure context. \"\"\" def __checkCanLink(context, source,","ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR","<db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR)","successfully compiled and linked against real_libs. Keyword arguments: source -- source to try","if not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave =","libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = '''","of actual libraries to link against (defaults to a list with one element,","int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context):","''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret )","source can be successfully compiled and linked against real_libs. Keyword arguments: source --","to show in the message output from scons real_libs -- list of actual","'.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() {","__checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that source can be successfully compiled","be \".c\") message_libname -- library name to show in the message output from","uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source,","libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t","if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source =","context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() {","ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37];","#include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return","0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return","byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = '''","bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret =","(probably should be \".c\") message_libname -- library name to show in the message","list of actual libraries to link against (defaults to a list with one","uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context):","'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t","to add to the configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]):","linked against real_libs. Keyword arguments: source -- source to try to compile source_type","real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret","security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source,","Keyword arguments: source -- source to try to compile source_type -- type of","&& DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif #else #error \"\" #endif","= ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF;","of source file, (probably should be \".c\") message_libname -- library name to show","uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return","\".c\") message_libname -- library name to show in the message output from scons","uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"])",") if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source","''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4","ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include","__checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int","from scons real_libs -- list of actual libraries to link against (defaults to","name to show in the message output from scons real_libs -- list of","source to try to compile source_type -- type of source file, (probably should","CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include","\"\"\" Check that source can be successfully compiled and linked against real_libs. Keyword","libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave","ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret","def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that source can be successfully","#endif ''' def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret = context.TryCompile(bdb_source, '.c')","list with one element, the value of messager_libname) \"\"\" if not real_libs: real_libs","in the message output from scons real_libs -- list of actual libraries to","def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h>","{ uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF;",">= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else","#else #error \"\" #endif #endif #else #error \"\" #endif ''' def CheckBDB(context): context.Message(\"Checking","-- source to try to compile source_type -- type of source file, (probably","one element, the value of messager_libname) \"\"\" if not real_libs: real_libs = [message_libname]","[\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx);","[message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source,","return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source","the configure context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that","<byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 =",">= 3 #else #error \"\" #endif #endif #else #error \"\" #endif ''' def","context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) &&","ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp',","\"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave","int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; }","0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source =","for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source =","CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int","that source can be successfully compiled and linked against real_libs. Keyword arguments: source","== 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif","<selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def","and linked against real_libs. Keyword arguments: source -- source to try to compile","= libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t","real_libs. Keyword arguments: source -- source to try to compile source_type -- type","ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else:","return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main()","to a list with one element, the value of messager_libname) \"\"\" if not","real_libs -- list of actual libraries to link against (defaults to a list","4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif #else","context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return","&& DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >=","the value of messager_libname) \"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking for","#if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\"","source, source_type, message_libname, real_libs=[]): \"\"\" Check that source can be successfully compiled and","link against (defaults to a list with one element, the value of messager_libname)","CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret","main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 =","context. \"\"\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \"\"\" Check that source can","a list with one element, the value of messager_libname) \"\"\" if not real_libs:","element, the value of messager_libname) \"\"\" if not real_libs: real_libs = [message_libname] context.Message(\"Checking","''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include","def CheckBDB(context): context.Message(\"Checking for BDB >= 4.3...\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret) return","#include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF;","Check that source can be successfully compiled and linked against real_libs. Keyword arguments:","return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4","byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 =","= ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR ==","are functions to add to the configure context. \"\"\" def __checkCanLink(context, source, source_type,","not real_libs: real_libs = [message_libname] context.Message(\"Checking for %s...\" % message_libname) libsave = context.env.get('LIBS')","#if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \"\" #endif #endif #else #error","% message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret )","#if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) &&","uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0;","compiled and linked against real_libs. Keyword arguments: source -- source to try to","for %s...\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result(","b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32);","return 0; } ''' def CheckByteswap(context): context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c')","main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return","del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int","context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS'])","type of source file, (probably should be \".c\") message_libname -- library name to","arguments: source -- source to try to compile source_type -- type of source","selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return","__checkCanLink(context, libuuid_source, \".c\", \"libuuid\", [\"uuid\"]) selinux_source = ''' #include <selinux/selinux.h> int main() {","context.Message(\"Checking for byteswap.h...\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source"],"string":"[\n \"<uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0;\",\n \"= 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64);\",\n \"['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16\",\n \"-- type of source file, (probably should be \\\".c\\\") message_libname -- library name\",\n \"= ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0;\",\n \"0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result(\",\n \") return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >=\",\n \"-- list of actual libraries to link against (defaults to a list with\",\n \"3 #else #error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif ''' def CheckBDB(context):\",\n \"libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu;\",\n \"to link against (defaults to a list with one element, the value of\",\n \"DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3\",\n \"\\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be\",\n \"context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] =\",\n \"message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be successfully compiled and linked against\",\n \"\\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0,\",\n \"context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if\",\n \"''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret)\",\n \"} ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = '''\",\n \"source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be successfully compiled and linked\",\n \"be successfully compiled and linked against real_libs. Keyword arguments: source -- source to\",\n \"uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16);\",\n \"context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type)\",\n \"4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error\",\n \"of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" %\",\n \"to compile source_type -- type of source file, (probably should be \\\".c\\\") message_libname\",\n \"= [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret =\",\n \"the message output from scons real_libs -- list of actual libraries to link\",\n \"= 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } '''\",\n \"with one element, the value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs =\",\n \"bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR\",\n \"return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source\",\n \"return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char\",\n \"library name to show in the message output from scons real_libs -- list\",\n \"is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include\",\n \"functions to add to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname,\",\n \"libraries to link against (defaults to a list with one element, the value\",\n \"uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\",\",\n \"compile source_type -- type of source file, (probably should be \\\".c\\\") message_libname --\",\n \"should be \\\".c\\\") message_libname -- library name to show in the message output\",\n \"source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave\",\n \"uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\",\",\n \"\\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx;\",\n \"getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux',\",\n \"DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif\",\n \"= 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for\",\n \"= context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is\",\n \"-- library name to show in the message output from scons real_libs --\",\n \"} ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret\",\n \"add to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\"\",\n \"#error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for\",\n \"defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif #else #error \\\"\\\"\",\n \"b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; }\",\n \"\\\"\\\"\\\" These are functions to add to the configure context. \\\"\\\"\\\" def __checkCanLink(context,\",\n \"return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h>\",\n \"CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret) return ret\",\n \"to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check\",\n \"= context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h>\",\n \"{ security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context,\",\n \"actual libraries to link against (defaults to a list with one element, the\",\n \"real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs)\",\n \"message_libname -- library name to show in the message output from scons real_libs\",\n \"to try to compile source_type -- type of source file, (probably should be\",\n \"try to compile source_type -- type of source file, (probably should be \\\".c\\\")\",\n \"context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None:\",\n \"libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu);\",\n \"#error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret =\",\n \"file, (probably should be \\\".c\\\") message_libname -- library name to show in the\",\n \"= context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS']\",\n \"message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if\",\n \"#else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret\",\n \"<stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t\",\n \"%s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret\",\n \"0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source =\",\n \"0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\")\",\n \"message output from scons real_libs -- list of actual libraries to link against\",\n \"''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; }\",\n \"messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname)\",\n \"def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return\",\n \"#include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if\",\n \"{ uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def\",\n \"#endif #endif #else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >=\",\n \"def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h>\",\n \"''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str);\",\n \"source file, (probably should be \\\".c\\\") message_libname -- library name to show in\",\n \"scons real_libs -- list of actual libraries to link against (defaults to a\",\n \"\\\"\\\" #endif #endif #else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB\",\n \"main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } '''\",\n \"real_libs=[]): \\\"\\\"\\\" Check that source can be successfully compiled and linked against real_libs.\",\n \"#include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32\",\n \"\\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret = context.TryCompile(bdb_source,\",\n \"can be successfully compiled and linked against real_libs. Keyword arguments: source -- source\",\n \"bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source,\",\n \"#endif #else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\")\",\n \"value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\"\",\n \"#include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } '''\",\n \"} ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = '''\",\n \"selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main()\",\n \"''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t\",\n \"defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR\",\n \"DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif '''\",\n \"source -- source to try to compile source_type -- type of source file,\",\n \"(defaults to a list with one element, the value of messager_libname) \\\"\\\"\\\" if\",\n \"bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret\",\n \"against real_libs. Keyword arguments: source -- source to try to compile source_type --\",\n \"0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def\",\n \"b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking\",\n \"int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64\",\n \"'.c') context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR)\",\n \"None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h>\",\n \"configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source\",\n \"source_type -- type of source file, (probably should be \\\".c\\\") message_libname -- library\",\n \"else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main()\",\n \"against (defaults to a list with one element, the value of messager_libname) \\\"\\\"\\\"\",\n \"= ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu,\",\n \"char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context,\",\n \"show in the message output from scons real_libs -- list of actual libraries\",\n \"uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context):\",\n \"output from scons real_libs -- list of actual libraries to link against (defaults\",\n \"&ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux'])\",\n \"ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if\",\n \"''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include\",\n \"These are functions to add to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source,\",\n \"ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR\",\n \"<db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR)\",\n \"successfully compiled and linked against real_libs. Keyword arguments: source -- source to try\",\n \"if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave =\",\n \"libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = '''\",\n \"of actual libraries to link against (defaults to a list with one element,\",\n \"int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context):\",\n \"''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret )\",\n \"source can be successfully compiled and linked against real_libs. Keyword arguments: source --\",\n \"to show in the message output from scons real_libs -- list of actual\",\n \"'.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() {\",\n \"__checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be successfully compiled\",\n \"be \\\".c\\\") message_libname -- library name to show in the message output from\",\n \"uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source,\",\n \"libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t\",\n \"if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source =\",\n \"context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() {\",\n \"ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37];\",\n \"#include <uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return\",\n \"0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return\",\n \"byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = '''\",\n \"bswap_32(b32); bswap_64(b64); return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret =\",\n \"(probably should be \\\".c\\\") message_libname -- library name to show in the message\",\n \"list of actual libraries to link against (defaults to a list with one\",\n \"uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context):\",\n \"'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t\",\n \"to add to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]):\",\n \"linked against real_libs. Keyword arguments: source -- source to try to compile source_type\",\n \"real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret\",\n \"security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source,\",\n \"Keyword arguments: source -- source to try to compile source_type -- type of\",\n \"&& DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif\",\n \"= ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF;\",\n \"of source file, (probably should be \\\".c\\\") message_libname -- library name to show\",\n \"uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; } ''' def CheckLibUUID(context): return\",\n \"\\\".c\\\") message_libname -- library name to show in the message output from scons\",\n \"uuid_str); return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"])\",\n \") if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source\",\n \"''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4\",\n \"ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source = ''' #include\",\n \"__checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int\",\n \"from scons real_libs -- list of actual libraries to link against (defaults to\",\n \"name to show in the message output from scons real_libs -- list of\",\n \"source to try to compile source_type -- type of source file, (probably should\",\n \"CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source = ''' #include <byteswap.h> #include\",\n \"\\\"\\\"\\\" Check that source can be successfully compiled and linked against real_libs. Keyword\",\n \"libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave\",\n \"ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret\",\n \"def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be successfully\",\n \"#endif ''' def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret = context.TryCompile(bdb_source, '.c')\",\n \"list with one element, the value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs\",\n \"in the message output from scons real_libs -- list of actual libraries to\",\n \"def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h>\",\n \"{ uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF;\",\n \">= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else\",\n \"#else #error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif ''' def CheckBDB(context): context.Message(\\\"Checking\",\n \"-- source to try to compile source_type -- type of source file, (probably\",\n \"one element, the value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname]\",\n \"[\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx);\",\n \"[message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source,\",\n \"return 0; } ''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source\",\n \"the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that\",\n \"<byteswap.h> #include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 =\",\n \">= 3 #else #error \\\"\\\" #endif #endif #else #error \\\"\\\" #endif ''' def\",\n \"context.Result( ret ) return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) &&\",\n \"ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp',\",\n \"\\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave\",\n \"int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0; }\",\n \"0; } ''' def CheckSeLinux(context): return __checkCanLink(context, selinux_source, '.cpp', 'selinux', ['selinux']) byteswap_source =\",\n \"for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source =\",\n \"CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int\",\n \"that source can be successfully compiled and linked against real_libs. Keyword arguments: source\",\n \"== 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif\",\n \"<selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def\",\n \"and linked against real_libs. Keyword arguments: source -- source to try to compile\",\n \"= libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int main() { uuid_t\",\n \"real_libs. Keyword arguments: source -- source to try to compile source_type -- type\",\n \"ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS']) else:\",\n \"return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main()\",\n \"to a list with one element, the value of messager_libname) \\\"\\\"\\\" if not\",\n \"real_libs -- list of actual libraries to link against (defaults to a list\",\n \"4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif #else\",\n \"context.Result( ret ) if libsave is None: del(context.env['LIBS']) else: context.env['LIBS'] = libsave return\",\n \"&& DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >=\",\n \"the value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for\",\n \"#if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\"\",\n \"source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can be successfully compiled and\",\n \"link against (defaults to a list with one element, the value of messager_libname)\",\n \"CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret\",\n \"main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 =\",\n \"context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type, message_libname, real_libs=[]): \\\"\\\"\\\" Check that source can\",\n \"a list with one element, the value of messager_libname) \\\"\\\"\\\" if not real_libs:\",\n \"element, the value of messager_libname) \\\"\\\"\\\" if not real_libs: real_libs = [message_libname] context.Message(\\\"Checking\",\n \"''' def CheckLibUUID(context): return __checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include\",\n \"def CheckBDB(context): context.Message(\\\"Checking for BDB >= 4.3...\\\") ret = context.TryCompile(bdb_source, '.c') context.Result(ret) return\",\n \"#include <stdint.h> int main() { uint16_t b16 = 0x00FF; uint32_t b32 = 0x0011EEFF;\",\n \"Check that source can be successfully compiled and linked against real_libs. Keyword arguments:\",\n \"return ret bdb_source = ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4\",\n \"byteswap_source = ''' #include <byteswap.h> #include <stdint.h> int main() { uint16_t b16 =\",\n \"= ''' #include <db.h> #if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR ==\",\n \"are functions to add to the configure context. \\\"\\\"\\\" def __checkCanLink(context, source, source_type,\",\n \"not real_libs: real_libs = [message_libname] context.Message(\\\"Checking for %s...\\\" % message_libname) libsave = context.env.get('LIBS')\",\n \"#if defined(DB_VERSION_MINOR) && DB_VERSION_MINOR >= 3 #else #error \\\"\\\" #endif #endif #else #error\",\n \"% message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret )\",\n \"#if defined(DB_VERSION_MAJOR) && DB_VERSION_MAJOR >= 4 #if DB_VERSION_MAJOR == 4 #if defined(DB_VERSION_MINOR) &&\",\n \"uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64); return 0;\",\n \"compiled and linked against real_libs. Keyword arguments: source -- source to try to\",\n \"for %s...\\\" % message_libname) libsave = context.env.get('LIBS') context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result(\",\n \"b16 = 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32);\",\n \"return 0; } ''' def CheckByteswap(context): context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c')\",\n \"main() { security_context_t ctx; getpeercon(0, &ctx); return 0; } ''' def CheckSeLinux(context): return\",\n \"del(context.env['LIBS']) else: context.env['LIBS'] = libsave return ret libuuid_source = ''' #include <uuid/uuid.h> int\",\n \"context.env.AppendUnique(LIBS=real_libs) ret = context.TryLink(source, source_type) context.Result( ret ) if libsave is None: del(context.env['LIBS'])\",\n \"type of source file, (probably should be \\\".c\\\") message_libname -- library name to\",\n \"arguments: source -- source to try to compile source_type -- type of source\",\n \"selinux_source = ''' #include <selinux/selinux.h> int main() { security_context_t ctx; getpeercon(0, &ctx); return\",\n \"__checkCanLink(context, libuuid_source, \\\".c\\\", \\\"libuuid\\\", [\\\"uuid\\\"]) selinux_source = ''' #include <selinux/selinux.h> int main() {\",\n \"context.Message(\\\"Checking for byteswap.h...\\\") ret = context.TryCompile(byteswap_source, '.c') context.Result( ret ) return ret bdb_source\"\n]","isConversational":false}}},{"rowIdx":2182,"cells":{"ngram":{"kind":"list like","value":["weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make","separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple","allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in a pytorch","tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We","len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except","KIND, either express or implied. # See the License for the specific language","# Find associated numpy array in pytorch model state dict if name not","Unless required by applicable law or agreed to in writing, software # distributed","to load PyTorch base models as well as derived models (with heads) #","array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) >","== list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel","{:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from","> 0: logger.info( \"Weights of {} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__,","[] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose","transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf # noqa: F401","s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights","is replaced by a level separation (can be used to convert TF2.0 lists","instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict =","sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state dict","When should we transpose the weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\"","to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero # When","pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints","\"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key =","or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF","or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" )","missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array, transpose =","pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of {}","name in a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch","in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose)","len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try:","array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array))","if needed from a PyTorch state_dict old_keys = [] new_keys = [] for","if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built","new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys,","from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights","this file except in compliance with the License. # You may obtain a","well as derived models (with heads) # TF models always have a prefix,","Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights","2.0 model. \"\"\" try: import torch # noqa: F401 import tensorflow as tf","tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF 2.0 model. \"\"\" try:","symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find","AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight","= pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array =","{} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) >",") tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt =","is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) #","is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\"","symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array","training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys)","allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array, transpose","weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name","as K except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both","(c) 2018, <NAME>. All rights reserved. # # Licensed under the Apache License,","installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict","if allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy()","Instantiate and load the associated TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__","weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()]","ANY KIND, either express or implied. # See the License for the specific","AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose:","should we transpose the weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in","\"\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0 # #####################","used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded from TF","e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight","prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1)","pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions:","for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a map","'.' separators tf_name = tf_name[1:] # Remove level zero # When should we","always have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove =","array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) >","TF 2.0 model. \"\"\" try: import torch # noqa: F401 import tensorflow as","to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf","we transpose the weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name","2.0 model in a pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights,","import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\"","easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf #","tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced by a level","except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch","ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow","logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0","loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if","\"Weights of {} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","'_._' is replaced by a level separation (can be used to convert TF2.0","a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs,","if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key","len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as","\"\"\" try: import torch # noqa: F401 import tensorflow as tf # noqa:","pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF 2.0 model. \"\"\"","models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in","allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" pt_state_dict =","pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values())))","sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated","reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t","PyTorch state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key","= [] for key in pt_state_dict.keys(): new_key = None if \"gamma\" in key:","[] new_keys = [] for key in pt_state_dict.keys(): new_key = None if \"gamma\"","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None,","to new format if needed from a PyTorch state_dict old_keys = [] new_keys","weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are","unexpected_keys) ) logger.info(\"Weights or buffers not loaded from TF 2.0 model: {}\".format(all_tf_weights)) return","convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove","= [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not","tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in a pytorch model \"\"\" weights","convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight","tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" pt_state_dict","conversions: - '$1___$2' is replaced by $2 (can be used to duplicate or","in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch model:","built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load","tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is","# When should we transpose the weights transpose = bool(tf_name[-1] == \"kernel\" or","tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] ==","key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in","2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do","OF ANY KIND, either express or implied. # See the License for the","language governing permissions and # limitations under the License. \"\"\" PyTorch - TF","pt_model.base_model_prefix + \".\" # Build a map from potential PyTorch weight names to","##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF","to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model","{}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name = \"TF\"","ImportError: logger.error( \"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow","def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF 2.0","Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to","Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1]","Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under the Apache","in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to","tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples =","a TF 2.0 model. \"\"\" try: import torch # noqa: F401 import tensorflow","weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load","logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict,","= [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name,","duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is replaced by","level zero # When should we transpose the weights transpose = bool(tf_name[-1] ==","= {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load","axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape,","TF2.0 and PyTorch weights matrices are transposed with regards to each other \"\"\"","2.0 general utilities.\"\"\" import logging import os import re import numpy logger =","sure model is built # Adapt state dict - TODO remove this and","= loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state dict","0: logger.info( \"Weights from TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys)","assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise","from TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or","= \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix +","start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt","# Find associated numpy array in pytorch model state dict if pt_weight_name not","training=False) # Make sure model is built # Adapt state dict - TODO","or \"out_projs\" in tf_name) # Convert standard TF2.0 names in PyTorch names if","tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False)","tf_name) # Remove empty levels at the end tf_name = tf_name.split(\"/\") # Convert","is built # Adapt state dict - TODO remove this and update the","Find associated numpy array in pytorch model state dict if pt_weight_name not in","tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None,","to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose","TF models always have a prefix, some of PyTorch models (base ones) don't","installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure","checkpoint in a PyTorch model We use HDF5 to easily do transfer learning","= 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name","model \"\"\" try: import tensorflow as tf # noqa: F401 import torch #","# device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is","tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \"kernel\"","old format to new format if needed from a PyTorch state_dict old_keys =","format if needed from a PyTorch state_dict old_keys = [] new_keys = []","(base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()):","= numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args","missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight","governing permissions and # limitations under the License. \"\"\" PyTorch - TF 2.0","Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed","# logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys,","Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced","##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False):","tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch","software # distributed under the License is distributed on an \"AS IS\" BASIS,","logging import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"):","tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### # PyTorch => TF","each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub(","array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e:","We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import","in a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute","noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading a PyTorch","model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False):","# '$1___$2' is replaced by $2 (can be used to duplicate or remove","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):","weights in a PyTorch model \"\"\" try: import tensorflow as tf # noqa:","PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys =","# Remove empty levels at the end tf_name = tf_name.split(\"/\") # Convert from","torch # noqa: F401 except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow,","for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model,",") def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF","PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # #####################","Adapt state dict - TODO remove this and update the AWS weights files","pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in","if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif","HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer","the end tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/' separators to PyTorch","== \"beta\": tf_name[-1] = \"bias\" # Remove prefix if needed tf_name = \".\".join(tf_name)","{}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in","and load the associated TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ #","PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle","= numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape)","= re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced by $2 (can","2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF","weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a","TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights from","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","'/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero","for key in pt_state_dict.keys(): new_key = None if \"gamma\" in key: new_key =","weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False)","a PyTorch state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys():","shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] =","Convert old format to new format if needed from a PyTorch state_dict old_keys","\"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters())","model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False):","= tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0","required by applicable law or agreed to in writing, software # distributed under","< len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0)","in a TF 2.0 model. \"\"\" try: import torch # noqa: F401 import","new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able","in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch model\".format(name)) array","if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array =","# Instantiate and load the associated TF 2.0 model tf_model_class_name = \"TF\" +","tuple with: - pytorch model weight name - transpose: boolean indicating weither TF2.0","applicable law or agreed to in writing, software # distributed under the License","import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0",") raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not","\" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict","numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) ==","tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights","Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers","levels at the end tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/' separators","tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" try:","in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._' is","load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load","F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading a PyTorch model","import torch # noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow model in","tensorflow.python.keras import backend as K except ImportError: logger.error( \"Loading a PyTorch model in","array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is","all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore","elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape)","for installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make","(base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()):","or agreed to in writing, software # distributed under the License is distributed","device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced","noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend","pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch","duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue #","re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF","TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name","are transposed with regards to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") #","tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove prefix","torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model(","new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) #","= tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0","logger.info( \"Weights of {} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) )","AttributeError(\"{} not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array =","transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF","is replaced by a new level separation (can be used to convert TF2.0","CONDITIONS OF ANY KIND, either express or implied. # See the License for","logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,}","All rights reserved. # # Licensed under the Apache License, Version 2.0 (the","= [] new_keys = [] for key in pt_state_dict.keys(): new_key = None if","allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if","tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\"","transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape)","model weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices","0: logger.info( \"Weights of {} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys)","files instead # Convert old format to new format if needed from a","\".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights","a map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map =","https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs","a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed.","tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) #","allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a PyTorch model \"\"\" try: import","under the Apache License, Version 2.0 (the \"License\"); # you may not use","in pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name)","writing, software # distributed under the License is distributed on an \"AS IS\"","lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels","\"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove prefix if needed","# Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in","and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs is None: tf_inputs =","return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights","== \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in tf_name) # Convert standard","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","in pt_state_dict.keys(): new_key = None if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\")","dict - TODO remove this and update the AWS weights files instead #","License. # You may obtain a copy of the License at # #","2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy","# limitations under the License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import","def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in a pytorch model","\"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2'","= pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None,","model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # ##################### def","array in pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys:","logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys","as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name))","in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see \"","\"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be","the License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import logging import os","pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch model\".format(name)) array =","tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array)","2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape)","compliance with the License. # You may obtain a copy of the License","if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove prefix if needed tf_name","{}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0 model","a PyTorch model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357).","AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018,","load PyTorch base models as well as derived models (with heads) # TF","installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict","logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable","to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name =","state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{}","list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e","needed from a PyTorch state_dict old_keys = [] new_keys = [] for key","2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the beggining","= tf_name[1:] # Remove level zero # When should we transpose the weights","to duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is replaced","in a PyTorch model \"\"\" try: import tensorflow as tf # noqa: F401","return tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path,",") raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path,","and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights from","PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight in","initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info(","a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed.","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)","def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable name in a","Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All",") # '_._' is replaced by a level separation (can be used to","\"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] =","\".\" # Build a map from potential PyTorch weight names to TF 2.0","try: import torch # noqa: F401 import tensorflow as tf # noqa: F401","pytorch state_dict in a TF 2.0 model. \"\"\" try: import torch # noqa:","tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state dict -","in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF 2.0","bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in tf_name) # Convert","in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) #","PyTorch and TensorFlow to be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for","installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path","names to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name,","convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable name in a pytorch","pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict,","=> PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0","2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model","as well as derived models (with heads) # TF models always have a","tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in a PyTorch model","instructions.\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is","transposed with regards to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device","\"out_projs\" in tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1]","{} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared","used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch","in a TF 2.0 model \"\"\" try: import tensorflow as tf # noqa:","raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name)","load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model","coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace","model state dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{}","don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove =","current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base","as tf # noqa: F401 from tensorflow.python.keras import backend as K except ImportError:","https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path))","= set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items():","set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name,","any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights","not use this file except in compliance with the License. # You may","buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0","= \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove prefix if","= numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape)","array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e:","\"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model,","0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name =","in pytorch model state dict if name not in pt_state_dict: if allow_missing_keys: continue","2.0 model variable name in a pytorch model weight name. Conventions for TF2.0","nn.ModulesList) return tuple with: - pytorch model weight name - transpose: boolean indicating","try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape)","remove layers in TF2.0 vs PyTorch) - '_._' is replaced by a new","License, Version 2.0 (the \"License\"); # you may not use this file except","\"/\" ) # '_._' is replaced by a level separation (can be used","t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model,","array in pytorch model state dict if name not in pt_state_dict: if allow_missing_keys:","# Add \"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config)","TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated","strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of {} not","model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0","- TF 2.0 general utilities.\"\"\" import logging import os import re import numpy","is replaced by $2 (can be used to duplicate or remove layers in","tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs =","F401 except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both PyTorch","HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. # #","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key = None","symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights =","PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel()","TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return","replaced by a new level separation (can be used to convert TF2.0 lists","except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size","or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1]","at the end tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/' separators to","+= (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name))","(can be used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name","Load pytorch state_dict in a TF 2.0 model. \"\"\" try: import torch #","tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built #","len(missing_keys) > 0: logger.info( \"Weights of {} not initialized from TF 2.0 model:","if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are","found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if","model. \"\"\" try: import torch # noqa: F401 import tensorflow as tf #","old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are","=> TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch","load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in a PyTorch","of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for","# you may not use this file except in compliance with the License.","derived models (with heads) # TF models always have a prefix, some of","= \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class = getattr(transformers,","= pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base models","replaced by $2 (can be used to duplicate or remove layers in TF2.0","symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove )","tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic","assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise","checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs,","unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights","in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key = key.replace(\"beta\",","agreed to in writing, software # distributed under the License is distributed on","team. # Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under","= re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at the end tf_name =","key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key)","vs PyTorch) - '_._' is replaced by a new level separation (can be",") # '$1___$2' is replaced by $2 (can be used to duplicate or","tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path,","TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints","\"\"\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5","= tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not","new_key = None if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\"","TF 2.0 model in a pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model,","from potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for","not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF","= numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args","needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name,","(the \"License\"); # you may not use this file except in compliance with","\"Weights from TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights","and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved.","new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch","F401 except ImportError: logger.error( \"Loading a TensorFlow model in PyTorch, requires both PyTorch","PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] ==","raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None:","pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in","TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers","None if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key:","= numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape):","key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key]","if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs,","# Unless required by applicable law or agreed to in writing, software #","\"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\",","is not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True)","- pytorch model weight name - transpose: boolean indicating weither TF2.0 and PyTorch","load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in","1) return tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def","not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 =>","by applicable law or agreed to in writing, software # distributed under the","level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name","general utilities.\"\"\" import logging import os import re import numpy logger = logging.getLogger(__name__)","axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape,","new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure","from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name =","# Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:]","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","Build a map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map","loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch","(can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\",","= (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for","= torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys +=","training=False) # Make sure restore ops are run logger.info(\"Loaded {:,} parameters in the","= tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples","transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape)","by a new level separation (can be used to convert TF2.0 lists in","= torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys)","pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\"","a PyTorch model \"\"\" try: import tensorflow as tf # noqa: F401 import","== list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e #","be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\",","at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is","associated TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at","<NAME>. All rights reserved. # # Licensed under the Apache License, Version 2.0","{} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name]","checkpoints in a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model,","beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs","PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load","pytorch checkpoints in a TF 2.0 model \"\"\" try: import tensorflow as tf","instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and","Load TF 2.0 model in a pytorch model \"\"\" weights = tf_model.weights return","current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr()","TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level","TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by $2","utilities.\"\"\" import logging import os import re import numpy logger = logging.getLogger(__name__) def","()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue","2018 The Google AI Language Team Authors and The HuggingFace Inc. team. #","tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\"Loaded {:,} parameters in","file except in compliance with the License. # You may obtain a copy","TF 2.0 general utilities.\"\"\" import logging import os import re import numpy logger","is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\"Loaded","def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0","sure we are able to load PyTorch base models as well as derived","allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF 2.0 model. \"\"\" try: import","not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\"Loaded {:,}","if len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0 model not used in","built # Adapt state dict - TODO remove this and update the AWS","== \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" #","\"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove","raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if","noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \"Loading a","def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in a","License for the specific language governing permissions and # limitations under the License.","F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as","under the License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import logging import","zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load","if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1]","indicating weither TF2.0 and PyTorch weights matrices are transposed with regards to each","AWS weights files instead # Convert old format to new format if needed","format to new format if needed from a PyTorch state_dict old_keys = []","some of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix)","weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict,","ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced by","weights files instead # Convert old format to new format if needed from","2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights from TF","\"\"\" Load TF 2.0 model in a pytorch model \"\"\" weights = tf_model.weights","dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found","# Remove prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove,","e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name)","to in writing, software # distributed under the License is distributed on an","used to duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is","from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove","by $2 (can be used to duplicate or remove layers in TF2.0 vs","2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in","are run logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers","tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in a","implied. # See the License for the specific language governing permissions and #","# Remove level zero # When should we transpose the weights transpose =","found in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array =","model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) <","\"License\"); # you may not use this file except in compliance with the","Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model,","both PyTorch and TensorFlow to be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/","have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \"\"","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys )","PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced by a","tf # noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error(","start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### # PyTorch =>","if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found in","convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr =","\"\"\" Convert a TF 2.0 model variable name in a pytorch model weight","state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key =","len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except","AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size #","= convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model","logger.error( \"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to","noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow model in PyTorch, requires both","return tuple with: - pytorch model weight name - transpose: boolean indicating weither","array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array","model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class","None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state dict","model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model #####################","F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \"Loading a PyTorch","tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a PyTorch model \"\"\" try:","in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at","PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero # When should","\"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\"","don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove =","TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys","e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF","in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a map from potential","The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. #","# Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def","= tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array =","as derived models (with heads) # TF models always have a prefix, some","permissions and # limitations under the License. \"\"\" PyTorch - TF 2.0 general","associated numpy array in pytorch model state dict if pt_weight_name not in tf_weights_map:","Find associated numpy array in pytorch model state dict if name not in","Add \"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if","missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of {} not initialized from TF","or implied. # See the License for the specific language governing permissions and","in a PyTorch model We use HDF5 to easily do transfer learning (see","Make sure model is built # Adapt state dict - TODO remove this","tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name))","the weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\"","len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0 model not used in {}:","Apache License, Version 2.0 (the \"License\"); # you may not use this file","installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs is None: tf_inputs","\"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key,","if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights","{}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) #","import torch # noqa: F401 except ImportError: logger.error( \"Loading a PyTorch model in","model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: -","# noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading a","def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a PyTorch model","e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name]","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","in writing, software # distributed under the License is distributed on an \"AS","models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in","PyTorch nn.ModulesList) return tuple with: - pytorch model weight name - transpose: boolean","Make sure we are able to load PyTorch base models as well as","None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\"Loaded {:,} parameters","PyTorch model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\"","in tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1] ==","Make sure restore ops are run logger.info(\"Loaded {:,} parameters in the TF 2.0","PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at the","all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0:","Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:","remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) #","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name)","symbolic weights in a PyTorch model \"\"\" try: import tensorflow as tf #","tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced by a level separation (can","tf_name[1:] # Remove level zero # When should we transpose the weights transpose","weither TF2.0 and PyTorch weights matrices are transposed with regards to each other","# Adapt state dict - TODO remove this and update the AWS weights","for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove","return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\"","return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\"","transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys()))","model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded","noqa: F401 except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both","\"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\"","in a pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def","by a level separation (can be used to convert TF2.0 lists in PyTorch","(with heads) # TF models always have a prefix, some of PyTorch models","= symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy","start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0","a TF 2.0 model \"\"\" try: import tensorflow as tf # noqa: F401","see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs is","or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1] =","return tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model,","in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array)","+ tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight","tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is","all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in","= key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if new_key:","logger.info(\"Weights or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### #","tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model,","new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to","tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove","+= (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)","tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as K except","\"beta\": tf_name[-1] = \"bias\" # Remove prefix if needed tf_name = \".\".join(tf_name) if","the Apache License, Version 2.0 (the \"License\"); # you may not use this","a new level separation (can be used to convert TF2.0 lists in PyTorch","tf_name or \"out_projs\" in tf_name) # Convert standard TF2.0 names in PyTorch names","tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path,","list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize","you may not use this file except in compliance with the License. #","> 0: logger.info( \"Weights from TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__,","(pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()]",") def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF","< len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0)","transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with regards","state dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not","models always have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove","transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight","TODO remove this and update the AWS weights files instead # Convert old","pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base","+= array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs","the AWS weights files instead # Convert old format to new format if","array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape):","if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### # PyTorch","{}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded from TF 2.0 model:","attribute names conversions: - '$1___$2' is replaced by $2 (can be used to","layers in TF2.0 vs PyTorch) - '_._' is replaced by a new level","regards to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name","not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array)","the associated TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\"","= \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose #####################","torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) >","in a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict,","getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if","or remove layers in TF2.0 vs PyTorch) - '_._' is replaced by a","we are able to load PyTorch base models as well as derived models","use this file except in compliance with the License. # You may obtain","for the specific language governing permissions and # limitations under the License. \"\"\"","= torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return","tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs","noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow","not found in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array","2.0 model \"\"\" try: import tensorflow as tf # noqa: F401 import torch","len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array,","name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch","Convert a TF 2.0 model variable name in a pytorch model weight name.","model in a pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys)","see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict = {}","\"\"\" Load TF2.0 symbolic weights in a PyTorch model \"\"\" try: import tensorflow","TF2.0 symbolic weights in a PyTorch model \"\"\" try: import tensorflow as tf","TF2.0 vs PyTorch) - '_._' is replaced by a new level separation (can","weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for","if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key)","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we","tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class =","+ pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model","https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) #","missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of {} not initialized","\"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict()","pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base models as","== \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\"","# Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \"kernel\" or","Remove level zero # When should we transpose the weights transpose = bool(tf_name[-1]","K except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both PyTorch","a level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)","load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model in a pytorch model \"\"\"","= dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base models","tf_name[-1] = \"bias\" # Remove prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove:","of {} not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys)","{}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False)","pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict","\"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\": tf_name[-1]","new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if","2.0 (the \"License\"); # you may not use this file except in compliance","logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable name in","PyTorch - TF 2.0 general utilities.\"\"\" import logging import os import re import","len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as","e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples)","# noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \"Loading","re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at the end tf_name = tf_name.split(\"/\")","load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a PyTorch model \"\"\"","r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced by $2 (can be used","contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys","- TODO remove this and update the AWS weights files instead # Convert","Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path =","import logging import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name,","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","a TF 2.0 model variable name in a pytorch model weight name. Conventions","and PyTorch weights matrices are transposed with regards to each other \"\"\" tf_name","tf_name ) # '$1___$2' is replaced by $2 (can be used to duplicate","as tf # noqa: F401 import torch # noqa: F401 except ImportError: logger.error(","logger.info( \"Weights from TF 2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) )","load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load","heads) # TF models always have a prefix, some of PyTorch models (base","\" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow","new_keys = [] for key in pt_state_dict.keys(): new_key = None if \"gamma\" in","lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name -","weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in","\" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs is None:","# # Unless required by applicable law or agreed to in writing, software","start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix","Load pytorch checkpoints in a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return","specific language governing permissions and # limitations under the License. \"\"\" PyTorch -","backend as K except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires","= None if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in","continue raise AttributeError(\"{} not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose:","+ \".\" # Build a map from potential PyTorch weight names to TF","express or implied. # See the License for the specific language governing permissions","be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise","transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in tf_name)","TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see \" \"https://pytorch.org/","key: new_key = key.replace(\"gamma\", \"weight\") if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\")","model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see","elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape)","load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model","= pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of","\"/\", tf_name) # Remove empty levels at the end tf_name = tf_name.split(\"/\") #","weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2'","except ImportError: logger.error( \"Loading a TensorFlow model in PyTorch, requires both PyTorch and","= set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name(","allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" try: import","= tf_name.split(\"/\") # Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name","pytorch checkpoints in a TF 2.0 model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model(","requires both PyTorch and TensorFlow to be installed. Please see \" \"https://pytorch.org/ and","pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights,","the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch model: {}\".format(all_pytorch_weights))","allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a","import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as K","either express or implied. # See the License for the specific language governing","old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make","boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with regards to","##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in","associated numpy array in pytorch model state dict if name not in pt_state_dict:","model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape):","vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced by","empty levels at the end tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/'","array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure","not None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state","raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able","matrices are transposed with regards to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\")","if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array =","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","be used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name =","TensorFlow to be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\"","array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] =","(see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf # noqa: F401 import torch","numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array","- '$1___$2' is replaced by $2 (can be used to duplicate or remove","tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in","in tf_name or \"out_projs\" in tf_name) # Convert standard TF2.0 names in PyTorch","tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF","pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel =","and TensorFlow to be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation","K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops","in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0 if","\"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import logging import os import re","Load pytorch checkpoints in a TF 2.0 model \"\"\" try: import tensorflow as","PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5","update the AWS weights files instead # Convert old format to new format","separators tf_name = tf_name[1:] # Remove level zero # When should we transpose","len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try:","numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args +=","as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize","be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: -","= tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced by a level separation","tf_name[-1] == \"beta\": tf_name[-1] = \"bias\" # Remove prefix if needed tf_name =","array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert","# TF models always have a prefix, some of PyTorch models (base ones)","new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state","\"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\"","model is built # Adapt state dict - TODO remove this and update","array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert","numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) ==","the License. # You may obtain a copy of the License at #","F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow model","tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in","logger.error( \"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to","some of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix)","start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix","current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a map from potential PyTorch","limitations under the License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import logging","used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace(","= key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys):","HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as","names conversions: - '$1___$2' is replaced by $2 (can be used to duplicate","# distributed under the License is distributed on an \"AS IS\" BASIS, #","numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array","import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated","to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) #","\"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\" try: import tensorflow","# '_._' is replaced by a level separation (can be used to convert","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight","\"bias\" # Remove prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name =","TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the","# noqa: F401 except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires","new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys","PyTorch base models as well as derived models (with heads) # TF models","# PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\"","\"\"\" try: import tensorflow as tf # noqa: F401 import torch # noqa:","name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in","this and update the AWS weights files instead # Convert old format to","# Make sure we are able to load PyTorch base models as well","tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in","\"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs","for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in","= tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model","{:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys )","\"weight\") if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key)","sure restore ops are run logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel))","in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None,","the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None:","not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights =","+= missing_keys_pt if len(missing_keys) > 0: logger.info( \"Weights of {} not initialized from","separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name =","tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0 #","= convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr","tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at the end tf_name","by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0","TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily","or \"emb_projs\" in tf_name or \"out_projs\" in tf_name) # Convert standard TF2.0 names","in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name - transpose:","in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded from TF 2.0","except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow, requires both PyTorch and","with the License. # You may obtain a copy of the License at","r\"/\\1/\", tf_name ) # '$1___$2' is replaced by $2 (can be used to","layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._'","# ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint","any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a","tensorflow as tf # noqa: F401 import torch # noqa: F401 except ImportError:","do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf # noqa:","transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch","tf_name = tf_name[1:] # Remove level zero # When should we transpose the","Remove prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\",","None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make","= logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable name","= getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs","# Make sure restore ops are run logger.info(\"Loaded {:,} parameters in the TF","zero # When should we transpose the weights transpose = bool(tf_name[-1] == \"kernel\"","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise","ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove","2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name(","remove this and update the AWS weights files instead # Convert old format","TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs,","TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if","dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not",") if len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0 model not used","PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can be used","= tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name )","model variable name in a pytorch model weight name. Conventions for TF2.0 scopes","Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c)","pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False):",") # Find associated numpy array in pytorch model state dict if name","raise AttributeError(\"{} not found in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array","PyTorch) - '_._' is replaced by a new level separation (can be used","rights reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\");","TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose =","pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue","Remove empty levels at the end tf_name = tf_name.split(\"/\") # Convert from TF2.0","is not None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt","not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\"{} not found in PyTorch model\".format(name))","for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] =","re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced by $2 (can be","\"\"\" Load pytorch state_dict in a TF 2.0 model. \"\"\" try: import torch","map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains {:,} parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model,","a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if","The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright","loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model","in key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key","missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info(","law or agreed to in writing, software # distributed under the License is","tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights:","the License for the specific language governing permissions and # limitations under the","# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc.","scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can","import torch # noqa: F401 import tensorflow as tf # noqa: F401 from","PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) <","= \"bias\" # Remove prefix if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name","Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if tf_inputs","tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None:","in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find","transpose the weights transpose = bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or","model \"\"\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def","list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel +=","return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \"\"\" Load TF 2.0 model","and update the AWS weights files instead # Convert old format to new","allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in a PyTorch","model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see","instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we","# Convert old format to new format if needed from a PyTorch state_dict","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please","if len(missing_keys) > 0: logger.info( \"Weights of {} not initialized from TF 2.0","2018, <NAME>. All rights reserved. # # Licensed under the Apache License, Version","pytorch model state dict if name not in pt_state_dict: if allow_missing_keys: continue raise","$2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)","TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\" ) # '_._' is replaced","new format if needed from a PyTorch state_dict old_keys = [] new_keys =","# Make sure model is built # Adapt state dict - TODO remove","and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights","torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt","logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None:","PyTorch weights matrices are transposed with regards to each other \"\"\" tf_name =","weights matrices are transposed with regards to each other \"\"\" tf_name = tf_name.replace(\":0\",","pt_state_dict.keys(): new_key = None if \"gamma\" in key: new_key = key.replace(\"gamma\", \"weight\") if","end tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/' separators to PyTorch '.'","TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model","names in PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or","with regards to each other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids","duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\", \"/\"","len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array,","pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model =","and # limitations under the License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\"","nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty levels at the end","from a PyTorch state_dict old_keys = [] new_keys = [] for key in","standard TF2.0 names in PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1] ==","TF 2.0 model \"\"\" try: import tensorflow as tf # noqa: F401 import","all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose =","[] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated","start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state dict if","(can be used to duplicate or remove layers in TF2.0 vs PyTorch) -","tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure","numpy array in pytorch model state dict if name not in pt_state_dict: if","see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers logger.info(\"Loading","in compliance with the License. # You may obtain a copy of the",") logger.info(\"Weights or buffers not loaded from TF 2.0 model: {}\".format(all_tf_weights)) return pt_model","[] for key in pt_state_dict.keys(): new_key = None if \"gamma\" in key: new_key","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name","\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights","try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape)","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","ops are run logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or","TF2.0 names in PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\"","installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise if","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" #","if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array","allow_missing_keys=False): \"\"\" Load TF 2.0 model in a pytorch model \"\"\" weights =","= {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch","\" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading","run logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not","if \"beta\" in key: new_key = key.replace(\"beta\", \"bias\") if new_key: old_keys.append(key) new_keys.append(new_key) for","for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by","= \"\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix +","= numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape):","torch # noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow model in PyTorch,","# noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import","See the License for the specific language governing permissions and # limitations under","allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a","use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\")","s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a map from","variable name in a pytorch model weight name. Conventions for TF2.0 scopes ->","installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise import","continue raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name]","pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False):","numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args +=","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","import tensorflow as tf # noqa: F401 import torch # noqa: F401 except","TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load","= pt_model.base_model_prefix + \".\" # Build a map from potential PyTorch weight names","names if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\":","key in pt_state_dict.keys(): new_key = None if \"gamma\" in key: new_key = key.replace(\"gamma\",","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] =","parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded from PyTorch","torch # noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras","https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf # noqa: F401 import torch #","a pytorch model \"\"\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model,","PyTorch model \"\"\" try: import tensorflow as tf # noqa: F401 import torch","(tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name,","to be installed. Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" )","# ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a","separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero #","import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a","tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run","(can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with:","missing_keys) ) if len(unexpected_keys) > 0: logger.info( \"Weights from TF 2.0 model not","for installation instructions.\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if","\"emb_projs\" in tf_name or \"out_projs\" in tf_name) # Convert standard TF2.0 names in","License. \"\"\" PyTorch - TF 2.0 general utilities.\"\"\" import logging import os import","checkpoints in a TF 2.0 model \"\"\" try: import tensorflow as tf #","state_dict in a TF 2.0 model. \"\"\" try: import torch # noqa: F401","restore ops are run logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights","replaced by a level separation (can be used to convert TF2.0 lists in","from tensorflow.python.keras import backend as K except ImportError: logger.error( \"Loading a PyTorch model","{}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model,","to duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \"_._\",","in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see \"","# Build a map from potential PyTorch weight names to TF 2.0 Variables","PyTorch, requires both PyTorch and TensorFlow to be installed. Please see \" \"https://pytorch.org/","None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model,","are able to load PyTorch base models as well as derived models (with","not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build","with: - pytorch model weight name - transpose: boolean indicating weither TF2.0 and","try: import tensorflow as tf # noqa: F401 import torch # noqa: F401","= os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint","= tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys()))","> len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError","numpy array in pytorch model state dict if pt_weight_name not in tf_weights_map: if","PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for s","base models as well as derived models (with heads) # TF models always","in PyTorch model\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape)","numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model","logger.info(\"Loaded {:,} parameters in the TF 2.0 model.\".format(tf_loaded_numel)) logger.info(\"Weights or buffers not loaded","able to load PyTorch base models as well as derived models (with heads)","https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from","for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights +","the specific language governing permissions and # limitations under the License. \"\"\" PyTorch","from PyTorch model: {}\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch #","loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state dict if","start_prefix_to_remove=\"\"): \"\"\" Convert a TF 2.0 model variable name in a pytorch model","if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found in TF 2.0 model\".format(pt_weight_name)) array,","'_._' is replaced by a new level separation (can be used to convert","name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is","name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed","Version 2.0 (the \"License\"); # you may not use this file except in","{}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded from TF 2.0 model: {}\".format(all_tf_weights))","except in compliance with the License. # You may obtain a copy of","if needed tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return","in TF2.0 vs PyTorch) - '_._' is replaced by a new level separation","tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {}","instead # Convert old format to new format if needed from a PyTorch","load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch state_dict in a TF 2.0 model.","of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(tf_model.base_model_prefix) for","tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(),","\"_._\", \"/\" ) # '_._' is replaced by a level separation (can be","pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0 model \"\"\"","start_prefix_to_remove = pt_model.base_model_prefix + \".\" # Build a map from potential PyTorch weight","installation instructions.\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs","- transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with","in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel","continue # Find associated numpy array in pytorch model state dict if pt_weight_name","-> PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can be","tf_name = re.sub( r\"/[^/]*___([^/]*)/\", r\"/\\1/\", tf_name ) # '$1___$2' is replaced by $2","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","pytorch model weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights","in PyTorch names if tf_name[-1] == \"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1]",") raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load","for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path))","dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base models as","not initialized from TF 2.0 model: {}\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0:","weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights:","== \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if tf_name[-1] == \"beta\":","\"kernel\" or tf_name[-1] == \"embeddings\" or tf_name[-1] == \"gamma\": tf_name[-1] = \"weight\" if","Please see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict =","\"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in tf_name) # Convert standard TF2.0","allow_missing_keys=False): \"\"\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We use","other \"\"\" tf_name = tf_name.replace(\":0\", \"\") # device ids tf_name = re.sub( r\"/[^/]*___([^/]*)/\",","# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The","os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch weights from {}\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\"cpu\") logger.info(\"PyTorch checkpoint contains","weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name","Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>.","transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array","- '_._' is replaced by a new level separation (can be used to","pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array)","load the associated TF 2.0 model tf_model_class_name = \"TF\" + pt_model.__class__.__name__ # Add","see \" \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path)","in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch","PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name]","TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\"//+\", \"/\", tf_name) # Remove empty","load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \"\"\" Load TF2.0 symbolic weights in","be used to duplicate or remove layers in TF2.0 vs PyTorch) - '_._'","Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.","\".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose ##################### #","ones) don't start_prefix_to_remove = \"\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove","tf_name.split(\"/\") # Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name =","+ \".\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = []","set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): #","pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0","# TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\"","convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state","# noqa: F401 except ImportError: logger.error( \"Loading a TensorFlow model in PyTorch, requires","# Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under the","'$1___$2' is replaced by $2 (can be used to duplicate or remove layers","map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {}","\"TF\" + pt_model.__class__.__name__ # Add \"TF\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name)","TF 2.0 model variable name in a pytorch model weight name. Conventions for","sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model,","def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch checkpoints in a TF 2.0","prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \"\" if not","import backend as K except ImportError: logger.error( \"Loading a PyTorch model in TensorFlow,","# logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not","os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\"\"): \"\"\" Convert","= bool(tf_name[-1] == \"kernel\" or \"emb_projs\" in tf_name or \"out_projs\" in tf_name) #","tf # noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \"Loading","Load TF2.0 symbolic weights in a PyTorch model \"\"\" try: import tensorflow as","e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] =","tf_name = \".\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \"\", 1) return tf_name, transpose","= {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove )","learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try: import tensorflow as tf # noqa: F401 import","if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\"{} not found","not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not loaded from","\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\"Loading PyTorch","tf_name = tf_name.split(\"/\") # Convert from TF2.0 '/' separators to PyTorch '.' separators","raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) # Instantiate and load the","parameters\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def","raise e # logger.warning(\"Initialize PyTorch weight {}\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)","model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \"\"\" try:",") raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are","models as well as derived models (with heads) # TF models always have","not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return","pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif","TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please","tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = []","2.0 model not used in {}: {}\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\"Weights or buffers not","tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for","state dict - TODO remove this and update the AWS weights files instead","for installation instructions.\" ) raise import transformers logger.info(\"Loading TensorFlow weights from {}\".format(tf_checkpoint_path)) #","{} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch","pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights =","tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if","Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name,","\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\" ) raise new_pt_params_dict = {} current_pt_params_dict =","a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names","list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e","(symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\"Initialize TF weight {}\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight,","tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \"\"\" Load pytorch","models (with heads) # TF models always have a prefix, some of PyTorch","level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return","Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] #","> len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError"],"string":"[\n \"weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make\",\n \"separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple\",\n \"allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a pytorch\",\n \"tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We\",\n \"len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"# Find associated numpy array in pytorch model state dict if name not\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"to load PyTorch base models as well as derived models (with heads) #\",\n \"array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) >\",\n \"== list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel\",\n \"{:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from\",\n \"> 0: logger.info( \\\"Weights of {} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__,\",\n \"[] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose\",\n \"transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401\",\n \"s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights\",\n \"is replaced by a level separation (can be used to convert TF2.0 lists\",\n \"instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict =\",\n \"sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state dict\",\n \"When should we transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\"\",\n \"to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero # When\",\n \"pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints\",\n \"\\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key =\",\n \"or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF\",\n \"or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" )\",\n \"missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose =\",\n \"pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of {}\",\n \"name in a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch\",\n \"in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose)\",\n \"len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try:\",\n \"array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array))\",\n \"if needed from a PyTorch state_dict old_keys = [] new_keys = [] for\",\n \"if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built\",\n \"new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys,\",\n \"from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"well as derived models (with heads) # TF models always have a prefix,\",\n \"Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights\",\n \"2.0 model. \\\"\\\"\\\" try: import torch # noqa: F401 import tensorflow as tf\",\n \"tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\" try:\",\n \"symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find\",\n \"AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight\",\n \"= pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array =\",\n \"{} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) >\",\n \") tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt =\",\n \"is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) #\",\n \"is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array\",\n \"training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys)\",\n \"allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose\",\n \"weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name\",\n \"as K except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both\",\n \"(c) 2018, <NAME>. All rights reserved. # # Licensed under the Apache License,\",\n \"installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict\",\n \"if allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy()\",\n \"Instantiate and load the associated TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__\",\n \"weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()]\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose:\",\n \"should we transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in\",\n \"\\\"\\\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0 # #####################\",\n \"used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from TF\",\n \"e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight\",\n \"prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1)\",\n \"pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions:\",\n \"for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a map\",\n \"'.' separators tf_name = tf_name[1:] # Remove level zero # When should we\",\n \"always have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove =\",\n \"array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) >\",\n \"TF 2.0 model. \\\"\\\"\\\" try: import torch # noqa: F401 import tensorflow as\",\n \"to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf\",\n \"we transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name\",\n \"2.0 model in a pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights,\",\n \"import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\"\",\n \"easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf #\",\n \"tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by a level\",\n \"except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch\",\n \"ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow\",\n \"logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0\",\n \"loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if\",\n \"\\\"Weights of {} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"'_._' is replaced by a level separation (can be used to convert TF2.0\",\n \"a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs,\",\n \"if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key\",\n \"len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as\",\n \"\\\"\\\"\\\" try: import torch # noqa: F401 import tensorflow as tf # noqa:\",\n \"pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\"\",\n \"models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict =\",\n \"pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values())))\",\n \"sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated\",\n \"reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t\",\n \"PyTorch state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key\",\n \"= [] for key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key:\",\n \"[] new_keys = [] for key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\"\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None,\",\n \"to new format if needed from a PyTorch state_dict old_keys = [] new_keys\",\n \"weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are\",\n \"unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from TF 2.0 model: {}\\\".format(all_tf_weights)) return\",\n \"convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove\",\n \"= [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not\",\n \"tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a pytorch model \\\"\\\"\\\" weights\",\n \"convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight\",\n \"tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict\",\n \"conversions: - '$1___$2' is replaced by $2 (can be used to duplicate or\",\n \"in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch model:\",\n \"built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is\",\n \"# When should we transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or\",\n \"tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] ==\",\n \"key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in\",\n \"2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"language governing permissions and # limitations under the License. \\\"\\\"\\\" PyTorch - TF\",\n \"pt_model.base_model_prefix + \\\".\\\" # Build a map from potential PyTorch weight names to\",\n \"##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF\",\n \"to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model\",\n \"{}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name = \\\"TF\\\"\",\n \"ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow\",\n \"def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF 2.0\",\n \"Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to\",\n \"Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1]\",\n \"Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under the Apache\",\n \"in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to\",\n \"tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples =\",\n \"a TF 2.0 model. \\\"\\\"\\\" try: import torch # noqa: F401 import tensorflow\",\n \"weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict,\",\n \"= [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name,\",\n \"duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is replaced by\",\n \"level zero # When should we transpose the weights transpose = bool(tf_name[-1] ==\",\n \"= {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load\",\n \"axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape,\",\n \"TF2.0 and PyTorch weights matrices are transposed with regards to each other \\\"\\\"\\\"\",\n \"2.0 general utilities.\\\"\\\"\\\" import logging import os import re import numpy logger =\",\n \"sure model is built # Adapt state dict - TODO remove this and\",\n \"= loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state dict\",\n \"0: logger.info( \\\"Weights from TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys)\",\n \"assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise\",\n \"from TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or\",\n \"= \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix +\",\n \"start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt\",\n \"# Find associated numpy array in pytorch model state dict if pt_weight_name not\",\n \"training=False) # Make sure model is built # Adapt state dict - TODO\",\n \"or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names in PyTorch names if\",\n \"tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False)\",\n \"tf_name) # Remove empty levels at the end tf_name = tf_name.split(\\\"/\\\") # Convert\",\n \"is built # Adapt state dict - TODO remove this and update the\",\n \"Find associated numpy array in pytorch model state dict if pt_weight_name not in\",\n \"tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None,\",\n \"to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose\",\n \"TF models always have a prefix, some of PyTorch models (base ones) don't\",\n \"installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure\",\n \"checkpoint in a PyTorch model We use HDF5 to easily do transfer learning\",\n \"= 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name\",\n \"model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch #\",\n \"# device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is\",\n \"tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \\\"kernel\\\"\",\n \"old format to new format if needed from a PyTorch state_dict old_keys =\",\n \"format if needed from a PyTorch state_dict old_keys = [] new_keys = []\",\n \"(base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()):\",\n \"= numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args\",\n \"missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight\",\n \"governing permissions and # limitations under the License. \\\"\\\"\\\" PyTorch - TF 2.0\",\n \"Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed\",\n \"# logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys,\",\n \"Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced\",\n \"##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False):\",\n \"tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"logging import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"):\",\n \"tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### # PyTorch => TF\",\n \"each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub(\",\n \"array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e:\",\n \"We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import\",\n \"in a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute\",\n \"noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch\",\n \"model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False):\",\n \"# '$1___$2' is replaced by $2 (can be used to duplicate or remove\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):\",\n \"weights in a PyTorch model \\\"\\\"\\\" try: import tensorflow as tf # noqa:\",\n \"PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys =\",\n \"# Remove empty levels at the end tf_name = tf_name.split(\\\"/\\\") # Convert from\",\n \"torch # noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow,\",\n \"for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model,\",\n \") def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF\",\n \"PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # #####################\",\n \"Adapt state dict - TODO remove this and update the AWS weights files\",\n \"pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in\",\n \"if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif\",\n \"HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer\",\n \"the end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to PyTorch\",\n \"== \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if needed tf_name = \\\".\\\".join(tf_name)\",\n \"{}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in\",\n \"and load the associated TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ #\",\n \"PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle\",\n \"= numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape)\",\n \"= re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by $2 (can\",\n \"2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF\",\n \"weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a\",\n \"TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights from\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"'/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero\",\n \"for key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key: new_key =\",\n \"weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False)\",\n \"a PyTorch state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys():\",\n \"shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] =\",\n \"Convert old format to new format if needed from a PyTorch state_dict old_keys\",\n \"\\\"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters())\",\n \"model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False):\",\n \"= tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"< len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0)\",\n \"in a TF 2.0 model. \\\"\\\"\\\" try: import torch # noqa: F401 import\",\n \"new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able\",\n \"in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array\",\n \"if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array =\",\n \"# Instantiate and load the associated TF 2.0 model tf_model_class_name = \\\"TF\\\" +\",\n \"tuple with: - pytorch model weight name - transpose: boolean indicating weither TF2.0\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0\",\n \") raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not\",\n \"\\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict\",\n \"numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) ==\",\n \"tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights\",\n \"Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers\",\n \"levels at the end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators\",\n \"tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" try:\",\n \"in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is\",\n \"load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch model\",\n \"import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow model in\",\n \"tensorflow.python.keras import backend as K except ImportError: logger.error( \\\"Loading a PyTorch model in\",\n \"array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is\",\n \"all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore\",\n \"elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape)\",\n \"for installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make\",\n \"(base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()):\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced\",\n \"noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend\",\n \"pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch\",\n \"duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue #\",\n \"re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF\",\n \"TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name\",\n \"are transposed with regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") #\",\n \"tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix\",\n \"torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model(\",\n \"new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) #\",\n \"= tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0\",\n \"logger.info( \\\"Weights of {} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) )\",\n \"AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array =\",\n \"transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF\",\n \"is replaced by a new level separation (can be used to convert TF2.0\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,}\",\n \"All rights reserved. # # Licensed under the Apache License, Version 2.0 (the\",\n \"= [] new_keys = [] for key in pt_state_dict.keys(): new_key = None if\",\n \"allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if\",\n \"tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\"\",\n \"transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape)\",\n \"model weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices\",\n \"0: logger.info( \\\"Weights of {} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys)\",\n \"files instead # Convert old format to new format if needed from a\",\n \"\\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights\",\n \"a map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map =\",\n \"https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs\",\n \"a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed.\",\n \"tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) #\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\" try: import\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"in pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name)\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels\",\n \"\\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if needed\",\n \"# Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in\",\n \"and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs is None: tf_inputs =\",\n \"return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights\",\n \"== \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) # Convert standard\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\")\",\n \"dict - TODO remove this and update the AWS weights files instead #\",\n \"License. # You may obtain a copy of the License at # #\",\n \"2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy\",\n \"# limitations under the License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import\",\n \"def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a pytorch model\",\n \"\\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2'\",\n \"= pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None,\",\n \"model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # ##################### def\",\n \"array in pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys:\",\n \"logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys\",\n \"as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name))\",\n \"in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see \\\"\",\n \"\\\"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be\",\n \"the License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging import os\",\n \"pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array =\",\n \"tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array)\",\n \"2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape)\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if needed tf_name\",\n \"{}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0 model\",\n \"a PyTorch model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357).\",\n \"AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018,\",\n \"load PyTorch base models as well as derived models (with heads) # TF\",\n \"installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict\",\n \"logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable\",\n \"to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name =\",\n \"state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{}\",\n \"list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e\",\n \"needed from a PyTorch state_dict old_keys = [] new_keys = [] for key\",\n \"2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining\",\n \"= tf_name[1:] # Remove level zero # When should we transpose the weights\",\n \"to duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is replaced\",\n \"in a PyTorch model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401\",\n \"return tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path,\",\n \") raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path,\",\n \"and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights from\",\n \"PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight in\",\n \"initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info(\",\n \"a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed.\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)\",\n \"def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable name in a\",\n \"Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All\",\n \") # '_._' is replaced by a level separation (can be used to\",\n \"\\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] =\",\n \"\\\".\\\" # Build a map from potential PyTorch weight names to TF 2.0\",\n \"try: import torch # noqa: F401 import tensorflow as tf # noqa: F401\",\n \"pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\" try: import torch # noqa:\",\n \"tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state dict -\",\n \"in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF 2.0\",\n \"bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) # Convert\",\n \"in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) #\",\n \"PyTorch and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for\",\n \"installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path\",\n \"names to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name,\",\n \"convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable name in a pytorch\",\n \"pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict,\",\n \"=> PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0\",\n \"2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model\",\n \"as well as derived models (with heads) # TF models always have a\",\n \"tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a PyTorch model\",\n \"instructions.\\\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is\",\n \"transposed with regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device\",\n \"\\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1]\",\n \"{} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared\",\n \"used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: - pytorch\",\n \"in a TF 2.0 model \\\"\\\"\\\" try: import tensorflow as tf # noqa:\",\n \"raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name)\",\n \"load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model\",\n \"coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace\",\n \"model state dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{}\",\n \"don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove =\",\n \"current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base\",\n \"as tf # noqa: F401 from tensorflow.python.keras import backend as K except ImportError:\",\n \"https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path))\",\n \"= set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items():\",\n \"set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name,\",\n \"any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights\",\n \"not use this file except in compliance with the License. # You may\",\n \"buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0\",\n \"= \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if\",\n \"= numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape)\",\n \"array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e:\",\n \"\\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model,\",\n \"0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name =\",\n \"in pytorch model state dict if name not in pt_state_dict: if allow_missing_keys: continue\",\n \"2.0 model variable name in a pytorch model weight name. Conventions for TF2.0\",\n \"nn.ModulesList) return tuple with: - pytorch model weight name - transpose: boolean indicating\",\n \"try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape)\",\n \"remove layers in TF2.0 vs PyTorch) - '_._' is replaced by a new\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"\\\"/\\\" ) # '_._' is replaced by a level separation (can be used\",\n \"t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model,\",\n \"array in pytorch model state dict if name not in pt_state_dict: if allow_missing_keys:\",\n \"# Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config)\",\n \"TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated\",\n \"strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of {} not\",\n \"model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0\",\n \"- TF 2.0 general utilities.\\\"\\\"\\\" import logging import os import re import numpy\",\n \"is replaced by $2 (can be used to duplicate or remove layers in\",\n \"tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs =\",\n \"F401 except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both PyTorch\",\n \"HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. # #\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key = None\",\n \"symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights =\",\n \"PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel()\",\n \"TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return\",\n \"replaced by a new level separation (can be used to convert TF2.0 lists\",\n \"except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size\",\n \"or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1]\",\n \"at the end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to\",\n \"+= (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name))\",\n \"(can be used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name\",\n \"Load pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\" try: import torch #\",\n \"tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built #\",\n \"len(missing_keys) > 0: logger.info( \\\"Weights of {} not initialized from TF 2.0 model:\",\n \"if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are\",\n \"found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if\",\n \"model. \\\"\\\"\\\" try: import torch # noqa: F401 import tensorflow as tf #\",\n \"old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are\",\n \"=> TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch\",\n \"load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a PyTorch\",\n \"of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for\",\n \"# you may not use this file except in compliance with the License.\",\n \"derived models (with heads) # TF models always have a prefix, some of\",\n \"= \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers,\",\n \"= pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base models\",\n \"replaced by $2 (can be used to duplicate or remove layers in TF2.0\",\n \"symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove )\",\n \"tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic\",\n \"assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise\",\n \"checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs,\",\n \"unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights\",\n \"in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\",\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"team. # Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under\",\n \"= re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at the end tf_name =\",\n \"key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key)\",\n \"vs PyTorch) - '_._' is replaced by a new level separation (can be\",\n \") # '$1___$2' is replaced by $2 (can be used to duplicate or\",\n \"tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path,\",\n \"TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints\",\n \"\\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5\",\n \"= tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not\",\n \"new_key = None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\"\",\n \"TF 2.0 model in a pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model,\",\n \"from potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for\",\n \"not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF\",\n \"= numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args\",\n \"needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name,\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"\\\"Weights from TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights\",\n \"and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved.\",\n \"new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch\",\n \"F401 except ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch, requires both PyTorch\",\n \"PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] ==\",\n \"raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None:\",\n \"pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in\",\n \"TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers\",\n \"None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key:\",\n \"= numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape):\",\n \"key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key]\",\n \"if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs,\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"\\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\",\",\n \"is not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True)\",\n \"- pytorch model weight name - transpose: boolean indicating weither TF2.0 and PyTorch\",\n \"load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in\",\n \"1) return tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def\",\n \"not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 =>\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name\",\n \"general utilities.\\\"\\\"\\\" import logging import os import re import numpy logger = logging.getLogger(__name__)\",\n \"axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape,\",\n \"new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure\",\n \"from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model tf_model_class_name =\",\n \"# Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:]\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"Build a map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map\",\n \"loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch\",\n \"(can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\",\",\n \"= (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for\",\n \"= torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys +=\",\n \"training=False) # Make sure restore ops are run logger.info(\\\"Loaded {:,} parameters in the\",\n \"= tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples\",\n \"transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape)\",\n \"by a new level separation (can be used to convert TF2.0 lists in\",\n \"= torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys)\",\n \"pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\"\",\n \"a PyTorch model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import\",\n \"== list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e #\",\n \"be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\",\",\n \"at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is\",\n \"associated TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at\",\n \"<NAME>. All rights reserved. # # Licensed under the Apache License, Version 2.0\",\n \"{} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name]\",\n \"checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model,\",\n \"beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs\",\n \"PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" try: import tensorflow as tf\",\n \"instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and\",\n \"Load TF 2.0 model in a pytorch model \\\"\\\"\\\" weights = tf_model.weights return\",\n \"current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr()\",\n \"TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level\",\n \"TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by $2\",\n \"utilities.\\\"\\\"\\\" import logging import os import re import numpy logger = logging.getLogger(__name__) def\",\n \"()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue\",\n \"2018 The Google AI Language Team Authors and The HuggingFace Inc. team. #\",\n \"tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\\\"Loaded {:,} parameters in\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"TF 2.0 general utilities.\\\"\\\"\\\" import logging import os import re import numpy logger\",\n \"is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\\\"Loaded\",\n \"def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0\",\n \"sure we are able to load PyTorch base models as well as derived\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\" try: import\",\n \"not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\\\"Loaded {:,}\",\n \"if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0 model not used in\",\n \"built # Adapt state dict - TODO remove this and update the AWS\",\n \"== \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" #\",\n \"\\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove\",\n \"raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if\",\n \"noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \\\"Loading a\",\n \"def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as\",\n \"under the License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging import\",\n \"zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load\",\n \"if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1]\",\n \"indicating weither TF2.0 and PyTorch weights matrices are transposed with regards to each\",\n \"AWS weights files instead # Convert old format to new format if needed\",\n \"format to new format if needed from a PyTorch state_dict old_keys = []\",\n \"some of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix)\",\n \"weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict,\",\n \"ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by\",\n \"weights files instead # Convert old format to new format if needed from\",\n \"2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF\",\n \"\\\"\\\"\\\" Load TF 2.0 model in a pytorch model \\\"\\\"\\\" weights = tf_model.weights\",\n \"dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found\",\n \"# Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove,\",\n \"e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name)\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"used to duplicate or remove layers in TF2.0 vs PyTorch) - '_._' is\",\n \"from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove\",\n \"by $2 (can be used to duplicate or remove layers in TF2.0 vs\",\n \"2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in\",\n \"are run logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers\",\n \"tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"# Remove level zero # When should we transpose the weights transpose =\",\n \"found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array =\",\n \"model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) <\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model,\",\n \"both PyTorch and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/\",\n \"have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\"\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys )\",\n \"PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by a\",\n \"tf # noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error(\",\n \"start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### # PyTorch =>\",\n \"if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found in\",\n \"convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr =\",\n \"\\\"\\\"\\\" Convert a TF 2.0 model variable name in a pytorch model weight\",\n \"state_dict old_keys = [] new_keys = [] for key in pt_state_dict.keys(): new_key =\",\n \"len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except\",\n \"AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size #\",\n \"= convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model\",\n \"logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to\",\n \"noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch, requires both\",\n \"return tuple with: - pytorch model weight name - transpose: boolean indicating weither\",\n \"array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array\",\n \"model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class\",\n \"None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state dict\",\n \"model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model #####################\",\n \"F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \\\"Loading a PyTorch\",\n \"tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\" try:\",\n \"in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at\",\n \"PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero # When should\",\n \"\\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove =\",\n \"TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys\",\n \"e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF\",\n \"in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a map from potential\",\n \"The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>. All rights reserved. #\",\n \"# Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def\",\n \"= tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array =\",\n \"as derived models (with heads) # TF models always have a prefix, some\",\n \"permissions and # limitations under the License. \\\"\\\"\\\" PyTorch - TF 2.0 general\",\n \"associated numpy array in pytorch model state dict if pt_weight_name not in tf_weights_map:\",\n \"Find associated numpy array in pytorch model state dict if name not in\",\n \"Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if\",\n \"missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of {} not initialized from TF\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"in a PyTorch model We use HDF5 to easily do transfer learning (see\",\n \"Make sure model is built # Adapt state dict - TODO remove this\",\n \"tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name))\",\n \"the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\"\",\n \"len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0 model not used in {}:\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"\\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs is None: tf_inputs\",\n \"\\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key,\",\n \"if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights\",\n \"{}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) #\",\n \"import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch model in\",\n \"model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: -\",\n \"# noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a\",\n \"def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch model\",\n \"e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name]\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in\",\n \"PyTorch nn.ModulesList) return tuple with: - pytorch model weight name - transpose: boolean\",\n \"Make sure we are able to load PyTorch base models as well as\",\n \"None: tf_model(tf_inputs, training=False) # Make sure restore ops are run logger.info(\\\"Loaded {:,} parameters\",\n \"PyTorch model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\"\",\n \"in tf_name) # Convert standard TF2.0 names in PyTorch names if tf_name[-1] ==\",\n \"Make sure restore ops are run logger.info(\\\"Loaded {:,} parameters in the TF 2.0\",\n \"PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at the\",\n \"all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0:\",\n \"Handle PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:\",\n \"remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) #\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name)\",\n \"symbolic weights in a PyTorch model \\\"\\\"\\\" try: import tensorflow as tf #\",\n \"tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by a level separation (can\",\n \"tf_name[1:] # Remove level zero # When should we transpose the weights transpose\",\n \"weither TF2.0 and PyTorch weights matrices are transposed with regards to each other\",\n \"# Adapt state dict - TODO remove this and update the AWS weights\",\n \"for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove\",\n \"return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys()))\",\n \"model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded\",\n \"noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both\",\n \"\\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\"\",\n \"in a pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def\",\n \"by a level separation (can be used to convert TF2.0 lists in PyTorch\",\n \"(with heads) # TF models always have a prefix, some of PyTorch models\",\n \"= symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy\",\n \"start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0\",\n \"a TF 2.0 model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401\",\n \"see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs is\",\n \"or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1] =\",\n \"return tf_name, transpose ##################### # PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model,\",\n \"in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array)\",\n \"+ tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight\",\n \"tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is\",\n \"all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in\",\n \"= key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key:\",\n \"logger.info(\\\"Weights or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### #\",\n \"tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model,\",\n \"new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to\",\n \"tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove\",\n \"+= (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)\",\n \"tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as K except\",\n \"\\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"a new level separation (can be used to convert TF2.0 lists in PyTorch\",\n \"tf_name or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names in PyTorch names\",\n \"tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path,\",\n \"list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize\",\n \"you may not use this file except in compliance with the License. #\",\n \"> 0: logger.info( \\\"Weights from TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__,\",\n \"(pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()]\",\n \") def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF\",\n \"< len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0)\",\n \"transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with regards\",\n \"state dict if name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not\",\n \"models always have a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove\",\n \"transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight\",\n \"TODO remove this and update the AWS weights files instead # Convert old\",\n \"pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base\",\n \"+= array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs\",\n \"the AWS weights files instead # Convert old format to new format if\",\n \"array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape):\",\n \"if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### # PyTorch\",\n \"{}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from TF 2.0 model:\",\n \"attribute names conversions: - '$1___$2' is replaced by $2 (can be used to\",\n \"layers in TF2.0 vs PyTorch) - '_._' is replaced by a new level\",\n \"regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name\",\n \"not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array)\",\n \"the associated TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\"\",\n \"= \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose #####################\",\n \"torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) >\",\n \"in a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict,\",\n \"getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if\",\n \"or remove layers in TF2.0 vs PyTorch) - '_._' is replaced by a\",\n \"we are able to load PyTorch base models as well as derived models\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"for the specific language governing permissions and # limitations under the License. \\\"\\\"\\\"\",\n \"= torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return\",\n \"tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs\",\n \"noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow\",\n \"not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array\",\n \"2.0 model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch\",\n \"len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array,\",\n \"name not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch\",\n \"Convert a TF 2.0 model variable name in a pytorch model weight name.\",\n \"model in a pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys)\",\n \"see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict = {}\",\n \"\\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\" try: import tensorflow\",\n \"TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\" try: import tensorflow as tf\",\n \"TF2.0 vs PyTorch) - '_._' is replaced by a new level separation (can\",\n \"weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for\",\n \"if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key)\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make sure we\",\n \"tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class =\",\n \"+ pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model\",\n \"https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) #\",\n \"missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of {} not initialized\",\n \"\\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict()\",\n \"pt_state_dict.pop(old_key) # Make sure we are able to load PyTorch base models as\",\n \"== \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\"\",\n \"# Convert standard TF2.0 names in PyTorch names if tf_name[-1] == \\\"kernel\\\" or\",\n \"Remove level zero # When should we transpose the weights transpose = bool(tf_name[-1]\",\n \"K except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both PyTorch\",\n \"a level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)\",\n \"load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a pytorch model \\\"\\\"\\\"\",\n \"= dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base models\",\n \"tf_name[-1] = \\\"bias\\\" # Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove:\",\n \"of {} not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys)\",\n \"{}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False)\",\n \"pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict\",\n \"\\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\": tf_name[-1]\",\n \"new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable name in\",\n \"PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging import os import re import\",\n \"len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as\",\n \"e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples)\",\n \"# noqa: F401 from tensorflow.python.keras import backend as K except ImportError: logger.error( \\\"Loading\",\n \"re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at the end tf_name = tf_name.split(\\\"/\\\")\",\n \"load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\"\",\n \"r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by $2 (can be used\",\n \"contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys\",\n \"- TODO remove this and update the AWS weights files instead # Convert\",\n \"Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path =\",\n \"import logging import os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name,\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"a TF 2.0 model variable name in a pytorch model weight name. Conventions\",\n \"and PyTorch weights matrices are transposed with regards to each other \\\"\\\"\\\" tf_name\",\n \"tf_name ) # '$1___$2' is replaced by $2 (can be used to duplicate\",\n \"as tf # noqa: F401 import torch # noqa: F401 except ImportError: logger.error(\",\n \"logger.info( \\\"Weights from TF 2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) )\",\n \"load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"heads) # TF models always have a prefix, some of PyTorch models (base\",\n \"\\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow\",\n \"new_keys = [] for key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in\",\n \"lists in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name -\",\n \"weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in\",\n \"\\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs is None:\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix\",\n \"Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return\",\n \"specific language governing permissions and # limitations under the License. \\\"\\\"\\\" PyTorch -\",\n \"backend as K except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires\",\n \"= None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in\",\n \"continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose:\",\n \"+ \\\".\\\" # Build a map from potential PyTorch weight names to TF\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise\",\n \"transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name)\",\n \"TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/\",\n \"key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\")\",\n \"model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see\",\n \"elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape)\",\n \"load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model\",\n \"= pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of\",\n \"\\\"/\\\", tf_name) # Remove empty levels at the end tf_name = tf_name.split(\\\"/\\\") #\",\n \"weight name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2'\",\n \"except ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch, requires both PyTorch and\",\n \"= set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose = convert_tf_weight_name_to_pt_weight_name(\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" try: import\",\n \"= tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name\",\n \"pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model(\",\n \"requires both PyTorch and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/ and\",\n \"pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights,\",\n \"the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch model: {}\\\".format(all_pytorch_weights))\",\n \"allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a\",\n \"import tensorflow as tf # noqa: F401 from tensorflow.python.keras import backend as K\",\n \"either express or implied. # See the License for the specific language governing\",\n \"old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): pt_state_dict[new_key] = pt_state_dict.pop(old_key) # Make\",\n \"boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with regards to\",\n \"##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in\",\n \"associated numpy array in pytorch model state dict if name not in pt_state_dict:\",\n \"model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape):\",\n \"vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by\",\n \"empty levels at the end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/'\",\n \"array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure\",\n \"not None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt state\",\n \"raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able\",\n \"matrices are transposed with regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\")\",\n \"if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array =\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"be used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name =\",\n \"TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\"\",\n \"array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] =\",\n \"(see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch\",\n \"numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array\",\n \"- '$1___$2' is replaced by $2 (can be used to duplicate or remove\",\n \"tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in\",\n \"in tf_name or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names in PyTorch\",\n \"tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF\",\n \"pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel =\",\n \"and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation\",\n \"K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops\",\n \"in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0 if\",\n \"\\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging import os import re\",\n \"Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" try: import tensorflow as\",\n \"PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5\",\n \"update the AWS weights files instead # Convert old format to new format\",\n \"separators tf_name = tf_name[1:] # Remove level zero # When should we transpose\",\n \"len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try:\",\n \"numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args +=\",\n \"as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize\",\n \"be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with: -\",\n \"= tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by a level separation\",\n \"tf_name[-1] == \\\"beta\\\": tf_name[-1] = \\\"bias\\\" # Remove prefix if needed tf_name =\",\n \"array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert\",\n \"# TF models always have a prefix, some of PyTorch models (base ones)\",\n \"new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state\",\n \"\\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\"\",\n \"model is built # Adapt state dict - TODO remove this and update\",\n \"array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert\",\n \"numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) ==\",\n \"the License. # You may obtain a copy of the License at #\",\n \"F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow model\",\n \"tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in\",\n \"logger.error( \\\"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to\",\n \"some of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix)\",\n \"start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix\",\n \"current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a map from potential PyTorch\",\n \"limitations under the License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging\",\n \"used to duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace(\",\n \"= key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys):\",\n \"HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as\",\n \"names conversions: - '$1___$2' is replaced by $2 (can be used to duplicate\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape): array\",\n \"import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated\",\n \"to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) #\",\n \"\\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\" try: import tensorflow\",\n \"# '_._' is replaced by a level separation (can be used to convert\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight\",\n \"\\\"bias\\\" # Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name =\",\n \"TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the\",\n \"# noqa: F401 except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires\",\n \"new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys\",\n \"PyTorch base models as well as derived models (with heads) # TF models\",\n \"# PyTorch => TF 2.0 # ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch # noqa:\",\n \"name, transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in\",\n \"this and update the AWS weights files instead # Convert old format to\",\n \"# Make sure we are able to load PyTorch base models as well\",\n \"tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in\",\n \"\\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs\",\n \"for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in\",\n \"= tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure model\",\n \"{:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys )\",\n \"\\\"weight\\\") if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key)\",\n \"sure restore ops are run logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel))\",\n \"in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None,\",\n \"the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None:\",\n \"not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights =\",\n \"+= missing_keys_pt if len(missing_keys) > 0: logger.info( \\\"Weights of {} not initialized from\",\n \"separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) tf_name =\",\n \"tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### # PyTorch => TF 2.0 #\",\n \"= convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr\",\n \"tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at the end tf_name\",\n \"by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0\",\n \"TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily\",\n \"or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names\",\n \"in PyTorch nn.ModulesList) return tuple with: - pytorch model weight name - transpose:\",\n \"in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from TF 2.0\",\n \"except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow, requires both PyTorch and\",\n \"with the License. # You may obtain a copy of the License at\",\n \"r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by $2 (can be used to\",\n \"layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._'\",\n \"# ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint\",\n \"any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a\",\n \"tensorflow as tf # noqa: F401 import torch # noqa: F401 except ImportError:\",\n \"do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf # noqa:\",\n \"transpose = convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch\",\n \"tf_name = tf_name[1:] # Remove level zero # When should we transpose the\",\n \"Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\",\",\n \"None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make\",\n \"= logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable name\",\n \"= getattr(transformers, tf_model_class_name) tf_model = tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs\",\n \"# Make sure restore ops are run logger.info(\\\"Loaded {:,} parameters in the TF\",\n \"zero # When should we transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\"\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise\",\n \"ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove\",\n \"2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name(\",\n \"remove this and update the AWS weights files instead # Convert old format\",\n \"TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs,\",\n \"TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if\",\n \"dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not\",\n \") if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0 model not used\",\n \"PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can be used\",\n \"= tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name )\",\n \"model variable name in a pytorch model weight name. Conventions for TF2.0 scopes\",\n \"Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c)\",\n \"pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False):\",\n \") # Find associated numpy array in pytorch model state dict if name\",\n \"raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array\",\n \"PyTorch) - '_._' is replaced by a new level separation (can be used\",\n \"rights reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose =\",\n \"pytorch model state dict if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue\",\n \"Remove empty levels at the end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0\",\n \"is not None: tf_model(tf_inputs, training=False) # Make sure model is built # Adapt\",\n \"not in pt_state_dict: if allow_missing_keys: continue raise AttributeError(\\\"{} not found in PyTorch model\\\".format(name))\",\n \"for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] =\",\n \"re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by $2 (can be\",\n \"\\\"\\\"\\\" Load pytorch state_dict in a TF 2.0 model. \\\"\\\"\\\" try: import torch\",\n \"map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains {:,} parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model,\",\n \"a prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if\",\n \"The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright\",\n \"loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model\",\n \"in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key\",\n \"missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt if len(missing_keys) > 0: logger.info(\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights:\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc.\",\n \"scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can\",\n \"import torch # noqa: F401 import tensorflow as tf # noqa: F401 from\",\n \"PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) <\",\n \"= \\\"bias\\\" # Remove prefix if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name\",\n \"Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if tf_inputs\",\n \"tf_model_class(pt_model.config) if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs is not None:\",\n \"in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find\",\n \"transpose the weights transpose = bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or\",\n \"model \\\"\\\"\\\" pt_state_dict = pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def\",\n \"list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e tf_loaded_numel +=\",\n \"return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model\",\n \"and update the AWS weights files instead # Convert old format to new\",\n \"allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in a PyTorch\",\n \"model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see\",\n \"instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we\",\n \"# Convert old format to new format if needed from a PyTorch state_dict\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please\",\n \"if len(missing_keys) > 0: logger.info( \\\"Weights of {} not initialized from TF 2.0\",\n \"2018, <NAME>. All rights reserved. # # Licensed under the Apache License, Version\",\n \"pytorch model state dict if name not in pt_state_dict: if allow_missing_keys: continue raise\",\n \"$2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)\",\n \"TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\" ) # '_._' is replaced\",\n \"new format if needed from a PyTorch state_dict old_keys = [] new_keys =\",\n \"# Make sure model is built # Adapt state dict - TODO remove\",\n \"and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights\",\n \"torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) all_tf_weights.discard(pt_weight_name) missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) missing_keys += missing_keys_pt\",\n \"logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None:\",\n \"PyTorch weights matrices are transposed with regards to each other \\\"\\\"\\\" tf_name =\",\n \"weights matrices are transposed with regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\",\",\n \"pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\", \\\"weight\\\") if\",\n \"end tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to PyTorch '.'\",\n \"TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the associated TF 2.0 model\",\n \"names in PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or\",\n \"with regards to each other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids\",\n \"duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\", \\\"/\\\"\",\n \"len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(pt_weight.shape) > len(array.shape): array = numpy.expand_dims(array,\",\n \"pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name) tf_model =\",\n \"and # limitations under the License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\"\",\n \"nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty levels at the end\",\n \"from a PyTorch state_dict old_keys = [] new_keys = [] for key in\",\n \"standard TF2.0 names in PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] ==\",\n \"TF 2.0 model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import\",\n \"all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name name, transpose =\",\n \"[] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated\",\n \"start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state dict if\",\n \"(can be used to duplicate or remove layers in TF2.0 vs PyTorch) -\",\n \"tf_inputs = tf_model.dummy_inputs if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure\",\n \"numpy array in pytorch model state dict if name not in pt_state_dict: if\",\n \"see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \") logger.info(\\\"Weights or buffers not loaded from TF 2.0 model: {}\\\".format(all_tf_weights)) return pt_model\",\n \"[] for key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key: new_key\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name\",\n \"\\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights\",\n \"try: assert list(pt_weight.shape) == list(array.shape) except AssertionError as e: e.args += (pt_weight.shape, array.shape)\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"ops are run logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or\",\n \"TF2.0 names in PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\"\",\n \"installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise if\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" #\",\n \"if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 model in a pytorch model \\\"\\\"\\\" weights =\",\n \"= {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch\",\n \"\\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading\",\n \"run logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not\",\n \"if \\\"beta\\\" in key: new_key = key.replace(\\\"beta\\\", \\\"bias\\\") if new_key: old_keys.append(key) new_keys.append(new_key) for\",\n \"for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is replaced by\",\n \"= \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix +\",\n \"= numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif len(symbolic_weight.shape) > len(array.shape):\",\n \"torch # noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch,\",\n \"# noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras import\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a\",\n \"use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\")\",\n \"s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a map from\",\n \"variable name in a pytorch model weight name. Conventions for TF2.0 scopes ->\",\n \"installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise import\",\n \"continue raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array, transpose = tf_weights_map[pt_weight_name]\",\n \"pt_model.state_dict() return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False):\",\n \"numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args +=\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"import tensorflow as tf # noqa: F401 import torch # noqa: F401 except\",\n \"TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load\",\n \"= pt_model.base_model_prefix + \\\".\\\" # Build a map from potential PyTorch weight names\",\n \"names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\":\",\n \"key in pt_state_dict.keys(): new_key = None if \\\"gamma\\\" in key: new_key = key.replace(\\\"gamma\\\",\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] =\",\n \"parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded from PyTorch\",\n \"torch # noqa: F401 import tensorflow as tf # noqa: F401 from tensorflow.python.keras\",\n \"https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch #\",\n \"a pytorch model \\\"\\\"\\\" weights = tf_model.weights return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model,\",\n \"PyTorch model \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import torch\",\n \"(tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name,\",\n \"to be installed. Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" )\",\n \"# ##################### def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a\",\n \"separators to PyTorch '.' separators tf_name = tf_name[1:] # Remove level zero #\",\n \"import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a\",\n \"tf_inputs is not None: tf_model(tf_inputs, training=False) # Make sure restore ops are run\",\n \"(can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple with:\",\n \"missing_keys) ) if len(unexpected_keys) > 0: logger.info( \\\"Weights from TF 2.0 model not\",\n \"for installation instructions.\\\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if\",\n \"\\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0 names in\",\n \"License. \\\"\\\"\\\" PyTorch - TF 2.0 general utilities.\\\"\\\"\\\" import logging import os import\",\n \"checkpoints in a TF 2.0 model \\\"\\\"\\\" try: import tensorflow as tf #\",\n \"state_dict in a TF 2.0 model. \\\"\\\"\\\" try: import torch # noqa: F401\",\n \"restore ops are run logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights\",\n \"replaced by a level separation (can be used to convert TF2.0 lists in\",\n \"from tensorflow.python.keras import backend as K except ImportError: logger.error( \\\"Loading a PyTorch model\",\n \"{}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model,\",\n \"to duplicate or remove layers in TF2.0 vs PyTorch) tf_name = tf_name.replace( \\\"_._\\\",\",\n \"in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see \\\"\",\n \"# Build a map from potential PyTorch weight names to TF 2.0 Variables\",\n \"PyTorch, requires both PyTorch and TensorFlow to be installed. Please see \\\" \\\"https://pytorch.org/\",\n \"None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model,\",\n \"are able to load PyTorch base models as well as derived models (with\",\n \"not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build\",\n \"with: - pytorch model weight name - transpose: boolean indicating weither TF2.0 and\",\n \"try: import tensorflow as tf # noqa: F401 import torch # noqa: F401\",\n \"= os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint\",\n \"= tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys()))\",\n \"> len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(symbolic_weight.shape) == list(array.shape) except AssertionError\",\n \"numpy array in pytorch model state dict if pt_weight_name not in tf_weights_map: if\",\n \"PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for s\",\n \"base models as well as derived models (with heads) # TF models always\",\n \"in PyTorch model\\\".format(name)) array = pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape)\",\n \"numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model\",\n \"logger.info(\\\"Loaded {:,} parameters in the TF 2.0 model.\\\".format(tf_loaded_numel)) logger.info(\\\"Weights or buffers not loaded\",\n \"able to load PyTorch base models as well as derived models (with heads)\",\n \"https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from\",\n \"for s in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights +\",\n \"the specific language governing permissions and # limitations under the License. \\\"\\\"\\\" PyTorch\",\n \"from PyTorch model: {}\\\".format(all_pytorch_weights)) return tf_model ##################### # TF 2.0 => PyTorch #\",\n \"loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch model state dict if\",\n \"start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert a TF 2.0 model variable name in a pytorch model\",\n \"if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found in TF 2.0 model\\\".format(pt_weight_name)) array,\",\n \"'_._' is replaced by a new level separation (can be used to convert\",\n \"name. Conventions for TF2.0 scopes -> PyTorch attribute names conversions: - '$1___$2' is\",\n \"name - transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"{}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from TF 2.0 model: {}\\\".format(all_tf_weights))\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"if needed tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return\",\n \"in TF2.0 vs PyTorch) - '_._' is replaced by a new level separation\",\n \"tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {}\",\n \"instead # Convert old format to new format if needed from a PyTorch\",\n \"load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch state_dict in a TF 2.0 model.\",\n \"of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(tf_model.base_model_prefix) for\",\n \"tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(),\",\n \"\\\"_._\\\", \\\"/\\\" ) # '_._' is replaced by a level separation (can be\",\n \"pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0 model \\\"\\\"\\\"\",\n \"start_prefix_to_remove = pt_model.base_model_prefix + \\\".\\\" # Build a map from potential PyTorch weight\",\n \"installation instructions.\\\" ) raise if tf_inputs is None: tf_inputs = tf_model.dummy_inputs if tf_inputs\",\n \"- transpose: boolean indicating weither TF2.0 and PyTorch weights matrices are transposed with\",\n \"in pt_state_dict.keys()): start_prefix_to_remove = tf_model.base_model_prefix + \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel\",\n \"continue # Find associated numpy array in pytorch model state dict if pt_weight_name\",\n \"-> PyTorch attribute names conversions: - '$1___$2' is replaced by $2 (can be\",\n \"tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\", r\\\"/\\\\1/\\\", tf_name ) # '$1___$2' is replaced by $2\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"pytorch model weight name - transpose: boolean indicating weither TF2.0 and PyTorch weights\",\n \"in PyTorch names if tf_name[-1] == \\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1]\",\n \") raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load\",\n \"for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path))\",\n \"dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch base models as\",\n \"not initialized from TF 2.0 model: {}\\\".format(pt_model.__class__.__name__, missing_keys) ) if len(unexpected_keys) > 0:\",\n \"weight names to TF 2.0 Variables tf_weights_map = {} for tf_weight in tf_weights:\",\n \"== \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if tf_name[-1] == \\\"beta\\\":\",\n \"\\\"kernel\\\" or tf_name[-1] == \\\"embeddings\\\" or tf_name[-1] == \\\"gamma\\\": tf_name[-1] = \\\"weight\\\" if\",\n \"Please see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict =\",\n \"\\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) # Convert standard TF2.0\",\n \"allow_missing_keys=False): \\\"\\\"\\\" Load TF 2.0 HDF5 checkpoint in a PyTorch model We use\",\n \"other \\\"\\\"\\\" tf_name = tf_name.replace(\\\":0\\\", \\\"\\\") # device ids tf_name = re.sub( r\\\"/[^/]*___([^/]*)/\\\",\",\n \"# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The\",\n \"os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch weights from {}\\\".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location=\\\"cpu\\\") logger.info(\\\"PyTorch checkpoint contains\",\n \"weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: sw_name = symbolic_weight.name\",\n \"Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, <NAME>.\",\n \"transpose = tf_weights_map[pt_weight_name] if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array\",\n \"- '_._' is replaced by a new level separation (can be used to\",\n \"pt_state_dict[name].numpy() if transpose: array = numpy.transpose(array) if len(symbolic_weight.shape) < len(array.shape): array = numpy.squeeze(array)\",\n \"load the associated TF 2.0 model tf_model_class_name = \\\"TF\\\" + pt_model.__class__.__name__ # Add\",\n \"see \\\" \\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path)\",\n \"in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] continue # Find associated numpy array in pytorch\",\n \"PyTorch shared weight ()not duplicated in TF 2.0 if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: new_pt_params_dict[pt_weight_name]\",\n \"TF2.0 lists in PyTorch nn.ModulesList) tf_name = re.sub(r\\\"//+\\\", \\\"/\\\", tf_name) # Remove empty\",\n \"load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False): \\\"\\\"\\\" Load TF2.0 symbolic weights in\",\n \"be used to duplicate or remove layers in TF2.0 vs PyTorch) - '_._'\",\n \"Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\",\n \"\\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose ##################### #\",\n \"ones) don't start_prefix_to_remove = \\\"\\\" if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): start_prefix_to_remove\",\n \"tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name =\",\n \"+ \\\".\\\" symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = []\",\n \"set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = [] for pt_weight_name, pt_weight in current_pt_params_dict.items(): #\",\n \"pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF 2.0\",\n \"# TF 2.0 => PyTorch # ##################### def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\"\",\n \"convert_tf_weight_name_to_pt_weight_name( sw_name, start_prefix_to_remove=start_prefix_to_remove ) # Find associated numpy array in pytorch model state\",\n \"# noqa: F401 except ImportError: logger.error( \\\"Loading a TensorFlow model in PyTorch, requires\",\n \"# Copyright (c) 2018, <NAME>. All rights reserved. # # Licensed under the\",\n \"'$1___$2' is replaced by $2 (can be used to duplicate or remove layers\",\n \"map from potential PyTorch weight names to TF 2.0 Variables tf_weights_map = {}\",\n \"\\\"TF\\\" + pt_model.__class__.__name__ # Add \\\"TF\\\" at the beggining tf_model_class = getattr(transformers, tf_model_class_name)\",\n \"TF 2.0 model variable name in a pytorch model weight name. Conventions for\",\n \"sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys) def load_tf2_model_in_pytorch_model(pt_model, tf_model,\",\n \"def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch checkpoints in a TF 2.0\",\n \"prefix, some of PyTorch models (base ones) don't start_prefix_to_remove = \\\"\\\" if not\",\n \"import backend as K except ImportError: logger.error( \\\"Loading a PyTorch model in TensorFlow,\",\n \"# logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not\",\n \"os import re import numpy logger = logging.getLogger(__name__) def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=\\\"\\\"): \\\"\\\"\\\" Convert\",\n \"= bool(tf_name[-1] == \\\"kernel\\\" or \\\"emb_projs\\\" in tf_name or \\\"out_projs\\\" in tf_name) #\",\n \"tf # noqa: F401 import torch # noqa: F401 except ImportError: logger.error( \\\"Loading\",\n \"Load TF2.0 symbolic weights in a PyTorch model \\\"\\\"\\\" try: import tensorflow as\",\n \"e.args += (pt_weight.shape, array.shape) raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] =\",\n \"tf_name = \\\".\\\".join(tf_name) if start_prefix_to_remove: tf_name = tf_name.replace(start_prefix_to_remove, \\\"\\\", 1) return tf_name, transpose\",\n \"= {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove )\",\n \"learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try: import tensorflow as tf # noqa: F401 import\",\n \"if pt_weight_name not in tf_weights_map: if allow_missing_keys: missing_keys_pt.append(pt_weight_name) continue raise AttributeError(\\\"{} not found\",\n \"not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not loaded from\",\n \"\\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise pt_path = os.path.abspath(pytorch_checkpoint_path) logger.info(\\\"Loading PyTorch\",\n \"tf_name = tf_name.split(\\\"/\\\") # Convert from TF2.0 '/' separators to PyTorch '.' separators\",\n \"raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) # Instantiate and load the\",\n \"parameters\\\".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model( tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def\",\n \"raise e # logger.warning(\\\"Initialize PyTorch weight {}\\\".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)\",\n \"model We use HDF5 to easily do transfer learning (see https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). \\\"\\\"\\\" try:\",\n \") raise new_pt_params_dict = {} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are\",\n \"models as well as derived models (with heads) # TF models always have\",\n \"not None: tf_model(tf_inputs, training=False) # Make sure model is built tf_model.load_weights(tf_checkpoint_path, by_name=True) return\",\n \"pt_weight_name, pt_weight in current_pt_params_dict.items(): # Handle PyTorch shared weight ()not duplicated in TF\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"if transpose: array = numpy.transpose(array) if len(pt_weight.shape) < len(array.shape): array = numpy.squeeze(array) elif\",\n \"TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please\",\n \"tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights = set(list(tf_weights_map.keys())) loaded_pt_weights_data_ptr = {} missing_keys_pt = []\",\n \"2.0 model not used in {}: {}\\\".format(pt_model.__class__.__name__, unexpected_keys) ) logger.info(\\\"Weights or buffers not\",\n \"tf_model.trainable_weights + tf_model.non_trainable_weights tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for\",\n \"state dict - TODO remove this and update the AWS weights files instead\",\n \"for installation instructions.\\\" ) raise import transformers logger.info(\\\"Loading TensorFlow weights from {}\\\".format(tf_checkpoint_path)) #\",\n \"{} current_pt_params_dict = dict(pt_model.named_parameters()) # Make sure we are able to load PyTorch\",\n \"pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name, start_prefix_to_remove=start_prefix_to_remove ) tf_weights_map[pt_name] = (tf_weight.numpy(), transpose) all_tf_weights =\",\n \"tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if\",\n \"Variables tf_weights_map = {} for tf_weight in tf_weights: pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( tf_weight.name,\",\n \"\\\"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\\\" ) raise new_pt_params_dict = {} current_pt_params_dict =\",\n \"a pytorch model weight name. Conventions for TF2.0 scopes -> PyTorch attribute names\",\n \"list(symbolic_weight.shape) == list(array.shape) except AssertionError as e: e.args += (symbolic_weight.shape, array.shape) raise e\",\n \"(symbolic_weight.shape, array.shape) raise e tf_loaded_numel += array.size # logger.warning(\\\"Initialize TF weight {}\\\".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight,\",\n \"tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys ) def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): \\\"\\\"\\\" Load pytorch\",\n \"models (with heads) # TF models always have a prefix, some of PyTorch\",\n \"level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return\",\n \"Convert from TF2.0 '/' separators to PyTorch '.' separators tf_name = tf_name[1:] #\",\n \"> len(array.shape): array = numpy.expand_dims(array, axis=0) try: assert list(pt_weight.shape) == list(array.shape) except AssertionError\"\n]","isConversational":false}}},{"rowIdx":2183,"cells":{"ngram":{"kind":"list like","value":["res != v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds","return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt)","return x _dataset = None def get_dataset(): global _dataset if _dataset is None:",".annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def","'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x,","resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds","= os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def","timer() x = f() end = timer() assert (start - end) < max_duration","**kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try:","default_timer as timer import unittest import pytest from decorator import decorator from hail.utils.java","start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32,","hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global","elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return","list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for","= pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local backend\", strict=True) def","if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start","= timer() x = f() end = timer() assert (start - end) <","return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset =","= flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower,","x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if","hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected):","astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr),","timer() assert (start - end) < max_duration print(f'took {end - start:.3f}') return x","1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), )","prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table():","res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\n expected: {v}')","in x.items()} else: return x _dataset = None def get_dataset(): global _dataset if","get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def","False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if","None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x","hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized:","return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return","expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower =","+ k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return","import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs)","d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')),","isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list):","zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags()","expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags","def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only')","= None def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache()","y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return","elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return","'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix","isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend =","def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e)","max_duration): start = timer() x = f() end = timer() assert (start -","v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected)","decorator import decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend","hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized =","os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds","(create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs):","startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name ==","assert_time(f, max_duration): start = timer() x = f() end = timer() assert (start","_doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir,","isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k:","global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f,","prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only) return","k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_'))","= True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data')","backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def","= flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only) return wrapper","def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def","mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}),","{end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32),","phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return","elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif","import decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend import","lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only","'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def","def assert_time(f, max_duration): start = timer() x = f() end = timer() assert","for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x])","pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir,","def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def","return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for","return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local","t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s,","hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if","_test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename)","from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not","import pytest from decorator import decorator from hail.utils.java import Env import hail as","in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x,","skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend):","os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename):","= dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k:","start = timer() x = f() end = timer() assert (start - end)","for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v","dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset","from hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized","stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return","return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache())","convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif","= os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename)","elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return","hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall),","return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)),","assert (start - end) < max_duration print(f'took {end - start:.3f}') return x def","min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass","return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),","_initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR',","mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k:","convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt","x.items()} else: return x _dataset = None def get_dataset(): global _dataset if _dataset","hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k]","{v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def","strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e,","mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def","actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def","= os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() #","in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values)","unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work","create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if","fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local backend\", strict=True)","initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir =","= zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags =","return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND')",".cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator","@decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only =","Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on","create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values()","return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt","n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2,","flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return","0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0,","def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds","def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar',","def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires","== 'local', reason=\"doesn't yet work on local backend\", strict=True) def run_with_cxx_compile(): @decorator def","10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k","_dataset = None def get_dataset(): global _dataset if _dataset is None: _dataset =","m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x',","def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y):","_dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x =","end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct(","x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo',","func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') ==","filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds ==","timer import unittest import pytest from decorator import decorator from hail.utils.java import Env","5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in","wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1',","(hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3,","def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename):","= timer() assert (start - end) < max_duration print(f'took {end - start:.3f}') return","aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)),","{k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for",".annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2)","def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()}","convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset = None def","return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise","timeit import default_timer as timer import unittest import pytest from decorator import decorator",".annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import","def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def","(hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def","os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local backend\", strict=True) def run_with_cxx_compile(): @decorator",".annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from","2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix):","k, v in x.items()} else: return x _dataset = None def get_dataset(): global","else: return x _dataset = None def get_dataset(): global _dataset if _dataset is","**kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper","exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs):","hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args,","global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark':","wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if","999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)),","def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name","backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc()","dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v)","local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper","tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k,","create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func,","as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized","reason=\"doesn't yet work on local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args,","Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def","import os from timeit import default_timer as timer import unittest import pytest from","== 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True","ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo',","**kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res !=","@decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise","**kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local',","return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args,","s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3)","for k, v in x.items()} else: return x _dataset = None def get_dataset():","n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return","if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x,","x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple):","create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']),","= False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark')","- end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return","all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_'))","filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds","y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x):","dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct):","x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k,",".cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values,","LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name =","v): res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\n expected:","end = timer() assert (start - end) < max_duration print(f'took {end - start:.3f}')","x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict):","2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets():","in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x,","'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend():","md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1),","y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in","return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values =","assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower","hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'),","quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir","for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values,","'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend","def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower')","def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds =","{k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset = None","'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization","backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized =","0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1',","import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name","flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only)","for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in","ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds)",") def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s})","assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\n","raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs),","Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR',","i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5,","os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x)","force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir","tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v)","import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False","is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer()","def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_'))","def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v):","'../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return","== y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v","prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values","yet work on local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs):","'local', reason=\"doesn't yet work on local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func,","= hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args,","pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local backend\", strict=True) def run_with_cxx_compile():","isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x","create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values,","- start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),","from timeit import default_timer as timer import unittest import pytest from decorator import","x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else:","{res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only():","on local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return","*args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return","'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values,","assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args,","hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x = f() end","[convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in","k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x]","def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(),",".annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_'))","return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return","schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def","wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet work on local backend\",","os from timeit import default_timer as timer import unittest import pytest from decorator","all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values","y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32,","if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend","hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999),","_dataset def assert_time(f, max_duration): start = timer() x = f() end = timer()","return _dataset def assert_time(f, max_duration): start = timer() x = f() end =","doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y)","v in x.items()} else: return x _dataset = None def get_dataset(): global _dataset","_initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]',","def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset","print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64),","elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in","else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir =","from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return","f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']):","hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1',","33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2,","return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v:","import unittest import pytest from decorator import decorator from hail.utils.java import Env import","(start - end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values():","i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]),","work on local backend\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return","nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for","hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values()","'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True),","unittest import pytest from decorator import decorator from hail.utils.java import Env import hail","raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't yet","return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for","f() end = timer() assert (start - end) < max_duration print(f'took {end -","= hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\n expected: {v}') def","from decorator import decorator from hail.utils.java import Env import hail as hl from","x _dataset = None def get_dataset(): global _dataset if _dataset is None: _dataset","import default_timer as timer import unittest import pytest from decorator import decorator from","import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext():","isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt)","'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table())","'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978,","prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally:","run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res","mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'),","decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend","_dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start =","x = f() end = timer() assert (start - end) < max_duration print(f'took","max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9),","h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1,","s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache())","'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend","in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()}","= create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values =","<gh_stars>0 import os from timeit import default_timer as timer import unittest import pytest","c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)),","< max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14),","create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_'))","pytest from decorator import decorator from hail.utils.java import Env import hail as hl","_initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else:","_dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration):","'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval(","hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs)","SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail(","hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0,","'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)),","= create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values,","1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2,","not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True)","True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def","SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else:","os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def","hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext():","v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif","= f() end = timer() assert (start - end) < max_duration print(f'took {end","else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\"doesn't","*args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res","wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f'","hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a',","as timer import unittest import pytest from decorator import decorator from hail.utils.java import","!= v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds =","= dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x,","hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix +","hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return","# force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources')","if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0,","_initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND',","expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator","def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func,","@decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res =","if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized","None def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return","def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f' actual:","= hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x = f()","'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22',","if res != v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs,","*args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1')","wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark')","os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force","create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table():","b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']):","return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds =",".annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(),","k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5,"],"string":"[\n \"res != v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds\",\n \"return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt)\",\n \"return x _dataset = None def get_dataset(): global _dataset if _dataset is None:\",\n \".annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def\",\n \"'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x,\",\n \"resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds\",\n \"= os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def\",\n \"timer() x = f() end = timer() assert (start - end) < max_duration\",\n \"**kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try:\",\n \"default_timer as timer import unittest import pytest from decorator import decorator from hail.utils.java\",\n \"start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32,\",\n \"hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global\",\n \"elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return\",\n \"list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for\",\n \"= pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local backend\\\", strict=True) def\",\n \"if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start\",\n \"= timer() x = f() end = timer() assert (start - end) <\",\n \"return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset =\",\n \"= flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower,\",\n \"x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if\",\n \"hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected):\",\n \"astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr),\",\n \"timer() assert (start - end) < max_duration print(f'took {end - start:.3f}') return x\",\n \"1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), )\",\n \"prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table():\",\n \"res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\\\n expected: {v}')\",\n \"in x.items()} else: return x _dataset = None def get_dataset(): global _dataset if\",\n \"get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def\",\n \"False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if\",\n \"None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x\",\n \"hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized:\",\n \"return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return\",\n \"expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower =\",\n \"+ k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return\",\n \"import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs)\",\n \"d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')),\",\n \"isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list):\",\n \"zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags()\",\n \"expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags\",\n \"def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only')\",\n \"= None def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache()\",\n \"y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return\",\n \"elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return\",\n \"'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix\",\n \"isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend =\",\n \"def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e)\",\n \"max_duration): start = timer() x = f() end = timer() assert (start -\",\n \"v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected)\",\n \"decorator import decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend\",\n \"hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized =\",\n \"os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds\",\n \"(create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs):\",\n \"startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name ==\",\n \"assert_time(f, max_duration): start = timer() x = f() end = timer() assert (start\",\n \"_doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir,\",\n \"isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k:\",\n \"global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f,\",\n \"prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only) return\",\n \"k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_'))\",\n \"= True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data')\",\n \"backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def\",\n \"= flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only) return wrapper\",\n \"def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def\",\n \"mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}),\",\n \"{end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32),\",\n \"phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return\",\n \"elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif\",\n \"import decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend import\",\n \"lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only\",\n \"'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def\",\n \"def assert_time(f, max_duration): start = timer() x = f() end = timer() assert\",\n \"for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x])\",\n \"pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir,\",\n \"def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def\",\n \"return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for\",\n \"return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local\",\n \"t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s,\",\n \"hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if\",\n \"_test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename)\",\n \"from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not\",\n \"import pytest from decorator import decorator from hail.utils.java import Env import hail as\",\n \"in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x,\",\n \"skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend):\",\n \"os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename):\",\n \"= dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k:\",\n \"start = timer() x = f() end = timer() assert (start - end)\",\n \"for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v\",\n \"dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset\",\n \"from hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized\",\n \"stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return\",\n \"return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache())\",\n \"convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif\",\n \"= os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename)\",\n \"elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return\",\n \"hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall),\",\n \"return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)),\",\n \"assert (start - end) < max_duration print(f'took {end - start:.3f}') return x def\",\n \"min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass\",\n \"return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),\",\n \"_initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR',\",\n \"mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k:\",\n \"convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt\",\n \"x.items()} else: return x _dataset = None def get_dataset(): global _dataset if _dataset\",\n \"hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k]\",\n \"{v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def\",\n \"strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e,\",\n \"mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def\",\n \"actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def\",\n \"= os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() #\",\n \"in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values)\",\n \"unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work\",\n \"create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if\",\n \"fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local backend\\\", strict=True)\",\n \"initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir =\",\n \"= zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags =\",\n \"return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND')\",\n \".cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator\",\n \"@decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only =\",\n \"Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on\",\n \"create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values()\",\n \"return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt\",\n \"n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2,\",\n \"flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return\",\n \"0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0,\",\n \"def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds\",\n \"def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar',\",\n \"def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires\",\n \"== 'local', reason=\\\"doesn't yet work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def\",\n \"10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k\",\n \"_dataset = None def get_dataset(): global _dataset if _dataset is None: _dataset =\",\n \"m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x',\",\n \"def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y):\",\n \"_dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x =\",\n \"end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct(\",\n \"x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo',\",\n \"func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') ==\",\n \"filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds ==\",\n \"timer import unittest import pytest from decorator import decorator from hail.utils.java import Env\",\n \"5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in\",\n \"wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1',\",\n \"(hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3,\",\n \"def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename):\",\n \"= timer() assert (start - end) < max_duration print(f'took {end - start:.3f}') return\",\n \"aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)),\",\n \"{k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for\",\n \".annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2)\",\n \"def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()}\",\n \"convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset = None def\",\n \"return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise\",\n \"timeit import default_timer as timer import unittest import pytest from decorator import decorator\",\n \".annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import\",\n \"def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def\",\n \"(hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def\",\n \"os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator\",\n \".annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from\",\n \"2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix):\",\n \"k, v in x.items()} else: return x _dataset = None def get_dataset(): global\",\n \"else: return x _dataset = None def get_dataset(): global _dataset if _dataset is\",\n \"**kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper\",\n \"exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs):\",\n \"hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args,\",\n \"global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark':\",\n \"wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if\",\n \"999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)),\",\n \"def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name\",\n \"backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc()\",\n \"dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v)\",\n \"local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper\",\n \"tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k,\",\n \"create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func,\",\n \"as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext(): global _initialized\",\n \"reason=\\\"doesn't yet work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args,\",\n \"Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def\",\n \"import os from timeit import default_timer as timer import unittest import pytest from\",\n \"== 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True\",\n \"ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo',\",\n \"**kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res !=\",\n \"@decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise\",\n \"**kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local',\",\n \"return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args,\",\n \"s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3)\",\n \"for k, v in x.items()} else: return x _dataset = None def get_dataset():\",\n \"n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return\",\n \"if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x,\",\n \"x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple):\",\n \"create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']),\",\n \"= False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark')\",\n \"- end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return\",\n \"all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_'))\",\n \"filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds\",\n \"y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x):\",\n \"dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct):\",\n \"x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k,\",\n \".cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values,\",\n \"LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name =\",\n \"v): res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\\\n expected:\",\n \"end = timer() assert (start - end) < max_duration print(f'took {end - start:.3f}')\",\n \"x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict):\",\n \"2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets():\",\n \"in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x,\",\n \"'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend():\",\n \"md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1),\",\n \"y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v in\",\n \"return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values =\",\n \"assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower\",\n \"hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'),\",\n \"quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir\",\n \"for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values,\",\n \"'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend\",\n \"def lower_only(): @decorator def wrapper(func, *args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower')\",\n \"def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds =\",\n \"{k: convert_struct_to_dict(v) for k, v in x.items()} else: return x _dataset = None\",\n \"'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization\",\n \"backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized =\",\n \"0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1',\",\n \"import LocalBackend _initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name\",\n \"flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally: hl._set_flags(lower=prev_lower, lower_only=prev_lower_only)\",\n \"for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in\",\n \"ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds)\",\n \") def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for k in s})\",\n \"assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\\\n\",\n \"raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs),\",\n \"Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR',\",\n \"i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5,\",\n \"os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x)\",\n \"force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir\",\n \"tuple): return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v)\",\n \"import Env import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False\",\n \"is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer()\",\n \"def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_'))\",\n \"def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v):\",\n \"'../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return\",\n \"== y_fds def convert_struct_to_dict(x): if isinstance(x, hl.Struct): return {k: convert_struct_to_dict(v) for k, v\",\n \"prefix): return hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values\",\n \"yet work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs):\",\n \"'local', reason=\\\"doesn't yet work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func,\",\n \"= hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args,\",\n \"pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local backend\\\", strict=True) def run_with_cxx_compile():\",\n \"isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else: return x\",\n \"create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values,\",\n \"- start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),\",\n \"from timeit import default_timer as timer import unittest import pytest from decorator import\",\n \"x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()} else:\",\n \"{res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only():\",\n \"on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return\",\n \"*args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return\",\n \"'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values,\",\n \"assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func, *args,\",\n \"hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x = f() end\",\n \"[convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt) for elt in\",\n \"k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x]\",\n \"def skip_unless_spark_backend(): from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(),\",\n \".annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values = create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_'))\",\n \"return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return\",\n \"schema_eq(x, y): x_fds = dict(x) y_fds = dict(y) return x_fds == y_fds def\",\n \"wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet work on local backend\\\",\",\n \"os from timeit import default_timer as timer import unittest import pytest from decorator\",\n \"all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values\",\n \"y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32,\",\n \"if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend\",\n \"hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999),\",\n \"_dataset def assert_time(f, max_duration): start = timer() x = f() end = timer()\",\n \"return _dataset def assert_time(f, max_duration): start = timer() x = f() end =\",\n \"doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds = dict(x) y_fds = dict(y)\",\n \"v in x.items()} else: return x _dataset = None def get_dataset(): global _dataset\",\n \"_initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]',\",\n \"def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset\",\n \"print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64),\",\n \"elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in\",\n \"else: Env.hc() # force initialization _initialized = True def stopTestHailContext(): pass _test_dir =\",\n \"from hail.backend.spark_backend import SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return\",\n \"f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']):\",\n \"hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1',\",\n \"33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2,\",\n \"return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v:\",\n \"import unittest import pytest from decorator import decorator from hail.utils.java import Env import\",\n \"(start - end) < max_duration print(f'took {end - start:.3f}') return x def create_all_values():\",\n \"i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]),\",\n \"work on local backend\\\", strict=True) def run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return\",\n \"nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix + k: s[k] for\",\n \"hl.struct(**{prefix + k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values()\",\n \"'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True),\",\n \"unittest import pytest from decorator import decorator from hail.utils.java import Env import hail\",\n \"raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't yet\",\n \"return tuple([convert_struct_to_dict(elt) for elt in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for\",\n \"f() end = timer() assert (start - end) < max_duration print(f'took {end -\",\n \"= hl.eval(e) if res != v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def\",\n \"from decorator import decorator from hail.utils.java import Env import hail as hl from\",\n \"x _dataset = None def get_dataset(): global _dataset if _dataset is None: _dataset\",\n \"import default_timer as timer import unittest import pytest from decorator import decorator from\",\n \"import hail as hl from hail.backend.local_backend import LocalBackend _initialized = False def startTestHailContext():\",\n \"isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif isinstance(x, tuple): return tuple([convert_struct_to_dict(elt)\",\n \"'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table())\",\n \"'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978,\",\n \"prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs) finally:\",\n \"run_with_cxx_compile(): @decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res\",\n \"mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'),\",\n \"decorator from hail.utils.java import Env import hail as hl from hail.backend.local_backend import LocalBackend\",\n \"_dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start =\",\n \"x = f() end = timer() assert (start - end) < max_duration print(f'took\",\n \"max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9),\",\n \"h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1,\",\n \"s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache())\",\n \"'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(), create_all_values_matrix_table()) def skip_unless_spark_backend(): from hail.backend.spark_backend\",\n \"in x]) elif isinstance(x, dict): return {k: convert_struct_to_dict(v) for k, v in x.items()}\",\n \"= create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table(): all_values =\",\n \"<gh_stars>0 import os from timeit import default_timer as timer import unittest import pytest\",\n \"c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)),\",\n \"< max_duration print(f'took {end - start:.3f}') return x def create_all_values(): return hl.struct( f32=hl.float32(3.14),\",\n \"create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_'))\",\n \"pytest from decorator import decorator from hail.utils.java import Env import hail as hl\",\n \"_initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else:\",\n \"_dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration):\",\n \"'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval(\",\n \"hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1') try: return func(*args, **kwargs)\",\n \"SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail(\",\n \"hl.locus('1', 1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0,\",\n \"'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22', 33878978, 'GRCh38'), ml=hl.null(hl.tlocus('GRCh37')), i=hl.interval( hl.locus('1', 999), hl.locus('1', 1001)),\",\n \"= create_all_values() return (hl.utils.range_matrix_table(3, 2, n_partitions=2) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values,\",\n \"1001)), c=hl.call(0, 1), mc=hl.null(hl.tcall), t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2,\",\n \"not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True)\",\n \"True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources') _doctest_dir = os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def\",\n \"SparkBackend @decorator def wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else:\",\n \"os.environ.get('HAIL_DOCTEST_DATA_DIR', 'hail/docs/data') def resource(filename): return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def\",\n \"hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized = True def stopTestHailContext():\",\n \"v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt) for elt in x] elif\",\n \"= f() end = timer() assert (start - end) < max_duration print(f'took {end\",\n \"else: raise unittest.SkipTest('requires Spark') return wrapper fails_local_backend = pytest.mark.xfail( os.environ.get('HAIL_QUERY_BACKEND') == 'local', reason=\\\"doesn't\",\n \"*args, **kwargs): return return wrapper def assert_evals_to(e, v): res = hl.eval(e) if res\",\n \"wrapper def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f'\",\n \"hl.struct( f32=hl.float32(3.14), i64=hl.int64(-9), m=hl.null(hl.tfloat64), astruct=hl.struct(a=hl.null(hl.tint32), b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a',\",\n \"as timer import unittest import pytest from decorator import decorator from hail.utils.java import\",\n \"!= v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds =\",\n \"= dict(x) y_fds = dict(y) return x_fds == y_fds def convert_struct_to_dict(x): if isinstance(x,\",\n \"hl.null(hl.tstr)]), mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)), nd=hl.nd.arange(0, 10).reshape((2, 5)), ) def prefix_struct(s, prefix): return hl.struct(**{prefix +\",\n \"hl.Struct): return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return\",\n \"# force initialization _initialized = True def stopTestHailContext(): pass _test_dir = os.environ.get('HAIL_TEST_RESOURCES_DIR', '../src/test/resources')\",\n \"if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0,\",\n \"_initialized = False def startTestHailContext(): global _initialized if not _initialized: backend_name = os.environ.get('HAIL_QUERY_BACKEND',\",\n \"expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator\",\n \"def assert_all_eval_to(*expr_and_expected): exprs, expecteds = zip(*expr_and_expected) assert_evals_to(hl.tuple(exprs), expecteds) def lower_only(): @decorator def wrapper(func,\",\n \"@decorator def wrapper(func, *args, **kwargs): return return wrapper def assert_evals_to(e, v): res =\",\n \"if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force initialization _initialized\",\n \"None def get_dataset(): global _dataset if _dataset is None: _dataset = hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return\",\n \"def assert_evals_to(e, v): res = hl.eval(e) if res != v: raise ValueError(f' actual:\",\n \"= hl.split_multi_hts(hl.import_vcf(resource('sample.vcf'))).cache() return _dataset def assert_time(f, max_duration): start = timer() x = f()\",\n \"'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}), md=hl.null(hl.tdict(hl.tint32, hl.tstr)), h38=hl.locus('chr22',\",\n \"if res != v: raise ValueError(f' actual: {res}\\\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs,\",\n \"*args, **kwargs): flags = hl._get_flags() prev_lower = flags.get('lower') prev_lower_only = flags.get('lower_only') hl._set_flags(lower='1', lower_only='1')\",\n \"wrapper(func, *args, **kwargs): if isinstance(hl.utils.java.Env.backend(), SparkBackend): return func(*args, **kwargs) else: raise unittest.SkipTest('requires Spark')\",\n \"os.environ.get('HAIL_QUERY_BACKEND', 'spark') if backend_name == 'spark': hl.init(master='local[1]', min_block_size=0, quiet=True) else: Env.hc() # force\",\n \"create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5, n_partitions=3) .annotate_globals(**prefix_struct(all_values, 'global_')) .annotate(**all_values) .cache()) def create_all_values_matrix_table():\",\n \"b=5.5), mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)), aset=hl.set(['foo', 'bar', 'baz']), mset=hl.null(hl.tset(hl.tfloat64)), d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']):\",\n \"return os.path.join(_test_dir, filename) def doctest_resource(filename): return os.path.join(_doctest_dir, filename) def schema_eq(x, y): x_fds =\",\n \".annotate_globals(**prefix_struct(all_values, 'global_')) .annotate_rows(**prefix_struct(all_values, 'row_')) .annotate_cols(**prefix_struct(all_values, 'col_')) .annotate_entries(**prefix_struct(all_values, 'entry_')) .cache()) def create_all_values_datasets(): return (create_all_values_table(),\",\n \"k: s[k] for k in s}) def create_all_values_table(): all_values = create_all_values() return (hl.utils.range_table(5,\"\n]","isConversational":false}}},{"rowIdx":2184,"cells":{"ngram":{"kind":"list like","value":["L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size)","Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device):","d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum(","scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name)","#true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self,","mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L]","ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def","### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights)","s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\")","context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels","= word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None:","self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s,","import tensorflow as tf import numpy as np from entity_linker.models.base import Model class","def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: #","self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed ==","= tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses =","= tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as","copyright (C) 2020 <NAME> \"\"\" import time import tensorflow as tf import numpy","batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as","if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else:","as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids,","<filename>src/entity_linker/models/figer_model/labeling_model.py \"\"\" Modifications copyright (C) 2020 <NAME> \"\"\" import time import tensorflow as","= num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if","shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights","# [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses)","tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable(","2020 <NAME> \"\"\" import time import tensorflow as tf import numpy as np","[B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) /","= tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded,","else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim,","self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with","d: if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0)))","= tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded,","tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights)","using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name,","tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering","L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES","num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels =","= tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids,","None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat(","(C) 2020 <NAME> \"\"\" import time import tensorflow as tf import numpy as","tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings,","name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs","PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs","Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed,","context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels],","tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings,","\"\"\" Modifications copyright (C) 2020 <NAME> \"\"\" import time import tensorflow as tf","FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores)","device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as","self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses","tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0)))","stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\",","self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu)","[context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B,","= tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss =","s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim,","shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs =","tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss = tf.reduce_sum(","self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores","true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim","tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed],","initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ###","\"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded,","mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with","num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores)","with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights =","logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores,","= batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device)","name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores","stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT","tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses) / tf.to_float(self.batch_size)","true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L]","mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded","== None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded =","<NAME> \"\"\" import time import tensorflow as tf import numpy as np from","tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels],","loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B,","self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1,","tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores,","scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses","= tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name)","tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name,","self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses)","np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def","LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim,","VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size","batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels","= tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings =","= tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores =","from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self,","initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable(","TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs =","tensorflow as tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model):","as s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\",","name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\")","self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s,","keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu):","num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights =","num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed","name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr')","self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5)","word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim =","as np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\"","as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss =","1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) #","time import tensorflow as tf import numpy as np from entity_linker.models.base import Model","class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings,","__init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size","tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as","def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size =","tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits(","targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids,","entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size,","tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss","with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits(","self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss","self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d:","numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State","word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights","self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings","import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using","/ tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\"entity_labeling_loss\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses) /","as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0,","device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses =","\"\"\" import time import tensorflow as tf import numpy as np from entity_linker.models.base","import Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels,","import time import tensorflow as tf import numpy as np from entity_linker.models.base import","context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim","[B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM","= tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0,","Modifications copyright (C) 2020 <NAME> \"\"\" import time import tensorflow as tf import","# [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES","as tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \"\"\"Unsupervised","Model class LabelingModel(Model): \"\"\"Unsupervised Clustering using Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, context_encoded_dim,","self.label_weights = tf.get_variable( name=\"label_weights\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores =","tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d:"],"string":"[\n \"L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size)\",\n \"Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device):\",\n \"d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum(\",\n \"scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name)\",\n \"#true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self,\",\n \"mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L]\",\n \"ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def\",\n \"### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights)\",\n \"s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\")\",\n \"context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels\",\n \"= word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None:\",\n \"self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s,\",\n \"import tensorflow as tf import numpy as np from entity_linker.models.base import Model class\",\n \"def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: #\",\n \"self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed ==\",\n \"= tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses =\",\n \"= tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as\",\n \"copyright (C) 2020 <NAME> \\\"\\\"\\\" import time import tensorflow as tf import numpy\",\n \"batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as\",\n \"if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else:\",\n \"as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids,\",\n \"<filename>src/entity_linker/models/figer_model/labeling_model.py \\\"\\\"\\\" Modifications copyright (C) 2020 <NAME> \\\"\\\"\\\" import time import tensorflow as\",\n \"= num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if\",\n \"shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights\",\n \"# [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses)\",\n \"tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable(\",\n \"2020 <NAME> \\\"\\\"\\\" import time import tensorflow as tf import numpy as np\",\n \"[B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) /\",\n \"= tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded,\",\n \"else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim,\",\n \"self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with\",\n \"d: if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0)))\",\n \"= tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded,\",\n \"tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights)\",\n \"using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name,\",\n \"tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering\",\n \"L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES\",\n \"num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels =\",\n \"= tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids,\",\n \"None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat(\",\n \"(C) 2020 <NAME> \\\"\\\"\\\" import time import tensorflow as tf import numpy as\",\n \"tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings,\",\n \"name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs\",\n \"PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs\",\n \"Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed,\",\n \"context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels],\",\n \"tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings,\",\n \"\\\"\\\"\\\" Modifications copyright (C) 2020 <NAME> \\\"\\\"\\\" import time import tensorflow as tf\",\n \"FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores)\",\n \"device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as\",\n \"self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses\",\n \"tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0)))\",\n \"stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\",\",\n \"self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu)\",\n \"[context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B,\",\n \"= tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss =\",\n \"s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim,\",\n \"shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs =\",\n \"tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss = tf.reduce_sum(\",\n \"self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores\",\n \"true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim\",\n \"tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed],\",\n \"initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ###\",\n \"\\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded,\",\n \"mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with\",\n \"num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores)\",\n \"with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights =\",\n \"logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores,\",\n \"= batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device)\",\n \"name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores\",\n \"stddev=1.0/(100.0))) # [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT\",\n \"tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses) / tf.to_float(self.batch_size)\",\n \"true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L]\",\n \"mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded\",\n \"== None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded =\",\n \"<NAME> \\\"\\\"\\\" import time import tensorflow as tf import numpy as np from\",\n \"tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels],\",\n \"loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B,\",\n \"self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1,\",\n \"tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores,\",\n \"scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses\",\n \"= tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name)\",\n \"tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name,\",\n \"self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses)\",\n \"np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def\",\n \"LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim,\",\n \"VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size\",\n \"batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels\",\n \"= tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings =\",\n \"= tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores =\",\n \"from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self,\",\n \"initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable(\",\n \"TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs =\",\n \"tensorflow as tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model):\",\n \"as s, tf.device(device) as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\",\",\n \"name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\")\",\n \"self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s,\",\n \"keep_prob=0.5) self.entity_label_scores = tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu):\",\n \"num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights =\",\n \"num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed\",\n \"name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) else: context_encoded = tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr')\",\n \"self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings = tf.nn.dropout(true_entity_embeddings, keep_prob=0.5)\",\n \"word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim =\",\n \"as np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\"\",\n \"as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss =\",\n \"1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) #\",\n \"time import tensorflow as tf import numpy as np from entity_linker.models.base import Model\",\n \"class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings,\",\n \"__init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size\",\n \"tf.matmul(true_entity_embeddings, self.label_weights) self.entity_label_probs = tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as\",\n \"def __init__(self, batch_size, num_labels, context_encoded_dim, true_entity_embeddings, word_embed_dim, context_encoded, mention_embed, scope_name, device): self.batch_size =\",\n \"tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits(\",\n \"targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids,\",\n \"entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size,\",\n \"tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\\\"labeling_loss\\\") self.labeling_loss\",\n \"with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits(\",\n \"self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss\",\n \"self.num_labels = num_labels self.word_embed_dim = word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d:\",\n \"numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State\",\n \"word_embed_dim with tf.variable_scope(scope_name) as s, tf.device(device) as d: if mention_embed == None: self.label_weights\",\n \"self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM ENTITIES #true_entity_embeddings\",\n \"import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using\",\n \"/ tf.to_float(self.batch_size) self.enlabel_cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.entity_label_scores, targets=true_label_ids, name=\\\"entity_labeling_loss\\\") self.entity_labeling_loss = tf.reduce_sum( self.enlabel_cross_entropy_losses) /\",\n \"as d: if mention_embed == None: self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0,\",\n \"device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d: # [B, L] self.cross_entropy_losses =\",\n \"\\\"\\\"\\\" import time import tensorflow as tf import numpy as np from entity_linker.models.base\",\n \"import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels,\",\n \"import time import tensorflow as tf import numpy as np from entity_linker.models.base import\",\n \"context_encoded, mention_embed, scope_name, device): self.batch_size = batch_size self.num_labels = num_labels self.word_embed_dim = word_embed_dim\",\n \"[B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES FROM\",\n \"= tf.concat( 1, [context_encoded, mention_embed], name='con_ment_repr') self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0,\",\n \"Modifications copyright (C) 2020 <NAME> \\\"\\\"\\\" import time import tensorflow as tf import\",\n \"# [B, L] self.label_scores = tf.matmul(context_encoded, self.label_weights) self.label_probs = tf.sigmoid(self.label_scores) ### PREDICT TYPES\",\n \"as tf import numpy as np from entity_linker.models.base import Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised\",\n \"Model class LabelingModel(Model): \\\"\\\"\\\"Unsupervised Clustering using Discrete-State VAE\\\"\\\"\\\" def __init__(self, batch_size, num_labels, context_encoded_dim,\",\n \"self.label_weights = tf.get_variable( name=\\\"label_weights\\\", shape=[context_encoded_dim+word_embed_dim, num_labels], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0/(100.0))) # [B, L] self.label_scores =\",\n \"tf.sigmoid(self.label_scores) def loss_graph(self, true_label_ids, scope_name, device_gpu): with tf.variable_scope(scope_name) as s, tf.device(device_gpu) as d:\"\n]","isConversational":false}}},{"rowIdx":2185,"cells":{"ngram":{"kind":"list like","value":["exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print","pmd import numpy as np from scipy.spatial import distance if len(sys.argv) < 2:","len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds =","import sys import parmed as pmd import numpy as np from scipy.spatial import","as pmd import numpy as np from scipy.spatial import distance if len(sys.argv) <","sys import parmed as pmd import numpy as np from scipy.spatial import distance","print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist =","molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds,","numpy as np from scipy.spatial import distance if len(sys.argv) < 2: print \"Usage:","import distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol =","mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print np.max(dist)","< 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates","= pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print np.max(dist) exit(0)","python import sys import parmed as pmd import numpy as np from scipy.spatial","\"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds,","parmed as pmd import numpy as np from scipy.spatial import distance if len(sys.argv)","if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds","2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist","import parmed as pmd import numpy as np from scipy.spatial import distance if","import numpy as np from scipy.spatial import distance if len(sys.argv) < 2: print","<gh_stars>1-10 #!/usr/bin/env python import sys import parmed as pmd import numpy as np","from scipy.spatial import distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1)","np from scipy.spatial import distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\"","as np from scipy.spatial import distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py","scipy.spatial import distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol","<mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean')","#!/usr/bin/env python import sys import parmed as pmd import numpy as np from","distance if len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.argv[1])"],"string":"[\n \"exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print\",\n \"pmd import numpy as np from scipy.spatial import distance if len(sys.argv) < 2:\",\n \"len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds =\",\n \"import sys import parmed as pmd import numpy as np from scipy.spatial import\",\n \"as pmd import numpy as np from scipy.spatial import distance if len(sys.argv) <\",\n \"sys import parmed as pmd import numpy as np from scipy.spatial import distance\",\n \"print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist =\",\n \"molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds,\",\n \"numpy as np from scipy.spatial import distance if len(sys.argv) < 2: print \\\"Usage:\",\n \"import distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol =\",\n \"mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print np.max(dist)\",\n \"< 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates\",\n \"= pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print np.max(dist) exit(0)\",\n \"python import sys import parmed as pmd import numpy as np from scipy.spatial\",\n \"\\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds,\",\n \"parmed as pmd import numpy as np from scipy.spatial import distance if len(sys.argv)\",\n \"if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds\",\n \"2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist\",\n \"import parmed as pmd import numpy as np from scipy.spatial import distance if\",\n \"import numpy as np from scipy.spatial import distance if len(sys.argv) < 2: print\",\n \"<gh_stars>1-10 #!/usr/bin/env python import sys import parmed as pmd import numpy as np\",\n \"from scipy.spatial import distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1)\",\n \"np from scipy.spatial import distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\"\",\n \"as np from scipy.spatial import distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py\",\n \"scipy.spatial import distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol\",\n \"<mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean')\",\n \"#!/usr/bin/env python import sys import parmed as pmd import numpy as np from\",\n \"distance if len(sys.argv) < 2: print \\\"Usage: molecular_diameter.py <mymolecule.mol2>\\\" exit(1) mol = pmd.load_file(sys.argv[1])\"\n]","isConversational":false}}},{"rowIdx":2186,"cells":{"ngram":{"kind":"list like","value":["= pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head()","= \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\",","== \"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif","from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None)","64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args)","\"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\":","train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type,","model_type == \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\":","model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions =","header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] ==","\"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True # } }","\"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\" model_name =","= \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif","= sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\":","= \"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif","print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head())","elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type =","pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1]","\"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\": model_name =","eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0]","model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions","model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\"","\"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\" elif model_type","+ \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] =","header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df","\"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name =","== \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model","\"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\":","= \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif","\"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type =","False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True # }","} if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create","model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\"","if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a","= 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train","\"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2,","64, # \"config\": { # \"output_hidden_states\": True # } } if model_type ==","\"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type =","model_type = sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type ==","== \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name","# \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\":","pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0]","= \"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\":","= \"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\":","\"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\" model_name =","\"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True # } } if model_type","\"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type","\", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\":","train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]}","eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type","import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df","regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\": model_name","\"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type","train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\",","# Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model","Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, #","# \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": {","model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\" elif model_type ==","model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\"","\"output_hidden_states\": True # } } if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"]","model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type ==","\"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int)","train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int)","the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs,","eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\"","model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\"","ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate","\"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\":","True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3,","train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\",","\"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model =","(eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]}","# \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, #","import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df =","train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None)","ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df =","model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args = {","a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df)","False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4,","2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the","model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\":","\"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128,","{\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\":","3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\":","\"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False,","model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # #","\"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\",","regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True),","# \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True","True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False,","elif model_type == \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type ==","# Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model #","train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) #","model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True,","# \"config\": { # \"output_hidden_states\": True # } } if model_type == \"xlnet\":","\" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type ==","== \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\" model_name","False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, #","= 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name,","model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\"","\"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\":","\"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type","if model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name =","model_type == \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\":","= pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type =","ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) #","== 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \"","# \"output_hidden_states\": True # } } if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64","\"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type","elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\":","\"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model","\"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args =","as pd from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix +","pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0]","= pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df =","{ # \"output_hidden_states\": True # } } if model_type == \"xlnet\": train_args[\"train_batch_size\"] =","model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 # Create a ClassificationModel","eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type","import sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \"data/\"","= (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\":","== \"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\" elif","simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head()","\"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\":","\"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\":","elif model_type == \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type ==","= { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\":","{\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if","\"bert-base-cased\" elif model_type == \"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name","elif model_type == \"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name =","eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions = model.eval_model(eval_df)","prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix","<reponame>liorshk/simpletransformers<gh_stars>1000+ import sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix =","model_type == \"roberta\": model_name = \"roberta-base\" elif model_type == \"distilbert\": model_name = \"distilbert-base-cased\"","f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\",","True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\":","128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True # } } if","2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head())","\"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\":","= \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\":","\"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True,","1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, #","eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df =","\"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\": 2, # \"manual_seed\":","128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\":","2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\":",") print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": eval_df[0]} )","model_type == \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\":","== \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type","= (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\":","\"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\":","= ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # #","(train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\",","= \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\" model_name","True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128,","\"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, #","sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df","model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the","model_type = \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\"","== \"electra-base\": model_type = \"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type","f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification","\" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \"","# } } if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2","pandas as pd from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix","train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame(","4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\":","\"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0]","model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", # \"n_gpu\":","{ \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True,","} } if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] = 2 #","\"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\":","Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result,","\"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False,",") print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\" elif","== 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} )","train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df","\"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { #","print(eval_df.head()) model_type = sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type","= \"electra\" model_name = \"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args","\"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type","\"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000, \"wandb_project\": \"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name},","Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df,","== \"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\":","True # } } if model_type == \"xlnet\": train_args[\"train_batch_size\"] = 64 train_args[\"gradient_accumulation_steps\"] =","\"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\",","pd from simpletransformers.classification import ClassificationModel prefix = \"data/\" train_df = pd.read_csv(prefix + \"train.csv\",","eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True),","2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \",","= \"data/\" train_df = pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix +","= pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] =","model_type == \"distilbert\": model_name = \"distilbert-base-cased\" elif model_type == \"distilroberta\": model_type = \"roberta\"","pd.DataFrame( {\"text\": train_df[1].replace(r\"\\n\", \" \", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame(","\"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\":","= \"roberta\" model_name = \"distilroberta-base\" elif model_type == \"electra-base\": model_type = \"electra\" model_name","+ \"test.csv\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] ==","\"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64, # \"config\": { # \"output_hidden_states\": True #","Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\":","# \"n_gpu\": 2, # \"manual_seed\": 4, # \"use_multiprocessing\": False, \"train_batch_size\": 128, \"eval_batch_size\": 64,","\"Classification Model Comparison\", \"wandb_kwargs\": {\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True,","sys.argv[1] if model_type == \"bert\": model_name = \"bert-base-cased\" elif model_type == \"roberta\": model_name","elif model_type == \"distilroberta\": model_type = \"roberta\" model_name = \"distilroberta-base\" elif model_type ==","\"electra\" model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\" model_name =","\"config\": { # \"output_hidden_states\": True # } } if model_type == \"xlnet\": train_args[\"train_batch_size\"]","True, \"output_dir\": f\"outputs/{model_type}\", \"best_model_dir\": f\"outputs/{model_type}/best_model\", \"evaluate_during_training\": True, \"max_seq_length\": 128, \"num_train_epochs\": 3, \"evaluate_during_training_steps\": 1000,","model_name = \"google/electra-base-discriminator\" elif model_type == \"electra-small\": model_type = \"electra\" model_name = \"google/electra-small-discriminator\"","{\"name\": model_name}, \"save_model_every_epoch\": False, \"save_eval_checkpoints\": False, # \"use_early_stopping\": True, # \"early_stopping_metric\": \"mcc\", #","\", regex=True), \"labels\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\"text\": eval_df[1].replace(r\"\\n\", \" \",","args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model","\"google/electra-small-discriminator\" elif model_type == \"xlnet\": model_name = \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True,"],"string":"[\n \"= pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head()\",\n \"= \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\",\",\n \"== \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif\",\n \"from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None)\",\n \"64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args)\",\n \"\\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\":\",\n \"train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type,\",\n \"model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\":\",\n \"model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions =\",\n \"header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] ==\",\n \"\\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True # } }\",\n \"\\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name =\",\n \"= \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif\",\n \"= sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\":\",\n \"= \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif\",\n \"print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head())\",\n \"elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type =\",\n \"pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1]\",\n \"\\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name =\",\n \"eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0]\",\n \"model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions\",\n \"model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\"\",\n \"\\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type\",\n \"+ \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] =\",\n \"header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df\",\n \"\\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name =\",\n \"== \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model\",\n \"\\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\":\",\n \"= \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif\",\n \"\\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type =\",\n \"False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True # }\",\n \"} if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create\",\n \"model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\"\",\n \"if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a\",\n \"= 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train\",\n \"\\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2,\",\n \"64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True # } } if model_type ==\",\n \"\\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type =\",\n \"model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type ==\",\n \"== \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name\",\n \"# \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\":\",\n \"pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0]\",\n \"= \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\":\",\n \"= \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\":\",\n \"\\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name =\",\n \"\\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True # } } if model_type\",\n \"\\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type\",\n \"\\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\":\",\n \"train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]}\",\n \"eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type\",\n \"import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df\",\n \"regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name\",\n \"\\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type\",\n \"train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\",\",\n \"# Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model\",\n \"Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, #\",\n \"# \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": {\",\n \"model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type ==\",\n \"model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\"\",\n \"\\\"output_hidden_states\\\": True # } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"]\",\n \"model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type ==\",\n \"\\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int)\",\n \"train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int)\",\n \"the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result, model_outputs,\",\n \"eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\"\",\n \"model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\"\",\n \"ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate\",\n \"\\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\":\",\n \"True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3,\",\n \"train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\",\",\n \"\\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model =\",\n \"(eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]}\",\n \"# \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, #\",\n \"import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df =\",\n \"train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None)\",\n \"ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df =\",\n \"model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = {\",\n \"a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df)\",\n \"False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4,\",\n \"2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the\",\n \"model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\":\",\n \"\\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128,\",\n \"{\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\":\",\n \"3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\":\",\n \"\\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False,\",\n \"model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # #\",\n \"\\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\",\",\n \"regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True),\",\n \"# \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True\",\n \"True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False,\",\n \"elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type ==\",\n \"# Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model #\",\n \"train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) #\",\n \"model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True,\",\n \"# \\\"config\\\": { # \\\"output_hidden_states\\\": True # } } if model_type == \\\"xlnet\\\":\",\n \"\\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type ==\",\n \"== \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name\",\n \"False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, #\",\n \"= 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel model = ClassificationModel(model_type, model_name,\",\n \"model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\"\",\n \"\\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\":\",\n \"\\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type\",\n \"if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name =\",\n \"model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\":\",\n \"= pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type =\",\n \"ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) #\",\n \"== 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\"\",\n \"# \\\"output_hidden_states\\\": True # } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64\",\n \"\\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type\",\n \"elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\":\",\n \"\\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model\",\n \"\\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args =\",\n \"as pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix +\",\n \"pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0]\",\n \"= pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df =\",\n \"{ # \\\"output_hidden_states\\\": True # } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] =\",\n \"model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 # Create a ClassificationModel\",\n \"eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if model_type\",\n \"import sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\"\",\n \"= (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\":\",\n \"== \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif\",\n \"simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head()\",\n \"\\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\":\",\n \"\\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\":\",\n \"elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type ==\",\n \"= { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\":\",\n \"{\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} ) print(eval_df.head()) model_type = sys.argv[1] if\",\n \"\\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name\",\n \"elif model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name =\",\n \"eval_df=eval_df) # # # Evaluate the model # result, model_outputs, wrong_predictions = model.eval_model(eval_df)\",\n \"prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix\",\n \"<reponame>liorshk/simpletransformers<gh_stars>1000+ import sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix =\",\n \"model_type == \\\"roberta\\\": model_name = \\\"roberta-base\\\" elif model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\"\",\n \"f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\",\",\n \"True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\":\",\n \"128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True # } } if\",\n \"2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head())\",\n \"\\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\":\",\n \"= \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\":\",\n \"\\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True,\",\n \"1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, #\",\n \"eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df =\",\n \"\\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\": 2, # \\\"manual_seed\\\":\",\n \"128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\":\",\n \"2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\":\",\n \") print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": eval_df[0]} )\",\n \"model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\":\",\n \"== \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type\",\n \"= (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\":\",\n \"\\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\":\",\n \"= ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # #\",\n \"(train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\",\",\n \"= \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name\",\n \"True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128,\",\n \"\\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, #\",\n \"sys import pandas as pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df\",\n \"model_name, args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the\",\n \"model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\"\",\n \"== \\\"electra-base\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type\",\n \"f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification\",\n \"\\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\"\",\n \"# } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2\",\n \"pandas as pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix\",\n \"train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame(\",\n \"4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\":\",\n \"\\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0]\",\n \"model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", # \\\"n_gpu\\\":\",\n \"{ \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True,\",\n \"} } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] = 2 #\",\n \"\\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\":\",\n \"Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model # result,\",\n \"\\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False,\",\n \") print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif\",\n \"== 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} )\",\n \"train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df\",\n \"\\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { #\",\n \"print(eval_df.head()) model_type = sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type\",\n \"= \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args\",\n \"\\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type\",\n \"\\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000, \\\"wandb_project\\\": \\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name},\",\n \"Create a ClassificationModel model = ClassificationModel(model_type, model_name, args=train_args) # Train the model model.train_model(train_df,\",\n \"== \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\":\",\n \"True # } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"] = 64 train_args[\\\"gradient_accumulation_steps\\\"] =\",\n \"\\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\",\",\n \"pd from simpletransformers.classification import ClassificationModel prefix = \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\",\",\n \"eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True),\",\n \"2).astype(int) eval_df[0] = (eval_df[0] == 2).astype(int) train_df = pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\",\",\n \"= \\\"data/\\\" train_df = pd.read_csv(prefix + \\\"train.csv\\\", header=None) train_df.head() eval_df = pd.read_csv(prefix +\",\n \"= pd.read_csv(prefix + \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] =\",\n \"model_type == \\\"distilbert\\\": model_name = \\\"distilbert-base-cased\\\" elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\"\",\n \"pd.DataFrame( {\\\"text\\\": train_df[1].replace(r\\\"\\\\n\\\", \\\" \\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame(\",\n \"\\\"reprocess_input_data\\\": True, \\\"overwrite_output_dir\\\": True, \\\"use_cached_eval_features\\\": True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\":\",\n \"= \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type == \\\"electra-base\\\": model_type = \\\"electra\\\" model_name\",\n \"+ \\\"test.csv\\\", header=None) eval_df.head() train_df[0] = (train_df[0] == 2).astype(int) eval_df[0] = (eval_df[0] ==\",\n \"\\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64, # \\\"config\\\": { # \\\"output_hidden_states\\\": True #\",\n \"Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\":\",\n \"# \\\"n_gpu\\\": 2, # \\\"manual_seed\\\": 4, # \\\"use_multiprocessing\\\": False, \\\"train_batch_size\\\": 128, \\\"eval_batch_size\\\": 64,\",\n \"\\\"Classification Model Comparison\\\", \\\"wandb_kwargs\\\": {\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True,\",\n \"sys.argv[1] if model_type == \\\"bert\\\": model_name = \\\"bert-base-cased\\\" elif model_type == \\\"roberta\\\": model_name\",\n \"elif model_type == \\\"distilroberta\\\": model_type = \\\"roberta\\\" model_name = \\\"distilroberta-base\\\" elif model_type ==\",\n \"\\\"electra\\\" model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name =\",\n \"\\\"config\\\": { # \\\"output_hidden_states\\\": True # } } if model_type == \\\"xlnet\\\": train_args[\\\"train_batch_size\\\"]\",\n \"True, \\\"output_dir\\\": f\\\"outputs/{model_type}\\\", \\\"best_model_dir\\\": f\\\"outputs/{model_type}/best_model\\\", \\\"evaluate_during_training\\\": True, \\\"max_seq_length\\\": 128, \\\"num_train_epochs\\\": 3, \\\"evaluate_during_training_steps\\\": 1000,\",\n \"model_name = \\\"google/electra-base-discriminator\\\" elif model_type == \\\"electra-small\\\": model_type = \\\"electra\\\" model_name = \\\"google/electra-small-discriminator\\\"\",\n \"{\\\"name\\\": model_name}, \\\"save_model_every_epoch\\\": False, \\\"save_eval_checkpoints\\\": False, # \\\"use_early_stopping\\\": True, # \\\"early_stopping_metric\\\": \\\"mcc\\\", #\",\n \"\\\", regex=True), \\\"labels\\\": train_df[0]} ) print(train_df.head()) eval_df = pd.DataFrame( {\\\"text\\\": eval_df[1].replace(r\\\"\\\\n\\\", \\\" \\\",\",\n \"args=train_args) # Train the model model.train_model(train_df, eval_df=eval_df) # # # Evaluate the model\",\n \"\\\"google/electra-small-discriminator\\\" elif model_type == \\\"xlnet\\\": model_name = \\\"xlnet-base-cased\\\" train_args = { \\\"reprocess_input_data\\\": True,\"\n]","isConversational":false}}},{"rowIdx":2187,"cells":{"ngram":{"kind":"list like","value":["not os.path.isfile(path): print(\"Error: file \" + path + \" not found!\") exit(-1) with","file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 =","float(line[3]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2,","\" + path + \" not found!\") exit(-1) with open(path) as file: for","graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 == v2)","v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p =","weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \" +","<reponame>mahdi-zafarmand/SNA import networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph","delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \" + path","+ \" not found!\") exit(-1) with open(path) as file: for line in file.readlines():","file \" + path + \" not found!\") exit(-1) with open(path) as file:","v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop","if not os.path.isfile(path): print(\"Error: file \" + path + \" not found!\") exit(-1)","\" not found!\") exit(-1) with open(path) as file: for line in file.readlines(): line","graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1 == v2)","graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1 ==","file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w","= float(line[2]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1,","for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1])","v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2])","= int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and","with open(path) as file: for line in file.readlines(): w = 1.0 line =","file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1])","graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 == v2) or","int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 ==","with open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1 =","+ path + \" not found!\") exit(-1) with open(path) as file: for line","== v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path,","networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph()","not found!\") exit(-1) with open(path) as file: for line in file.readlines(): w =","file: for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 =","line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1)","w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1)","load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \"","line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w =","graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if","for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0])","int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if","= int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if","1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if","if weighted: w = float(line[2]) if (self_loop and v1 == v2) or (v1","= float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or (v1","exit(-1) with open(path) as file: for line in file.readlines(): w = 1.0 line","= int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3])","self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \" + path +","(v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph","!= v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph =","os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error:","in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 =","import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path):","int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop","weighted: w = float(line[2]) if (self_loop and v1 == v2) or (v1 !=","if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w,","v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def","as nx import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if","as file: for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1","line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted:","path + \" not found!\") exit(-1) with open(path) as file: for line in","load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \" +","w = float(line[2]) if (self_loop and v1 == v2) or (v1 != v2):","v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t',","found!\") exit(-1) with open(path) as file: for line in file.readlines(): line = line.split(delimiter)","(self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return","w = float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or","weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path):","nx.Graph() if not os.path.isfile(path): print(\"Error: file \" + path + \" not found!\")","found!\") exit(-1) with open(path) as file: for line in file.readlines(): w = 1.0","p = float(line[3]) if (self_loop and v1 == v2) or (v1 != v2):","= line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2])","float(line[2]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2,","graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \" + path + \"","open(path) as file: for line in file.readlines(): w = 1.0 line = line.split(delimiter)","and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p) return","= 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2)","graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file","float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or (v1 !=","print(\"Error: file \" + path + \" not found!\") exit(-1) with open(path) as","or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False):","line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w =","= float(line[3]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1,","and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph","= line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w","as file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2","nx import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not","line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p","(self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p)","\" not found!\") exit(-1) with open(path) as file: for line in file.readlines(): w","= int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1","v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p) return graph","v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph()","open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0])","not found!\") exit(-1) with open(path) as file: for line in file.readlines(): line =","in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2)","v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and","def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file \"","os.path.isfile(path): print(\"Error: file \" + path + \" not found!\") exit(-1) with open(path)","import networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph =","int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1","def load_graph(path, weighted=False, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error: file","v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not","exit(-1) with open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1","line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2","return graph def load_graph_uncertain(path, delimiter='\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\"Error:","if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w)","= nx.Graph() if not os.path.isfile(path): print(\"Error: file \" + path + \" not"],"string":"[\n \"not os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\" not found!\\\") exit(-1) with\",\n \"file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 =\",\n \"float(line[3]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2,\",\n \"\\\" + path + \\\" not found!\\\") exit(-1) with open(path) as file: for\",\n \"graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 == v2)\",\n \"v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p =\",\n \"weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" +\",\n \"<reponame>mahdi-zafarmand/SNA import networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph\",\n \"delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" + path\",\n \"+ \\\" not found!\\\") exit(-1) with open(path) as file: for line in file.readlines():\",\n \"file \\\" + path + \\\" not found!\\\") exit(-1) with open(path) as file:\",\n \"v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop\",\n \"if not os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\" not found!\\\") exit(-1)\",\n \"\\\" not found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): line\",\n \"graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1 == v2)\",\n \"graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1 ==\",\n \"file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w\",\n \"= float(line[2]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1,\",\n \"for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1])\",\n \"v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2])\",\n \"= int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and\",\n \"with open(path) as file: for line in file.readlines(): w = 1.0 line =\",\n \"file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1])\",\n \"graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 == v2) or\",\n \"int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1 ==\",\n \"with open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1 =\",\n \"+ path + \\\" not found!\\\") exit(-1) with open(path) as file: for line\",\n \"== v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path,\",\n \"networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph()\",\n \"not found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): w =\",\n \"file: for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 =\",\n \"line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1)\",\n \"w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1)\",\n \"load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\"\",\n \"line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w =\",\n \"graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if\",\n \"for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0])\",\n \"int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if\",\n \"= int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if\",\n \"1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if\",\n \"if weighted: w = float(line[2]) if (self_loop and v1 == v2) or (v1\",\n \"= float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or (v1\",\n \"exit(-1) with open(path) as file: for line in file.readlines(): w = 1.0 line\",\n \"= int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3])\",\n \"self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" + path +\",\n \"(v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph\",\n \"!= v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph =\",\n \"os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error:\",\n \"in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 =\",\n \"import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path):\",\n \"int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop\",\n \"weighted: w = float(line[2]) if (self_loop and v1 == v2) or (v1 !=\",\n \"if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w,\",\n \"v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def\",\n \"as nx import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if\",\n \"as file: for line in file.readlines(): w = 1.0 line = line.split(delimiter) v1\",\n \"line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted:\",\n \"path + \\\" not found!\\\") exit(-1) with open(path) as file: for line in\",\n \"load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" +\",\n \"w = float(line[2]) if (self_loop and v1 == v2) or (v1 != v2):\",\n \"v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t',\",\n \"found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): line = line.split(delimiter)\",\n \"(self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return\",\n \"w = float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or\",\n \"weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path):\",\n \"nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\" not found!\\\")\",\n \"found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): w = 1.0\",\n \"p = float(line[3]) if (self_loop and v1 == v2) or (v1 != v2):\",\n \"= line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2])\",\n \"float(line[2]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2,\",\n \"graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\"\",\n \"open(path) as file: for line in file.readlines(): w = 1.0 line = line.split(delimiter)\",\n \"and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p) return\",\n \"= 1.0 line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2)\",\n \"graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file\",\n \"float(line[2]) p = float(line[3]) if (self_loop and v1 == v2) or (v1 !=\",\n \"print(\\\"Error: file \\\" + path + \\\" not found!\\\") exit(-1) with open(path) as\",\n \"or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False):\",\n \"line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w =\",\n \"= float(line[3]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1,\",\n \"and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w) return graph\",\n \"= line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w\",\n \"as file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2\",\n \"nx import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not\",\n \"line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p\",\n \"(self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p)\",\n \"\\\" not found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): w\",\n \"= int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and v1\",\n \"v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w, prob=p) return graph\",\n \"v2): graph.add_edge(v1, v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph()\",\n \"open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0])\",\n \"not found!\\\") exit(-1) with open(path) as file: for line in file.readlines(): line =\",\n \"in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2)\",\n \"v2 = int(line[1]) graph.add_node(v1) graph.add_node(v2) if weighted: w = float(line[2]) if (self_loop and\",\n \"def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\"\",\n \"os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\" not found!\\\") exit(-1) with open(path)\",\n \"import networkx as nx import os.path def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph =\",\n \"int(line[1]) graph.add_node(v1) graph.add_node(v2) w = float(line[2]) p = float(line[3]) if (self_loop and v1\",\n \"def load_graph(path, weighted=False, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error: file\",\n \"v2, weight=w) return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not\",\n \"exit(-1) with open(path) as file: for line in file.readlines(): line = line.split(delimiter) v1\",\n \"line in file.readlines(): w = 1.0 line = line.split(delimiter) v1 = int(line[0]) v2\",\n \"return graph def load_graph_uncertain(path, delimiter='\\\\t', self_loop=False): graph = nx.Graph() if not os.path.isfile(path): print(\\\"Error:\",\n \"if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2, weight=w)\",\n \"= nx.Graph() if not os.path.isfile(path): print(\\\"Error: file \\\" + path + \\\" not\"\n]","isConversational":false}}},{"rowIdx":2188,"cells":{"ngram":{"kind":"list like","value":["as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import","as exception: # Not signed logger.debug( 'detached signature verification error; %s', exception )","**kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open()","_ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key","from django.db import models from django.urls import reverse from django.utils.encoding import force_text from","will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With","= EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document","Key ID. The Key ID is also used to locate a key in","else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint =","null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public","with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as","class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True,","fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document","key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) #","_('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self,","detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature'))","is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel):","signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date = verify_result.date","viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id","signature IDs are the same, even when using the same key. \"\"\" document_version","**kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier -","import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import","verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint')","self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded')","ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models","mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes","'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') )","from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager","in the world have the same Key ID. The Key ID is also","- Key Identifier - This is what identifies uniquely a key. Not two","def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse(","signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id",") # With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature","file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: #","a key is used to sign something it will generate a unique signature","signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def","No two signature IDs are the same, even when using the same key.","verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached","key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name =","file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects","self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature, self).save(*args, **kwargs)","_('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs):","also used to locate a key in the key servers: http://pgp.mit.edu * signature_id","Fields: * key_id - Key Identifier - This is what identifies uniquely a","{}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if","the same, even when using the same key. \"\"\" document_version = models.ForeignKey( editable=False,","def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object,","# Not signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0)","EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields:","a key in the key servers: http://pgp.mit.edu * signature_id - Signature ID -","This is what identifies uniquely a key. Not two keys in the world","models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date =","unique signature ID. No two signature IDs are the same, even when using","'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id","key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a key","editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40,","import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from","Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached","max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False,","key is used to sign something it will generate a unique signature ID.","objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural =","get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:]","*args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with",") except VerificationError as exception: # Not signed logger.debug( 'detached signature verification error;",") objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural","kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def","raw = True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result","try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed","signature_id - Signature ID - Every time a key is used to sign","get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return","key_id - Key Identifier - This is what identifies uniquely a key. Not","VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy","# With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID')","DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy(","_('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self,",") except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error;","= ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def","* key_id - Key Identifier - This is what identifies uniquely a key.","blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True,","error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id =","signature') if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as","same Key ID. The Key ID is also used to locate a key","_('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def","except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error; %s',","self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else:","Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version","class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature')","import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model):","else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object","DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__)","embedded signature') if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw)","**kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file(","models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the","version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if","InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion","verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id","http://pgp.mit.edu * signature_id - Signature ID - Every time a key is used","models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy","the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a","on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True,","_('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args,","models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False,","= _('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id)","= InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural","force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions","blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta:","= models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True,","to locate a key in the key servers: http://pgp.mit.edu * signature_id - Signature","a unique signature ID. No two signature IDs are the same, even when","objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature')","_('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return","help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file')","as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception:","*args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file )","return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs)","key in the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every","try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not","self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self):","Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE","verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date,","signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached signature verification","verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False, null=True,","Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id =","django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError","Signature ID - Every time a key is used to sign something it","document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True,","def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return",".literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args,","- Every time a key is used to sign something it will generate","With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') )","upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier","reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers","= _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args,","return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else:","def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return","verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file",".managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class","*args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw","logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date","STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return","), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class","except VerificationError as exception: # Not signed logger.debug( 'detached signature verification error; %s',","self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try:","import models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import","time a key is used to sign something it will generate a unique","file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature')","as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: #","def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature,","two keys in the world have the same Key ID. The Key ID","Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures')","- This is what identifies uniquely a key. Not two keys in the","version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version,","verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self,","import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from",") # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') )","super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result","- Signature ID - Every time a key is used to sign something","DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager,","error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id","verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name","self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self):","document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields","self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature,","ID. No two signature IDs are the same, even when using the same","get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self,","**kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except","\"\"\" Fields: * key_id - Key Identifier - This is what identifies uniquely","Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached signatures')","models.CharField( help_text=_('ID of the key that will be used to sign the document.'),","even when using the same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures',","same, even when using the same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE,","= models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager()","self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property","max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key","self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file =","__str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details',","signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for","is also used to locate a key in the key servers: http://pgp.mit.edu *","from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from","locate a key in the key servers: http://pgp.mit.edu * signature_id - Signature ID","signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date =","= models.CharField( help_text=_('ID of the key that will be used to sign the",") else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint","blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature","signed') ) key_id = models.CharField( help_text=_('ID of the key that will be used","max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',)","model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models","key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint =","ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64,","import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import",") public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects","self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result =","what identifies uniquely a key. Not two keys in the world have the","# Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id","@property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class","used to sign something it will generate a unique signature ID. No two","self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception:","def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached:","signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id =","Key Identifier - This is what identifies uniquely a key. Not two keys","file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not","the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField(","verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return","to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True,","signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return","('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self):","detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name:","two signature IDs are the same, even when using the same key. \"\"\"","delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs):","signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField(","logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw = False","= _('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self):","sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id =","self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as file_object: try:","self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_(","sign something it will generate a unique signature ID. No two signature IDs","VerificationError as exception: # Not signed logger.debug( 'detached signature verification error; %s', exception","EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version","= logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id","identifies uniquely a key. Not two keys in the world have the same","import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import","if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached')","to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id","Identifier - This is what identifies uniquely a key. Not two keys in","SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier - This is what identifies","from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import","class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded","be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper",") key_id = models.CharField( help_text=_('ID of the key that will be used to","'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager()","verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that will be","%s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id","import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs):","import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import","- {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self):","True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file(","have the same Key ID. The Key ID is also used to locate","used to locate a key in the key servers: http://pgp.mit.edu * signature_id -","upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version","signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name)","generate a unique signature ID. No two signature IDs are the same, even","same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') )","logger.debug( 'embedded signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id","verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def","date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID","else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property","embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw","exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint","import uuid from django.db import models from django.urls import reverse from django.utils.encoding import","editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField(","or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} )","is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name","class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document","world have the same Key ID. The Key ID is also used to","version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self):","@property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file')","ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') )","embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking","exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception ) else:","from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import","something it will generate a unique signature ID. No two signature IDs are","version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk:","reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return","self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object:","verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True,","objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural =","for embedded signature') if self.pk: raw = True else: raw = False with","not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document","keys in the world have the same Key ID. The Key ID is","to sign something it will generate a unique signature ID. No two signature","EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural","from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4())","False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError","raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object )","save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else:","self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as","editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that","django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager","django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _","Not signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date","**kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw =","models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to,","return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def","uniquely a key. Not two keys in the world have the same Key","'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded","IDs are the same, even when using the same key. \"\"\" document_version =","Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded signature","exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id","null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that will","the same Key ID. The Key ID is also used to locate a","logging import uuid from django.db import models from django.urls import reverse from django.utils.encoding","= True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result =","return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def","as exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception )","storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name","verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature, self).save(*args,","null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta:","generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager()","ID. The Key ID is also used to locate a key in the","related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False,","servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a key is","DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version","self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint:","save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file","is used to sign something it will generate a unique signature ID. No","return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return","\"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic","return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id':","%s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id =","Not two keys in the world have the same Key ID. The Key","self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.'","version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs):","_('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if","when using the same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion,","= verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature","django.db import models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation","__str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args,","'embedded signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id =","= verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature,","ID is also used to locate a key in the key servers: http://pgp.mit.edu","hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects =","exception: # Not signed logger.debug( 'detached signature verification error; %s', exception ) else:","= False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except","verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def","editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering","else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return","self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint","verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded","= _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self,","using the same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document","file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification","**kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ),","self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk}","are the same, even when using the same key. \"\"\" document_version = models.ForeignKey(","super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously","Not signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date =","of the key that will be used to sign the document.'), max_length=40, verbose_name=_('Key","), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document","mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers","proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint","ID - Every time a key is used to sign something it will","= Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded","'{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def","class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached","key_id = models.CharField( help_text=_('ID of the key that will be used to sign","verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed","used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key","return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name =","public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects =","file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached signature","if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as file_object:","signature ID. No two signature IDs are the same, even when using the","the same key. \"\"\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version')","return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects","force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier - This is","it will generate a unique signature ID. No two signature IDs are the",") else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint =","= Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug(","verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date","help_text=_('ID of the key that will be used to sign the document.'), max_length=40,","signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE","from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from","version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{}",") def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if","from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as","mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger","# Not signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date","previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects =","if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file')","ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures')","logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id -","self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class",") objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version","VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception","Every time a key is used to sign something it will generate a","'{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def","signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw =","The Key ID is also used to locate a key in the key","self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return","version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed')","= _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id","fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField(","import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger =","= models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date","import logging import uuid from django.db import models from django.urls import reverse from","return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not","will generate a unique signature ID. No two signature IDs are the same,","= verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField(","with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError","def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True","is what identifies uniquely a key. Not two keys in the world have","Key ID is also used to locate a key in the key servers:","logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: *","blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key","the world have the same Key ID. The Key ID is also used","name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name =","verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs)","= verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel):","in the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time","that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) #","uuid from django.db import models from django.urls import reverse from django.utils.encoding import force_text","from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from","hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version","def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class","from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def","def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta:","from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from","verification error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id","class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier - This is what","key that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') )","verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug(","* signature_id - Signature ID - Every time a key is used to","import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from","models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class","= verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args,","InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural =","= DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document","= _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature')","return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key Identifier - This","else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint","verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file","a key. Not two keys in the world have the same Key ID.","key. Not two keys in the world have the same Key ID. The","if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as","signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{} -","mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals","= models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of","null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering =","the key that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID')","DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\"","_('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self):","= models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ),","def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args,","_('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or","def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \"\"\" Fields: * key_id - Key"],"string":"[\n \"as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import\",\n \"as exception: # Not signed logger.debug( 'detached signature verification error; %s', exception )\",\n \"**kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open()\",\n \"_ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key\",\n \"from django.db import models from django.urls import reverse from django.utils.encoding import force_text from\",\n \"will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With\",\n \"= EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document\",\n \"Key ID. The Key ID is also used to locate a key in\",\n \"else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint =\",\n \"null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public\",\n \"with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as\",\n \"class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True,\",\n \"fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document\",\n \"key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) #\",\n \"_('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self,\",\n \"detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature'))\",\n \"is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel):\",\n \"signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date = verify_result.date\",\n \"viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id\",\n \"signature IDs are the same, even when using the same key. \\\"\\\"\\\" document_version\",\n \"**kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier -\",\n \"import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import\",\n \"verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint')\",\n \"self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded')\",\n \"ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models\",\n \"mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes\",\n \"'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') )\",\n \"from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager\",\n \"in the world have the same Key ID. The Key ID is also\",\n \"- Key Identifier - This is what identifies uniquely a key. Not two\",\n \"def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse(\",\n \"signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id\",\n \") # With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature\",\n \"file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: #\",\n \"a key is used to sign something it will generate a unique signature\",\n \"signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def\",\n \"No two signature IDs are the same, even when using the same key.\",\n \"verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached\",\n \"key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name =\",\n \"file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects\",\n \"self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature, self).save(*args, **kwargs)\",\n \"_('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs):\",\n \"also used to locate a key in the key servers: http://pgp.mit.edu * signature_id\",\n \"Fields: * key_id - Key Identifier - This is what identifies uniquely a\",\n \"{}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if\",\n \"the same, even when using the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False,\",\n \"def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object,\",\n \"# Not signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0)\",\n \"EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields:\",\n \"a key in the key servers: http://pgp.mit.edu * signature_id - Signature ID -\",\n \"This is what identifies uniquely a key. Not two keys in the world\",\n \"models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date =\",\n \"unique signature ID. No two signature IDs are the same, even when using\",\n \"'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id\",\n \"key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a key\",\n \"editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40,\",\n \"import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from\",\n \"Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached\",\n \"max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False,\",\n \"key is used to sign something it will generate a unique signature ID.\",\n \"objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural =\",\n \"get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:]\",\n \"*args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with\",\n \") except VerificationError as exception: # Not signed logger.debug( 'detached signature verification error;\",\n \") objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural\",\n \"kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def\",\n \"raw = True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result\",\n \"try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed\",\n \"signature_id - Signature ID - Every time a key is used to sign\",\n \"get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return\",\n \"key_id - Key Identifier - This is what identifies uniquely a key. Not\",\n \"VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy\",\n \"# With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID')\",\n \"DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy(\",\n \"_('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self,\",\n \") except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error;\",\n \"= ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def\",\n \"* key_id - Key Identifier - This is what identifies uniquely a key.\",\n \"blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True,\",\n \"error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id =\",\n \"signature') if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as\",\n \"same Key ID. The Key ID is also used to locate a key\",\n \"_('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def\",\n \"except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error; %s',\",\n \"self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else:\",\n \"Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version\",\n \"class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature')\",\n \"import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model):\",\n \"else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object\",\n \"DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__)\",\n \"embedded signature') if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw)\",\n \"**kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file(\",\n \"models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the\",\n \"version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if\",\n \"InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion\",\n \"verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id\",\n \"http://pgp.mit.edu * signature_id - Signature ID - Every time a key is used\",\n \"models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy\",\n \"the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a\",\n \"on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True,\",\n \"_('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args,\",\n \"models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False,\",\n \"= _('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id)\",\n \"= InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural\",\n \"force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions\",\n \"blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta:\",\n \"= models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True,\",\n \"to locate a key in the key servers: http://pgp.mit.edu * signature_id - Signature\",\n \"a unique signature ID. No two signature IDs are the same, even when\",\n \"objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature')\",\n \"_('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return\",\n \"help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file')\",\n \"as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception:\",\n \"*args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file )\",\n \"return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs)\",\n \"key in the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every\",\n \"try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not\",\n \"self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self):\",\n \"Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE\",\n \"verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date,\",\n \"signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached signature verification\",\n \"verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False, null=True,\",\n \"Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id =\",\n \"django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError\",\n \"Signature ID - Every time a key is used to sign something it\",\n \"document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField( blank=True,\",\n \"def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return\",\n \".literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args,\",\n \"- Every time a key is used to sign something it will generate\",\n \"With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') )\",\n \"upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier\",\n \"reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers\",\n \"= _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args,\",\n \"return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else:\",\n \"def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return\",\n \"verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file\",\n \".managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class\",\n \"*args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw\",\n \"logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date\",\n \"STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return\",\n \"), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class\",\n \"except VerificationError as exception: # Not signed logger.debug( 'detached signature verification error; %s',\",\n \"self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try:\",\n \"import models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import\",\n \"time a key is used to sign something it will generate a unique\",\n \"file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature')\",\n \"as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: #\",\n \"def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature,\",\n \"two keys in the world have the same Key ID. The Key ID\",\n \"Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures')\",\n \"- This is what identifies uniquely a key. Not two keys in the\",\n \"version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version,\",\n \"verbose_name_plural = _('Document version detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self,\",\n \"import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from\",\n \") # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') )\",\n \"super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result\",\n \"- Signature ID - Every time a key is used to sign something\",\n \"DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager,\",\n \"error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id\",\n \"verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name\",\n \"self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self):\",\n \"document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields\",\n \"self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature,\",\n \"ID. No two signature IDs are the same, even when using the same\",\n \"get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self,\",\n \"**kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except\",\n \"\\\"\\\"\\\" Fields: * key_id - Key Identifier - This is what identifies uniquely\",\n \"Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached signatures')\",\n \"models.CharField( help_text=_('ID of the key that will be used to sign the document.'),\",\n \"even when using the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures',\",\n \"same, even when using the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE,\",\n \"= models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager()\",\n \"self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property\",\n \"max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key\",\n \"self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file =\",\n \"__str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details',\",\n \"signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for\",\n \"is also used to locate a key in the key servers: http://pgp.mit.edu *\",\n \"from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from\",\n \"locate a key in the key servers: http://pgp.mit.edu * signature_id - Signature ID\",\n \"signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date =\",\n \"= models.CharField( help_text=_('ID of the key that will be used to sign the\",\n \") else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint\",\n \"blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature\",\n \"signed') ) key_id = models.CharField( help_text=_('ID of the key that will be used\",\n \"max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering = ('pk',)\",\n \"model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models\",\n \"key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint =\",\n \"ID') ) # With proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64,\",\n \"import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import\",\n \") public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects\",\n \"self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result =\",\n \"what identifies uniquely a key. Not two keys in the world have the\",\n \"# Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id\",\n \"@property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class\",\n \"used to sign something it will generate a unique signature ID. No two\",\n \"self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception:\",\n \"def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached:\",\n \"signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id =\",\n \"Key Identifier - This is what identifies uniquely a key. Not two keys\",\n \"file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not\",\n \"the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id = models.CharField(\",\n \"verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return\",\n \"to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True,\",\n \"signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return\",\n \"('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self):\",\n \"detached signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name:\",\n \"two signature IDs are the same, even when using the same key. \\\"\\\"\\\"\",\n \"delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs):\",\n \"signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint = models.CharField(\",\n \"logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw = False\",\n \"= _('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def __str__(self):\",\n \"sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id =\",\n \"self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as file_object: try:\",\n \"self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_(\",\n \"sign something it will generate a unique signature ID. No two signature IDs\",\n \"VerificationError as exception: # Not signed logger.debug( 'detached signature verification error; %s', exception\",\n \"EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version\",\n \"= logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id\",\n \"identifies uniquely a key. Not two keys in the world have the same\",\n \"import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import\",\n \"if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached')\",\n \"to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key signature_id\",\n \"Identifier - This is what identifies uniquely a key. Not two keys in\",\n \"SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier - This is what identifies\",\n \"from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import\",\n \"class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded\",\n \"be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper\",\n \") key_id = models.CharField( help_text=_('ID of the key that will be used to\",\n \"'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager()\",\n \"verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that will be\",\n \"%s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id\",\n \"import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs):\",\n \"import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import\",\n \"- {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self):\",\n \"True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file(\",\n \"have the same Key ID. The Key ID is also used to locate\",\n \"used to locate a key in the key servers: http://pgp.mit.edu * signature_id -\",\n \"upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document version\",\n \"signatures') def __str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name)\",\n \"generate a unique signature ID. No two signature IDs are the same, even\",\n \"same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') )\",\n \"logger.debug( 'embedded signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id\",\n \"verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached signatures') def\",\n \"date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID\",\n \"else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property\",\n \"embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw\",\n \"exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint\",\n \"import uuid from django.db import models from django.urls import reverse from django.utils.encoding import\",\n \"editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField(\",\n \"or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} )\",\n \"is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name\",\n \"class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document\",\n \"world have the same Key ID. The Key ID is also used to\",\n \"version signatures') def __str__(self): return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self):\",\n \"@property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file')\",\n \"ID') ) public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') )\",\n \"embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking\",\n \"exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception ) else:\",\n \"from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import\",\n \"something it will generate a unique signature ID. No two signature IDs are\",\n \"version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk:\",\n \"reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return\",\n \"self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as file_object:\",\n \"verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True,\",\n \"objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural =\",\n \"for embedded signature') if self.pk: raw = True else: raw = False with\",\n \"not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document\",\n \"keys in the world have the same Key ID. The Key ID is\",\n \"to sign something it will generate a unique signature ID. No two signature\",\n \"EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded signature') verbose_name_plural\",\n \"from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4())\",\n \"False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError\",\n \"raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object )\",\n \"save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else:\",\n \"self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as\",\n \"editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that\",\n \"django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager\",\n \"django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as _\",\n \"Not signed logger.debug( 'detached signature verification error; %s', exception ) else: self.signature_file.seek(0) self.date\",\n \"**kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True else: raw =\",\n \"models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to,\",\n \"return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return _('Embedded') @property def\",\n \"uniquely a key. Not two keys in the world have the same Key\",\n \"'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version embedded\",\n \"IDs are the same, even when using the same key. \\\"\\\"\\\" document_version =\",\n \"Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded signature\",\n \"exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id\",\n \"null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key that will\",\n \"the same Key ID. The Key ID is also used to locate a\",\n \"logging import uuid from django.db import models from django.urls import reverse from django.utils.encoding\",\n \"= True else: raw = False with self.document_version.open(raw=raw) as file_object: try: verify_result =\",\n \"return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def\",\n \"as exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception )\",\n \"storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name\",\n \"verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature, self).save(*args,\",\n \"null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta:\",\n \"generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager()\",\n \"ID. The Key ID is also used to locate a key in the\",\n \"related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False,\",\n \"servers: http://pgp.mit.edu * signature_id - Signature ID - Every time a key is\",\n \"DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version\",\n \"self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def get_key_id(self): if self.public_key_fingerprint:\",\n \"save(self, *args, **kwargs): with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file\",\n \"is used to sign something it will generate a unique signature ID. No\",\n \"return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if self.is_detached: return _('Detached') else: return\",\n \"\\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic\",\n \"return self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id':\",\n \"%s', exception ) else: self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id =\",\n \"Not two keys in the world have the same Key ID. The Key\",\n \"self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.'\",\n \"version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs):\",\n \"_('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if\",\n \"when using the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion,\",\n \"= verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature\",\n \"django.db import models from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation\",\n \"__str__(self): return '{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args,\",\n \"'embedded signature verification error; %s', exception ) else: self.date = verify_result.date self.key_id =\",\n \"= verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return super(DetachedSignature,\",\n \"ID is also used to locate a key in the key servers: http://pgp.mit.edu\",\n \"hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects =\",\n \"exception: # Not signed logger.debug( 'detached signature verification error; %s', exception ) else:\",\n \"= False with self.document_version.open(raw=raw) as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object ) except\",\n \"verbose_name = _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def\",\n \"editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering\",\n \"else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return\",\n \"self.signature_file.seek(0) self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint\",\n \"verbose_name_plural = _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded\",\n \"= _('Document version embedded signature') verbose_name_plural = _('Document version embedded signatures') def save(self,\",\n \"using the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document\",\n \"file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded signature verification\",\n \"**kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ),\",\n \"self.signature_id or '{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk}\",\n \"are the same, even when using the same key. \\\"\\\"\\\" document_version = models.ForeignKey(\",\n \"super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously\",\n \"Not signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date =\",\n \"of the key that will be used to sign the document.'), max_length=40, verbose_name=_('Key\",\n \"), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name = _('Document\",\n \"mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers\",\n \"proper key signature_id = models.CharField( blank=True, editable=False, null=True, max_length=64, verbose_name=_('Signature ID') ) public_key_fingerprint\",\n \"ID - Every time a key is used to sign something it will\",\n \"= Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug( 'embedded\",\n \"'{}-{}'.format(self.document_version, _('signature')) def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def\",\n \"class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document version detached\",\n \"key_id = models.CharField( help_text=_('ID of the key that will be used to sign\",\n \"verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed\",\n \"used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) # With proper key\",\n \"return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name =\",\n \"public_key_fingerprint = models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects =\",\n \"file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug( 'detached signature\",\n \"if self.pk: raw = True else: raw = False with self.document_version.open(raw=raw) as file_object:\",\n \"signature ID. No two signature IDs are the same, even when using the\",\n \"the same key. \\\"\\\"\\\" document_version = models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version')\",\n \"return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects\",\n \"force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier - This is\",\n \"it will generate a unique signature ID. No two signature IDs are the\",\n \") else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint =\",\n \"= Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError as exception: # Not signed logger.debug(\",\n \"verbose_name=_('Document version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date\",\n \"help_text=_('ID of the key that will be used to sign the document.'), max_length=40,\",\n \"signature_file = models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE\",\n \"from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from\",\n \"version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{}\",\n \") def get_key_id(self): if self.public_key_fingerprint: return self.public_key_fingerprint[-16:] else: return self.key_id def get_signature_type_display(self): if\",\n \"from django.urls import reverse from django.utils.encoding import force_text from django.utils.translation import ugettext_lazy as\",\n \"mayan.apps.storage.classes import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger\",\n \"# Not signed logger.debug( 'embedded signature verification error; %s', exception ) else: self.date\",\n \"previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects =\",\n \"if self.is_detached: return _('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file')\",\n \"ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural = _('Document version signatures')\",\n \"logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id -\",\n \"self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class\",\n \") objects = InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version\",\n \"VerificationError as exception: # Not signed logger.debug( 'embedded signature verification error; %s', exception\",\n \"Every time a key is used to sign something it will generate a\",\n \"'{} - {}'.format(self.date, self.key_id) def get_absolute_url(self): return reverse( viewname='signatures:document_version_signature_details', kwargs={'signature_id': self.pk} ) def\",\n \"signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw =\",\n \"The Key ID is also used to locate a key in the key\",\n \"self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint return\",\n \"version') ) # Basic fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed')\",\n \"= _('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id\",\n \"fields date = models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField(\",\n \"import DefinedStorageLazy from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger =\",\n \"= models.ForeignKey( editable=False, on_delete=models.CASCADE, related_name='signatures', to=DocumentVersion, verbose_name=_('Document version') ) # Basic fields date\",\n \"import logging import uuid from django.db import models from django.urls import reverse from\",\n \"return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not\",\n \"will generate a unique signature ID. No two signature IDs are the same,\",\n \"= verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField(\",\n \"with self.document_version.open() as file_object: try: verify_result = Key.objects.verify_file( file_object=file_object, signature_file=self.signature_file ) except VerificationError\",\n \"def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature') if self.pk: raw = True\",\n \"is what identifies uniquely a key. Not two keys in the world have\",\n \"Key ID is also used to locate a key in the key servers:\",\n \"logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: *\",\n \"blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of the key\",\n \"the world have the same Key ID. The Key ID is also used\",\n \"name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ), upload_to=upload_to, verbose_name=_('Signature file') ) objects = DetachedSignatureManager() class Meta: verbose_name =\",\n \"verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs)\",\n \"= verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel):\",\n \"in the key servers: http://pgp.mit.edu * signature_id - Signature ID - Every time\",\n \"that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') ) #\",\n \"uuid from django.db import models from django.urls import reverse from django.utils.encoding import force_text\",\n \"from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from\",\n \"hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta: verbose_name = _('Document version\",\n \"def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self): return not hasattr(self, 'signature_file') class\",\n \"from .literals import STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE from .managers import DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def\",\n \"def is_embedded(self): return not hasattr(self, 'signature_file') class EmbeddedSignature(SignatureBaseModel): objects = EmbeddedSignatureManager() class Meta:\",\n \"from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from\",\n \"verification error; %s', exception ) else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id\",\n \"class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier - This is what\",\n \"key that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID') )\",\n \"verify_result = Key.objects.verify_file( file_object=file_object ) except VerificationError as exception: # Not signed logger.debug(\",\n \"* signature_id - Signature ID - Every time a key is used to\",\n \"import force_text from django.utils.translation import ugettext_lazy as _ from model_utils.managers import InheritanceManager from\",\n \"models.CharField( blank=True, editable=False, null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class\",\n \"= verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args,\",\n \"InheritanceManager() class Meta: ordering = ('pk',) verbose_name = _('Document version signature') verbose_name_plural =\",\n \"= DetachedSignatureManager() class Meta: verbose_name = _('Document version detached signature') verbose_name_plural = _('Document\",\n \"= _('Document version embedded signatures') def save(self, *args, **kwargs): logger.debug(msg='checking for embedded signature')\",\n \"return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key Identifier - This\",\n \"else: self.date = verify_result.date self.key_id = verify_result.key_id self.signature_id = verify_result.signature_id self.public_key_fingerprint = verify_result.pubkey_fingerprint\",\n \"verify_result.pubkey_fingerprint super(EmbeddedSignature, self).save(*args, **kwargs) class DetachedSignature(SignatureBaseModel): signature_file = models.FileField( blank=True, help_text=_( 'Signature file\",\n \"a key. Not two keys in the world have the same Key ID.\",\n \"key. Not two keys in the world have the same Key ID. The\",\n \"if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args, **kwargs): with self.document_version.open() as\",\n \"signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or '{} -\",\n \"mayan.apps.django_gpg.models import Key from mayan.apps.documents.models import DocumentVersion from mayan.apps.storage.classes import DefinedStorageLazy from .literals\",\n \"= models.DateField( blank=True, editable=False, null=True, verbose_name=_('Date signed') ) key_id = models.CharField( help_text=_('ID of\",\n \"null=True, max_length=40, verbose_name=_('Public key fingerprint') ) objects = InheritanceManager() class Meta: ordering =\",\n \"the key that will be used to sign the document.'), max_length=40, verbose_name=_('Key ID')\",\n \"DetachedSignatureManager, EmbeddedSignatureManager logger = logging.getLogger(name=__name__) def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\"\",\n \"_('Detached') else: return _('Embedded') @property def is_detached(self): return hasattr(self, 'signature_file') @property def is_embedded(self):\",\n \"= models.FileField( blank=True, help_text=_( 'Signature file previously generated.' ), null=True, storage=DefinedStorageLazy( name=STORAGE_NAME_DOCUMENT_SIGNATURES_DETACHED_SIGNATURE ),\",\n \"def delete(self, *args, **kwargs): if self.signature_file.name: self.signature_file.storage.delete(name=self.signature_file.name) super(DetachedSignature, self).delete(*args, **kwargs) def save(self, *args,\",\n \"_('Document version signature') verbose_name_plural = _('Document version signatures') def __str__(self): return self.signature_id or\",\n \"def upload_to(*args, **kwargs): return force_text(s=uuid.uuid4()) class SignatureBaseModel(models.Model): \\\"\\\"\\\" Fields: * key_id - Key\"\n]","isConversational":false}}},{"rowIdx":2189,"cells":{"ngram":{"kind":"list like","value":["argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser =","= set([]) with open(reports_config_file, \"w\") as f: # Write all properties from reports","replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): #","(replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to","def sync(): # Add or replace the relevant properites from galaxy.ini # into","(synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__","replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property):","reports config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) =","universe_config): # Cycle through properties to replace and perform replacement on # this","on # this line if needed. synced_line = reports_line replaced_property = None for","universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\"","get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement on #","f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as f: #","these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not","reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\"","f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as f: # Write all properties","replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" %","= None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line","ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties = set([])","If any properties appear in universe config and not in # reports write","for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property,","as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as f:","\"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add or replace the relevant properites","in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser))","# this line if needed. synced_line = reports_line replaced_property = None for replacement_property","# needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property:","\"w\") as f: # Write all properties from reports config replacing as #","# Add or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file","replace the relevant properites from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if","# Cycle through properties to replace and perform replacement on # this line","from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file","get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe","in # reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION,","in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property","properties to replace and perform replacement on # this line if needed. synced_line","replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ ==","break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name))","reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and","\"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv)","= f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as f: # Write all","from ConfigParser import ConfigParser from sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"]","properites from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1:","parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with","parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config):","reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) #","len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\")","\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line,","for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line)","with open(reports_config_file, \"w\") as f: # Write all properties from reports config replacing","reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION,","into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file","needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property)","through properties to replace and perform replacement on # this line if needed.","import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): #","# into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1]","and perform replacement on # this line if needed. synced_line = reports_line replaced_property","in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace","Add or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file =","synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and","if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property","parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config","relevant properites from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) >","= ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties =","replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and not in","universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser()","sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync():","f.write(line) # If any properties appear in universe config and not in #","as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property","= \"app:main\" def sync(): # Add or replace the relevant properites from galaxy.ini","from reports config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property)","in universe config and not in # reports write these as well. for","set([]) with open(reports_config_file, \"w\") as f: # Write all properties from reports config","replace and perform replacement on # this line if needed. synced_line = reports_line","and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle","argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add","any properties appear in universe config and not in # reports write these","config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line,","not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties","replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config)","synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name,","replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def","def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement on","(line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties","= get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in","line if needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES:","\"r\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as","parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties","in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If","reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break","the relevant properites from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if len(argv)","# If any properties appear in universe config and not in # reports","and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return","= replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name,","get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return","as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if","\"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add or replace the relevant","open(reports_config_file, \"w\") as f: # Write all properties from reports config replacing as","if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and","if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file,","argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines()","= get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config):","replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser)","= \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file)","= reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\","REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property =","get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ == '__main__': sync()","replaced_properties = set([]) with open(reports_config_file, \"w\") as f: # Write all properties from","replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property,","this line if needed. synced_line = reports_line replaced_property = None for replacement_property in","well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in","import ConfigParser from sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION =","f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform","ConfigParser import ConfigParser from sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION","parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement","Write all properties from reports config replacing as # needed. for reports_config_line in","galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file =","if needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if","universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property)","return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if","def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ == '__main__':","all properties from reports config replacing as # needed. for reports_config_line in reports_config_lines:","appear in universe config and not in # reports write these as well.","[\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add or replace the","MAIN_SECTION = \"app:main\" def sync(): # Add or replace the relevant properites from","= argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser","len(argv) > 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2:","if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line,","\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through","2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f:","and not in # reports write these as well. for replacement_property in REPLACE_PROPERTIES:","replacement on # this line if needed. synced_line = reports_line replaced_property = None","REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def","open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\")","to replace and perform replacement on # this line if needed. synced_line =","properties from reports config replacing as # needed. for reports_config_line in reports_config_lines: (line,","write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\","Cycle through properties to replace and perform replacement on # this line if","= \"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if","reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2]","f: # Write all properties from reports config replacing as # needed. for","if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) >","for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\ not (replacement_property in replaced_properties):","reports.ini reports_config_file = \"config/reports.ini\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file =","= [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add or replace","from sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def","replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and","reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \"w\") as f: # Write","reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any","properties appear in universe config and not in # reports write these as","> 1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file","replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and not","REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\" def sync(): # Add or","with open(reports_config_file, \"r\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file,","not in # reports write these as well. for replacement_property in REPLACE_PROPERTIES: if","= argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines =","or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file = \"config/reports.ini\"","replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear","config and not in # reports write these as well. for replacement_property in","# reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property)","None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line =","\"app:main\" def sync(): # Add or replace the relevant properites from galaxy.ini #","replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \"%s=%s\\n\" % (property_name, universe_config.get(MAIN_SECTION,","> 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as","# Write all properties from reports config replacing as # needed. for reports_config_line","needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property)","universe config and not in # reports write these as well. for replacement_property","perform replacement on # this line if needed. synced_line = reports_line replaced_property =","ConfigParser from sys import argv REPLACE_PROPERTIES = [\"file_path\", \"database_connection\", \"new_file_path\"] MAIN_SECTION = \"app:main\"","universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \"r\") as f: reports_config_lines","as f: # Write all properties from reports config replacing as # needed.","\"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with","sync(): # Add or replace the relevant properites from galaxy.ini # into reports.ini","1: reports_config_file = argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file ="],"string":"[\n \"argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2] parser =\",\n \"= set([]) with open(reports_config_file, \\\"w\\\") as f: # Write all properties from reports\",\n \"replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): #\",\n \"(replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to\",\n \"def sync(): # Add or replace the relevant properites from galaxy.ini # into\",\n \"(synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__\",\n \"replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property):\",\n \"reports config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) =\",\n \"universe_config): # Cycle through properties to replace and perform replacement on # this\",\n \"on # this line if needed. synced_line = reports_line replaced_property = None for\",\n \"universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\"\",\n \"get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement on #\",\n \"f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f: #\",\n \"these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not\",\n \"reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\"\",\n \"f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f: # Write all properties\",\n \"replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" %\",\n \"= None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line\",\n \"ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties = set([])\",\n \"If any properties appear in universe config and not in # reports write\",\n \"for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property,\",\n \"as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f:\",\n \"\\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or replace the relevant properites\",\n \"in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser))\",\n \"# this line if needed. synced_line = reports_line replaced_property = None for replacement_property\",\n \"# needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property:\",\n \"\\\"w\\\") as f: # Write all properties from reports config replacing as #\",\n \"# Add or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file\",\n \"replace the relevant properites from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if\",\n \"# Cycle through properties to replace and perform replacement on # this line\",\n \"from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file\",\n \"get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe\",\n \"in # reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION,\",\n \"in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property\",\n \"properties to replace and perform replacement on # this line if needed. synced_line\",\n \"replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ ==\",\n \"break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name))\",\n \"reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and\",\n \"\\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv)\",\n \"= f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f: # Write all\",\n \"from ConfigParser import ConfigParser from sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"]\",\n \"properites from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1:\",\n \"parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with\",\n \"parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config):\",\n \"reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) #\",\n \"len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\")\",\n \"\\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line,\",\n \"for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line)\",\n \"with open(reports_config_file, \\\"w\\\") as f: # Write all properties from reports config replacing\",\n \"reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION,\",\n \"into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file\",\n \"needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property)\",\n \"through properties to replace and perform replacement on # this line if needed.\",\n \"import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): #\",\n \"# into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1]\",\n \"and perform replacement on # this line if needed. synced_line = reports_line replaced_property\",\n \"in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace\",\n \"Add or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file =\",\n \"synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and\",\n \"if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property\",\n \"parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config\",\n \"relevant properites from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) >\",\n \"= ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties =\",\n \"replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and not in\",\n \"universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser()\",\n \"sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync():\",\n \"f.write(line) # If any properties appear in universe config and not in #\",\n \"as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property\",\n \"= \\\"app:main\\\" def sync(): # Add or replace the relevant properites from galaxy.ini\",\n \"from reports config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property)\",\n \"in universe config and not in # reports write these as well. for\",\n \"set([]) with open(reports_config_file, \\\"w\\\") as f: # Write all properties from reports config\",\n \"replace and perform replacement on # this line if needed. synced_line = reports_line\",\n \"and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle\",\n \"argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add\",\n \"any properties appear in universe config and not in # reports write these\",\n \"config replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line,\",\n \"not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties\",\n \"replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config)\",\n \"synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name,\",\n \"replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def\",\n \"def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement on\",\n \"(line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties\",\n \"= get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in\",\n \"line if needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES:\",\n \"\\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as\",\n \"parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties\",\n \"in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If\",\n \"reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break\",\n \"the relevant properites from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv)\",\n \"# If any properties appear in universe config and not in # reports\",\n \"and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return\",\n \"= replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name,\",\n \"get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return\",\n \"as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if\",\n \"\\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or replace the relevant\",\n \"open(reports_config_file, \\\"w\\\") as f: # Write all properties from reports config replacing as\",\n \"if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and\",\n \"if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file,\",\n \"argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines()\",\n \"= get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config):\",\n \"replacing as # needed. for reports_config_line in reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser)\",\n \"= \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file)\",\n \"= reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\\",\n \"REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property =\",\n \"get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ == '__main__': sync()\",\n \"replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f: # Write all properties from\",\n \"replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property,\",\n \"this line if needed. synced_line = reports_line replaced_property = None for replacement_property in\",\n \"well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in\",\n \"import ConfigParser from sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION =\",\n \"f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform\",\n \"ConfigParser import ConfigParser from sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION\",\n \"parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and perform replacement\",\n \"Write all properties from reports config replacing as # needed. for reports_config_line in\",\n \"galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file =\",\n \"if needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if\",\n \"universe_config.has_option(MAIN_SECTION, replacement_property): synced_line = get_universe_line(replacement_property, universe_config) replaced_property = replacement_property break return (synced_line, replaced_property)\",\n \"return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if\",\n \"def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION, property_name)) if __name__ == '__main__':\",\n \"all properties from reports config replacing as # needed. for reports_config_line in reports_config_lines:\",\n \"appear in universe config and not in # reports write these as well.\",\n \"[\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or replace the\",\n \"MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or replace the relevant properites from\",\n \"= argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2] parser\",\n \"len(argv) > 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2:\",\n \"if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line,\",\n \"\\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through\",\n \"2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f:\",\n \"and not in # reports write these as well. for replacement_property in REPLACE_PROPERTIES:\",\n \"replacement on # this line if needed. synced_line = reports_line replaced_property = None\",\n \"REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties): f.write(get_universe_line(replacement_property, parser)) def\",\n \"open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\")\",\n \"to replace and perform replacement on # this line if needed. synced_line =\",\n \"properties from reports config replacing as # needed. for reports_config_line in reports_config_lines: (line,\",\n \"write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\\",\n \"Cycle through properties to replace and perform replacement on # this line if\",\n \"= \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if\",\n \"reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2]\",\n \"f: # Write all properties from reports config replacing as # needed. for\",\n \"if len(argv) > 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) >\",\n \"for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property) and \\\\ not (replacement_property in replaced_properties):\",\n \"reports.ini reports_config_file = \\\"config/reports.ini\\\" if len(argv) > 1: reports_config_file = argv[1] universe_config_file =\",\n \"= [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or replace\",\n \"from sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def\",\n \"replaced_properties): f.write(get_universe_line(replacement_property, parser)) def get_synced_line(reports_line, universe_config): # Cycle through properties to replace and\",\n \"reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file, \\\"w\\\") as f: # Write\",\n \"reports_config_lines: (line, replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any\",\n \"properties appear in universe config and not in # reports write these as\",\n \"> 1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file\",\n \"replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear in universe config and not\",\n \"REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\" def sync(): # Add or\",\n \"with open(reports_config_file, \\\"r\\\") as f: reports_config_lines = f.readlines() replaced_properties = set([]) with open(reports_config_file,\",\n \"not in # reports write these as well. for replacement_property in REPLACE_PROPERTIES: if\",\n \"= argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines =\",\n \"or replace the relevant properites from galaxy.ini # into reports.ini reports_config_file = \\\"config/reports.ini\\\"\",\n \"replaced_property) = get_synced_line(reports_config_line, parser) if replaced_property: replaced_properties.add(replaced_property) f.write(line) # If any properties appear\",\n \"config and not in # reports write these as well. for replacement_property in\",\n \"# reports write these as well. for replacement_property in REPLACE_PROPERTIES: if parser.has_option(MAIN_SECTION, replacement_property)\",\n \"None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property) and \\\\ universe_config.has_option(MAIN_SECTION, replacement_property): synced_line =\",\n \"\\\"app:main\\\" def sync(): # Add or replace the relevant properites from galaxy.ini #\",\n \"replacement_property break return (synced_line, replaced_property) def get_universe_line(property_name, universe_config): return \\\"%s=%s\\\\n\\\" % (property_name, universe_config.get(MAIN_SECTION,\",\n \"> 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as\",\n \"# Write all properties from reports config replacing as # needed. for reports_config_line\",\n \"needed. synced_line = reports_line replaced_property = None for replacement_property in REPLACE_PROPERTIES: if reports_line.startswith(replacement_property)\",\n \"universe config and not in # reports write these as well. for replacement_property\",\n \"perform replacement on # this line if needed. synced_line = reports_line replaced_property =\",\n \"ConfigParser from sys import argv REPLACE_PROPERTIES = [\\\"file_path\\\", \\\"database_connection\\\", \\\"new_file_path\\\"] MAIN_SECTION = \\\"app:main\\\"\",\n \"universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with open(reports_config_file, \\\"r\\\") as f: reports_config_lines\",\n \"as f: # Write all properties from reports config replacing as # needed.\",\n \"\\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file = argv[2] parser = ConfigParser() parser.read(universe_config_file) with\",\n \"sync(): # Add or replace the relevant properites from galaxy.ini # into reports.ini\",\n \"1: reports_config_file = argv[1] universe_config_file = \\\"config/galaxy.ini\\\" if len(argv) > 2: universe_config_file =\"\n]","isConversational":false}}},{"rowIdx":2190,"cells":{"ngram":{"kind":"list like","value":["copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE","-l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e","Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. # You","conditions of the Mulan PSL v2. # You may obtain a copy of","your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline |","information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs","-0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL'))","THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY","SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND,","# # openGauss is licensed under Mulan PSL v2. # You can use","ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat","PURPOSE. # See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- #","grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello","You may obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2","PSL v2. # You can use this software according to the terms and","OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for","-e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\"))","PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION","to the terms and conditions of the Mulan PSL v2. # You may","at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \"AS","/proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo","\\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if __name__","PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER","# You can use this software according to the terms and conditions of","terms and conditions of the Mulan PSL v2. # You may obtain a","2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2.","the terms and conditions of the Mulan PSL v2. # You may obtain","xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo","\\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe =","# # THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES","------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your","$SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if __name__ == \"__main__\": test_remote()","(c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL","# You may obtain a copy of Mulan PSL v2 at: # #","WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED","test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\"","-e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local():","= ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc","from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information","# # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \"AS IS\"","is licensed under Mulan PSL v2. # You can use this software according","/proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n","import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo","according to the terms and conditions of the Mulan PSL v2. # You","# # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh","------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from","world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor()","LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See","Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED","padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline","v2. # You may obtain a copy of Mulan PSL v2 at: #","| wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n'))","KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, #","# src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe =","world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if __name__ ==","AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR","more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py #","'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe","# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL","exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" |","the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py #","software according to the terms and conditions of the Mulan PSL v2. #","IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe","# http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS,","You can use this software according to the terms and conditions of the","of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS","FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more","obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # #","# test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import","# Copyright (c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under","NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan","Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. #","Mulan PSL v2. # You may obtain a copy of Mulan PSL v2","IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A","ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,","# padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat","# openGauss is licensed under Mulan PSL v2. # You can use this","# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY","WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT","PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details. # -------------------------------------------------------------------------","for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py","EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT","Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. # You can","# # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def","PSL v2. # You may obtain a copy of Mulan PSL v2 at:","wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo","A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details. #","a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS","\"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED,","MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2","print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello","NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. #","v2. # You can use this software according to the terms and conditions","v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN","# See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # #","\\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n","IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING","details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # #","print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\"))","INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR","Mulan PSL v2. # You can use this software according to the terms","test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory","# THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF","PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON","Copyright (c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan","OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO","can use this software according to the terms and conditions of the Mulan","this software according to the terms and conditions of the Mulan PSL v2.","openGauss is licensed under Mulan PSL v2. # You can use this software","the Mulan PSL v2. # You may obtain a copy of Mulan PSL","print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if __name__ == \"__main__\":","under Mulan PSL v2. # You can use this software according to the","'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if","print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping","of the Mulan PSL v2. # You may obtain a copy of Mulan","def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep","print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0]) print(exe.exec_command_sync('echo $SHELL')) def","| grep \\\"processor\\\" | wc -l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e","See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py","ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS","and conditions of the Mulan PSL v2. # You may obtain a copy","v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION #","ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo |","may obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 #","def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\"ping -h\")) if __name__ == \"__main__\": test_remote() test_local()","OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR","# ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # -------------------------------------------------------------------------","Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # #","| xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0])","licensed under Mulan PSL v2. # You can use this software according to","ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\"cat /proc/cpuinfo | grep \\\"processor\\\" | wc -l\"))","use this software according to the terms and conditions of the Mulan PSL","BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT","# IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote():","IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, #","# ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding","FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details.","EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR","src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor()","# # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() #","http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS, WITHOUT","TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the","BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE."],"string":"[\n \"copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE\",\n \"-l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e\",\n \"Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. # You\",\n \"conditions of the Mulan PSL v2. # You may obtain a copy of\",\n \"your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline |\",\n \"information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs\",\n \"-0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL'))\",\n \"THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY\",\n \"SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND,\",\n \"# # openGauss is licensed under Mulan PSL v2. # You can use\",\n \"ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat\",\n \"PURPOSE. # See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- #\",\n \"grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello\",\n \"You may obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2\",\n \"PSL v2. # You can use this software according to the terms and\",\n \"OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for\",\n \"-e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\"))\",\n \"PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION\",\n \"to the terms and conditions of the Mulan PSL v2. # You may\",\n \"at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \\\"AS\",\n \"/proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo\",\n \"\\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if __name__\",\n \"PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER\",\n \"# You can use this software according to the terms and conditions of\",\n \"terms and conditions of the Mulan PSL v2. # You may obtain a\",\n \"2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2.\",\n \"the terms and conditions of the Mulan PSL v2. # You may obtain\",\n \"xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo\",\n \"\\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe =\",\n \"# # THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES\",\n \"------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your\",\n \"$SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if __name__ == \\\"__main__\\\": test_remote()\",\n \"(c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL\",\n \"# You may obtain a copy of Mulan PSL v2 at: # #\",\n \"WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\",\n \"test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\"\",\n \"-e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local():\",\n \"= ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc\",\n \"from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information\",\n \"# # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\"\",\n \"is licensed under Mulan PSL v2. # You can use this software according\",\n \"/proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n\",\n \"import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo\",\n \"according to the terms and conditions of the Mulan PSL v2. # You\",\n \"# # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh\",\n \"------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from\",\n \"world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor()\",\n \"LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See\",\n \"Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED\",\n \"padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline\",\n \"v2. # You may obtain a copy of Mulan PSL v2 at: #\",\n \"| wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n'))\",\n \"KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, #\",\n \"# src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe =\",\n \"world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if __name__ ==\",\n \"AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR\",\n \"more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py #\",\n \"'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe\",\n \"# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL\",\n \"exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" |\",\n \"the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py #\",\n \"software according to the terms and conditions of the Mulan PSL v2. #\",\n \"IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe\",\n \"# http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS,\",\n \"You can use this software according to the terms and conditions of the\",\n \"of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS\",\n \"FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more\",\n \"obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # #\",\n \"# test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import\",\n \"# Copyright (c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under\",\n \"NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan\",\n \"Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. #\",\n \"Mulan PSL v2. # You may obtain a copy of Mulan PSL v2\",\n \"IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A\",\n \"ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,\",\n \"# padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat\",\n \"# openGauss is licensed under Mulan PSL v2. # You can use this\",\n \"# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY\",\n \"WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT\",\n \"PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details. # -------------------------------------------------------------------------\",\n \"for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py\",\n \"EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT\",\n \"Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. # You can\",\n \"# # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def\",\n \"PSL v2. # You may obtain a copy of Mulan PSL v2 at:\",\n \"wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo\",\n \"A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details. #\",\n \"a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS\",\n \"\\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED,\",\n \"MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2\",\n \"print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello\",\n \"NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. #\",\n \"v2. # You can use this software according to the terms and conditions\",\n \"v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN\",\n \"# See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # #\",\n \"\\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n\",\n \"IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING\",\n \"details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # #\",\n \"print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\"))\",\n \"INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR\",\n \"Mulan PSL v2. # You can use this software according to the terms\",\n \"test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory\",\n \"# THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF\",\n \"PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON\",\n \"Copyright (c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan\",\n \"OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\",\n \"can use this software according to the terms and conditions of the Mulan\",\n \"this software according to the terms and conditions of the Mulan PSL v2.\",\n \"openGauss is licensed under Mulan PSL v2. # You can use this software\",\n \"the Mulan PSL v2. # You may obtain a copy of Mulan PSL\",\n \"print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if __name__ == \\\"__main__\\\":\",\n \"under Mulan PSL v2. # You can use this software according to the\",\n \"'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if\",\n \"print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping\",\n \"of the Mulan PSL v2. # You may obtain a copy of Mulan\",\n \"def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep\",\n \"print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0]) print(exe.exec_command_sync('echo $SHELL')) def\",\n \"| grep \\\\\\\"processor\\\\\\\" | wc -l\\\")) print(exe.exec_command_sync(\\\"cat /proc/self/cmdline | xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e\",\n \"See the Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py\",\n \"ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS\",\n \"and conditions of the Mulan PSL v2. # You may obtain a copy\",\n \"v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION #\",\n \"ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo |\",\n \"may obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 #\",\n \"def test_local(): exe = ExecutorFactory().get_executor() print(exe.exec_command_sync(\\\"ping -h\\\")) if __name__ == \\\"__main__\\\": test_remote() test_local()\",\n \"OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR\",\n \"# ------------------------------------------------------------------------- # # test_ssh.py # # IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # -------------------------------------------------------------------------\",\n \"Mulan PSL v2 for more details. # ------------------------------------------------------------------------- # # test_ssh.py # #\",\n \"| xargs -0\\\")) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0].count('\\\\n')) print(exe.exec_command_sync(\\\"echo -e 'hello \\\\\\\\n world'\\\")[0])\",\n \"licensed under Mulan PSL v2. # You can use this software according to\",\n \"ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding your information print(exe.exec_command_sync(\\\"cat /proc/cpuinfo | grep \\\\\\\"processor\\\\\\\" | wc -l\\\"))\",\n \"use this software according to the terms and conditions of the Mulan PSL\",\n \"BASIS, WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT\",\n \"# IDENTIFICATION # src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote():\",\n \"IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OF ANY KIND, #\",\n \"# ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() # padding\",\n \"FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details.\",\n \"EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR\",\n \"src/gausskernel/dbmind/xtuner/test/test_ssh.py # # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor()\",\n \"# # ------------------------------------------------------------------------- from ssh import ExecutorFactory def test_remote(): exe = ExecutorFactory().set_host('').set_user('').set_pwd('').get_executor() #\",\n \"http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN \\\"AS IS\\\" BASIS, WITHOUT\",\n \"TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the\",\n \"BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.\"\n]","isConversational":false}}},{"rowIdx":2191,"cells":{"ngram":{"kind":"list like","value":["tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std =","tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar","sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp","return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the","dimension is the sentence length. The rank must be 3 The padding should","import tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from","+ tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension","work Copyright 2018 <NAME>. import tensorflow as tf import numpy as np from","is the embedding The second last dimension is the sentence length. The rank","- prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return","- recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu,","The second last dimension is the sentence length. The rank must be 3","length. The rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return","The rank must be 3 The padding should have zero \"\"\" with tf.variable_scope(scope,","None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell,","= tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob:","is the sentence length. The rank must be 3 The padding should have","from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in","encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last","\"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor","<NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu","the last dimension is the embedding The second last dimension is the sentence","<NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as","tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return","np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 +","encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input,","scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar,","tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats:","last dimension is the sentence length. The rank must be 3 The padding",":param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples","[1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats)","= tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def","(recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1)","\"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1)","tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor,","hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu,","get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is the embedding The second last","logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\")","-0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar))","except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5","hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def","embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None):","hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores),","must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size","work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018","tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask =","encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell,","50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\"",":param vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50))","tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is","return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2),","get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the","rank must be 3 The padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding',","tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x,","import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def","embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent,","Assumption, the last dimension is the embedding The second last dimension is the",":param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\"",":param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1]","name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z","_, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return","return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param","- tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) +","recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) -","= tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input,","length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param","Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>.","cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding The","b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding The","have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask =","def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z=","def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1)","sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu","1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent:","nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = []","= tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask,","repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor,","as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import","recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar):","tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor =","The rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding,","Copyright 2018 <NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score","smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar):","tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False):","return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None,","sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None):","is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell,","sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats):","scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 *","weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld","2018 <NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score import","mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu,","embedding The second last dimension is the sentence length. The rank must be","with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask","<reponame>wyshi/Unsupervised-Structure-Learning<gh_stars>10-100 # Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified","3 The padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask","embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\"","np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores","should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask","in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores)","is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell,","avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell,","tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding,","50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"):","length. The rank must be 3 The padding should have zero \"\"\" with","tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None,","return z def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is the embedding","kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)),","f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding","get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent,","tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return","repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] +","dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption,","reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input","z def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is the embedding The","length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size","2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow","dimension is the embedding The second last dimension is the sentence length. The","tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None,","encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param","return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1","dimension is the sentence length. The rank must be 3 \"\"\" embedding_size =","np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar","tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar +","last dimension is the sentence length. The rank must be 3 \"\"\" embedding_size","2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return","= tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon)","+ repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats) return","Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as tf import","hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3,","[] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0)","def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the","# Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work","mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption, the last","length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input =","'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask)","return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the","= [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except:","second last dimension is the sentence length. The rank must be 3 The","= tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size","= tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32)","multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor)","tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor =","def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar -","is the sentence length. The rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value","kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu,","* logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\"","expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)","be 3 The padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if","+ (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)),","embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is","numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref,","logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu +","last dimension is the embedding The second last dimension is the sentence length.","reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar)","epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std,","the sentence length. The rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if","sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\"","tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1)","reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None):","the sentence length. The rank must be 3 The padding should have zero","tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score","import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for","\"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param",":param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor,","repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats) return repeated_tensor","- tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def","\"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding,","tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32)","def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask: :return: \"\"\"","= [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) *","tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction","tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar),","z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption, the","as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps):","def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is",":return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor:","reuse=None): \"\"\" Assumption, the last dimension is the embedding The second last dimension","+ logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar),","_, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell,","\"\"\" :param vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask,","f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask: :return:","vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def",":return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats","3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return","length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding The second","prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu","embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]),","def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp,","nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps:","must be 3 The padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse):","cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension","dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\"","logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar):","SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref],","tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi)","std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def","if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding,","embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def","length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding,","prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu -","University # Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy","padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None:","rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]),","get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7,","-0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon =","avg=False): \"\"\" Assumption, the last dimension is the embedding The second last dimension","None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding,","be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else:","2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5","-1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor,","= tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor","from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores =","tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param","import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try:","sentence length. The rank must be 3 The padding should have zero \"\"\"","logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption,","# Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy as","Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy as np","if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input","reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input,","= tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None,","sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1,","second last dimension is the sentence length. The rank must be 3 \"\"\"","norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def","\"\"\" Assumption, the last dimension is the embedding The second last dimension is","reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension","length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask,","1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld =","tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\" with","reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _,","Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright","length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input =","tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples =","reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu),","length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask: :return: \"\"\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]),","The padding should have zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is","\"\"\" :param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1)","epsilon) return z def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is the","return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon","Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as tf","= embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size","gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar)","scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding The second last","else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption,","scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3]))","prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld","with tf.variable_scope(\"repeat\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor =","[-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return:","def tf_repeat(tensor, repeats): \"\"\" :param tensor: :param repeats: :return: \"\"\" with tf.variable_scope(\"repeat\"): expanded_tensor","+ tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std","tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last","b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob,","(C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import","* tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) -","the embedding The second last dimension is the sentence length. The rank must","def get_bow(embedding, avg=False): \"\"\" Assumption, the last dimension is the embedding The second","tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def","zero \"\"\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding),","encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \"\"\" :param vocab_prob: :param sent: :param length_mask:","sentence length. The rank must be 3 \"\"\" embedding_size = embedding.get_shape()[2].value if avg:","= -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2),","= tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding,","get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \"\"\" Assumption, the last dimension is the embedding","tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std = tf.exp(0.5 *","try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu,","for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return"],"string":"[\n \"tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std =\",\n \"tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar\",\n \"sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp\",\n \"return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the\",\n \"dimension is the sentence length. The rank must be 3 The padding should\",\n \"import tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from\",\n \"+ tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension\",\n \"work Copyright 2018 <NAME>. import tensorflow as tf import numpy as np from\",\n \"is the embedding The second last dimension is the sentence length. The rank\",\n \"- prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return\",\n \"- recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu,\",\n \"The second last dimension is the sentence length. The rank must be 3\",\n \"length. The rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return\",\n \"The rank must be 3 The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope,\",\n \"None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell,\",\n \"= tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob:\",\n \"is the sentence length. The rank must be 3 The padding should have\",\n \"from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in\",\n \"encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last\",\n \"\\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor\",\n \"<NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu\",\n \"the last dimension is the embedding The second last dimension is the sentence\",\n \"<NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as\",\n \"tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return\",\n \"np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 +\",\n \"encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input,\",\n \"scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar,\",\n \"tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats:\",\n \"last dimension is the sentence length. The rank must be 3 The padding\",\n \":param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples\",\n \"[1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats)\",\n \"= tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def\",\n \"(recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1)\",\n \"\\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1)\",\n \"tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor,\",\n \"hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu,\",\n \"get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second last\",\n \"logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\")\",\n \"-0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar))\",\n \"except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5\",\n \"hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def\",\n \"embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None):\",\n \"hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores),\",\n \"must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size\",\n \"work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018\",\n \"tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask =\",\n \"encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell,\",\n \"50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\"\",\n \":param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50))\",\n \"tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is\",\n \"return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2),\",\n \"get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the\",\n \"rank must be 3 The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding',\",\n \"tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x,\",\n \"import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def\",\n \"embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent,\",\n \"Assumption, the last dimension is the embedding The second last dimension is the\",\n \":param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\"\",\n \":param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1]\",\n \"name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z\",\n \"_, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return\",\n \"return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param\",\n \"- tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) +\",\n \"recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) -\",\n \"= tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input,\",\n \"length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param\",\n \"Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>.\",\n \"cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding The\",\n \"b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding The\",\n \"have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask =\",\n \"def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z=\",\n \"def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1)\",\n \"sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu\",\n \"1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent:\",\n \"nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = []\",\n \"= tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask,\",\n \"repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor,\",\n \"as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import\",\n \"recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar):\",\n \"tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor =\",\n \"The rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding,\",\n \"Copyright 2018 <NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score\",\n \"smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar):\",\n \"tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False):\",\n \"return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None,\",\n \"sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None):\",\n \"is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell,\",\n \"sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats):\",\n \"scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 *\",\n \"weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld\",\n \"2018 <NAME>. import tensorflow as tf import numpy as np from nltk.translate.bleu_score import\",\n \"mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu,\",\n \"embedding The second last dimension is the sentence length. The rank must be\",\n \"with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask\",\n \"<reponame>wyshi/Unsupervised-Structure-Learning<gh_stars>10-100 # Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified\",\n \"3 The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask\",\n \"embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\"\",\n \"np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores\",\n \"should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask\",\n \"in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores)\",\n \"is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell,\",\n \"avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell,\",\n \"tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding,\",\n \"50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"):\",\n \"length. The rank must be 3 The padding should have zero \\\"\\\"\\\" with\",\n \"tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None,\",\n \"return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is the embedding\",\n \"kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)),\",\n \"f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding\",\n \"get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent,\",\n \"tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return\",\n \"repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] +\",\n \"dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption,\",\n \"reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input\",\n \"z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is the embedding The\",\n \"length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size\",\n \"2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow\",\n \"dimension is the embedding The second last dimension is the sentence length. The\",\n \"tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None,\",\n \"encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param\",\n \"return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1\",\n \"dimension is the sentence length. The rank must be 3 \\\"\\\"\\\" embedding_size =\",\n \"np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar\",\n \"tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar +\",\n \"last dimension is the sentence length. The rank must be 3 \\\"\\\"\\\" embedding_size\",\n \"2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return\",\n \"= tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon)\",\n \"+ repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats) return\",\n \"Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as tf import\",\n \"hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3,\",\n \"[] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0)\",\n \"def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the\",\n \"# Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work\",\n \"mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last\",\n \"length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input =\",\n \"'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask)\",\n \"return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the\",\n \"= [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except:\",\n \"second last dimension is the sentence length. The rank must be 3 The\",\n \"= tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size\",\n \"= tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32)\",\n \"multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor)\",\n \"tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor =\",\n \"def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar -\",\n \"is the sentence length. The rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value\",\n \"kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu,\",\n \"* logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\"\",\n \"expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)\",\n \"be 3 The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if\",\n \"+ (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)),\",\n \"embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is\",\n \"numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref,\",\n \"logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu +\",\n \"last dimension is the embedding The second last dimension is the sentence length.\",\n \"reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar)\",\n \"epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std,\",\n \"the sentence length. The rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if\",\n \"sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\"\",\n \"tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1)\",\n \"reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None):\",\n \"the sentence length. The rank must be 3 The padding should have zero\",\n \"tensorflow as tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score\",\n \"import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for\",\n \"\\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param\",\n \":param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor,\",\n \"repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats) return repeated_tensor\",\n \"- tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def\",\n \"\\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding,\",\n \"tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32)\",\n \"def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\"\",\n \"= [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor, tf.shape(tensor) *\",\n \"tf import numpy as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction\",\n \"tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar),\",\n \"z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the\",\n \"as np from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps):\",\n \"def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is\",\n \":return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor:\",\n \"reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second last dimension\",\n \"+ logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar),\",\n \"_, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell,\",\n \"\\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask,\",\n \"f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return:\",\n \"vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]), tf.sequence_mask(length_mask, 50)) def\",\n \":return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats\",\n \"3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return\",\n \"length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second\",\n \"prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu\",\n \"embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]),\",\n \"def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp,\",\n \"nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps:\",\n \"must be 3 The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse):\",\n \"cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension\",\n \"dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\"\",\n \"logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar):\",\n \"SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref],\",\n \"tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi)\",\n \"std = tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def\",\n \"if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding,\",\n \"embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def\",\n \"length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding,\",\n \"prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu -\",\n \"University # Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy\",\n \"padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None:\",\n \"rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]),\",\n \"get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7,\",\n \"-0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon =\",\n \"avg=False): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second last dimension\",\n \"None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding,\",\n \"be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else:\",\n \"2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5\",\n \"-1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor = tf.reshape(tiled_tensor,\",\n \"= tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor = tf.tile(expanded_tensor, multiples=multiples) repeated_tensor\",\n \"from nltk.translate.bleu_score import sentence_bleu from nltk.translate.bleu_score import SmoothingFunction def get_bleu_stats(ref, hyps): scores =\",\n \"tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param\",\n \"import SmoothingFunction def get_bleu_stats(ref, hyps): scores = [] for hyp in hyps: try:\",\n \"sentence length. The rank must be 3 The padding should have zero \\\"\\\"\\\"\",\n \"logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption,\",\n \"# Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy as\",\n \"Modified work Copyright 2018 <NAME>. import tensorflow as tf import numpy as np\",\n \"if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input\",\n \"reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input,\",\n \"= tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask, dtype=tf.float32) return encoded_input, cell.state_size def get_bi_rnn_encode(embedding, f_cell, b_cell, length_mask=None,\",\n \"sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1,\",\n \"second last dimension is the sentence length. The rank must be 3 \\\"\\\"\\\"\",\n \"norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def\",\n \"\\\"\\\"\\\" Assumption, the last dimension is the embedding The second last dimension is\",\n \"reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension\",\n \"length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input = tf.nn.dynamic_rnn(cell, embedding, sequence_length=length_mask,\",\n \"1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld =\",\n \"tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with\",\n \"reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _,\",\n \"Original work Copyright (C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright\",\n \"length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding), reduction_indices=2)),reduction_indices=1) length_mask = tf.to_int32(length_mask) _, encoded_input =\",\n \"tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples =\",\n \"reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu),\",\n \"length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask: :return: \\\"\\\"\\\" tf.boolean_mask(tf.reshape(usr_input_sent, [-1, 50]),\",\n \"The padding should have zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is\",\n \"\\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1)\",\n \"epsilon) return z def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is the\",\n \"return -0.5*tf.reduce_sum(tf.log(2*np.pi) + logvar + tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon\",\n \"Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import tensorflow as tf\",\n \"= embedding.get_shape()[2].value if avg: return tf.reduce_mean(embedding, reduction_indices=[1]), embedding_size else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size\",\n \"gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar): kld = -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar)\",\n \"scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second last\",\n \"else: return tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption,\",\n \"scores = [] for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3]))\",\n \"prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) - tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld\",\n \"with tf.variable_scope(\\\"repeat\\\"): expanded_tensor = tf.expand_dims(tensor, -1) multiples = [1] + repeats tiled_tensor =\",\n \"[-1, 50]), tf.sequence_mask(length_mask, 50)) def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return:\",\n \"def tf_repeat(tensor, repeats): \\\"\\\"\\\" :param tensor: :param repeats: :return: \\\"\\\"\\\" with tf.variable_scope(\\\"repeat\\\"): expanded_tensor\",\n \"+ tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std\",\n \"tf.reduce_sum(embedding, reduction_indices=[1]), embedding_size def get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last\",\n \"b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob,\",\n \"(C) 2017 <NAME>, Carnegie Mellon University # Modified work Copyright 2018 <NAME>. import\",\n \"* tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2), tf.exp(prior_logvar)) -\",\n \"the embedding The second last dimension is the sentence length. The rank must\",\n \"def get_bow(embedding, avg=False): \\\"\\\"\\\" Assumption, the last dimension is the embedding The second\",\n \"tf.nn.bidirectional_dynamic_rnn(f_cell, b_cell, embedding, sequence_length=length_mask, dtype=tf.float32) encoded_input = tf.concat(encoded_input, 1) return encoded_input, f_cell.state_size+b_cell.state_size def\",\n \"zero \\\"\\\"\\\" with tf.variable_scope(scope, 'RnnEncoding', reuse=reuse): if length_mask is None: length_mask = tf.reduce_sum(tf.sign(tf.reduce_max(tf.abs(embedding),\",\n \"encoded_input, f_cell.state_size+b_cell.state_size def get_prob_for_one_sent(vocab_prob, sent, length_mask=None): \\\"\\\"\\\" :param vocab_prob: :param sent: :param length_mask:\",\n \"sentence length. The rank must be 3 \\\"\\\"\\\" embedding_size = embedding.get_shape()[2].value if avg:\",\n \"= -0.5 * tf.reduce_sum(1 + (recog_logvar - prior_logvar) - tf.div(tf.pow(prior_mu - recog_mu, 2),\",\n \"= tf.exp(0.5 * logvar) z= mu + tf.multiply(std, epsilon) return z def get_bow(embedding,\",\n \"get_rnn_encode(embedding, cell, length_mask=None, scope=None, reuse=None): \\\"\\\"\\\" Assumption, the last dimension is the embedding\",\n \"tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\\\"epsilon\\\") std = tf.exp(0.5 *\",\n \"try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return np.max(scores), np.mean(scores) def gaussian_kld(recog_mu,\",\n \"for hyp in hyps: try: scores.append(sentence_bleu([ref], hyp, smoothing_function=SmoothingFunction().method7, weights=[1./3, 1./3,1./3])) except: scores.append(0.0) return\"\n]","isConversational":false}}},{"rowIdx":2192,"cells":{"ngram":{"kind":"list like","value":["distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY","size arg') if random.uniform(0, 1) > self.probability: return x width, height, _ =","values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N))","not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise","License for the # specific language governing permissions and limitations # under the","(ASF) under one # or more contributor license agreements. See the NOTICE file","Wi x C) shape. Outputs: - **out**: output tensor with (size[0] x size[1]","= pad width for all axes. interpolation : int Interpolation method for resizing.","tuple of (W, H) Size of the final output. pad: int or tuple","or tuple of (W, H) Size of the final output. pad: int or","super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max =","software distributed under the License is distributed on an # \"AS IS\" BASIS,","s_max : float Max area to all area. ratio : float The ratio","h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height:","the value in erasing area. Parameters ---------- probability : float Probability of erasing.","TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg')","class RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between `s_min` and `s_max` with","super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size,","and after pad for each axis. (pad,) or int is a shortcut for","interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else","from mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing']","not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise","of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each","`size`. Parameters ---------- size : int or tuple of (W, H) Size of","- w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0]","probability : float Probability of erasing. s_min : float Min area to all","value in erasing area. Inputs: - **data**: input tensor with (Hi x Wi","* aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h","- h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w,","self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h","`src` with `size` (width, height). Padding is optional. Upsample result if `src` is","under the License is distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES","additional information # regarding copyright ownership. The ASF licenses this file # to","height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1]","# \"License\"); you may not use this file except in compliance # with","not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise","Licensed to the Apache Software Foundation (ASF) under one # or more contributor","0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array(","= ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def","(width, height). Padding is optional. Upsample result if `src` is smaller than `size`.","size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not","OpenCV's resize function for available choices. Inputs: - **data**: input tensor with (Hi","or more contributor license agreements. See the NOTICE file # distributed with this","for resizing. By default uses bilinear interpolation. See OpenCV's resize function for available","= (size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad),","ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg')","if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float):","OR CONDITIONS OF ANY # KIND, either express or implied. See the License","Foundation (ASF) under one # or more contributor license agreements. See the NOTICE","axis. (pad,) or int is a shortcut for before = after = pad","int Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's resize","Apache Software Foundation (ASF) under one # or more contributor license agreements. See","a shortcut for before = after = pad width for all axes. interpolation","before = after = pad width for all axes. interpolation : int Interpolation","if int, size of the zero-padding if tuple, number of values padded to","= int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width","float The ratio between width and height. mean : int or tuple of","the area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the","in compliance # with the License. You may obtain a copy of the","implied. See the License for the # specific language governing permissions and limitations","or agreed to in writing, # software distributed under the License is distributed","height). Padding is optional. Upsample result if `src` is smaller than `size`. Parameters","x1 = random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w,","__init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability","height, _ = x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max)","of the final output. pad: int or tuple if int, size of the","= (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args =","forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return","shortcut for before = after = pad width for all axes. interpolation :","*self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in `src`","image transforms.\" import random import math import numpy as np from mxnet import","license agreements. See the NOTICE file # distributed with this work for additional","probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio = ratio","x size[1] x C) or (size x size x C) shape. \"\"\" def","pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0]","- **data**: input tensor with (Hi x Wi x C) shape. Outputs: -","*self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between `s_min` and `s_max`","113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max","\"License\"); you may not use this file except in compliance # with the","of (R, G, B) The value in erasing area. Inputs: - **data**: input","resizing. By default uses bilinear interpolation. See OpenCV's resize function for available choices.","language governing permissions and limitations # under the License. # coding: utf-8 #","'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height). Padding is optional.","either express or implied. See the License for the # specific language governing","TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size","mean : int or tuple of (R, G, B) The value in erasing","(W, H) Size of the final output. pad: int or tuple if int,","tensor with (Hi x Wi x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02,","w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w,","1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w","not use this file except in compliance # with the License. You may","else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between","(size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0,","same before and after pad for each axis. (pad,) or int is a","target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","# or more contributor license agreements. See the NOTICE file # distributed with","disable= missing-docstring \"Addtional image transforms.\" import random import math import numpy as np","... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same","((before, after),) yields same before and after pad for each axis. (pad,) or","available choices. Inputs: - **data**: input tensor with (Hi x Wi x C)","area. s_max : float Max area to all area. ratio : float The","s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean","bilinear interpolation. See OpenCV's resize function for available choices. Inputs: - **data**: input","erasing the area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls","See OpenCV's resize function for available choices. Inputs: - **data**: input tensor with","interpolation : int Interpolation method for resizing. By default uses bilinear interpolation. See","['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height). Padding is","WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the","width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio)","TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg')",": int or tuple of (R, G, B) The value in erasing area.","__all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height).","pad: int or tuple if int, size of the zero-padding if tuple, number","output tensor with (size[0] x size[1] x C) or (size x size x","= s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if not",": float Probability of erasing. s_min : float Min area to all area.","tensor with (size[0] x size[1] x C) or (size x size x C)","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","crop `src` with `size` (width, height). Padding is optional. Upsample result if `src`","to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad","smaller than `size`. Parameters ---------- size : int or tuple of (W, H)","self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if","(0, 0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad: return","default uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs: -","int, size of the zero-padding if tuple, number of values padded to the","erasing. s_min : float Min area to all area. s_max : float Max","ratio : float The ratio between width and height. mean : int or","self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate","The ratio between width and height. mean : int or tuple of (R,","# regarding copyright ownership. The ASF licenses this file # to you under","> self.probability: return x width, height, _ = x.shape area = width *","more contributor license agreements. See the NOTICE file # distributed with this work","* height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w","is distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF","= x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) * area","or tuple of (R, G, B) The value in erasing area. Inputs: -","function for available choices. Inputs: - **data**: input tensor with (Hi x Wi","numpy as np from mxnet import image, nd from mxnet.gluon import Block __all__","axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before,","with (Hi x Wi x C) shape. Outputs: - **out**: output tensor with","np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad","CONDITIONS OF ANY # KIND, either express or implied. See the License for","work for additional information # regarding copyright ownership. The ASF licenses this file","number of values padded to the edges of each axis. ((before_1, after_1), ...","erasing area. Inputs: - **data**: input tensor with (Hi x Wi x C)","for each axis. (pad,) or int is a shortcut for before = after","licenses this file # to you under the Apache License, Version 2.0 (the","x C) shape. Outputs: - **out**: output tensor with (size[0] x size[1] x","# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either","self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size)","express or implied. See the License for the # specific language governing permissions","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float):","limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ #","the final output. pad: int or tuple if int, size of the zero-padding","ratio between width and height. mean : int or tuple of (R, G,","you under the Apache License, Version 2.0 (the # \"License\"); you may not","G, B) The value in erasing area. Inputs: - **data**: input tensor with","License is distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS","pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad:","aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area /","utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\" import","size = (size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad,","Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's resize function","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","C) shape. Outputs: - **out**: output tensor with (Hi x Wi x C)","is smaller than `size`. Parameters ---------- size : int or tuple of (W,","uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs: - **data**:","under the Apache License, Version 2.0 (the # \"License\"); you may not use","between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between width and","Min area to all area. s_max : float Max area to all area.","and `s_max` with `probability`. `ratio` controls the ratio between width and height. `mean`","if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float):","self.probability = probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio","Inputs: - **data**: input tensor with (Hi x Wi x C) shape. Outputs:","License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","and height. `mean` means the value in erasing area. Parameters ---------- probability :","and height. mean : int or tuple of (R, G, B) The value","height. mean : int or tuple of (R, G, B) The value in","or (size x size x C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2):","import numpy as np from mxnet import image, nd from mxnet.gluon import Block","or implied. See the License for the # specific language governing permissions and","isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got","with (size[0] x size[1] x C) or (size x size x C) shape.","(int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return","distributed under the License is distributed on an # \"AS IS\" BASIS, WITHOUT","`s_min` and `s_max` with `probability`. `ratio` controls the ratio between width and height.","if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad =","import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size`","(before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before","probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean","result if `src` is smaller than `size`. Parameters ---------- size : int or","= probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio =","the zero-padding if tuple, number of values padded to the edges of each","pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\" import random import","RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample result","Padding is optional. Upsample result if `src` is smaller than `size`. Parameters ----------","raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size","of (W, H) Size of the final output. pad: int or tuple if","unique pad widths for each axis. ((before, after),) yields same before and after","**out**: output tensor with (Hi x Wi x C) shape. \"\"\" def __init__(self,","Unless required by applicable law or agreed to in writing, # software distributed","tuple of (R, G, B) The value in erasing area. Inputs: - **data**:","distributed with this work for additional information # regarding copyright ownership. The ASF","size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types):","122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min","random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape area =","pad width for all axes. interpolation : int Interpolation method for resizing. By","numeric_types): size = (size, size) self._args = (size, interpolation) self.pad = ((pad, pad),","# coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image","`mean` means the value in erasing area. Parameters ---------- probability : float Probability","regarding copyright ownership. The ASF licenses this file # to you under the","self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max = s_max","inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg')","import random import math import numpy as np from mxnet import image, nd","than `size`. Parameters ---------- size : int or tuple of (W, H) Size","# KIND, either express or implied. See the License for the # specific","float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got","height. `mean` means the value in erasing area. Parameters ---------- probability : float","h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h,","this work for additional information # regarding copyright ownership. The ASF licenses this","ANY # KIND, either express or implied. See the License for the #","RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between `s_min` and `s_max` with `probability`.","contributor license agreements. See the NOTICE file # distributed with this work for","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width,","def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability =","if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int,","if w < width and h < height: x1 = random.randint(0, width -","tuple, number of values padded to the edges of each axis. ((before_1, after_1),","area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area","TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width, height,","np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing",": int or tuple of (W, H) Size of the final output. pad:","`size` (width, height). Padding is optional. Upsample result if `src` is smaller than","(pad,) or int is a shortcut for before = after = pad width","size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not","of the zero-padding if tuple, number of values padded to the edges of","int is a shortcut for before = after = pad width for all","= ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size","random import math import numpy as np from mxnet import image, nd from","return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block):","aspect_ratio))) if w < width and h < height: x1 = random.randint(0, width","and h < height: x1 = random.randint(0, width - w) y1 = random.randint(0,","(Hi x Wi x C) shape. Outputs: - **out**: output tensor with (size[0]","raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size","See the License for the # specific language governing permissions and limitations #","IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or","2.0 (the # \"License\"); you may not use this file except in compliance","mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area","int or tuple of (W, H) Size of the final output. pad: int",": float Max area to all area. ratio : float The ratio between","(size[0] x size[1] x C) or (size x size x C) shape. \"\"\"","x width, height, _ = x.shape area = width * height target_area =","< width and h < height: x1 = random.randint(0, width - w) y1","return x width, height, _ = x.shape area = width * height target_area","method for resizing. By default uses bilinear interpolation. See OpenCV's resize function for","area to all area. s_max : float Max area to all area. ratio","math import numpy as np from mxnet import image, nd from mxnet.gluon import","the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring","KIND, either express or implied. See the License for the # specific language","\"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic)","area. Parameters ---------- probability : float Probability of erasing. s_min : float Min","under the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable=","yields same before and after pad for each axis. (pad,) or int is","float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless",": int Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's","output tensor with (Hi x Wi x C) shape. \"\"\" def __init__(self, probability=0.5,","output. pad: int or tuple if int, size of the zero-padding if tuple,","< height: x1 = random.randint(0, width - w) y1 = random.randint(0, height -","C) shape. Outputs: - **out**: output tensor with (size[0] x size[1] x C)","__init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size,","arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0,","compliance # with the License. You may obtain a copy of the License","x Wi x C) shape. Outputs: - **out**: output tensor with (Hi x","\"\"\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample result if","interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size =","height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w =","WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See","with the License. You may obtain a copy of the License at #","axis. ((before, after),) yields same before and after pad for each axis. (pad,)","information # regarding copyright ownership. The ASF licenses this file # to you","erasing area. Parameters ---------- probability : float Probability of erasing. s_min : float","for each axis. ((before, after),) yields same before and after pad for each","size : int or tuple of (W, H) Size of the final output.","inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if","x size x C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__()","shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int,","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between","area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio","one # or more contributor license agreements. See the NOTICE file # distributed","**data**: input tensor with (Hi x Wi x C) shape. Outputs: - **out**:","random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if","inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if","except in compliance # with the License. You may obtain a copy of","Upsample result if `src` is smaller than `size`. Parameters ---------- size : int","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","you may not use this file except in compliance # with the License.","pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x):","of values padded to the edges of each axis. ((before_1, after_1), ... (before_N,","the ratio between width and height. `mean` means the value in erasing area.","between width and height. `mean` means the value in erasing area. Parameters ----------","an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND,","int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation)","w < width and h < height: x1 = random.randint(0, width - w)","self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad","isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad,","(Hi x Wi x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3,","import math import numpy as np from mxnet import image, nd from mxnet.gluon","random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0]","size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not","self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if","Parameters ---------- size : int or tuple of (W, H) Size of the","tuple if int, size of the zero-padding if tuple, number of values padded","this file # to you under the Apache License, Version 2.0 (the #","with `probability`. `ratio` controls the ratio between width and height. `mean` means the","in erasing area. Parameters ---------- probability : float Probability of erasing. s_min :","width and h < height: x1 = random.randint(0, width - w) y1 =","= ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height). Padding","# # Unless required by applicable law or agreed to in writing, #","area. ratio : float The ratio between width and height. mean : int","all axes. interpolation : int Interpolation method for resizing. By default uses bilinear","x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x,","padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique","size[1] x C) or (size x size x C) shape. \"\"\" def __init__(self,","isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got","in erasing area. Inputs: - **data**: input tensor with (Hi x Wi x","`s_max` with `probability`. `ratio` controls the ratio between width and height. `mean` means","interpolation. See OpenCV's resize function for available choices. Inputs: - **data**: input tensor","Version 2.0 (the # \"License\"); you may not use this file except in","for the # specific language governing permissions and limitations # under the License.","after = pad width for all axes. interpolation : int Interpolation method for","after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields","x Wi x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31,","- **out**: output tensor with (Hi x Wi x C) shape. \"\"\" def","shape. Outputs: - **out**: output tensor with (size[0] x size[1] x C) or","inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width, height, _","arg') if random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape","each axis. (pad,) or int is a shortcut for before = after =","* area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h =","OF ANY # KIND, either express or implied. See the License for the","BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied.","By default uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs:","y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] =","`ratio` controls the ratio between width and height. `mean` means the value in","choices. Inputs: - **data**: input tensor with (Hi x Wi x C) shape.","int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and","`src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between width","forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not","shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__()","final output. pad: int or tuple if int, size of the zero-padding if","/ aspect_ratio))) if w < width and h < height: x1 = random.randint(0,","edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for",": float The ratio between width and height. mean : int or tuple","License, Version 2.0 (the # \"License\"); you may not use this file except","Size of the final output. pad: int or tuple if int, size of",": float Min area to all area. s_max : float Max area to","width and height. mean : int or tuple of (R, G, B) The","this file except in compliance # with the License. You may obtain a","Max area to all area. ratio : float The ratio between width and","= random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1]","arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\" import random import math import","before and after pad for each axis. (pad,) or int is a shortcut","if isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(),","may not use this file except in compliance # with the License. You","as np from mxnet import image, nd from mxnet.gluon import Block __all__ =","value in erasing area. Parameters ---------- probability : float Probability of erasing. s_min","int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height: x1 =","to all area. ratio : float The ratio between width and height. mean","ASF licenses this file # to you under the Apache License, Version 2.0","Parameters ---------- probability : float Probability of erasing. s_min : float Min area","(R, G, B) The value in erasing area. Inputs: - **data**: input tensor","B) The value in erasing area. Inputs: - **data**: input tensor with (Hi","**out**: output tensor with (size[0] x size[1] x C) or (size x size","# distributed with this work for additional information # regarding copyright ownership. The","if random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape area","- **out**: output tensor with (size[0] x size[1] x C) or (size x","of erasing. s_min : float Min area to all area. s_max : float","s_min : float Min area to all area. s_max : float Max area","on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY #","pylint: disable= missing-docstring \"Addtional image transforms.\" import random import math import numpy as","area = width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio =","after pad for each axis. (pad,) or int is a shortcut for before","arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean,","with this work for additional information # regarding copyright ownership. The ASF licenses","size of the zero-padding if tuple, number of values padded to the edges","means the value in erasing area. Parameters ---------- probability : float Probability of","after_N)) unique pad widths for each axis. ((before, after),) yields same before and","the License. You may obtain a copy of the License at # #","pad widths for each axis. ((before, after),) yields same before and after pad","between width and height. mean : int or tuple of (R, G, B)","return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between `s_min`","pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size","agreements. See the NOTICE file # distributed with this work for additional information","if `src` is smaller than `size`. Parameters ---------- size : int or tuple","nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop","controls the ratio between width and height. `mean` means the value in erasing","int or tuple of (R, G, B) The value in erasing area. Inputs:","writing, # software distributed under the License is distributed on an # \"AS","raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size","\"Addtional image transforms.\" import random import math import numpy as np from mxnet","C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing,","The value in erasing area. Inputs: - **data**: input tensor with (Hi x","(size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int)","resize function for available choices. Inputs: - **data**: input tensor with (Hi x","image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly","isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad = ((pad,","= mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self,","NOTICE file # distributed with this work for additional information # regarding copyright","= random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio)))","H) Size of the final output. pad: int or tuple if int, size","x C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types =","= width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio,","widths for each axis. ((before, after),) yields same before and after pad for","float Min area to all area. s_max : float Max area to all","missing-docstring \"Addtional image transforms.\" import random import math import numpy as np from","if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1)","pad for each axis. (pad,) or int is a shortcut for before =","x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio","np from mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop',","width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] =","disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\" import random import math","= random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area *","for available choices. Inputs: - **data**: input tensor with (Hi x Wi x","s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got","the Apache License, Version 2.0 (the # \"License\"); you may not use this","(float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size,","C) or (size x size x C) shape. \"\"\" def __init__(self, size, pad=None,","aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h <","# specific language governing permissions and limitations # under the License. # coding:","random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio)))","x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2]","self.probability: return x width, height, _ = x.shape area = width * height","The ASF licenses this file # to you under the Apache License, Version","file except in compliance # with the License. You may obtain a copy","is optional. Upsample result if `src` is smaller than `size`. Parameters ---------- size","(pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x): if","float Probability of erasing. s_min : float Min area to all area. s_max","float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate","file # to you under the Apache License, Version 2.0 (the # \"License\");","((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),)","# pylint: disable= missing-docstring \"Addtional image transforms.\" import random import math import numpy","all area. ratio : float The ratio between width and height. mean :","= int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height: x1","C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float,","= after = pad width for all axes. interpolation : int Interpolation method","(Hi x Wi x C) shape. Outputs: - **out**: output tensor with (Hi","width and height. `mean` means the value in erasing area. Parameters ---------- probability","ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min","zero-padding if tuple, number of values padded to the edges of each axis.","all area. s_max : float Max area to all area. ratio : float","if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0]","raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate","(the # \"License\"); you may not use this file except in compliance #","mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class","or tuple if int, size of the zero-padding if tuple, number of values","random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] =","Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width,","isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise","size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0))","= self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2] return","law or agreed to in writing, # software distributed under the License is","class RandomCrop(Block): \"\"\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample","self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the","isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability:","# software distributed under the License is distributed on an # \"AS IS\"","to you under the Apache License, Version 2.0 (the # \"License\"); you may","def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else:","= random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h,","file # distributed with this work for additional information # regarding copyright ownership.","# Licensed to the Apache Software Foundation (ASF) under one # or more","image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in `src` between `s_min` and","s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability,","copyright ownership. The ASF licenses this file # to you under the Apache","ownership. The ASF licenses this file # to you under the Apache License,","mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src` with","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","= s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise","# Unless required by applicable law or agreed to in writing, # software","constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \"\"\"Randomly erasing the area in","\"\"\"Randomly erasing the area in `src` between `s_min` and `s_max` with `probability`. `ratio`","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","to in writing, # software distributed under the License is distributed on an","with `size` (width, height). Padding is optional. Upsample result if `src` is smaller","agreed to in writing, # software distributed under the License is distributed on","mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min =","# pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\" import random","shape. Outputs: - **out**: output tensor with (Hi x Wi x C) shape.","\"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability","self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def","arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio,","\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express","`probability`. `ratio` controls the ratio between width and height. `mean` means the value","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self,","area to all area. ratio : float The ratio between width and height.","---------- size : int or tuple of (W, H) Size of the final","x Wi x C) shape. Outputs: - **out**: output tensor with (size[0] x","to the Apache Software Foundation (ASF) under one # or more contributor license","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","each axis. ((before, after),) yields same before and after pad for each axis.","the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths","inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if","is a shortcut for before = after = pad width for all axes.","float Max area to all area. ratio : float The ratio between width","use this file except in compliance # with the License. You may obtain","w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w <","coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional image transforms.\"","axes. interpolation : int Interpolation method for resizing. By default uses bilinear interpolation.","x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min,","Software Foundation (ASF) under one # or more contributor license agreements. See the","the License is distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR","int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant',","width, height, _ = x.shape area = width * height target_area = random.uniform(self.s_min,","self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2] return x","width for all axes. interpolation : int Interpolation method for resizing. By default","tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x","---------- probability : float Probability of erasing. s_min : float Min area to","x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)):","for all axes. interpolation : int Interpolation method for resizing. By default uses","transforms.\" import random import math import numpy as np from mxnet import image,","for before = after = pad width for all axes. interpolation : int","input tensor with (Hi x Wi x C) shape. Outputs: - **out**: output","size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if","Outputs: - **out**: output tensor with (size[0] x size[1] x C) or (size","the # specific language governing permissions and limitations # under the License. #","isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got","# under the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint:","tensor with (Hi x Wi x C) shape. Outputs: - **out**: output tensor","See the NOTICE file # distributed with this work for additional information #","after),) yields same before and after pad for each axis. (pad,) or int","arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max,","def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if","_ = x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) *","TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg')","import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block):","Probability of erasing. s_min : float Min area to all area. s_max :","the NOTICE file # distributed with this work for additional information # regarding","in writing, # software distributed under the License is distributed on an #","the Apache Software Foundation (ASF) under one # or more contributor license agreements.","def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if","`src` is smaller than `size`. Parameters ---------- size : int or tuple of","x C) or (size x size x C) shape. \"\"\" def __init__(self, size,","y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h,","to all area. s_max : float Max area to all area. ratio :","size x C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types","area. Inputs: - **data**: input tensor with (Hi x Wi x C) shape.","each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis.","optional. Upsample result if `src` is smaller than `size`. Parameters ---------- size :","height: x1 = random.randint(0, width - w) y1 = random.randint(0, height - h)","not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) >","self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class","ratio between width and height. `mean` means the value in erasing area. Parameters","License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \"Addtional","else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)),","s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean =","permissions and limitations # under the License. # coding: utf-8 # pylint: disable=","self.s_max = s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float):","# with the License. You may obtain a copy of the License at","(size x size x C) shape. \"\"\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop,","Wi x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96,","Apache License, Version 2.0 (the # \"License\"); you may not use this file","and limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ","= (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad,","h < height: x1 = random.randint(0, width - w) y1 = random.randint(0, height","under one # or more contributor license agreements. See the NOTICE file #","Wi x C) shape. Outputs: - **out**: output tensor with (Hi x Wi","numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args","# to you under the Apache License, Version 2.0 (the # \"License\"); you","required by applicable law or agreed to in writing, # software distributed under","specific language governing permissions and limitations # under the License. # coding: utf-8","or int is a shortcut for before = after = pad width for","with (Hi x Wi x C) shape. \"\"\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4,","0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2]","image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly","by applicable law or agreed to in writing, # software distributed under the","mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self, x):","for additional information # regarding copyright ownership. The ASF licenses this file #","int or tuple if int, size of the zero-padding if tuple, number of","float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate","governing permissions and limitations # under the License. # coding: utf-8 # pylint:","the License for the # specific language governing permissions and limitations # under","from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \"\"\"Randomly crop `src`","Outputs: - **out**: output tensor with (Hi x Wi x C) shape. \"\"\"","applicable law or agreed to in writing, # software distributed under the License","if tuple, number of values padded to the edges of each axis. ((before_1,","not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)):","x C) shape. Outputs: - **out**: output tensor with (Hi x Wi x","1) > self.probability: return x width, height, _ = x.shape area = width"],"string":"[\n \"distributed on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"size arg') if random.uniform(0, 1) > self.probability: return x width, height, _ =\",\n \"values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N))\",\n \"not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise\",\n \"License for the # specific language governing permissions and limitations # under the\",\n \"(ASF) under one # or more contributor license agreements. See the NOTICE file\",\n \"Wi x C) shape. Outputs: - **out**: output tensor with (size[0] x size[1]\",\n \"= pad width for all axes. interpolation : int Interpolation method for resizing.\",\n \"tuple of (W, H) Size of the final output. pad: int or tuple\",\n \"or tuple of (W, H) Size of the final output. pad: int or\",\n \"super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max =\",\n \"software distributed under the License is distributed on an # \\\"AS IS\\\" BASIS,\",\n \"s_max : float Max area to all area. ratio : float The ratio\",\n \"h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height:\",\n \"the value in erasing area. Parameters ---------- probability : float Probability of erasing.\",\n \"TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg')\",\n \"class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between `s_min` and `s_max` with\",\n \"super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size,\",\n \"and after pad for each axis. (pad,) or int is a shortcut for\",\n \"interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else\",\n \"from mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing']\",\n \"not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise\",\n \"of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each\",\n \"`size`. Parameters ---------- size : int or tuple of (W, H) Size of\",\n \"- w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0]\",\n \"probability : float Probability of erasing. s_min : float Min area to all\",\n \"value in erasing area. Inputs: - **data**: input tensor with (Hi x Wi\",\n \"* aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h\",\n \"- h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w,\",\n \"self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h\",\n \"`src` with `size` (width, height). Padding is optional. Upsample result if `src` is\",\n \"under the License is distributed on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES\",\n \"additional information # regarding copyright ownership. The ASF licenses this file # to\",\n \"height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1]\",\n \"# \\\"License\\\"); you may not use this file except in compliance # with\",\n \"not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise\",\n \"Licensed to the Apache Software Foundation (ASF) under one # or more contributor\",\n \"0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array(\",\n \"= ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def\",\n \"(width, height). Padding is optional. Upsample result if `src` is smaller than `size`.\",\n \"size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not\",\n \"OpenCV's resize function for available choices. Inputs: - **data**: input tensor with (Hi\",\n \"or more contributor license agreements. See the NOTICE file # distributed with this\",\n \"for resizing. By default uses bilinear interpolation. See OpenCV's resize function for available\",\n \"= (size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad),\",\n \"ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg')\",\n \"if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float):\",\n \"OR CONDITIONS OF ANY # KIND, either express or implied. See the License\",\n \"Foundation (ASF) under one # or more contributor license agreements. See the NOTICE\",\n \"axis. (pad,) or int is a shortcut for before = after = pad\",\n \"int Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's resize\",\n \"Apache Software Foundation (ASF) under one # or more contributor license agreements. See\",\n \"a shortcut for before = after = pad width for all axes. interpolation\",\n \"before = after = pad width for all axes. interpolation : int Interpolation\",\n \"if int, size of the zero-padding if tuple, number of values padded to\",\n \"= int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width\",\n \"float The ratio between width and height. mean : int or tuple of\",\n \"the area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the\",\n \"in compliance # with the License. You may obtain a copy of the\",\n \"implied. See the License for the # specific language governing permissions and limitations\",\n \"or agreed to in writing, # software distributed under the License is distributed\",\n \"height). Padding is optional. Upsample result if `src` is smaller than `size`. Parameters\",\n \"x1 = random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w,\",\n \"__init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability\",\n \"height, _ = x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max)\",\n \"of the final output. pad: int or tuple if int, size of the\",\n \"= (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args =\",\n \"forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return\",\n \"shortcut for before = after = pad width for all axes. interpolation :\",\n \"*self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src`\",\n \"image transforms.\\\" import random import math import numpy as np from mxnet import\",\n \"license agreements. See the NOTICE file # distributed with this work for additional\",\n \"probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio = ratio\",\n \"x size[1] x C) or (size x size x C) shape. \\\"\\\"\\\" def\",\n \"pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0]\",\n \"- **data**: input tensor with (Hi x Wi x C) shape. Outputs: -\",\n \"*self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between `s_min` and `s_max`\",\n \"113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max\",\n \"\\\"License\\\"); you may not use this file except in compliance # with the\",\n \"of (R, G, B) The value in erasing area. Inputs: - **data**: input\",\n \"resizing. By default uses bilinear interpolation. See OpenCV's resize function for available choices.\",\n \"language governing permissions and limitations # under the License. # coding: utf-8 #\",\n \"'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding is optional.\",\n \"either express or implied. See the License for the # specific language governing\",\n \"TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size\",\n \"mean : int or tuple of (R, G, B) The value in erasing\",\n \"(W, H) Size of the final output. pad: int or tuple if int,\",\n \"tensor with (Hi x Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02,\",\n \"w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w,\",\n \"1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w\",\n \"not use this file except in compliance # with the License. You may\",\n \"else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between\",\n \"(size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0,\",\n \"same before and after pad for each axis. (pad,) or int is a\",\n \"target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"# or more contributor license agreements. See the NOTICE file # distributed with\",\n \"disable= missing-docstring \\\"Addtional image transforms.\\\" import random import math import numpy as np\",\n \"... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same\",\n \"((before, after),) yields same before and after pad for each axis. (pad,) or\",\n \"available choices. Inputs: - **data**: input tensor with (Hi x Wi x C)\",\n \"area. s_max : float Max area to all area. ratio : float The\",\n \"s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean\",\n \"bilinear interpolation. See OpenCV's resize function for available choices. Inputs: - **data**: input\",\n \"erasing the area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls\",\n \"See OpenCV's resize function for available choices. Inputs: - **data**: input tensor with\",\n \"interpolation : int Interpolation method for resizing. By default uses bilinear interpolation. See\",\n \"['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding is\",\n \"WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the\",\n \"width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio)\",\n \"TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg')\",\n \": int or tuple of (R, G, B) The value in erasing area.\",\n \"__all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height).\",\n \"pad: int or tuple if int, size of the zero-padding if tuple, number\",\n \"output tensor with (size[0] x size[1] x C) or (size x size x\",\n \"= s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if not\",\n \": float Probability of erasing. s_min : float Min area to all area.\",\n \"tensor with (size[0] x size[1] x C) or (size x size x C)\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"crop `src` with `size` (width, height). Padding is optional. Upsample result if `src`\",\n \"to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad\",\n \"smaller than `size`. Parameters ---------- size : int or tuple of (W, H)\",\n \"self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if\",\n \"(0, 0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad: return\",\n \"default uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs: -\",\n \"int, size of the zero-padding if tuple, number of values padded to the\",\n \"erasing. s_min : float Min area to all area. s_max : float Max\",\n \"ratio : float The ratio between width and height. mean : int or\",\n \"self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate\",\n \"The ratio between width and height. mean : int or tuple of (R,\",\n \"# regarding copyright ownership. The ASF licenses this file # to you under\",\n \"> self.probability: return x width, height, _ = x.shape area = width *\",\n \"more contributor license agreements. See the NOTICE file # distributed with this work\",\n \"* height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w\",\n \"is distributed on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"= x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) * area\",\n \"or tuple of (R, G, B) The value in erasing area. Inputs: -\",\n \"function for available choices. Inputs: - **data**: input tensor with (Hi x Wi\",\n \"numpy as np from mxnet import image, nd from mxnet.gluon import Block __all__\",\n \"axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before,\",\n \"with (Hi x Wi x C) shape. Outputs: - **out**: output tensor with\",\n \"np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad\",\n \"CONDITIONS OF ANY # KIND, either express or implied. See the License for\",\n \"work for additional information # regarding copyright ownership. The ASF licenses this file\",\n \"number of values padded to the edges of each axis. ((before_1, after_1), ...\",\n \"erasing area. Inputs: - **data**: input tensor with (Hi x Wi x C)\",\n \"for each axis. (pad,) or int is a shortcut for before = after\",\n \"licenses this file # to you under the Apache License, Version 2.0 (the\",\n \"x C) shape. Outputs: - **out**: output tensor with (size[0] x size[1] x\",\n \"# \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either\",\n \"self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size)\",\n \"express or implied. See the License for the # specific language governing permissions\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float):\",\n \"limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ #\",\n \"the final output. pad: int or tuple if int, size of the zero-padding\",\n \"ratio between width and height. mean : int or tuple of (R, G,\",\n \"you under the Apache License, Version 2.0 (the # \\\"License\\\"); you may not\",\n \"G, B) The value in erasing area. Inputs: - **data**: input tensor with\",\n \"License is distributed on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS\",\n \"pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x): if self.pad:\",\n \"aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area /\",\n \"utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import\",\n \"size = (size, size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad,\",\n \"Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's resize function\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"C) shape. Outputs: - **out**: output tensor with (Hi x Wi x C)\",\n \"is smaller than `size`. Parameters ---------- size : int or tuple of (W,\",\n \"uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs: - **data**:\",\n \"under the Apache License, Version 2.0 (the # \\\"License\\\"); you may not use\",\n \"between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between width and\",\n \"Min area to all area. s_max : float Max area to all area.\",\n \"and `s_max` with `probability`. `ratio` controls the ratio between width and height. `mean`\",\n \"if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float):\",\n \"self.probability = probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio\",\n \"Inputs: - **data**: input tensor with (Hi x Wi x C) shape. Outputs:\",\n \"License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"and height. `mean` means the value in erasing area. Parameters ---------- probability :\",\n \"and height. mean : int or tuple of (R, G, B) The value\",\n \"height. mean : int or tuple of (R, G, B) The value in\",\n \"or (size x size x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2):\",\n \"import numpy as np from mxnet import image, nd from mxnet.gluon import Block\",\n \"or implied. See the License for the # specific language governing permissions and\",\n \"isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got\",\n \"with (size[0] x size[1] x C) or (size x size x C) shape.\",\n \"(int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return\",\n \"distributed under the License is distributed on an # \\\"AS IS\\\" BASIS, WITHOUT\",\n \"`s_min` and `s_max` with `probability`. `ratio` controls the ratio between width and height.\",\n \"if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad =\",\n \"import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size`\",\n \"(before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before\",\n \"probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean\",\n \"result if `src` is smaller than `size`. Parameters ---------- size : int or\",\n \"= probability self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio =\",\n \"the zero-padding if tuple, number of values padded to the edges of each\",\n \"pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random import\",\n \"RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample result\",\n \"Padding is optional. Upsample result if `src` is smaller than `size`. Parameters ----------\",\n \"raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size\",\n \"of (W, H) Size of the final output. pad: int or tuple if\",\n \"unique pad widths for each axis. ((before, after),) yields same before and after\",\n \"**out**: output tensor with (Hi x Wi x C) shape. \\\"\\\"\\\" def __init__(self,\",\n \"Unless required by applicable law or agreed to in writing, # software distributed\",\n \"tuple of (R, G, B) The value in erasing area. Inputs: - **data**:\",\n \"distributed with this work for additional information # regarding copyright ownership. The ASF\",\n \"size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types):\",\n \"122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min = s_min\",\n \"random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape area =\",\n \"pad width for all axes. interpolation : int Interpolation method for resizing. By\",\n \"numeric_types): size = (size, size) self._args = (size, interpolation) self.pad = ((pad, pad),\",\n \"# coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image\",\n \"`mean` means the value in erasing area. Parameters ---------- probability : float Probability\",\n \"regarding copyright ownership. The ASF licenses this file # to you under the\",\n \"self).__init__() self.probability = probability self.mean = mean self.s_min = s_min self.s_max = s_max\",\n \"inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg')\",\n \"import random import math import numpy as np from mxnet import image, nd\",\n \"than `size`. Parameters ---------- size : int or tuple of (W, H) Size\",\n \"# KIND, either express or implied. See the License for the # specific\",\n \"float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got\",\n \"height. `mean` means the value in erasing area. Parameters ---------- probability : float\",\n \"h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h,\",\n \"this work for additional information # regarding copyright ownership. The ASF licenses this\",\n \"ANY # KIND, either express or implied. See the License for the #\",\n \"RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between `s_min` and `s_max` with `probability`.\",\n \"contributor license agreements. See the NOTICE file # distributed with this work for\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width,\",\n \"def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability =\",\n \"if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int,\",\n \"if w < width and h < height: x1 = random.randint(0, width -\",\n \"tuple, number of values padded to the edges of each axis. ((before_1, after_1),\",\n \"area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area\",\n \"TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width, height,\",\n \"np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing\",\n \": int or tuple of (W, H) Size of the final output. pad:\",\n \"`size` (width, height). Padding is optional. Upsample result if `src` is smaller than\",\n \"(pad,) or int is a shortcut for before = after = pad width\",\n \"size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not\",\n \"of the zero-padding if tuple, number of values padded to the edges of\",\n \"int is a shortcut for before = after = pad width for all\",\n \"= ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size\",\n \"random import math import numpy as np from mxnet import image, nd from\",\n \"return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block):\",\n \"aspect_ratio))) if w < width and h < height: x1 = random.randint(0, width\",\n \"and h < height: x1 = random.randint(0, width - w) y1 = random.randint(0,\",\n \"(Hi x Wi x C) shape. Outputs: - **out**: output tensor with (size[0]\",\n \"raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size\",\n \"See the License for the # specific language governing permissions and limitations #\",\n \"IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or\",\n \"2.0 (the # \\\"License\\\"); you may not use this file except in compliance\",\n \"mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area\",\n \"int or tuple of (W, H) Size of the final output. pad: int\",\n \": float Max area to all area. ratio : float The ratio between\",\n \"(size[0] x size[1] x C) or (size x size x C) shape. \\\"\\\"\\\"\",\n \"x width, height, _ = x.shape area = width * height target_area =\",\n \"< width and h < height: x1 = random.randint(0, width - w) y1\",\n \"return x width, height, _ = x.shape area = width * height target_area\",\n \"method for resizing. By default uses bilinear interpolation. See OpenCV's resize function for\",\n \"area to all area. s_max : float Max area to all area. ratio\",\n \"math import numpy as np from mxnet import image, nd from mxnet.gluon import\",\n \"the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring\",\n \"KIND, either express or implied. See the License for the # specific language\",\n \"\\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic)\",\n \"area. Parameters ---------- probability : float Probability of erasing. s_min : float Min\",\n \"under the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable=\",\n \"yields same before and after pad for each axis. (pad,) or int is\",\n \"float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \": int Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's\",\n \"output tensor with (Hi x Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5,\",\n \"output. pad: int or tuple if int, size of the zero-padding if tuple,\",\n \"< height: x1 = random.randint(0, width - w) y1 = random.randint(0, height -\",\n \"C) shape. Outputs: - **out**: output tensor with (size[0] x size[1] x C)\",\n \"__init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size,\",\n \"arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0,\",\n \"compliance # with the License. You may obtain a copy of the License\",\n \"x Wi x C) shape. Outputs: - **out**: output tensor with (Hi x\",\n \"\\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample result if\",\n \"interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size =\",\n \"height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w =\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See\",\n \"with the License. You may obtain a copy of the License at #\",\n \"axis. ((before, after),) yields same before and after pad for each axis. (pad,)\",\n \"information # regarding copyright ownership. The ASF licenses this file # to you\",\n \"erasing area. Parameters ---------- probability : float Probability of erasing. s_min : float\",\n \"for each axis. ((before, after),) yields same before and after pad for each\",\n \"size : int or tuple of (W, H) Size of the final output.\",\n \"inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if\",\n \"x size x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__()\",\n \"shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int,\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between\",\n \"area in `src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio\",\n \"one # or more contributor license agreements. See the NOTICE file # distributed\",\n \"**data**: input tensor with (Hi x Wi x C) shape. Outputs: - **out**:\",\n \"random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if\",\n \"inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if\",\n \"except in compliance # with the License. You may obtain a copy of\",\n \"Upsample result if `src` is smaller than `size`. Parameters ---------- size : int\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"you may not use this file except in compliance # with the License.\",\n \"pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x):\",\n \"of values padded to the edges of each axis. ((before_1, after_1), ... (before_N,\",\n \"the ratio between width and height. `mean` means the value in erasing area.\",\n \"between width and height. `mean` means the value in erasing area. Parameters ----------\",\n \"an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND,\",\n \"int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation)\",\n \"w < width and h < height: x1 = random.randint(0, width - w)\",\n \"self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad\",\n \"isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad,\",\n \"(Hi x Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3,\",\n \"import math import numpy as np from mxnet import image, nd from mxnet.gluon\",\n \"random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0]\",\n \"size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not\",\n \"self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if\",\n \"Parameters ---------- size : int or tuple of (W, H) Size of the\",\n \"tuple if int, size of the zero-padding if tuple, number of values padded\",\n \"this file # to you under the Apache License, Version 2.0 (the #\",\n \"with `probability`. `ratio` controls the ratio between width and height. `mean` means the\",\n \"in erasing area. Parameters ---------- probability : float Probability of erasing. s_min :\",\n \"width and h < height: x1 = random.randint(0, width - w) y1 =\",\n \"= ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding\",\n \"# # Unless required by applicable law or agreed to in writing, #\",\n \"area. ratio : float The ratio between width and height. mean : int\",\n \"all axes. interpolation : int Interpolation method for resizing. By default uses bilinear\",\n \"x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x,\",\n \"padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique\",\n \"size[1] x C) or (size x size x C) shape. \\\"\\\"\\\" def __init__(self,\",\n \"isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got\",\n \"in erasing area. Inputs: - **data**: input tensor with (Hi x Wi x\",\n \"`s_max` with `probability`. `ratio` controls the ratio between width and height. `mean` means\",\n \"interpolation. See OpenCV's resize function for available choices. Inputs: - **data**: input tensor\",\n \"Version 2.0 (the # \\\"License\\\"); you may not use this file except in\",\n \"for the # specific language governing permissions and limitations # under the License.\",\n \"after = pad width for all axes. interpolation : int Interpolation method for\",\n \"after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields\",\n \"x Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31,\",\n \"- **out**: output tensor with (Hi x Wi x C) shape. \\\"\\\"\\\" def\",\n \"shape. Outputs: - **out**: output tensor with (size[0] x size[1] x C) or\",\n \"inappropriate size arg') if random.uniform(0, 1) > self.probability: return x width, height, _\",\n \"arg') if random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape\",\n \"each axis. (pad,) or int is a shortcut for before = after =\",\n \"* area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h =\",\n \"OF ANY # KIND, either express or implied. See the License for the\",\n \"BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied.\",\n \"By default uses bilinear interpolation. See OpenCV's resize function for available choices. Inputs:\",\n \"y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] =\",\n \"`ratio` controls the ratio between width and height. `mean` means the value in\",\n \"choices. Inputs: - **data**: input tensor with (Hi x Wi x C) shape.\",\n \"int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and\",\n \"`src` between `s_min` and `s_max` with `probability`. `ratio` controls the ratio between width\",\n \"forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not\",\n \"shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__()\",\n \"final output. pad: int or tuple if int, size of the zero-padding if\",\n \"/ aspect_ratio))) if w < width and h < height: x1 = random.randint(0,\",\n \"edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for\",\n \": float The ratio between width and height. mean : int or tuple\",\n \"License, Version 2.0 (the # \\\"License\\\"); you may not use this file except\",\n \"Size of the final output. pad: int or tuple if int, size of\",\n \": float Min area to all area. s_max : float Max area to\",\n \"width and height. mean : int or tuple of (R, G, B) The\",\n \"this file except in compliance # with the License. You may obtain a\",\n \"Max area to all area. ratio : float The ratio between width and\",\n \"= random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1]\",\n \"arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random import math import\",\n \"before and after pad for each axis. (pad,) or int is a shortcut\",\n \"if isinstance(pad, int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(),\",\n \"may not use this file except in compliance # with the License. You\",\n \"as np from mxnet import image, nd from mxnet.gluon import Block __all__ =\",\n \"value in erasing area. Parameters ---------- probability : float Probability of erasing. s_min\",\n \"int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height: x1 =\",\n \"to all area. ratio : float The ratio between width and height. mean\",\n \"ASF licenses this file # to you under the Apache License, Version 2.0\",\n \"Parameters ---------- probability : float Probability of erasing. s_min : float Min area\",\n \"(R, G, B) The value in erasing area. Inputs: - **data**: input tensor\",\n \"B) The value in erasing area. Inputs: - **data**: input tensor with (Hi\",\n \"**out**: output tensor with (size[0] x size[1] x C) or (size x size\",\n \"# distributed with this work for additional information # regarding copyright ownership. The\",\n \"if random.uniform(0, 1) > self.probability: return x width, height, _ = x.shape area\",\n \"- **out**: output tensor with (size[0] x size[1] x C) or (size x\",\n \"of erasing. s_min : float Min area to all area. s_max : float\",\n \"s_min : float Min area to all area. s_max : float Max area\",\n \"on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY #\",\n \"pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random import math import numpy as\",\n \"area = width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio =\",\n \"after pad for each axis. (pad,) or int is a shortcut for before\",\n \"arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean,\",\n \"with this work for additional information # regarding copyright ownership. The ASF licenses\",\n \"size of the zero-padding if tuple, number of values padded to the edges\",\n \"means the value in erasing area. Parameters ---------- probability : float Probability of\",\n \"after_N)) unique pad widths for each axis. ((before, after),) yields same before and\",\n \"the License. You may obtain a copy of the License at # #\",\n \"pad widths for each axis. ((before, after),) yields same before and after pad\",\n \"between width and height. mean : int or tuple of (R, G, B)\",\n \"return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between `s_min`\",\n \"pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size\",\n \"agreements. See the NOTICE file # distributed with this work for additional information\",\n \"if `src` is smaller than `size`. Parameters ---------- size : int or tuple\",\n \"nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop\",\n \"controls the ratio between width and height. `mean` means the value in erasing\",\n \"int or tuple of (R, G, B) The value in erasing area. Inputs:\",\n \"writing, # software distributed under the License is distributed on an # \\\"AS\",\n \"raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate size\",\n \"\\\"Addtional image transforms.\\\" import random import math import numpy as np from mxnet\",\n \"C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing,\",\n \"The value in erasing area. Inputs: - **data**: input tensor with (Hi x\",\n \"(size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int)\",\n \"resize function for available choices. Inputs: - **data**: input tensor with (Hi x\",\n \"image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly\",\n \"isinstance(size, numeric_types): size = (size, size) self._args = (size, interpolation) self.pad = ((pad,\",\n \"= mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self,\",\n \"NOTICE file # distributed with this work for additional information # regarding copyright\",\n \"= random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio)))\",\n \"H) Size of the final output. pad: int or tuple if int, size\",\n \"x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types =\",\n \"= width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio,\",\n \"widths for each axis. ((before, after),) yields same before and after pad for\",\n \"float Min area to all area. s_max : float Max area to all\",\n \"missing-docstring \\\"Addtional image transforms.\\\" import random import math import numpy as np from\",\n \"if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1)\",\n \"pad for each axis. (pad,) or int is a shortcut for before =\",\n \"x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) * area aspect_ratio\",\n \"np from mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop',\",\n \"width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] =\",\n \"disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random import math\",\n \"= random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area *\",\n \"for available choices. Inputs: - **data**: input tensor with (Hi x Wi x\",\n \"s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got\",\n \"the Apache License, Version 2.0 (the # \\\"License\\\"); you may not use this\",\n \"(float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args = (size,\",\n \"C) or (size x size x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None,\",\n \"aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h <\",\n \"# specific language governing permissions and limitations # under the License. # coding:\",\n \"random.uniform(self.s_min, self.s_max) * area aspect_ratio = random.uniform(self.ratio, 1/self.ratio) w = int(round(math.sqrt(target_area * aspect_ratio)))\",\n \"x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2]\",\n \"self.probability: return x width, height, _ = x.shape area = width * height\",\n \"The ASF licenses this file # to you under the Apache License, Version\",\n \"file except in compliance # with the License. You may obtain a copy\",\n \"is optional. Upsample result if `src` is smaller than `size`. Parameters ---------- size\",\n \"(pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self, x): if\",\n \"float Probability of erasing. s_min : float Min area to all area. s_max\",\n \"float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate\",\n \"file # to you under the Apache License, Version 2.0 (the # \\\"License\\\");\",\n \"((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),)\",\n \"# pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random import math import numpy\",\n \"all area. ratio : float The ratio between width and height. mean :\",\n \"= int(round(math.sqrt(target_area / aspect_ratio))) if w < width and h < height: x1\",\n \"C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float,\",\n \"= after = pad width for all axes. interpolation : int Interpolation method\",\n \"(Hi x Wi x C) shape. Outputs: - **out**: output tensor with (Hi\",\n \"width and height. `mean` means the value in erasing area. Parameters ---------- probability\",\n \"ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min\",\n \"zero-padding if tuple, number of values padded to the edges of each axis.\",\n \"all area. s_max : float Max area to all area. ratio : float\",\n \"if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0]\",\n \"raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate\",\n \"(the # \\\"License\\\"); you may not use this file except in compliance #\",\n \"mxnet import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class\",\n \"or tuple if int, size of the zero-padding if tuple, number of values\",\n \"random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] =\",\n \"Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width,\",\n \"isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)): raise\",\n \"size) self._args = (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0))\",\n \"= self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2] return\",\n \"law or agreed to in writing, # software distributed under the License is\",\n \"class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with `size` (width, height). Padding is optional. Upsample\",\n \"self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the\",\n \"isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability:\",\n \"# software distributed under the License is distributed on an # \\\"AS IS\\\"\",\n \"to you under the Apache License, Version 2.0 (the # \\\"License\\\"); you may\",\n \"def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else:\",\n \"= random.randint(0, width - w) y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h,\",\n \"file # distributed with this work for additional information # regarding copyright ownership.\",\n \"# Licensed to the Apache Software Foundation (ASF) under one # or more\",\n \"image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in `src` between `s_min` and\",\n \"s_min self.s_max = s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability,\",\n \"copyright ownership. The ASF licenses this file # to you under the Apache\",\n \"ownership. The ASF licenses this file # to you under the Apache License,\",\n \"mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src` with\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"= s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float): raise\",\n \"# Unless required by applicable law or agreed to in writing, # software\",\n \"constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class RandomErasing(Block): \\\"\\\"\\\"Randomly erasing the area in\",\n \"\\\"\\\"\\\"Randomly erasing the area in `src` between `s_min` and `s_max` with `probability`. `ratio`\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"to in writing, # software distributed under the License is distributed on an\",\n \"with `size` (width, height). Padding is optional. Upsample result if `src` is smaller\",\n \"agreed to in writing, # software distributed under the License is distributed on\",\n \"mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean = mean self.s_min =\",\n \"# pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\" import random\",\n \"shape. Outputs: - **out**: output tensor with (Hi x Wi x C) shape.\",\n \"\\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability\",\n \"self.mean = mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def\",\n \"arg') if not isinstance(self.s_max, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio,\",\n \"\\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express\",\n \"`probability`. `ratio` controls the ratio between width and height. `mean` means the value\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"((pad, pad), (pad, pad), (0, 0)) if isinstance(pad, int) else pad def forward(self,\",\n \"area to all area. ratio : float The ratio between width and height.\",\n \"---------- size : int or tuple of (W, H) Size of the final\",\n \"x Wi x C) shape. Outputs: - **out**: output tensor with (size[0] x\",\n \"to the Apache Software Foundation (ASF) under one # or more contributor license\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"each axis. ((before, after),) yields same before and after pad for each axis.\",\n \"the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths\",\n \"inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if\",\n \"is a shortcut for before = after = pad width for all axes.\",\n \"float Max area to all area. ratio : float The ratio between width\",\n \"use this file except in compliance # with the License. You may obtain\",\n \"w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w <\",\n \"coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional image transforms.\\\"\",\n \"axes. interpolation : int Interpolation method for resizing. By default uses bilinear interpolation.\",\n \"x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_min,\",\n \"Software Foundation (ASF) under one # or more contributor license agreements. See the\",\n \"the License is distributed on an # \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR\",\n \"int) else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant',\",\n \"width, height, _ = x.shape area = width * height target_area = random.uniform(self.s_min,\",\n \"self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2] return x\",\n \"width for all axes. interpolation : int Interpolation method for resizing. By default\",\n \"tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) > self.probability: return x\",\n \"---------- probability : float Probability of erasing. s_min : float Min area to\",\n \"x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)):\",\n \"for all axes. interpolation : int Interpolation method for resizing. By default uses\",\n \"transforms.\\\" import random import math import numpy as np from mxnet import image,\",\n \"for before = after = pad width for all axes. interpolation : int\",\n \"input tensor with (Hi x Wi x C) shape. Outputs: - **out**: output\",\n \"size arg') if not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if\",\n \"Outputs: - **out**: output tensor with (size[0] x size[1] x C) or (size\",\n \"the # specific language governing permissions and limitations # under the License. #\",\n \"isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max, float): raise TypeError('Got\",\n \"# under the License. # coding: utf-8 # pylint: disable= arguments-differ # pylint:\",\n \"tensor with (Hi x Wi x C) shape. Outputs: - **out**: output tensor\",\n \"See the NOTICE file # distributed with this work for additional information #\",\n \"after),) yields same before and after pad for each axis. (pad,) or int\",\n \"arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.s_max,\",\n \"def forward(self, x): if not isinstance(self.probability, float): raise TypeError('Got inappropriate size arg') if\",\n \"_ = x.shape area = width * height target_area = random.uniform(self.s_min, self.s_max) *\",\n \"TypeError('Got inappropriate size arg') if not isinstance(self.s_min, float): raise TypeError('Got inappropriate size arg')\",\n \"import image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block):\",\n \"Probability of erasing. s_min : float Min area to all area. s_max :\",\n \"the NOTICE file # distributed with this work for additional information # regarding\",\n \"in writing, # software distributed under the License is distributed on an #\",\n \"the Apache Software Foundation (ASF) under one # or more contributor license agreements.\",\n \"def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types = (float, int, np.generic) if\",\n \"`src` is smaller than `size`. Parameters ---------- size : int or tuple of\",\n \"x C) or (size x size x C) shape. \\\"\\\"\\\" def __init__(self, size,\",\n \"y1 = random.randint(0, height - h) x[x1:x1+w, y1:y1+h, 0] = self.mean[0] x[x1:x1+w, y1:y1+h,\",\n \"to all area. s_max : float Max area to all area. ratio :\",\n \"size x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop, self).__init__() numeric_types\",\n \"area. Inputs: - **data**: input tensor with (Hi x Wi x C) shape.\",\n \"each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis.\",\n \"optional. Upsample result if `src` is smaller than `size`. Parameters ---------- size :\",\n \"height: x1 = random.randint(0, width - w) y1 = random.randint(0, height - h)\",\n \"not isinstance(self.mean, (int, tuple)): raise TypeError('Got inappropriate size arg') if random.uniform(0, 1) >\",\n \"self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)), *self._args)[0] else: return image.random_crop(x, *self._args)[0] class\",\n \"ratio between width and height. `mean` means the value in erasing area. Parameters\",\n \"License. # coding: utf-8 # pylint: disable= arguments-differ # pylint: disable= missing-docstring \\\"Addtional\",\n \"else pad def forward(self, x): if self.pad: return image.random_crop(nd.array( np.pad(x.asnumpy(), self.pad, mode='constant', constant_values=0)),\",\n \"s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96, 113.86)): super(RandomErasing, self).__init__() self.probability = probability self.mean =\",\n \"permissions and limitations # under the License. # coding: utf-8 # pylint: disable=\",\n \"self.s_max = s_max self.ratio = ratio def forward(self, x): if not isinstance(self.probability, float):\",\n \"# with the License. You may obtain a copy of the License at\",\n \"(size x size x C) shape. \\\"\\\"\\\" def __init__(self, size, pad=None, interpolation=2): super(RandomCrop,\",\n \"Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4, ratio=0.3, mean=(125.31, 122.96,\",\n \"Apache License, Version 2.0 (the # \\\"License\\\"); you may not use this file\",\n \"and limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ\",\n \"= (size, interpolation) self.pad = ((pad, pad), (pad, pad), (0, 0)) if isinstance(pad,\",\n \"h < height: x1 = random.randint(0, width - w) y1 = random.randint(0, height\",\n \"under one # or more contributor license agreements. See the NOTICE file #\",\n \"Wi x C) shape. Outputs: - **out**: output tensor with (Hi x Wi\",\n \"numeric_types = (float, int, np.generic) if isinstance(size, numeric_types): size = (size, size) self._args\",\n \"# to you under the Apache License, Version 2.0 (the # \\\"License\\\"); you\",\n \"required by applicable law or agreed to in writing, # software distributed under\",\n \"specific language governing permissions and limitations # under the License. # coding: utf-8\",\n \"or int is a shortcut for before = after = pad width for\",\n \"with (Hi x Wi x C) shape. \\\"\\\"\\\" def __init__(self, probability=0.5, s_min=0.02, s_max=0.4,\",\n \"0] = self.mean[0] x[x1:x1+w, y1:y1+h, 1] = self.mean[1] x[x1:x1+w, y1:y1+h, 2] = self.mean[2]\",\n \"image, nd from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly\",\n \"by applicable law or agreed to in writing, # software distributed under the\",\n \"mean self.s_min = s_min self.s_max = s_max self.ratio = ratio def forward(self, x):\",\n \"for additional information # regarding copyright ownership. The ASF licenses this file #\",\n \"int or tuple if int, size of the zero-padding if tuple, number of\",\n \"float): raise TypeError('Got inappropriate size arg') if not isinstance(self.ratio, float): raise TypeError('Got inappropriate\",\n \"governing permissions and limitations # under the License. # coding: utf-8 # pylint:\",\n \"the License for the # specific language governing permissions and limitations # under\",\n \"from mxnet.gluon import Block __all__ = ['RandomCrop', 'RandomErasing'] class RandomCrop(Block): \\\"\\\"\\\"Randomly crop `src`\",\n \"Outputs: - **out**: output tensor with (Hi x Wi x C) shape. \\\"\\\"\\\"\",\n \"applicable law or agreed to in writing, # software distributed under the License\",\n \"if tuple, number of values padded to the edges of each axis. ((before_1,\",\n \"not isinstance(self.ratio, float): raise TypeError('Got inappropriate size arg') if not isinstance(self.mean, (int, tuple)):\",\n \"x C) shape. Outputs: - **out**: output tensor with (Hi x Wi x\",\n \"1) > self.probability: return x width, height, _ = x.shape area = width\"\n]","isConversational":false}}},{"rowIdx":2193,"cells":{"ngram":{"kind":"list like","value":[": {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df =","= pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1)","import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids","wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration '''","record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record","are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df","signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values,","st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals","which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are","import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration","./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record =","= go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) '''","annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}`","annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot","{record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record","pandas as pd from utils import * import glob import wfdb import os","0: st.write('Warning ! No data could be found under the ./data/ directory.', '*\\*.dat*,","= full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal],","count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select","and such should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort()","pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count","pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum()","the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each","Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] =","st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}` :","counts for each annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts')","Plot counts for each annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\","./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be placed ', 'immediately","record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :')","= wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for","= pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count =",": in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for","frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df","annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'],","y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{} (annot)'.format(annot)))","fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot,","record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning !","No data could be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files","annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal","* import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH","annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}`","= [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No","[os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data","record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot:","the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record",".reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations')","for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample,","record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}`","wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx,","# MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')]","the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations","for each annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig","= go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows","pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) '''","streamlit as st import pandas as pd from utils import * import glob","bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal","go import streamlit as st import pandas as pd from utils import *","go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in","such should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id","a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}')","''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values","`{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments",": {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml')","== annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal))","for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency","*\\*.hea*, *\\*.atr* files and such should be placed ', 'immediately under the ./data/","st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here :","'*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be placed ', 'immediately under the","in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs","unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here","file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be","available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df =","signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME]","annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}')","import streamlit as st import pandas as pd from utils import * import","' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal,","non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among","f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for annot in","beat annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'-","followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on","annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are","import pandas as pd from utils import * import glob import wfdb import",".rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count',","signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot in","\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts',","annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers',","axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot =","' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for annot","= pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations '''",":') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the","go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ##","'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\ .value_counts() \\","signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot =","found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` :","import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' #","data could be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and","ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4]","and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for","`{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the","in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with","annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for","this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME)","{record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df,","under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids)","unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat","'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id',","xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a signal',","f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name)","\\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by","annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot","directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be placed ', 'immediately under","glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be found under","record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation =","signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of '","on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for","textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset","contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non","`{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : '","*\\*.atr* files and such should be placed ', 'immediately under the ./data/ directory')","', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record","annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal","and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] ==","the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be placed ',","st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for","= 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for","id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this","''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains","in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in","''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot","for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{}","idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of","with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values,","placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a","full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines',","! No data could be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr*","from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations'","y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore","MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if","st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot","= annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'],","''' ## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) #","'atr') st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'-",": ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\","# Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot]","in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this","beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings","st import pandas as pd from utils import * import glob import wfdb","import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia","DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) ==","= wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal in","with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record :","in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available","= annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations","''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning","could be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such","## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This","= st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr')","st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol,","under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be placed","enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs *","text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items():","be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select","= annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which","matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values,","full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count","{annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') #","import plotly.graph_objects as go import streamlit as st import pandas as pd from","== 0: st.write('Warning ! No data could be found under the ./data/ directory.',","a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot","if len(record_ids) == 0: st.write('Warning ! No data could be found under the","be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should","signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie],","index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count =","in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be found","else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation","columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ##","should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id =","text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full","* record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie","this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]},","are the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More","fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{}","directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}')","' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The","dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations","yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a","annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME],","os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids =","mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{} (annot)'.format(annot))) st.plotly_chart(fig)","annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation","annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations","`{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat","f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations","## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot","st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz')","' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\ .value_counts()","st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}`",":') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a","st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name)","Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record","glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB","full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and","record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in","here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie","Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids)","of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df =","record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}',","\\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )])","bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig)","matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig","= {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig =","= st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {}","as go import streamlit as st import pandas as pd from utils import","found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*, *\\*.atr* files and such should be","non beat annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot:","{} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title(","unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal))))","beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}`","files and such should be placed ', 'immediately under the ./data/ directory') else:","annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto'","Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals","a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found","by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal =",")]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset '''","annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :')","annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations '","as pd from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME","st.write('Warning ! No data could be found under the ./data/ directory.', '*\\*.dat*, *\\*.hea*,","record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with","# Plot counts for each annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME)","for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could","''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in","= annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations","'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file","annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and","as st import pandas as pd from utils import * import glob import","pd from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME =","len(record_ids) == 0: st.write('Warning ! No data could be found under the ./data/",".value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations","each annotation annot_counts_df = annot_serie \\ .value_counts() \\ .rename_axis(ANNOTATIONS_COL_NAME) \\ .reset_index(name='counts') bar_fig =","record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie =","explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts","wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name):","st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and '","annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).')","plotly.graph_objects as go import streamlit as st import pandas as pd from utils","utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' '''","record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df","Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0:","for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations","name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum()"],"string":"[\n \": {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df =\",\n \"= pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1)\",\n \"import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids\",\n \"wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration '''\",\n \"record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record\",\n \"are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df\",\n \"signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values,\",\n \"st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals\",\n \"which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are\",\n \"import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration\",\n \"./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record =\",\n \"= go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) '''\",\n \"annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}`\",\n \"annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot\",\n \"{record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record\",\n \"pandas as pd from utils import * import glob import wfdb import os\",\n \"0: st.write('Warning ! No data could be found under the ./data/ directory.', '*\\\\*.dat*,\",\n \"= full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal],\",\n \"count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select\",\n \"and such should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort()\",\n \"pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count\",\n \"pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum()\",\n \"the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each\",\n \"Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] =\",\n \"st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}` :\",\n \"counts for each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts')\",\n \"Plot counts for each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\\",\n \"./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be placed ', 'immediately\",\n \"record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :')\",\n \"= wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for\",\n \"= pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count =\",\n \": in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for\",\n \"frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df\",\n \"annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'],\",\n \"y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{} (annot)'.format(annot)))\",\n \"fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot,\",\n \"record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning !\",\n \"No data could be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files\",\n \"annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal\",\n \"* import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH\",\n \"annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}`\",\n \"= [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No\",\n \"[os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data\",\n \"record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot:\",\n \"the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record\",\n \".reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations')\",\n \"for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample,\",\n \"record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}`\",\n \"wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx,\",\n \"# MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')]\",\n \"the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations\",\n \"for each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig\",\n \"= go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows\",\n \"pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) '''\",\n \"streamlit as st import pandas as pd from utils import * import glob\",\n \"bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal\",\n \"go import streamlit as st import pandas as pd from utils import *\",\n \"go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in\",\n \"such should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id\",\n \"a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}')\",\n \"''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values\",\n \"`{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments\",\n \": {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml')\",\n \"== annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal))\",\n \"for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency\",\n \"*\\\\*.hea*, *\\\\*.atr* files and such should be placed ', 'immediately under the ./data/\",\n \"st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here :\",\n \"'*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be placed ', 'immediately under the\",\n \"in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs\",\n \"unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here\",\n \"file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be\",\n \"available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df =\",\n \"signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME]\",\n \"annotations are the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}')\",\n \"import streamlit as st import pandas as pd from utils import * import\",\n \"' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal,\",\n \"non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among\",\n \"f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for annot in\",\n \"beat annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot: st.write(f'-\",\n \"followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on\",\n \"annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are\",\n \"import pandas as pd from utils import * import glob import wfdb import\",\n \".rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count',\",\n \"signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot in\",\n \"\\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts',\",\n \"annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers',\",\n \"axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot =\",\n \"' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for annot\",\n \"= pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations '''\",\n \":') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the\",\n \"go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ##\",\n \"'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\\",\n \"signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot =\",\n \"found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` :\",\n \"import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' #\",\n \"data could be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and\",\n \"ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4]\",\n \"and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :') for\",\n \"`{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the\",\n \"in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with\",\n \"annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for\",\n \"this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME)\",\n \"{record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df,\",\n \"under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids)\",\n \"unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat\",\n \"'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record id',\",\n \"xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a signal',\",\n \"f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name)\",\n \"\\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by\",\n \"annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot\",\n \"directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be placed ', 'immediately under\",\n \"glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be found under\",\n \"record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation =\",\n \"signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of '\",\n \"on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for\",\n \"textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset\",\n \"contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non\",\n \"`{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : '\",\n \"*\\\\*.atr* files and such should be placed ', 'immediately under the ./data/ directory')\",\n \"', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a record\",\n \"annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal\",\n \"and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] ==\",\n \"the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be placed ',\",\n \"st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for\",\n \"= 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for\",\n \"id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this\",\n \"''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains\",\n \"in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in\",\n \"''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot\",\n \"for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{}\",\n \"idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of\",\n \"with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values,\",\n \"placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select a\",\n \"full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines',\",\n \"! No data could be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr*\",\n \"from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations'\",\n \"y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore\",\n \"MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if\",\n \"st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot\",\n \"= annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'],\",\n \"''' ## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) #\",\n \"'atr') st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'-\",\n \": ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\\\\",\n \"# Plot signals and annotations matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot]\",\n \"in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this\",\n \"beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings\",\n \"st import pandas as pd from utils import * import glob import wfdb\",\n \"import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia\",\n \"DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) ==\",\n \"= wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal in\",\n \"with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record :\",\n \"in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations are available\",\n \"= annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations\",\n \"''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning\",\n \"could be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such\",\n \"## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This\",\n \"= st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr')\",\n \"st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol,\",\n \"under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be placed\",\n \"enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs *\",\n \"text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items():\",\n \"be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id = st.selectbox('Select\",\n \"= annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which\",\n \"matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal)))) fig.add_trace(go.Scatter(x=full_df.index.values,\",\n \"full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count\",\n \"{annotation_definitions[annot]}') st.write('More explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') #\",\n \"import plotly.graph_objects as go import streamlit as st import pandas as pd from\",\n \"== 0: st.write('Warning ! No data could be found under the ./data/ directory.',\",\n \"a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {} for annot\",\n \"if len(record_ids) == 0: st.write('Warning ! No data could be found under the\",\n \"be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should\",\n \"signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie],\",\n \"index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count =\",\n \"in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could be found\",\n \"else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation\",\n \"columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ##\",\n \"should be placed ', 'immediately under the ./data/ directory') else: record_ids.sort() record_id =\",\n \"text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full\",\n \"* record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie\",\n \"this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]},\",\n \"are the followings :') for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More\",\n \"fig.add_trace(go.Scatter(x=full_df.index.values, y=full_df[signal], mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{}\",\n \"directory') else: record_ids.sort() record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}')\",\n \"' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The\",\n \"dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations\",\n \"yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a\",\n \"annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME],\",\n \"os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids =\",\n \"mode='lines', name=signal)) for annot, annot_matching_rows in matching_rows_by_annot.items(): fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values, y=full_df[annot_matching_rows][signal].values, mode='markers', name='{} (annot)'.format(annot))) st.plotly_chart(fig)\",\n \"annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation\",\n \"annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations\",\n \"`{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat\",\n \"f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations\",\n \"## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot\",\n \"st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of ' f'{record.fs * record.samps_per_frame[idx]}hz')\",\n \"' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie \\\\ .value_counts()\",\n \"st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}`\",\n \":') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with a\",\n \"st.write(bar_fig) ''' ## Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name)\",\n \"Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record\",\n \"glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' ''' # MIT-BIH Arrhythmia DB\",\n \"full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals and\",\n \"record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in\",\n \"here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts for each annotation annot_counts_df = annot_serie\",\n \"Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids)\",\n \"of ' f'{record.fs * record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df =\",\n \"record_id = st.selectbox('Select a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}',\",\n \"\\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )])\",\n \"bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig)\",\n \"matching_rows_by_annot = {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig\",\n \"= {} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig =\",\n \"= st.selectbox('Select a signal', record.sig_name) # Plot signals and annotations matching_rows_by_annot = {}\",\n \"as go import streamlit as st import pandas as pd from utils import\",\n \"found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*, *\\\\*.atr* files and such should be\",\n \"non beat annotation(s).') st.write('The annotations are the followings :') for annot in unique_annot:\",\n \"{} for annot in unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title(\",\n \"unique_annot: matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot fig = go.Figure(layout=go.Layout(title=go.layout.Title( text='{} signal with annotations'.format(signal))))\",\n \"beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}`\",\n \"files and such should be placed ', 'immediately under the ./data/ directory') else:\",\n \"annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto'\",\n \"Explore full dataset ''' signal = st.selectbox('Select a signal', record.sig_name) # Plot signals\",\n \"a record id', record_ids) record = wfdb.rdrecord(f'data/{record_id}') annotation = wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found\",\n \"by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset ''' signal =\",\n \")]) bar_fig.update_layout(title='Annotations by count', yaxis_title='counts', xaxis_title='annotations') st.write(bar_fig) ''' ## Explore full dataset '''\",\n \"annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).') st.write('The annotations are the followings :')\",\n \"annot_serie.isin(dict(beat_annotations)).sum() non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum() unique_annot = annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations '\",\n \"as pd from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME\",\n \"st.write('Warning ! No data could be found under the ./data/ directory.', '*\\\\*.dat*, *\\\\*.hea*,\",\n \"record :') for idx, signal in enumerate(record.sig_name): st.write(f'- `{signal}` : in {record.units[idx]}, with\",\n \"# Plot counts for each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME)\",\n \"for file in glob.glob('data/*.dat')] if len(record_ids) == 0: st.write('Warning ! No data could\",\n \"''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in\",\n \"= annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations\",\n \"'annotations' ''' # MIT-BIH Arrhythmia DB Exploration ''' record_ids = [os.path.basename(file)[:-4] for file\",\n \"annot_serie.value_counts().index.values st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and\",\n \"as st import pandas as pd from utils import * import glob import\",\n \"pd from utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME =\",\n \"len(record_ids) == 0: st.write('Warning ! No data could be found under the ./data/\",\n \".value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME], y=annot_counts_df['counts'], text=annot_counts_df['counts'], textposition='auto' )]) bar_fig.update_layout(title='Annotations\",\n \"each annotation annot_counts_df = annot_serie \\\\ .value_counts() \\\\ .rename_axis(ANNOTATIONS_COL_NAME) \\\\ .reset_index(name='counts') bar_fig =\",\n \"record.samps_per_frame[idx]}hz') st.write(f'Comments for this record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie =\",\n \"explanations on the annotations are available here : ' 'https://archive.physionet.org/physiobank/annotations.shtml') # Plot counts\",\n \"wfdb.rdann(f'data/{record_id}', 'atr') st.write('Signals found in this record :') for idx, signal in enumerate(record.sig_name):\",\n \"st.write(f'This record contains `{annot_serie.size}` annotations ' f'among which `{beat_annot_count}` beat annotations and '\",\n \"annotations ' f'among which `{beat_annot_count}` beat annotations and ' f'`{non_beat_annot_count}` non beat annotation(s).')\",\n \"plotly.graph_objects as go import streamlit as st import pandas as pd from utils\",\n \"utils import * import glob import wfdb import os ANNOTATIONS_COL_NAME = 'annotations' '''\",\n \"record : {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df\",\n \"Exploration ''' record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')] if len(record_ids) == 0:\",\n \"for annot in unique_annot: st.write(f'- `{annot}` : {annotation_definitions[annot]}') st.write('More explanations on the annotations\",\n \"name=ANNOTATIONS_COL_NAME) full_df = pd.concat([signals_df, annot_serie], axis=1) ''' ## Annotations ''' beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum()\"\n]","isConversational":false}}},{"rowIdx":2194,"cells":{"ngram":{"kind":"list like","value":["pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of","current System.CharEnumerator object to the next character of the enumerated string. Returns: true","be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ()","\"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an","see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\" pass def","str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently","Supports iterating over a System.String object and reading its individual characters. This class","\"\"\" Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object.","\"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes","__repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda","cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator()","Creates a copy of the current System.CharEnumerator object. Returns: An System.Object that is","by the current instance of the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\"","the enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object) -> object","a position logically before the first character of the enumerated string. \"\"\" pass","of the current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases","copy of the current System.CharEnumerator object. Returns: An System.Object that is a copy","character in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) ->","System.CharEnumerator object to the next character of the enumerated string. Returns: true if","characters. This class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the","\"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object","the currently referenced character in the string enumerated by this System.CharEnumerator object. Get:","object) -> object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index","copy of the current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator)","Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used by the current instance of","object to the next character of the enumerated string. Returns: true if the","next character of the enumerated string. Returns: true if the index is successfully","enumerated string. Returns: true if the index is successfully incremented and within the","initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes","\"\"\" Reset(self: CharEnumerator) Initializes the index to a position logically before the first","signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def","next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator)","x; see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) ->","signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature","object and reading its individual characters. This class cannot be inherited. \"\"\" def","System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used","def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to a position logically before","of the current System.CharEnumerator object. Returns: An System.Object that is a copy of","an instance of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates","character of the enumerated string. Returns: true if the index is successfully incremented","the current System.CharEnumerator object. Returns: An System.Object that is a copy of the","of the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool","-> object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) ->","next(self: object) -> object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the","None,lambda self: None) \"\"\"Gets the currently referenced character in the string enumerated by","def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance","-> object Creates a copy of the current System.CharEnumerator object. Returns: An System.Object","__iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args):","System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the","the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a copy of","is successfully incremented and within the enumerated string; otherwise,false. \"\"\" pass def next(self,*args):","\"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\"","Increments the internal index of the current System.CharEnumerator object to the next character","resources used by the current instance of the System.CharEnumerator class. \"\"\" pass def","__exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...)","iterating over a System.String object and reading its individual characters. This class cannot","IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object)","System.Object that is a copy of the current System.CharEnumerator object. \"\"\" pass def","ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of","to the next character of the enumerated string. Returns: true if the index","the enumerated string. Returns: true if the index is successfully incremented and within","for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\"","initializes x; see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator)","pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass","pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass","current instance of the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator)","class CharEnumerator(object): \"\"\" Supports iterating over a System.String object and reading its individual","instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) ->","__exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x;","all resources used by the current instance of the System.CharEnumerator class. \"\"\" pass","the next character of the enumerated string. Returns: true if the index is","This class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\"","instance of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a","class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a copy of the","used by the current instance of the System.CharEnumerator class. \"\"\" pass def MoveNext(self):","the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool","pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args):","\"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable)","a System.String object and reading its individual characters. This class cannot be inherited.","the index is successfully incremented and within the enumerated string; otherwise,false. \"\"\" pass","\"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\"","\"\"\" next(self: object) -> object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes","the first character of the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator:","pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently referenced character","x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\"","System.CharEnumerator object. Returns: An System.Object that is a copy of the current System.CharEnumerator","def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\"","and within the enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object)","Dispose(self: CharEnumerator) Releases all resources used by the current instance of the System.CharEnumerator","object. Returns: An System.Object that is a copy of the current System.CharEnumerator object.","\"\"\" Dispose(self: CharEnumerator) Releases all resources used by the current instance of the","Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to a position logically before the","logically before the first character of the enumerated string. \"\"\" pass def __contains__(self,*args):","Returns: An System.Object that is a copy of the current System.CharEnumerator object. \"\"\"","\"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value:","\"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes","instance of the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) ->","__contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) ->","see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object","def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see","signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self:","\"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the","def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used by the current instance","\"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to a position","__repr__(self: object) -> str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None)","of the enumerated string. Returns: true if the index is successfully incremented and","the internal index of the current System.CharEnumerator object to the next character of","true if the index is successfully incremented and within the enumerated string; otherwise,false.","class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self): \"\"\"","pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x;","\"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used by the","\"\"\" Supports iterating over a System.String object and reading its individual characters. This","before the first character of the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\"","\"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently referenced","return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self): \"\"\" Clone(self:","self: None) \"\"\"Gets the currently referenced character in the string enumerated by this","object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently referenced character in the string","IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see","-> bool Increments the internal index of the current System.CharEnumerator object to the","\"\"\"Gets the currently referenced character in the string enumerated by this System.CharEnumerator object.","of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a copy","\"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def","pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda self: object(),lambda","CharEnumerator(object): \"\"\" Supports iterating over a System.String object and reading its individual characters.","Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently referenced character in","the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self):","class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return","object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to a","__enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type:","object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass","-> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass","__iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self:","string. Returns: true if the index is successfully incremented and within the enumerated","in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) -> Char","IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object)","IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object","self,v: None,lambda self: None) \"\"\"Gets the currently referenced character in the string enumerated","bool Increments the internal index of the current System.CharEnumerator object to the next","CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object. Returns: An","object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...)","the index to a position logically before the first character of the enumerated","see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see","position logically before the first character of the enumerated string. \"\"\" pass def","inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns","MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator object","first character of the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value:","string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass","current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources","\"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for","\"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self:","index to a position logically before the first character of the enumerated string.","within the enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object) ->","__enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back:","instance of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\"","x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\" pass def __iter__(self,*args):","CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator object to","otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass def","a copy of the current System.CharEnumerator object. Returns: An System.Object that is a","if the index is successfully incremented and within the enumerated string; otherwise,false. \"\"\"","character of the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char)","System.String object and reading its individual characters. This class cannot be inherited. \"\"\"","its individual characters. This class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance","and reading its individual characters. This class cannot be inherited. \"\"\" def ZZZ(self):","index of the current System.CharEnumerator object to the next character of the enumerated","def next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass def Reset(self): \"\"\" Reset(self:","for signature \"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass","of the current System.CharEnumerator object to the next character of the enumerated string.","object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__","__init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__","object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\" x.__init__(...) initializes x; see x.__class__.__doc__ for","incremented and within the enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self:","\"\"\" pass def next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass def Reset(self):","object) -> str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets","pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used by the current","An System.Object that is a copy of the current System.CharEnumerator object. \"\"\" pass","MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current","self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the currently referenced character in the","current System.CharEnumerator object. Returns: An System.Object that is a copy of the current","None) \"\"\"Gets the currently referenced character in the string enumerated by this System.CharEnumerator","__reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda self:","to a position logically before the first character of the enumerated string. \"\"\"","def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args): \"\"\"","-> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\"","Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\"","for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for","object \"\"\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str","Reset(self: CharEnumerator) Initializes the index to a position logically before the first character","of the class\"\"\" return CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def","individual characters. This class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of","enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\"","x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...)","x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x;","x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\" pass","the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) -> Char \"\"\"","def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args): pass def","the current System.CharEnumerator object to the next character of the enumerated string. Returns:","__contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self:","def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda self: object(),lambda self,v:","Initializes the index to a position logically before the first character of the","pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def __init__(self,*args):","class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal","a copy of the current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self:","the current instance of the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self:","-> object \"\"\" pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to","of the enumerated string. \"\"\" pass def __contains__(self,*args): \"\"\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) ->","over a System.String object and reading its individual characters. This class cannot be","def __reduce_ex__(self,*args): pass def __repr__(self,*args): \"\"\" __repr__(self: object) -> str \"\"\" pass Current=property(lambda","def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the","\"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index","object Creates a copy of the current System.CharEnumerator object. Returns: An System.Object that","CharEnumerator) Initializes the index to a position logically before the first character of","the System.CharEnumerator class. \"\"\" pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments","\"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator","def Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a copy of the current","-> str \"\"\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \"\"\"Gets the","pass def Reset(self): \"\"\" Reset(self: CharEnumerator) Initializes the index to a position logically","def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self:","reading its individual characters. This class cannot be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock","\"\"\" pass def __iter__(self,*args): \"\"\" __iter__(self: IEnumerator) -> object \"\"\" pass def __reduce_ex__(self,*args):","referenced character in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator)","x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__","bool \"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def","is a copy of the current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\"","pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args): \"\"\"","Returns: true if the index is successfully incremented and within the enumerated string;","Releases all resources used by the current instance of the System.CharEnumerator class. \"\"\"","currently referenced character in the string enumerated by this System.CharEnumerator object. Get: Current(self:","\"\"\" pass def __enter__(self,*args): \"\"\" __enter__(self: IDisposable) -> object \"\"\" pass def __exit__(self,*args):","pass def next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass def Reset(self): \"\"\"","index is successfully incremented and within the enumerated string; otherwise,false. \"\"\" pass def","CharEnumerator() instance=ZZZ() \"\"\"hardcoded/returns an instance of the class\"\"\" def Clone(self): \"\"\" Clone(self: CharEnumerator)","the current System.CharEnumerator object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all","internal index of the current System.CharEnumerator object to the next character of the","Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object. Returns:","successfully incremented and within the enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\"","CharEnumerator) Releases all resources used by the current instance of the System.CharEnumerator class.","enumerated string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object) -> object \"\"\"","string; otherwise,false. \"\"\" pass def next(self,*args): \"\"\" next(self: object) -> object \"\"\" pass","Clone(self): \"\"\" Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator","initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \"\"\"","that is a copy of the current System.CharEnumerator object. \"\"\" pass def Dispose(self):","object. \"\"\" pass def Dispose(self): \"\"\" Dispose(self: CharEnumerator) Releases all resources used by","\"\"\" pass def __exit__(self,*args): \"\"\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \"\"\" pass def"],"string":"[\n \"pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of\",\n \"current System.CharEnumerator object to the next character of the enumerated string. Returns: true\",\n \"be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ()\",\n \"\\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an\",\n \"see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def\",\n \"str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently\",\n \"Supports iterating over a System.String object and reading its individual characters. This class\",\n \"\\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object.\",\n \"\\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes\",\n \"__repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda\",\n \"cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator()\",\n \"Creates a copy of the current System.CharEnumerator object. Returns: An System.Object that is\",\n \"by the current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\"\",\n \"the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object\",\n \"a position logically before the first character of the enumerated string. \\\"\\\"\\\" pass\",\n \"of the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases\",\n \"copy of the current System.CharEnumerator object. Returns: An System.Object that is a copy\",\n \"character in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) ->\",\n \"System.CharEnumerator object to the next character of the enumerated string. Returns: true if\",\n \"characters. This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the\",\n \"\\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object\",\n \"the currently referenced character in the string enumerated by this System.CharEnumerator object. Get:\",\n \"object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index\",\n \"copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator)\",\n \"Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by the current instance of\",\n \"object to the next character of the enumerated string. Returns: true if the\",\n \"next character of the enumerated string. Returns: true if the index is successfully\",\n \"enumerated string. Returns: true if the index is successfully incremented and within the\",\n \"initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes\",\n \"\\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a position logically before the first\",\n \"signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def\",\n \"next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator)\",\n \"x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) ->\",\n \"signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature\",\n \"object and reading its individual characters. This class cannot be inherited. \\\"\\\"\\\" def\",\n \"System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used\",\n \"def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a position logically before\",\n \"of the current System.CharEnumerator object. Returns: An System.Object that is a copy of\",\n \"an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates\",\n \"character of the enumerated string. Returns: true if the index is successfully incremented\",\n \"the current System.CharEnumerator object. Returns: An System.Object that is a copy of the\",\n \"of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool\",\n \"-> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) ->\",\n \"next(self: object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the\",\n \"None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character in the string enumerated by\",\n \"def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance\",\n \"-> object Creates a copy of the current System.CharEnumerator object. Returns: An System.Object\",\n \"__iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args):\",\n \"System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the\",\n \"the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy of\",\n \"is successfully incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args):\",\n \"\\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\"\",\n \"Increments the internal index of the current System.CharEnumerator object to the next character\",\n \"resources used by the current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def\",\n \"__exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...)\",\n \"iterating over a System.String object and reading its individual characters. This class cannot\",\n \"IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object)\",\n \"System.Object that is a copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass def\",\n \"ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of\",\n \"to the next character of the enumerated string. Returns: true if the index\",\n \"the enumerated string. Returns: true if the index is successfully incremented and within\",\n \"for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\"\",\n \"initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator)\",\n \"pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass\",\n \"pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass\",\n \"current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator)\",\n \"class CharEnumerator(object): \\\"\\\"\\\" Supports iterating over a System.String object and reading its individual\",\n \"instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) ->\",\n \"__exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x;\",\n \"all resources used by the current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass\",\n \"the next character of the enumerated string. Returns: true if the index is\",\n \"This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\"\",\n \"instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a\",\n \"class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy of the\",\n \"used by the current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self):\",\n \"the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool\",\n \"pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args):\",\n \"\\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable)\",\n \"a System.String object and reading its individual characters. This class cannot be inherited.\",\n \"the index is successfully incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass\",\n \"\\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes\",\n \"the first character of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator:\",\n \"pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character\",\n \"x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\"\",\n \"System.CharEnumerator object. Returns: An System.Object that is a copy of the current System.CharEnumerator\",\n \"def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\"\",\n \"and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object)\",\n \"Dispose(self: CharEnumerator) Releases all resources used by the current instance of the System.CharEnumerator\",\n \"object. Returns: An System.Object that is a copy of the current System.CharEnumerator object.\",\n \"\\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by the current instance of the\",\n \"Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a position logically before the\",\n \"logically before the first character of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args):\",\n \"Returns: An System.Object that is a copy of the current System.CharEnumerator object. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value:\",\n \"\\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes\",\n \"instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) ->\",\n \"__contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) ->\",\n \"see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object\",\n \"def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see\",\n \"signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self:\",\n \"\\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the\",\n \"def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by the current instance\",\n \"\\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a position\",\n \"__repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None)\",\n \"of the enumerated string. Returns: true if the index is successfully incremented and\",\n \"the internal index of the current System.CharEnumerator object to the next character of\",\n \"true if the index is successfully incremented and within the enumerated string; otherwise,false.\",\n \"class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\"\",\n \"pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x;\",\n \"\\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by the\",\n \"\\\"\\\"\\\" Supports iterating over a System.String object and reading its individual characters. This\",\n \"before the first character of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced\",\n \"return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self:\",\n \"self: None) \\\"\\\"\\\"Gets the currently referenced character in the string enumerated by this\",\n \"object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character in the string\",\n \"IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see\",\n \"-> bool Increments the internal index of the current System.CharEnumerator object to the\",\n \"\\\"\\\"\\\"Gets the currently referenced character in the string enumerated by this System.CharEnumerator object.\",\n \"of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy\",\n \"\\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def\",\n \"pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda\",\n \"CharEnumerator(object): \\\"\\\"\\\" Supports iterating over a System.String object and reading its individual characters.\",\n \"Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character in\",\n \"the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self):\",\n \"class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return\",\n \"object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a\",\n \"__enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type:\",\n \"object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass\",\n \"-> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass\",\n \"__iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self:\",\n \"string. Returns: true if the index is successfully incremented and within the enumerated\",\n \"in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) -> Char\",\n \"IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object)\",\n \"IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object\",\n \"self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character in the string enumerated\",\n \"bool Increments the internal index of the current System.CharEnumerator object to the next\",\n \"CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object. Returns: An\",\n \"object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...)\",\n \"the index to a position logically before the first character of the enumerated\",\n \"see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see\",\n \"position logically before the first character of the enumerated string. \\\"\\\"\\\" pass def\",\n \"inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns\",\n \"MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator object\",\n \"first character of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value:\",\n \"string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass\",\n \"current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources\",\n \"\\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for\",\n \"\\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self:\",\n \"index to a position logically before the first character of the enumerated string.\",\n \"within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) ->\",\n \"__enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back:\",\n \"instance of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\"\",\n \"x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass def __iter__(self,*args):\",\n \"CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator object to\",\n \"otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def\",\n \"a copy of the current System.CharEnumerator object. Returns: An System.Object that is a\",\n \"if the index is successfully incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\"\",\n \"character of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char)\",\n \"System.String object and reading its individual characters. This class cannot be inherited. \\\"\\\"\\\"\",\n \"its individual characters. This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance\",\n \"and reading its individual characters. This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self):\",\n \"index of the current System.CharEnumerator object to the next character of the enumerated\",\n \"def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self:\",\n \"for signature \\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass\",\n \"of the current System.CharEnumerator object to the next character of the enumerated string.\",\n \"object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__\",\n \"__init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__\",\n \"object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\" x.__init__(...) initializes x; see x.__class__.__doc__ for\",\n \"incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self:\",\n \"\\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def Reset(self):\",\n \"object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets\",\n \"pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by the current\",\n \"An System.Object that is a copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass\",\n \"MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current\",\n \"self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the currently referenced character in the\",\n \"current System.CharEnumerator object. Returns: An System.Object that is a copy of the current\",\n \"None) \\\"\\\"\\\"Gets the currently referenced character in the string enumerated by this System.CharEnumerator\",\n \"__reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self:\",\n \"to a position logically before the first character of the enumerated string. \\\"\\\"\\\"\",\n \"def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args): \\\"\\\"\\\"\",\n \"-> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\"\",\n \"Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\"\",\n \"for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for\",\n \"object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str\",\n \"Reset(self: CharEnumerator) Initializes the index to a position logically before the first character\",\n \"of the class\\\"\\\"\\\" return CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def\",\n \"individual characters. This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock instance of\",\n \"enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\"\",\n \"x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...)\",\n \"x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x;\",\n \"x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\" pass\",\n \"the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator) -> Char \\\"\\\"\\\"\",\n \"def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args): pass def\",\n \"the current System.CharEnumerator object to the next character of the enumerated string. Returns:\",\n \"__contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) -> bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self:\",\n \"def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v:\",\n \"Initializes the index to a position logically before the first character of the\",\n \"pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def __init__(self,*args):\",\n \"class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal\",\n \"a copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self:\",\n \"the current instance of the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self:\",\n \"-> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to\",\n \"of the enumerated string. \\\"\\\"\\\" pass def __contains__(self,*args): \\\"\\\"\\\" __contains__[Char](enumerator: IEnumerator[Char],value: Char) ->\",\n \"over a System.String object and reading its individual characters. This class cannot be\",\n \"def __reduce_ex__(self,*args): pass def __repr__(self,*args): \\\"\\\"\\\" __repr__(self: object) -> str \\\"\\\"\\\" pass Current=property(lambda\",\n \"def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the\",\n \"\\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal index\",\n \"object Creates a copy of the current System.CharEnumerator object. Returns: An System.Object that\",\n \"CharEnumerator) Initializes the index to a position logically before the first character of\",\n \"the System.CharEnumerator class. \\\"\\\"\\\" pass def MoveNext(self): \\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments\",\n \"\\\"\\\"\\\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of the current System.CharEnumerator\",\n \"def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy of the current\",\n \"-> str \\\"\\\"\\\" pass Current=property(lambda self: object(),lambda self,v: None,lambda self: None) \\\"\\\"\\\"Gets the\",\n \"pass def Reset(self): \\\"\\\"\\\" Reset(self: CharEnumerator) Initializes the index to a position logically\",\n \"def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self:\",\n \"reading its individual characters. This class cannot be inherited. \\\"\\\"\\\" def ZZZ(self): \\\"\\\"\\\"hardcoded/mock\",\n \"\\\"\\\"\\\" pass def __iter__(self,*args): \\\"\\\"\\\" __iter__(self: IEnumerator) -> object \\\"\\\"\\\" pass def __reduce_ex__(self,*args):\",\n \"referenced character in the string enumerated by this System.CharEnumerator object. Get: Current(self: CharEnumerator)\",\n \"x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__\",\n \"bool \\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def\",\n \"is a copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\"\",\n \"pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\"\",\n \"Returns: true if the index is successfully incremented and within the enumerated string;\",\n \"Releases all resources used by the current instance of the System.CharEnumerator class. \\\"\\\"\\\"\",\n \"currently referenced character in the string enumerated by this System.CharEnumerator object. Get: Current(self:\",\n \"\\\"\\\"\\\" pass def __enter__(self,*args): \\\"\\\"\\\" __enter__(self: IDisposable) -> object \\\"\\\"\\\" pass def __exit__(self,*args):\",\n \"pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass def Reset(self): \\\"\\\"\\\"\",\n \"index is successfully incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def\",\n \"CharEnumerator() instance=ZZZ() \\\"\\\"\\\"hardcoded/returns an instance of the class\\\"\\\"\\\" def Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator)\",\n \"the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all\",\n \"internal index of the current System.CharEnumerator object to the next character of the\",\n \"Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator object. Returns:\",\n \"successfully incremented and within the enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\"\",\n \"CharEnumerator) Releases all resources used by the current instance of the System.CharEnumerator class.\",\n \"enumerated string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\"\",\n \"string; otherwise,false. \\\"\\\"\\\" pass def next(self,*args): \\\"\\\"\\\" next(self: object) -> object \\\"\\\"\\\" pass\",\n \"Clone(self): \\\"\\\"\\\" Clone(self: CharEnumerator) -> object Creates a copy of the current System.CharEnumerator\",\n \"initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature \\\"\\\"\\\"\",\n \"that is a copy of the current System.CharEnumerator object. \\\"\\\"\\\" pass def Dispose(self):\",\n \"object. \\\"\\\"\\\" pass def Dispose(self): \\\"\\\"\\\" Dispose(self: CharEnumerator) Releases all resources used by\",\n \"\\\"\\\"\\\" pass def __exit__(self,*args): \\\"\\\"\\\" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) \\\"\\\"\\\" pass def\"\n]","isConversational":false}}},{"rowIdx":2195,"cells":{"ngram":{"kind":"list like","value":["<filename>src/home_automation_hub/config.py from .config_store import ConfigStore config = ConfigStore() config.set_mqtt_broker(\"mqtt\", 1883) config.set_redis_config(\"redis\", 6379, 0)"],"string":"[\n \"<filename>src/home_automation_hub/config.py from .config_store import ConfigStore config = ConfigStore() config.set_mqtt_broker(\\\"mqtt\\\", 1883) config.set_redis_config(\\\"redis\\\", 6379, 0)\"\n]","isConversational":false}}},{"rowIdx":2196,"cells":{"ngram":{"kind":"list like","value":["send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent","#f float float 4 #d double float 8 #s char[] string #p char[]","0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if","from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine","self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print","def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data = analyseMfg.mgf.read()","self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size =","pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent == 0:","= self.sock.send(data) if sent == 0: print \"Connexion lost\" return False return True","receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'': print \"Connexion","return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size:","2 #H unsigned short integer 2 #i int integer 4 #I unsigned int","i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size =","return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format","None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C","of length 1 #b signed char integer 1 #B unsigned char integer 1","%s>\" % (self.status,msg) else: print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return","integer 1 #B unsigned char integer 1 #? _Bool bool 1 #h short","len(recv) + len(self.buffer) #Format C Type Python type Standard size #x pad byte","_size + self.buffer sent = self.sock.send(data) if sent == 0: print \"Connexion lost\"","#l long integer 4 #L unsigned long integer 4 #q long long integer","verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str() if msg","long integer 4 #L unsigned long integer 4 #q long long integer 8","code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock","float 4 #d double float 8 #s char[] string #p char[] string #P","len(types) < len(args): print \"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if","Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port)","create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser","msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" %","no value #c char string of length 1 #b signed char integer 1","import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX)","0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print","def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg","Type Python type Standard size #x pad byte no value #c char string","recv == b'': print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer =","= 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number of args/type\"","print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv)","self.sock.recv(6) if recv == b'': print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv)","b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if","def receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'': print","len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if","long integer 8 #f float float 4 #d double float 8 #s char[]","%d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP)","#Q unsigned long long integer 8 #f float float 4 #d double float","unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not","= b'' recv = self.sock.recv(6) if recv == b'': print \"Connexion lost\" return","def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer","%s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!=","lost\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer)","code = 404 if self.receive() > 0: msg = self._unpack_str() if msg ==","print \"Connexion lost\" return False return True def receive(self): recv = b'' recv","= _size + self.buffer sent = self.sock.send(data) if sent == 0: print \"Connexion","self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1","sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return None return sock","and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg) else: print","if not sock: return False data = analyseMfg.mgf.read() + '\\0' return sock.call(\"i\",HarpeServer.FUNCTION_ID.ANALYSE,\"i%ds\" %","enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole)","type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down","pad byte no value #c char string of length 1 #b signed char","print \"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send()","self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard size #x","while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res","self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if","#c char string of length 1 #b signed char integer 1 #B unsigned","long integer 8 #Q unsigned long long integer 8 #f float float 4","recv = self.sock.recv(6) if recv == b'': print \"Connexion lost\" return None size,self.status","unsigned short integer 2 #i int integer 4 #I unsigned int integer 4","#H unsigned short integer 2 #i int integer 4 #I unsigned int integer","res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer)","def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False","self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res =","Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock","class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG","not sock: return False data = analyseMfg.mgf.read() + '\\0' return sock.call(\"i\",HarpeServer.FUNCTION_ID.ANALYSE,\"i%ds\" % (analyseMfg.mgf.size+1)","0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0:","== b'': print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size)","+=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types)","= len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data)","char[] string #p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args)","occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock:","return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res","csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code","= self.receive() if size: if self.status != 0: print \"recive error code :","string of length 1 #b signed char integer 1 #B unsigned char integer","msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion","self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg) else:","return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser:","% self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser","return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if","None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False","Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0","404 if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and","= \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer =","print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i","self.sock.send(data) if sent == 0: print \"Connexion lost\" return False return True def","= HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion()","integer 1 #? _Bool bool 1 #h short integer 2 #H unsigned short","if self.status != 0: print \"recive error code : %d\" % self.status else:","from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (),","0: print \"recive error code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return","send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data = analyseMfg.mgf.read() +","if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status","\"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) +","\"An error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if","self.buffer sent = self.sock.send(data) if sent == 0: print \"Connexion lost\" return False","(self.status,msg) else: print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return self.status def","unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type","ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if",": %s>\" % (self.status,msg) else: print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear()","% (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0':","#? _Bool bool 1 #h short integer 2 #H unsigned short integer 2","#L unsigned long integer 4 #q long long integer 8 #Q unsigned long","value #c char string of length 1 #b signed char integer 1 #B","= create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\0' return","signed char integer 1 #B unsigned char integer 1 #? _Bool bool 1","Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\"","self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d :","#h short integer 2 #H unsigned short integer 2 #i int integer 4","float 8 #s char[] string #p char[] string #P void * integer def","sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg):","self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status","args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if","# -*- coding: utf-8 -*- import socket as csocket from struct import pack,unpack","#B unsigned char integer 1 #? _Bool bool 1 #h short integer 2","False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\"","\"verify_connexion <%d : %s>\" % (self.status,msg) else: print \"verify_connexion <%d : %s>\" %","not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print","(), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down =","float float 4 #d double float 8 #s char[] string #p char[] string","len(self.buffer) #Format C Type Python type Standard size #x pad byte no value","res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size +","#s char[] string #p char[] string #P void * integer def add(self,typ,*args): self.buffer","struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums)","= 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:]","4 #l long integer 4 #L unsigned long integer 4 #q long long","self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str()","% (self.status,msg) else: print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return self.status","ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return","= ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return None","!= sock.NTW_ERROR_NO: print \"An error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock","unsigned long integer 4 #q long long integer 8 #Q unsigned long long","byte no value #c char string of length 1 #b signed char integer","sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser =","<%d : %s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0","#p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self):","False return True def receive(self): recv = b'' recv = self.sock.recv(6) if recv","types: self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0: print","as csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return","if len(types) < len(args): print \"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id)","int integer 4 #l long integer 4 #L unsigned long integer 4 #q","<%d : %s>\" % (self.status,msg) else: print \"verify_connexion <%d : %s>\" % (self.status,msg)","socket as csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums):","def verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str() if","== 0: print \"Connexion lost\" return False return True def receive(self): recv =","8 #f float float 4 #d double float 8 #s char[] string #p","NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status","short integer 2 #H unsigned short integer 2 #i int integer 4 #I","ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An","return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM)","connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg =","= enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO","long integer 4 #q long long integer 8 #Q unsigned long long integer","return False return True def receive(self): recv = b'' recv = self.sock.recv(6) if","char string of length 1 #b signed char integer 1 #B unsigned char","self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser =","0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return","self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg) else: print \"verify_connexion","enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO =","self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def","return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return",": %s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while","string #p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def","\"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i =","integer 8 #Q unsigned long long integer 8 #f float float 4 #d",": %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock =","self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg) else: print \"verify_connexion <%d :","return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size","b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number","= self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data","error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not","#P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\"","if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return None return sock def","integer 2 #i int integer 4 #I unsigned int integer 4 #l long","if sent == 0: print \"Connexion lost\" return False return True def receive(self):","+ self.buffer sent = self.sock.send(data) if sent == 0: print \"Connexion lost\" return","4 #I unsigned int integer 4 #l long integer 4 #L unsigned long","char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer","else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1]","enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2)","ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error occur\" return None return","call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number of args/type\" return 0 self.clear()","== self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg)","self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data =","return len(recv) + len(self.buffer) #Format C Type Python type Standard size #x pad","self.send() size = self.receive() if size: if self.status != 0: print \"recive error","= csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self):","def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status = 0 def","def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type =","enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__","bool 1 #h short integer 2 #H unsigned short integer 2 #i int","print \"An error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket()","\"Connexion lost\" return False return True def receive(self): recv = b'' recv =","0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0","4 #q long long integer 8 #Q unsigned long long integer 8 #f","4 #d double float 8 #s char[] string #p char[] string #P void","enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM,","print \"recive error code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0","= 404 if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG","* integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status =","1 #? _Bool bool 1 #h short integer 2 #H unsigned short integer","self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status)","sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data =","size = self.receive() if size: if self.status != 0: print \"recive error code","length 1 #b signed char integer 1 #B unsigned char integer 1 #?","int integer 4 #I unsigned int integer 4 #l long integer 4 #L","integer 2 #H unsigned short integer 2 #i int integer 4 #I unsigned","0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number of args/type\" return","utf-8 -*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models import","else: print \"verify_connexion <%d : %s>\" % (self.status,msg) self.clear() return self.status def _unpack_str(self):","import socket as csocket from struct import pack,unpack from website.contrib.communication.models import * def","integer 8 #f float float 4 #d double float 8 #s char[] string","= Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0]","= unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python","= pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent ==","self.buffer = b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code =","integer 4 #l long integer 4 #L unsigned long integer 4 #q long","+ len(self.buffer) #Format C Type Python type Standard size #x pad byte no","integer 4 #I unsigned int integer 4 #l long integer 4 #L unsigned","print \"verify_connexion <%d : %s>\" % (self.status,msg) else: print \"verify_connexion <%d : %s>\"","(self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port))","short integer 2 #i int integer 4 #I unsigned int integer 4 #l","of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive()","HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() !=","Python type Standard size #x pad byte no value #c char string of","recv = b'' recv = self.sock.recv(6) if recv == b'': print \"Connexion lost\"","char integer 1 #B unsigned char integer 1 #? _Bool bool 1 #h","= self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size","< len(args): print \"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types:","!= 0: print \"recive error code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0]","b'': print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return","= self.sock.recv(6) if recv == b'': print \"Connexion lost\" return None size,self.status =","0: print \"Connexion lost\" return False return True def receive(self): recv = b''","lost\" return False return True def receive(self): recv = b'' recv = self.sock.recv(6)","long long integer 8 #f float float 4 #d double float 8 #s","def clear(self): self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) <","-*- coding: utf-8 -*- import socket as csocket from struct import pack,unpack from","self.status != 0: print \"recive error code : %d\" % self.status else: return","website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine =","4 #L unsigned long integer 4 #q long long integer 8 #Q unsigned","self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive()","if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO:","sent == 0: print \"Connexion lost\" return False return True def receive(self): recv","char integer 1 #? _Bool bool 1 #h short integer 2 #H unsigned","#b signed char integer 1 #B unsigned char integer 1 #? _Bool bool","if recv == b'': print \"Connexion lost\" return None size,self.status = unpack('!Ih',recv) self.buffer","self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0: print \"recive","return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data","sock = create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\0'","sock.NTW_ERROR_NO: print \"An error occur\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock =","import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class","__init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def connect(self,host,port):","long long integer 8 #Q unsigned long long integer 8 #f float float","number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size =","sent = self.sock.send(data) if sent == 0: print \"Connexion lost\" return False return","string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer =","pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket:","_unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer","\"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size","2 #i int integer 4 #I unsigned int integer 4 #l long integer","= 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status =","unsigned int integer 4 #l long integer 4 #L unsigned long integer 4","self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status !=","data = _size + self.buffer sent = self.sock.send(data) if sent == 0: print","return True def receive(self): recv = b'' recv = self.sock.recv(6) if recv ==","\"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\"","== self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\" % (self.status,msg) else: print \"verify_connexion <%d","1 #B unsigned char integer 1 #? _Bool bool 1 #h short integer","def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i]","= enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock =","8 #Q unsigned long long integer 8 #f float float 4 #d double","\"recive error code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def","type Standard size #x pad byte no value #c char string of length","#i int integer 4 #I unsigned int integer 4 #l long integer 4","integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status = 0","integer 4 #L unsigned long integer 4 #q long long integer 8 #Q","if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d : %s>\"","NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer","self.receive() if size: if self.status != 0: print \"recive error code : %d\"","'\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self):","i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size","if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0:","= 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() >","self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def","(self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\0': i+=1","#Format C Type Python type Standard size #x pad byte no value #c","> 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO:","_size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent","size: if self.status != 0: print \"recive error code : %d\" % self.status","-*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models import *","size #x pad byte no value #c char string of length 1 #b","1 #h short integer 2 #H unsigned short integer 2 #i int integer","error code : %d\" % self.status else: return unpack(\"!\"+ret_type,self.buffer)[0] return 0 def create_socket():","#q long long integer 8 #Q unsigned long long integer 8 #f float","csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum',","unsigned char integer 1 #? _Bool bool 1 #h short integer 2 #H","* def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type","= self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard size","Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG =","clear(self): self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args):","return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \"An error","void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status","_Bool bool 1 #h short integer 2 #H unsigned short integer 2 #i","unsigned long long integer 8 #f float float 4 #d double float 8","= self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \"verify_connexion <%d","self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status ==","UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0):","i = 0 while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer =","size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type","len(args): print \"Wrong number of args/type\" return 0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args)","C Type Python type Standard size #x pad byte no value #c char","def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number of args/type\" return 0","double float 8 #s char[] string #p char[] string #P void * integer","coding: utf-8 -*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models","self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong number of","= enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \"hello!\\0\" NTW_ERROR_NO = 0 def","#I unsigned int integer 4 #l long integer 4 #L unsigned long integer","True def receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'':","Standard size #x pad byte no value #c char string of length 1","integer 4 #q long long integer 8 #Q unsigned long long integer 8","size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent =","def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\"\" self.status = 0 def","#d double float 8 #s char[] string #p char[] string #P void *","8 #s char[] string #p char[] string #P void * integer def add(self,typ,*args):","if size: if self.status != 0: print \"recive error code : %d\" %","sock: return False data = analyseMfg.mgf.read() + '\\0' return sock.call(\"i\",HarpeServer.FUNCTION_ID.ANALYSE,\"i%ds\" % (analyseMfg.mgf.size+1) ,analyseMfg.pk,data)","0 while self.buffer[i]!= '\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return","#x pad byte no value #c char string of length 1 #b signed","add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args):","= b\"\" self.status = 0 def call(self,ret_type,func_id,types=\"\",*args): if len(types) < len(args): print \"Wrong","create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\0' return sock.call(\"i\",HarpeServer.FUNCTION_ID.ANALYSE,\"i%ds\"","b'' recv = self.sock.recv(6) if recv == b'': print \"Connexion lost\" return None","1 #b signed char integer 1 #B unsigned char integer 1 #? _Bool","self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard","= b\"\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404"],"string":"[\n \"send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent\",\n \"#f float float 4 #d double float 8 #s char[] string #p char[]\",\n \"0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if\",\n \"from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine\",\n \"self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print\",\n \"def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data = analyseMfg.mgf.read()\",\n \"self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size =\",\n \"pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent == 0:\",\n \"= self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\" return False return True\",\n \"receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'': print \\\"Connexion\",\n \"return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size:\",\n \"2 #H unsigned short integer 2 #i int integer 4 #I unsigned int\",\n \"i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size =\",\n \"return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format\",\n \"None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C\",\n \"of length 1 #b signed char integer 1 #B unsigned char integer 1\",\n \"%s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return\",\n \"integer 1 #B unsigned char integer 1 #? _Bool bool 1 #h short\",\n \"len(recv) + len(self.buffer) #Format C Type Python type Standard size #x pad byte\",\n \"_size + self.buffer sent = self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\"\",\n \"#l long integer 4 #L unsigned long integer 4 #q long long integer\",\n \"verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str() if msg\",\n \"long integer 4 #L unsigned long integer 4 #q long long integer 8\",\n \"code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock\",\n \"float 4 #d double float 8 #s char[] string #p char[] string #P\",\n \"len(types) < len(args): print \\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if\",\n \"Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port)\",\n \"create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser\",\n \"msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" %\",\n \"no value #c char string of length 1 #b signed char integer 1\",\n \"import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX)\",\n \"0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print\",\n \"def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg\",\n \"Type Python type Standard size #x pad byte no value #c char string\",\n \"recv == b'': print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer =\",\n \"= 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number of args/type\\\"\",\n \"print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv)\",\n \"self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv)\",\n \"b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if\",\n \"def receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'': print\",\n \"len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if\",\n \"long integer 8 #f float float 4 #d double float 8 #s char[]\",\n \"%d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP)\",\n \"#Q unsigned long long integer 8 #f float float 4 #d double float\",\n \"unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not\",\n \"= b'' recv = self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\" return\",\n \"def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer\",\n \"%s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!=\",\n \"lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer)\",\n \"code = 404 if self.receive() > 0: msg = self._unpack_str() if msg ==\",\n \"print \\\"Connexion lost\\\" return False return True def receive(self): recv = b'' recv\",\n \"= _size + self.buffer sent = self.sock.send(data) if sent == 0: print \\\"Connexion\",\n \"self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1\",\n \"sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return sock\",\n \"and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print\",\n \"if not sock: return False data = analyseMfg.mgf.read() + '\\\\0' return sock.call(\\\"i\\\",HarpeServer.FUNCTION_ID.ANALYSE,\\\"i%ds\\\" %\",\n \"enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole)\",\n \"type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down\",\n \"pad byte no value #c char string of length 1 #b signed char\",\n \"print \\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send()\",\n \"self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard size #x\",\n \"while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res\",\n \"self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if\",\n \"#c char string of length 1 #b signed char integer 1 #B unsigned\",\n \"long integer 8 #Q unsigned long long integer 8 #f float float 4\",\n \"recv = self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\" return None size,self.status\",\n \"unsigned short integer 2 #i int integer 4 #I unsigned int integer 4\",\n \"#H unsigned short integer 2 #i int integer 4 #I unsigned int integer\",\n \"res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer)\",\n \"def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False\",\n \"self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res =\",\n \"Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock\",\n \"class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG\",\n \"not sock: return False data = analyseMfg.mgf.read() + '\\\\0' return sock.call(\\\"i\\\",HarpeServer.FUNCTION_ID.ANALYSE,\\\"i%ds\\\" % (analyseMfg.mgf.size+1)\",\n \"0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0:\",\n \"== b'': print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size)\",\n \"+=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types)\",\n \"= len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data)\",\n \"char[] string #p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args)\",\n \"occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock:\",\n \"return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res\",\n \"csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code\",\n \"= self.receive() if size: if self.status != 0: print \\\"recive error code :\",\n \"string of length 1 #b signed char integer 1 #B unsigned char integer\",\n \"msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion\",\n \"self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else:\",\n \"return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser:\",\n \"% self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser\",\n \"return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if\",\n \"None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False\",\n \"Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0\",\n \"404 if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and\",\n \"= \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer =\",\n \"print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i\",\n \"self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\" return False return True def\",\n \"= HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion()\",\n \"integer 1 #? _Bool bool 1 #h short integer 2 #H unsigned short\",\n \"if self.status != 0: print \\\"recive error code : %d\\\" % self.status else:\",\n \"from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (),\",\n \"0: print \\\"recive error code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return\",\n \"send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data = analyseMfg.mgf.read() +\",\n \"if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status\",\n \"\\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) +\",\n \"\\\"An error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if\",\n \"self.buffer sent = self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\" return False\",\n \"(self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return self.status def\",\n \"unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type\",\n \"ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if\",\n \": %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear()\",\n \"% (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0':\",\n \"#? _Bool bool 1 #h short integer 2 #H unsigned short integer 2\",\n \"#L unsigned long integer 4 #q long long integer 8 #Q unsigned long\",\n \"value #c char string of length 1 #b signed char integer 1 #B\",\n \"= create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\\\0' return\",\n \"signed char integer 1 #B unsigned char integer 1 #? _Bool bool 1\",\n \"Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\"\",\n \"self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d :\",\n \"#h short integer 2 #H unsigned short integer 2 #i int integer 4\",\n \"float 8 #s char[] string #p char[] string #P void * integer def\",\n \"sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg):\",\n \"self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status\",\n \"args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if\",\n \"# -*- coding: utf-8 -*- import socket as csocket from struct import pack,unpack\",\n \"#B unsigned char integer 1 #? _Bool bool 1 #h short integer 2\",\n \"False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\"\",\n \"\\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" %\",\n \"not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print\",\n \"(), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down =\",\n \"float float 4 #d double float 8 #s char[] string #p char[] string\",\n \"len(self.buffer) #Format C Type Python type Standard size #x pad byte no value\",\n \"res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size +\",\n \"#s char[] string #p char[] string #P void * integer def add(self,typ,*args): self.buffer\",\n \"struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums)\",\n \"= 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:]\",\n \"4 #l long integer 4 #L unsigned long integer 4 #q long long\",\n \"self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str()\",\n \"% (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return self.status\",\n \"ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return\",\n \"= ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None\",\n \"!= sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock\",\n \"unsigned long integer 4 #q long long integer 8 #Q unsigned long long\",\n \"byte no value #c char string of length 1 #b signed char integer\",\n \"sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser =\",\n \"<%d : %s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0\",\n \"#p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self):\",\n \"False return True def receive(self): recv = b'' recv = self.sock.recv(6) if recv\",\n \"types: self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0: print\",\n \"as csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return\",\n \"if len(types) < len(args): print \\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id)\",\n \"int integer 4 #l long integer 4 #L unsigned long integer 4 #q\",\n \"<%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg)\",\n \"socket as csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums):\",\n \"def verify_connexion(self): code = 404 if self.receive() > 0: msg = self._unpack_str() if\",\n \"== 0: print \\\"Connexion lost\\\" return False return True def receive(self): recv =\",\n \"8 #f float float 4 #d double float 8 #s char[] string #p\",\n \"NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status\",\n \"short integer 2 #H unsigned short integer 2 #i int integer 4 #I\",\n \"ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An\",\n \"return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM)\",\n \"connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() > 0: msg =\",\n \"= enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO\",\n \"long integer 4 #q long long integer 8 #Q unsigned long long integer\",\n \"return False return True def receive(self): recv = b'' recv = self.sock.recv(6) if\",\n \"char string of length 1 #b signed char integer 1 #B unsigned char\",\n \"self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser =\",\n \"0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return\",\n \"self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion\",\n \"enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO =\",\n \"self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def\",\n \"return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return\",\n \": %s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while\",\n \"string #p char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def\",\n \"\\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self): i =\",\n \"integer 8 #Q unsigned long long integer 8 #f float float 4 #d\",\n \": %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock =\",\n \"self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d :\",\n \"return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size\",\n \"b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number\",\n \"= self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data\",\n \"error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not\",\n \"#P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\"\",\n \"if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return sock def\",\n \"integer 2 #i int integer 4 #I unsigned int integer 4 #l long\",\n \"if sent == 0: print \\\"Connexion lost\\\" return False return True def receive(self):\",\n \"+ self.buffer sent = self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\" return\",\n \"4 #I unsigned int integer 4 #l long integer 4 #L unsigned long\",\n \"char[] string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer\",\n \"else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket(): sock = Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1]\",\n \"enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2)\",\n \"ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return\",\n \"call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number of args/type\\\" return 0 self.clear()\",\n \"== self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg)\",\n \"self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data =\",\n \"return len(recv) + len(self.buffer) #Format C Type Python type Standard size #x pad\",\n \"self.send() size = self.receive() if size: if self.status != 0: print \\\"recive error\",\n \"= csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self):\",\n \"def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status = 0 def\",\n \"def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type =\",\n \"enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__\",\n \"bool 1 #h short integer 2 #H unsigned short integer 2 #i int\",\n \"print \\\"An error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket()\",\n \"\\\"Connexion lost\\\" return False return True def receive(self): recv = b'' recv =\",\n \"0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0\",\n \"4 #q long long integer 8 #Q unsigned long long integer 8 #f\",\n \"4 #d double float 8 #s char[] string #p char[] string #P void\",\n \"enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM,\",\n \"print \\\"recive error code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0\",\n \"= 404 if self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG\",\n \"* integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status =\",\n \"1 #? _Bool bool 1 #h short integer 2 #H unsigned short integer\",\n \"self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status)\",\n \"sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data =\",\n \"size = self.receive() if size: if self.status != 0: print \\\"recive error code\",\n \"length 1 #b signed char integer 1 #B unsigned char integer 1 #?\",\n \"int integer 4 #I unsigned int integer 4 #l long integer 4 #L\",\n \"integer 2 #H unsigned short integer 2 #i int integer 4 #I unsigned\",\n \"0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number of args/type\\\" return\",\n \"utf-8 -*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models import\",\n \"else: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) self.clear() return self.status def _unpack_str(self):\",\n \"import socket as csocket from struct import pack,unpack from website.contrib.communication.models import * def\",\n \"integer 8 #f float float 4 #d double float 8 #s char[] string\",\n \"= Socket(Socket.Dommaine.IP,Socket.Type.TCP) ser = HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0]\",\n \"= unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python\",\n \"= pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent ==\",\n \"self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code =\",\n \"integer 4 #l long integer 4 #L unsigned long integer 4 #q long\",\n \"+ len(self.buffer) #Format C Type Python type Standard size #x pad byte no\",\n \"integer 4 #I unsigned int integer 4 #l long integer 4 #L unsigned\",\n \"print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d : %s>\\\"\",\n \"(self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port))\",\n \"short integer 2 #i int integer 4 #I unsigned int integer 4 #l\",\n \"of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive()\",\n \"HarpeServer.objects.filter(is_active=True)[:1] if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() !=\",\n \"Python type Standard size #x pad byte no value #c char string of\",\n \"recv = b'' recv = self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\"\",\n \"char integer 1 #B unsigned char integer 1 #? _Bool bool 1 #h\",\n \"= self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size = len(self.buffer) _size\",\n \"< len(args): print \\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types:\",\n \"!= 0: print \\\"recive error code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0]\",\n \"b'': print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return\",\n \"= self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\" return None size,self.status =\",\n \"0: print \\\"Connexion lost\\\" return False return True def receive(self): recv = b''\",\n \"lost\\\" return False return True def receive(self): recv = b'' recv = self.sock.recv(6)\",\n \"long long integer 8 #f float float 4 #d double float 8 #s\",\n \"def clear(self): self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) <\",\n \"-*- coding: utf-8 -*- import socket as csocket from struct import pack,unpack from\",\n \"self.status != 0: print \\\"recive error code : %d\\\" % self.status else: return\",\n \"website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine =\",\n \"4 #L unsigned long integer 4 #q long long integer 8 #Q unsigned\",\n \"self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive()\",\n \"if not ser: return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO:\",\n \"sent == 0: print \\\"Connexion lost\\\" return False return True def receive(self): recv\",\n \"char integer 1 #? _Bool bool 1 #h short integer 2 #H unsigned\",\n \"#b signed char integer 1 #B unsigned char integer 1 #? _Bool bool\",\n \"if recv == b'': print \\\"Connexion lost\\\" return None size,self.status = unpack('!Ih',recv) self.buffer\",\n \"self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0: print \\\"recive\",\n \"return sock def send_AnalyseMgf_to_calc(analyseMfg): sock = create_socket() if not sock: return False data\",\n \"sock = create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\\\0'\",\n \"sock.NTW_ERROR_NO: print \\\"An error occur\\\" return None return sock def send_AnalyseMgf_to_calc(analyseMfg): sock =\",\n \"import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class\",\n \"__init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port):\",\n \"long long integer 8 #Q unsigned long long integer 8 #f float float\",\n \"number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size =\",\n \"sent = self.sock.send(data) if sent == 0: print \\\"Connexion lost\\\" return False return\",\n \"string #P void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer =\",\n \"pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum', (), enums) class Socket:\",\n \"_unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer\",\n \"\\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size\",\n \"2 #i int integer 4 #I unsigned int integer 4 #l long integer\",\n \"= 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status =\",\n \"unsigned int integer 4 #l long integer 4 #L unsigned long integer 4\",\n \"self.add(\\\"i\\\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status !=\",\n \"data = _size + self.buffer sent = self.sock.send(data) if sent == 0: print\",\n \"return True def receive(self): recv = b'' recv = self.sock.recv(6) if recv ==\",\n \"\\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\"\",\n \"== self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\" % (self.status,msg) else: print \\\"verify_connexion <%d\",\n \"1 #B unsigned char integer 1 #? _Bool bool 1 #h short integer\",\n \"def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i]\",\n \"= enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock =\",\n \"8 #Q unsigned long long integer 8 #f float float 4 #d double\",\n \"\\\"recive error code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def\",\n \"type Standard size #x pad byte no value #c char string of length\",\n \"#i int integer 4 #I unsigned int integer 4 #l long integer 4\",\n \"integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status = 0\",\n \"integer 4 #L unsigned long integer 4 #q long long integer 8 #Q\",\n \"if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d : %s>\\\"\",\n \"NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer\",\n \"self.receive() if size: if self.status != 0: print \\\"recive error code : %d\\\"\",\n \"'\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self):\",\n \"i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return res def send(self): size\",\n \"if types: self.add(types,*args) self.send() size = self.receive() if size: if self.status != 0:\",\n \"= 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404 if self.receive() >\",\n \"self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def\",\n \"(self.status,msg) self.clear() return self.status def _unpack_str(self): i = 0 while self.buffer[i]!= '\\\\0': i+=1\",\n \"#Format C Type Python type Standard size #x pad byte no value #c\",\n \"> 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO:\",\n \"_size = pack('!Ih',size,self.status) data = _size + self.buffer sent = self.sock.send(data) if sent\",\n \"size: if self.status != 0: print \\\"recive error code : %d\\\" % self.status\",\n \"-*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models import *\",\n \"size #x pad byte no value #c char string of length 1 #b\",\n \"1 #h short integer 2 #H unsigned short integer 2 #i int integer\",\n \"error code : %d\\\" % self.status else: return unpack(\\\"!\\\"+ret_type,self.buffer)[0] return 0 def create_socket():\",\n \"#q long long integer 8 #Q unsigned long long integer 8 #f float\",\n \"csocket from struct import pack,unpack from website.contrib.communication.models import * def enum(**enums): return type('Enum',\",\n \"unsigned char integer 1 #? _Bool bool 1 #h short integer 2 #H\",\n \"* def enum(**enums): return type('Enum', (), enums) class Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type\",\n \"= self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard size\",\n \"Socket: Dommaine = enum(IP=csocket.AF_INET,LOCAL=csocket.AF_UNIX) Type = enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG =\",\n \"clear(self): self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args):\",\n \"return False ser = ser[0] sock.connect(ser.ip,ser.port) if sock.verify_connexion() != sock.NTW_ERROR_NO: print \\\"An error\",\n \"void * integer def add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status\",\n \"_Bool bool 1 #h short integer 2 #H unsigned short integer 2 #i\",\n \"unsigned long long integer 8 #f float float 4 #d double float 8\",\n \"= self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status == self.NTW_ERROR_NO: print \\\"verify_connexion <%d\",\n \"self.receive() > 0: msg = self._unpack_str() if msg == self.NTW_WELCOM_MSG and self.status ==\",\n \"UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def __init__ (self,dommaine,type,protocole=0):\",\n \"i = 0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer =\",\n \"size,self.status = unpack('!Ih',recv) self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type\",\n \"len(args): print \\\"Wrong number of args/type\\\" return 0 self.clear() self.add(\\\"i\\\",func_id) if types: self.add(types,*args)\",\n \"C Type Python type Standard size #x pad byte no value #c char\",\n \"def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number of args/type\\\" return 0\",\n \"double float 8 #s char[] string #p char[] string #P void * integer\",\n \"coding: utf-8 -*- import socket as csocket from struct import pack,unpack from website.contrib.communication.models\",\n \"self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong number of\",\n \"= enum(TCP=csocket.SOCK_STREAM, UDP=csocket.SOCK_DGRAM) Down = enum(SEND=0,RECIVE=1,BOTH=2) NTW_WELCOM_MSG = \\\"hello!\\\\0\\\" NTW_ERROR_NO = 0 def\",\n \"#I unsigned int integer 4 #l long integer 4 #L unsigned long integer\",\n \"True def receive(self): recv = b'' recv = self.sock.recv(6) if recv == b'':\",\n \"Standard size #x pad byte no value #c char string of length 1\",\n \"integer 4 #q long long integer 8 #Q unsigned long long integer 8\",\n \"size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent =\",\n \"def __init__ (self,dommaine,type,protocole=0): self.sock = csocket.socket(dommaine,type,protocole) self.buffer = b\\\"\\\" self.status = 0 def\",\n \"#d double float 8 #s char[] string #p char[] string #P void *\",\n \"8 #s char[] string #p char[] string #P void * integer def add(self,typ,*args):\",\n \"if size: if self.status != 0: print \\\"recive error code : %d\\\" %\",\n \"sock: return False data = analyseMfg.mgf.read() + '\\\\0' return sock.call(\\\"i\\\",HarpeServer.FUNCTION_ID.ANALYSE,\\\"i%ds\\\" % (analyseMfg.mgf.size+1) ,analyseMfg.pk,data)\",\n \"0 while self.buffer[i]!= '\\\\0': i+=1 i+=1 res = self.buffer[:i] self.buffer = self.buffer[i:] return\",\n \"#x pad byte no value #c char string of length 1 #b signed\",\n \"add(self,typ,*args): self.buffer +=pack('!'+typ,*args) def clear(self): self.buffer = b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args):\",\n \"= b\\\"\\\" self.status = 0 def call(self,ret_type,func_id,types=\\\"\\\",*args): if len(types) < len(args): print \\\"Wrong\",\n \"create_socket() if not sock: return False data = analyseMfg.mgf.read() + '\\\\0' return sock.call(\\\"i\\\",HarpeServer.FUNCTION_ID.ANALYSE,\\\"i%ds\\\"\",\n \"b'' recv = self.sock.recv(6) if recv == b'': print \\\"Connexion lost\\\" return None\",\n \"1 #b signed char integer 1 #B unsigned char integer 1 #? _Bool\",\n \"self.buffer = self.sock.recv(size) return len(recv) + len(self.buffer) #Format C Type Python type Standard\",\n \"= b\\\"\\\" self.status = 0 def connect(self,host,port): self.sock.connect((host,port)) def verify_connexion(self): code = 404\"\n]","isConversational":false}}},{"rowIdx":2197,"cells":{"ngram":{"kind":"list like","value":["name name_face = AttrFace(\"name\") else: # If internal node, draws label with smaller","style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified and add misclassified label","ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color =","\"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping,","* (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do","'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for","0, sep = \"\\t\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon,","gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col","else: style['fgcolor'] = 'green' if not n.name == \"N1\": branch_id = n.name +","feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats =","= v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts","= faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf","= 8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf)","= \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values]","%(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\"%s","if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style","faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to","i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the","rf = ete2.CircleFace(radius = 8, style = \"circle\", color = 'red') elif value","= 'green') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color =","= (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)),","h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) /","parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default","= [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels,","each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl","pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format = 1)","matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow","#for visualization of continuous feature get the range of values for each feature","events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf)","pt_tree.traverse(): #ignore the root if n.name == \"N1\": continue if not node_annotation is","= a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats =","= target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event","= 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] =","\"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state","zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0:","binary features for labeling the nodes\") a = parser.parse_args() pt_tree, feats, pf2color =","= get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping,","# Medium Gray # The following don't work well for people with defective","add misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index: tf","i in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for","= node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if","\"\\t\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\",","rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'orange') else: rf","top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event =","pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder'))","- val) / (recon_max - recon_min) v_new = v - (v * (scale_factor))","pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class =","n in pt_tree.traverse(): #ignore the root if n.name == \"N1\": continue if not","sep = \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in","Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, #","i, position = \"branch-top\") for n in pt_tree.traverse(): if n.name in node2name: n.name","= [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for","gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3","ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name","threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype]","\"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if using continuous features","if sample was misclassified and add misclassified label if not miscl is None:","gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf)","labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: #","if value == 0: rf = ete2.CircleFace(radius = 8, style = \"circle\", color","help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample","0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow","for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon","top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss","style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] =","features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain","#if pf_acc: # labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i","if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf","pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node","top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf","] def my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face","= 'green' if not n.name == \"N1\": branch_id = n.name + \"_\" +","# labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc:","Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light","max_feats = 10, miscl = None, node_annotation = None): #read target feats feats","%s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels","size name_face = AttrFace(\"name\", fsize=10) # Adds the name face to the image","# Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish","None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col = 0,","= pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for n in pt_tree.traverse(): #ignore","\"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold","= 8, style = \"circle\", color = 'orange') else: rf = faces.CircleFace(radius =","style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor']","= \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\",","type = int, default = 10, help = \"visualize at most max_feats features\")","a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype,","figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list,","node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False,","people with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish","column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to change in","mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help =","= False ts.show_scale = True ts.force_topology = False # Use my custom layout","layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color","node, draws its name name_face = AttrFace(\"name\") else: # If internal node, draws","if not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf,","style = \"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold:","features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help =","Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange","top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\")","not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i","loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf)","gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]),","call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and names\") parser.add_argument(\"out\",","6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color","Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, #","= 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style)","face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def","= None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation =","= \"circle\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style =","< 0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius =","def get_style(): ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name","ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color =","in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the style","labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\",","threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name == \"N1\":","= a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list =","a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold,","Grayish Yellow 0x817066, # Medium Gray # The following don't work well for","names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use","[\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node is","is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value =","\"branch-right\") #set species name instead of tax id if n.name in sample_mapping.index: node2name[n.name]","state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help","matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon,","= AttrFace(\"name\") else: # If internal node, draws label with smaller font size","0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray #","node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set()","is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0,","probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range of","# Adds the name face to the image at the preferred position faces.add_face_to_node(name_face,","= 0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if","0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8,","as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab","threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else:","== \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help","include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if","ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation,","events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf =","kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] >","i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon =","= \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids","Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800,","node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name =","my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype =","return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc =","and add misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index:","losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf =","of the branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,]","import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75,","target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style())","events ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\")","parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if using continuous features with a","= \"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target =","= 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read node","= 'orange') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color =","% (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index] #labels=","its name name_face = AttrFace(\"name\") else: # If internal node, draws label with","def my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face =","(v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() #","frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend","= 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample","abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0:","style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns","= parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon","argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\")","- recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new])))","nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous","features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set","pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i","> threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features","features with a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold to","style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for","\"table of binary features for labeling the nodes\") a = parser.parse_args() pt_tree, feats,","= 10, miscl = None, node_annotation = None): #read target feats feats =","8, style = \"circle\", color = 'red') elif value == 2: rf =","= \"branch-right\") #set species name instead of tax id if n.name in sample_mapping.index:","pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from","= 'grey') n.add_face(rf, column = i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype]","between sample ids and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type =","val, recon_min, recon_max): \"\"\"set brightness according to change in continuous reconstruction value\"\"\" h,","feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\"%s","[\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels","labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out","= 'preorder')) pfams_with_event = set() pfam2color = {} #set the style of the","with smaller font size name_face = AttrFace(\"name\", fsize=10) # Adds the name face","= 1 - (recon_max - val) / (recon_max - recon_min) v_new = v","[\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i],","ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state reconstruction\")","= a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node,","(recon_max - val) / (recon_max - recon_min) v_new = v - (v *","s, v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf names","Adds the name face to the image at the preferred position faces.add_face_to_node(name_face, node,","Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16,","color = 'orange') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color","recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def","= 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\")","Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00,","help = \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events","misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features for labeling the nodes\")","import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors","'black' n.set_style(style) #check if sample was misclassified and add misclassified label if not","= \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i","column = i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle()","\"output file\") parser.add_argument(\"--max_feats\", type = int, default = 10, help = \"visualize at","= 10, help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table","1]) for i in range(len(labels))] #if pf_acc: # labels = [\"%s %s\" %","a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats, a.out, pf2color)","feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping","= a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold =","= \"circle\", color = 'green') else: rf = faces.CircleFace(radius = 8, style =","Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D,","val) / (recon_max - recon_min) v_new = v - (v * (scale_factor)) return","= my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype","pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\",","0) for n in pt_tree.traverse(): #ignore the root if n.name == \"N1\": continue","hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, #","tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color","= a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl =","[i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if","in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels=","events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\")","the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max","\"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name","not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0)","style = \"circle\", color = 'red') elif value == 2: rf = faces.CircleFace(radius","filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] -","8, style = \"circle\", color = 'green') else: rf = faces.CircleFace(radius = 8,","scale_factor = 1 - (recon_max - val) / (recon_max - recon_min) v_new =","Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very","= False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend =","events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and names\") parser.add_argument(\"out\", help =","'orange') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'green')","# Grayish Yellow 0x817066, # Medium Gray # The following don't work well","0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue","style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] >","state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help","tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None,","= 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep =","#ignore the root if n.name == \"N1\": continue if not node_annotation is None:","are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m","position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to change in continuous","= \"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is","elif value == 2: rf = faces.CircleFace(radius = 8, style = \"circle\", color","\"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\")","the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the","# Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262,","miscl is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for","faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]]","= \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features for","AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex =","style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name == \"N1\": branch_id","adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id,","= faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style =","= 'store_true', help = \"set if using continuous features with a discrete phenotype\")","target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats","8, style = \"circle\", color = 'grey') n.add_face(rf, column = i, position =","to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get","0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange","i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print","numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out +","Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong","target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree =","#filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node","target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\",","== 0: rf = ete2.CircleFace(radius = 8, style = \"circle\", color = 'red')","loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype","gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep =","if not node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns,","%s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale =","get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node =","def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list =","The following don't work well for people with defective color vision 0x007D34, #","= i, position = \"branch-top\") for n in pt_tree.traverse(): if n.name in node2name:","parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list","pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i","1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color","n.add_face(tf, column = 0, position = \"branch-right\") #set species name instead of tax","v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style():","defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A,","target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat","pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf","\"\\t\", index_col = 0) for n in pt_tree.traverse(): #ignore the root if n.name","pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column =","node annotation return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import argparse parser","= a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node =","values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if","for labeling the nodes\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon =","recon_max): \"\"\"set brightness according to change in continuous reconstruction value\"\"\" h, s, v","pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read node and edge reconstruction matrices","for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if","= 8, style = \"circle\", color = 'green') else: rf = faces.CircleFace(radius =","feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target","#add majority feature gains and losses events = [] for i in range(top10_feats.shape[0]):","= kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf","pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return","according to change in continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor","feats, pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node","markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out +","if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None:","to change in continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor =","0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid","for n in pt_tree.traverse(): #ignore the root if n.name == \"N1\": continue if","\"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] =","#if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))]","0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target node","= argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state","'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if","%(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:,","[\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col","are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree,","1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] =","rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'grey') n.add_face(rf, column","position = \"branch-right\") #set species name instead of tax id if n.name in","10, help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of","faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\") #set species name instead of","node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep","parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help = \"target","node, draws label with smaller font size name_face = AttrFace(\"name\", fsize=10) # Adds","posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range","[\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: #","file\") parser.add_argument(\"--max_feats\", type = int, default = 10, help = \"visualize at most","Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315,","target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style","is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for","i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] =","Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If","n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams","of continuous feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype:","Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A,","gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep","\"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune","vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong","discrete phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold to call genotype/phenotype events\")","def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class = False):","sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read node and edge","reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon =","= node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids","#target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out,","continue if not node_annotation is None: if n.name in node_table.index: for attr,i in","value == 0: rf = ete2.CircleFace(radius = 8, style = \"circle\", color =","in range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\") for n in pt_tree.traverse():","#tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else:","Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, #","tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc =","None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for n in","style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] =","events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf =","annotation return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import argparse parser =","help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral","i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index","i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {}","argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral","in pt_tree.traverse(): #ignore the root if n.name == \"N1\": continue if not node_annotation","get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree","\"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help =","- (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle()","pf_desc = False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend","= False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def","recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m =","= faces.CircleFace(radius = 8, style = \"circle\", color = 'grey') n.add_face(rf, column =","= adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i],","in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False)","from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color,","pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style = \"circle\",","node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns):","def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node","< threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name ==","0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep","i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1","= faces.CircleFace(radius = 8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]","leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False #","'green' if not n.name == \"N1\": branch_id = n.name + \"_\" + n.up.name","= \"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]]","phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype","pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints =","n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event),","def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to change in continuous reconstruction","list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help","True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6))","8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf)","node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids =","filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__","[0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i","name face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\")","style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if","events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]])","help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if using","= 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1","feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is","color = 'green') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color","False, max_feats = 10, miscl = None, node_annotation = None): #read target feats","gains and losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype:","= \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if using continuous","Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, #","Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow","= \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal","help = \"mapping between sample ids and names\") parser.add_argument(\"out\", help = \"output file\")","[] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8,","= TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale","[\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale","= ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table =","nodes\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon =","column = 0, position = \"branch-right\") #set species name instead of tax id","faces.CircleFace(radius = 8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i])","using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help =","of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help =","image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min,","x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0]","\"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character","target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True,","parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if","features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]])","node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to change","\"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help","\"mapping between sample ids and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type","= sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for i","style = \"circle\", color = 'green') else: rf = faces.CircleFace(radius = 8, style","Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, #","v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts =","not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column","kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf =","to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color,","target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name)","# Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink","n.set_style(style) #check if sample was misclassified and add misclassified label if not miscl","# Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C,","most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help =","internal node, draws label with smaller font size name_face = AttrFace(\"name\", fsize=10) #","= gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl,","reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature","following don't work well for people with defective color vision 0x007D34, # Vivid","0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following don't work","ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\",","#set the style of the branches and nodes according to the posterior probability","Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium","Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf():","= a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype =","figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout()","Gray # The following don't work well for people with defective color vision","pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node =","Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851,","smaller font size name_face = AttrFace(\"name\", fsize=10) # Adds the name face to","ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10","ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif","#set species name instead of tax id if n.name in sample_mapping.index: node2name[n.name] =","pf_desc: # labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in","draws label with smaller font size name_face = AttrFace(\"name\", fsize=10) # Adds the","visualization of continuous feature get the range of values for each feature if","in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids","rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800,","value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val)","edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon","value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf =","= pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy =","if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else:","Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out):","% (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5,","for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams =","return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize","parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character","index_col = 0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize()","= 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon,","= True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0]","miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column =","fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" %","\"\"\"set brightness according to change in continuous reconstruction value\"\"\" h, s, v =","#read target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree","n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda","else: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'green') else:","with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink","help = \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of","Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws its","# Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The","help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features","# If terminal node, draws its name name_face = AttrFace(\"name\") else: # If","rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'green') else: rf","max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table","not node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))):","sep = \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col","n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if","'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check","\"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] =","in range(len(labels))] #if pf_desc: # labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1])","None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position","Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, #","in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index =","{} #set the style of the branches and nodes according to the posterior","= kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]]","AttrFace(\"name\", fsize=10) # Adds the name face to the image at the preferred","adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] <","= \"branch-top\") for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name]","list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event","a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping,","and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col = 0)","reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max -","draws its name name_face = AttrFace(\"name\") else: # If internal node, draws label","for i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i])","feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\"%s %s\"","feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping","pandas as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import","None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \"\\t\")","= rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max - recon_min)","'preorder')) pfams_with_event = set() pfam2color = {} #set the style of the branches","get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min)","style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0","\"branch-top\") for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams","threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif","pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table","n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events","%s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels =","the style of the branches and nodes according to the posterior probability top10_feats","0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out","species name instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0]","sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10,","= get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def","parser.add_argument(\"threshold\", type = float, help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help","Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green","node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius =","add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False","= [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node","the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val,","(scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not","sample ids and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type = int,","pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, #","'darkred' else: style['fgcolor'] = 'green' if not n.name == \"N1\": branch_id = n.name","= pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for","events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]","= 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"]","ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use my custom","#loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\")","(9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o',","pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping =","tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id,","If terminal node, draws its name name_face = AttrFace(\"name\") else: # If internal","parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\")","= [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))]","filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process","format = 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep","help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default =","node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping","= gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\",","'_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc","#fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree,","color = 'red') elif value == 2: rf = faces.CircleFace(radius = 8, style","# Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, #","return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not add","miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\") #set species","#process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import argparse","\"N1\": continue if not node_annotation is None: if n.name in node_table.index: for attr,i","= \"output file\") parser.add_argument(\"--max_feats\", type = int, default = 10, help = \"visualize","out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat =","= fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\"","# Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish","pfam2color if __name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of","Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown","== \"N1\": branch_id = n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] >","Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, #","misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index: tf =","set() pfam2color = {} #set the style of the branches and nodes according","phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor']","of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature","pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position =","loss_recon.index.values] #prune to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name","a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree,","get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats,","are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \"circle\", color = kelly_colors_hex[i]) #gain","range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max =","> threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"]","events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state","attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8,","TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale =","in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values)","pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style","terminal node, draws its name name_face = AttrFace(\"name\") else: # If internal node,","color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, #","Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws","= ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey'","loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target node if","= a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation =","\"circle\", color = 'green') else: rf = faces.CircleFace(radius = 8, style = \"circle\",","kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i],","+ \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping,","None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation = None):","if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col =","ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False","recon_min, recon_max): \"\"\"set brightness according to change in continuous reconstruction value\"\"\" h, s,","at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help","with a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold to call","not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style =","root if n.name == \"N1\": continue if not node_annotation is None: if n.name","state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help =","target node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name","miscl = None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col","\"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\",","Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, #","loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf =","in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \"circle\",","import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300,","a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node,","pf_acc: # labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in","= pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png',","labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines,","my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree,","get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc","pf2color, pf_desc = False, pf_acc = True, include_class = False): fig = pylab.figure()","= False # Use my custom layout ts.layout_fn = my_layout return ts def","= pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col =","#filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams,","id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and","phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else:","\"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes","miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for n in pt_tree.traverse():","threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl","(pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\"%s\"","= float, help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping","events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\") for","color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i","else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style = \"circle\", color","= \"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true',","tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\") #set species name","font size name_face = AttrFace(\"name\", fsize=10) # Adds the name face to the","internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of","from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid","character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help","ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import","0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish","tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold:","label if not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\")","\"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0)","0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal","'grey') n.add_face(rf, column = i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style","of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features for labeling the","instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority","faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style = \"circle\",","== 2: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'orange')","node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\"name\") else: #","i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node is not","sep = \"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation","= faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]]","get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist","= phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist =","are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation = None): #read","faces.CircleFace(radius = 8, style = \"circle\", color = 'green') else: rf = faces.CircleFace(radius","1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1,","= faces.CircleFace(radius = 8, style = \"circle\", color = 'green') else: rf =","sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = []","type = float, help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help =","Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following","tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold","0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node):","Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green","labels= [i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])]","0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None,","elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not","faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf =","feature gains and losses events = [] for i in range(top10_feats.shape[0]): if not","action = 'store_true', help = \"set if using continuous features with a discrete","if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col =","0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not","% (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels =","help = \"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action =","v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf names automatically","and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of","#top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ ==","a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats,","0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified and add misclassified","ts.show_scale = True ts.force_topology = False # Use my custom layout ts.layout_fn =","= \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col =","plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\",","rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf","= filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0]","= False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax =","target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return","Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws its name","2: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'orange') else:","sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for i in","of binary features for labeling the nodes\") a = parser.parse_args() pt_tree, feats, pf2color","range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \"circle\", color","False # Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree,","= \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep =","recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\")","= 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style())","automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use my","= 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified and add","not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \"circle\", color = kelly_colors_hex[i])","pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style =","= 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index =","majority feature gains and losses events = [] for i in range(top10_feats.shape[0]): if","pfam2color = {} #set the style of the branches and nodes according to","a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out)","= \"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size']","else: # If internal node, draws label with smaller font size name_face =","= 8, style = \"circle\", color = 'grey') n.add_face(rf, column = i, position","n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"]","continuous feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min","= [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid","top10_feats, pfam2color if __name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list","tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style","top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import","0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid","= None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col =","help = \"output file\") parser.add_argument(\"--max_feats\", type = int, default = 10, help =","0x817066, # Medium Gray # The following don't work well for people with","\"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon","+ \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon,","'_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats,","pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0,","range(len(labels))] #if pf_desc: # labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for","loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1]))","in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value ==","\"circle\", color = 'grey') n.add_face(rf, column = i, position = \"aligned\") ns =","rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong","preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness","i in loss_recon.index.values] #prune to target node if target_node is not None: pt_tree","parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree","pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i","__name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\",","gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype","'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"]","elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] =","= None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep =","color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]","figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5,","= pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format =","= set() pfam2color = {} #set the style of the branches and nodes","style of the branches and nodes according to the posterior probability top10_feats =","# Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive","\"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help","# Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish","labeling the nodes\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon,","ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout return ts","# Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red","= \"\\t\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep =","value == 2: rf = faces.CircleFace(radius = 8, style = \"circle\", color =","for i in loss_recon.index.values] #prune to target node if target_node is not None:","# Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish","+ '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color,","0: rf = ete2.CircleFace(radius = 8, style = \"circle\", color = 'red') elif","'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold:","= 0, position = \"branch-right\") #set species name instead of tax id if","= \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in","None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping,","10, miscl = None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list,","index_col = 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to","sep = \"\\t\", index_col = 0) for n in pt_tree.traverse(): #ignore the root","n.name == \"N1\": branch_id = n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype]","Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex","0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7,","style = \"circle\", color = 'grey') n.add_face(rf, column = i, position = \"aligned\")","gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list","\"class\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\"%s %s\" %","a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list,","well for people with defective color vision 0x007D34, # Vivid Green 0xF6768E, #","in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i","pf_acc = True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize =","= [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc:","index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index","8, style = \"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] >","\"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\",","= a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping =","pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x,","= pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation","False ts.show_scale = True ts.force_topology = False # Use my custom layout ts.layout_fn","[ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange","= 0) loss_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in loss_recon.index.values] #prune to target","0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet","lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in","style['fgcolor'] = 'green' if not n.name == \"N1\": branch_id = n.name + \"_\"","n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1","index_col = 0, sep = \"\\t\") #read node and edge reconstruction matrices node_recon","help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified","index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read","= 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold:","if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses","of features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help","the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set","True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out","#pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True,","i in range(len(labels))] #if pf_acc: # labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i],","for i in range(len(labels))] #if pf_desc: # labels = [\"%s %s\" % (labels[i],","= a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree =","target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event =","position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"] = 'square'","\"set if using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type = float,","node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\")","None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name,","target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out +","sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation","= True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9,","ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list","i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style =","figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon,","Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13,","return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype,","the branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for","node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col","return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold =","= 'red') elif value == 2: rf = faces.CircleFace(radius = 8, style =","\"circle\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style = \"circle\",","# Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish","s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max","if using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help","work well for people with defective color vision 0x007D34, # Vivid Green 0xF6768E,","# Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066,","target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation","branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization","= [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\",","for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not","#gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\")","range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\") for n in pt_tree.traverse(): if","\"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action","pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for n in pt_tree.traverse(): #ignore the","custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats,","= faces.CircleFace(radius = 8, style = \"circle\", color = 'orange') else: rf =","= 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns <","phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0","phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif","if __name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\")","Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ]","for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col = 0)","sep = \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in","= n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] =","a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl,","help = \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\",","= a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats, a.out,","import argparse parser = argparse.ArgumentParser(\"\"\"visualize target list of features\"\"\") parser.add_argument(\"node_recon\", help = \"node","= 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\")","= {} #set the style of the branches and nodes according to the","= \"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help","= False, max_feats = 10, miscl = None, node_annotation = None): #read target","# Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020,","v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max -","fsize=10) # Adds the name face to the image at the preferred position","continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max","Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, #","= \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values]","Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, #","0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read node and","= \"\\t\", index_col = 0) for n in pt_tree.traverse(): #ignore the root if","None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in","#if pf_desc: # labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i","a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation)","range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show()","pt_tree, top10_feats, pfam2color if __name__ == \"__main__\": import argparse parser = argparse.ArgumentParser(\"\"\"visualize target","for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set","figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines","#check if sample was misclassified and add misclassified label if not miscl is","= pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep =","1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"]","= faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\") #set species name instead","Yellow 0x817066, # Medium Gray # The following don't work well for people","i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \"\\t\", index_col =","= \"mapping between sample ids and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\",","top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous","import pandas as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle","range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i in","= 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl =","AttrFace(\"name\") else: # If internal node, draws label with smaller font size name_face","Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray","else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified","# Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow","with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list","# labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))]","1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return","if n.name == \"N1\": continue if not node_annotation is None: if n.name in","threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] =","phenotype\") parser.add_argument(\"--are_continuous_features_with_discrete_phenotype\", action = 'store_true', help = \"set if using continuous features with","= \"circle\", color = 'grey') n.add_face(rf, column = i, position = \"aligned\") ns","node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value","a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon,","style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] =","Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid","= i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\"shape\"]","name instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add","else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if","color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)):","0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish","= [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius =","get_style(): ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name =","False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111)","fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype, tree, gain_recon,","i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: #","= AttrFace(\"name\", fsize=10) # Adds the name face to the image at the","node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for","and losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf","to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and names\")","target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out +","loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3","and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type = int, default =","parser.add_argument(\"--node_annotation\", help = \"table of binary features for labeling the nodes\") a =","(recon_max - recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s,","include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax","reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help =","tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style = \"circle\", color =","= top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \"__main__\":","for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class:","# Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node,","\"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and","faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] =","parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss","sep = \"\\t\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep","#print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if","don't work well for people with defective color vision 0x007D34, # Vivid Green","= 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green'","not n.name == \"N1\": branch_id = n.name + \"_\" + n.up.name if gain_recon.loc[branch_id,","if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i])","0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark","#continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id,","of features\"\"\") parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help =","adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column","= kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color =","position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according","plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class = False): fig","Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, #","Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology","names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type = int, default = 10,","genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and names\") parser.add_argument(\"out\", help","= 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red'","tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains","= 'darkred' else: style['fgcolor'] = 'green' if not n.name == \"N1\": branch_id =","0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red","of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max)","the root if n.name == \"N1\": continue if not node_annotation is None: if","in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events =","== \"N1\": continue if not node_annotation is None: if n.name in node_table.index: for","8, style = \"circle\", color = 'orange') else: rf = faces.CircleFace(radius = 8,","# Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00,","pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels = [\"%s %s\"","2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out","in continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 -","= \"set if using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type =","help = \"table of binary features for labeling the nodes\") a = parser.parse_args()","float, help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\", help = \"mapping between","parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type = int, default = 10, help","(labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels = [\"%s","0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong","# If internal node, draws label with smaller font size name_face = AttrFace(\"name\",","True ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout return","according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature","'green') else: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'grey')","in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not","faces.CircleFace(radius = 8, style = \"circle\", color = 'orange') else: rf = faces.CircleFace(radius","If internal node, draws label with smaller font size name_face = AttrFace(\"name\", fsize=10)","0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree,","\"circle\", color = 'red') elif value == 2: rf = faces.CircleFace(radius = 8,","top10_feats.index[i]] < 0: tf = faces.TextFace(\"-\") else: tf = faces.TextFace(\"+\") cf = faces.CircleFace(radius","feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values for each","= \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\",","a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype,","pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats","gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1]))","1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\",","n.add_face(events[i], column = i, position = \"branch-top\") for n in pt_tree.traverse(): if n.name","i in range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\") for n in","if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] =","(labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints","Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, #","10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred'","Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def","continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold","Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following don't","= \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character","top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values","parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary","parser.add_argument(\"--max_feats\", type = int, default = 10, help = \"visualize at most max_feats","TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [","help = \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help = \"tree with","i in range(len(labels))] #if pf_desc: # labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i],","features for labeling the nodes\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon","style = ete2.NodeStyle() style[\"shape\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] =","False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize","0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep","= dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color","= 'black' n.set_style(style) #check if sample was misclassified and add misclassified label if","elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i])","character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\",","if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position =","cf = faces.CircleFace(radius = 8, style = \"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] =","= \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral","= node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius","attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value","hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple","\"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] =","3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was","\"circle\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in","max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats,","faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv","# Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True","character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state reconstruction\") parser.add_argument(\"loss_recon\",","Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish","/ (recon_max - recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h,","for i in range(len(labels))] #if pf_acc: # labels = [\"%s %s\" % (labels[i],","3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"]","= adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]]","node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value):","sample was misclassified and add misclassified label if not miscl is None: if","the name face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0,","= pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines =","int, default = 10, help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help","= kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i,","kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position","my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\"name\")","feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min =","pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color","not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology =","ete2.CircleFace(radius = 8, style = \"circle\", color = 'red') elif value == 2:","\"tree with internal nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help =","= pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x =","pfams_with_event = set() pfam2color = {} #set the style of the branches and","\"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index]","= faces.CircleFace(radius = 8, style = \"circle\", color = kelly_colors_hex[i]) #gain events if","= get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False,","events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius","parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon =","phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold to call genotype/phenotype events\") parser.add_argument(\"sample_mapping\",","feats feats = pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree = ete2.Tree(tree,","for i in range(len(events)): n.add_face(events[i], column = i, position = \"branch-top\") for n","help = \"set if using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\", type","False) #fig.show() fig.tight_layout() figlegend.savefig(out + \"_legend.svg\") figlegend.savefig(out + \"_legend.png\") return figlegend def get_tree(phenotype,","= faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]),","feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class","at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\"branch-right\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max):","for i in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i])","pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor']","0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue","faces.CircleFace(radius = 8, style = \"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id,","pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col","brightness according to change in continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color)))","- (recon_max - val) / (recon_max - recon_min) v_new = v - (v","= target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style =","import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb,","was misclassified and add misclassified label if not miscl is None: if node2name[n.name]","features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of","in loss_recon.index.values] #prune to target node if target_node is not None: pt_tree =","is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name =","\"\\t\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None:","Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong","out, pf2color, pf_desc = False, pf_acc = True, include_class = False): fig =","is None: node_table = pd.read_csv(node_annotation, sep = \"\\t\", index_col = 0) sample_mapping =","dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color =","nodes labeled\") parser.add_argument(\"pfam_mapping\", help = \"feature mapping/list\") parser.add_argument(\"feat_list\", help = \"list of features\")","range(len(labels))] #if pf_acc: # labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for","- 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats,","= \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help = \"table of misclassified samples\")","the nodes\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon","rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max - recon_min) v_new","threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl","name_face = AttrFace(\"name\", fsize=10) # Adds the name face to the image at","misclassified and add misclassified label if not miscl is None: if node2name[n.name] in","= int, default = 10, help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\",","node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col =","if not n.name == \"N1\": branch_id = n.name + \"_\" + n.up.name if","# labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))]","index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for i in gain_recon.index.values] loss_recon =","cf = faces.CircleFace(radius = 8, style = \"circle\", color = kelly_colors_hex[i]) #gain events","faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]],","'store_true', help = \"set if using continuous features with a discrete phenotype\") parser.add_argument(\"threshold\",","color = 'grey') n.add_face(rf, column = i, position = \"aligned\") ns = node_recon.loc[n.name,","kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, #","ids and names\") parser.add_argument(\"out\", help = \"output file\") parser.add_argument(\"--max_feats\", type = int, default","Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong","parser.add_argument(\"node_recon\", help = \"node ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events","#labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\"","= 0) for n in pt_tree.traverse(): #ignore the root if n.name == \"N1\":","pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1]","feats = pd.read_csv(feat_list, index_col = 0, sep = \"\\t\") pt_tree = ete2.Tree(tree, format","faces.CircleFace(radius = 8, style = \"circle\", color = 'grey') n.add_face(rf, column = i,","position = \"branch-top\") for n in pt_tree.traverse(): if n.name in node2name: n.name =","events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf)","reconstruction\") parser.add_argument(\"loss_recon\", help = \"loss events ancestral character state reconstruction\") parser.add_argument(\"tree\", help =","in range(feats.shape[0])] #if include_class: # labels= [\"%s %s\" %(labels[i], feats.loc[:, \"class\"].iloc[i]) for i","+ n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green'","name_face = AttrFace(\"name\") else: # If internal node, draws label with smaller font","samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features for labeling the nodes\") a","0) gain_recon = pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]),","adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \"\"\"set brightness according to change in continuous reconstruction value\"\"\"","= 8, style = \"circle\", color = 'red') elif value == 2: rf","index_col = 0) for n in pt_tree.traverse(): #ignore the root if n.name ==","= pd.read_csv(gain_recon, sep = \"\\t\", index_col = 0) gain_recon.index = [\"_\".join((\"_\".join(i.split(\"_\")[:-1]), i.split(\"_\")[-1])) for","n.add_face(rf, column = i, position = \"aligned\") ns = node_recon.loc[n.name, phenotype] style =","= get_tree(phenotype = phenotype, feat_list = \"top_cor\", is_ml_plus_phypat = True, target_node = target_node)","for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style","sep = \"\\t\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep","# Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if","branch_id = n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"]","pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf","ancestral character state reconstruction\") parser.add_argument(\"gain_recon\", help = \"gain events ancestral character state reconstruction\")","node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep","#prune to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name =","if node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\"name\") else:","'red') elif value == 2: rf = faces.CircleFace(radius = 8, style = \"circle\",","change in continuous reconstruction value\"\"\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1","label with smaller font size name_face = AttrFace(\"name\", fsize=10) # Adds the name","\"table of misclassified samples\") parser.add_argument(\"--node_annotation\", help = \"table of binary features for labeling","for people with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong","= target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf',","0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid","labels = [\"%s %s\" % (labels[i], pf2short_desc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1]) for i in range(len(labels))] #if","n.name == \"N1\": continue if not node_annotation is None: if n.name in node_table.index:","else: rf = faces.CircleFace(radius = 8, style = \"circle\", color = 'grey') n.add_face(rf,","+ \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"]","Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown","= True ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout","1 - (recon_max - val) / (recon_max - recon_min) v_new = v -","style[\"hz_line_color\"] = 'red' style[\"hz_line_width\"] = 3 else: style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black'","i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for","= feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values for","loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype =","is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col = 0) for n","parser.add_argument(\"sample_mapping\", help = \"mapping between sample ids and names\") parser.add_argument(\"out\", help = \"output","#read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \"\\t\", index_col","ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex,","if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr]","in miscl_m.index: tf = faces.TextFace(\"misclassified\") n.add_face(tf, column = 0, position = \"branch-right\") #set","default = 10, help = \"visualize at most max_feats features\") parser.add_argument(\"--miscl\", help =","Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): #","= ete2.CircleFace(radius = 8, style = \"circle\", color = 'red') elif value ==","= \"list of features\") parser.add_argument(\"--target_node\", default = \"N1\", help = \"list of features\")","\"N1\": branch_id = n.name + \"_\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold:","in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i:","# The following don't work well for people with defective color vision 0x007D34,","Medium Gray # The following don't work well for people with defective color","> threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events","recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf =","#print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,]","0, position = \"branch-right\") #set species name instead of tax id if n.name","a discrete phenotype\") parser.add_argument(\"threshold\", type = float, help = \"threshold to call genotype/phenotype","# Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node,","in range(len(labels))] #if pf_acc: # labels = [\"%s %s\" % (labels[i], pf2acc.loc[feats.loc[:,\"Pfam_acc\"].iloc[i], 1])","range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf","= a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype =","range(len(pf2color))] labels= [i for i in feats.index] #labels= [\"%s\" %(feats.loc[:,\"Pfam_acc\"].iloc[i]) for i in","not miscl is None: miscl_m = pd.read_csv(miscl, sep = \"\\t\", index_col = 0)","[ax.plot(x, pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels=","style = \"circle\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style","pd.np.ones(len(x)), 'o', color = \"#%06x\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i","= \"circle\", color = 'red') elif value == 2: rf = faces.CircleFace(radius =","= 8, style = \"circle\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]]","fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x","if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in","to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0]","node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\"Pfam_acc\"].values) #print","default = \"N1\", help = \"list of features\") parser.add_argument(\"phenotype\", help = \"target phenotype\")","+ '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target,","if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \"circle\", color =","recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep =","= \"table of binary features for labeling the nodes\") a = parser.parse_args() pt_tree,","pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the style of","= pd.read_csv(sample_mapping, index_col = 0, sep = \"\\t\") #read node and edge reconstruction","Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, #","style[\"hz_line_type\"] = 0 style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified and","column = i, position = \"branch-top\") for n in pt_tree.traverse(): if n.name in","if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name,","Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid","pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon,","pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\"-\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id,","> threshold: style[\"hz_line_type\"] = 1 style[\"hz_line_color\"] = 'green' style[\"hz_line_width\"] = 3 elif loss_recon.loc[branch_id,","= 0, sep = \"\\t\") #read node and edge reconstruction matrices node_recon =","pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy","= faces.TextFace(\"+\") cf = faces.CircleFace(radius = 8, style = \"circle\", color = adjusted_color)"],"string":"[\n \"name name_face = AttrFace(\\\"name\\\") else: # If internal node, draws label with smaller\",\n \"style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was misclassified and add misclassified label\",\n \"ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color =\",\n \"\\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping,\",\n \"* (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do\",\n \"'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for\",\n \"0, sep = \\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon,\",\n \"gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col\",\n \"else: style['fgcolor'] = 'green' if not n.name == \\\"N1\\\": branch_id = n.name +\",\n \"feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats =\",\n \"= v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts\",\n \"= faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf\",\n \"= 8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf)\",\n \"= \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values]\",\n \"%(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\\\"%s\",\n \"if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style\",\n \"faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to\",\n \"i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the\",\n \"rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\", color = 'red') elif value\",\n \"= 'green') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"= (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)),\",\n \"h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) /\",\n \"parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default\",\n \"= [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels,\",\n \"each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl\",\n \"pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1)\",\n \"matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow\",\n \"#for visualization of continuous feature get the range of values for each feature\",\n \"events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf)\",\n \"pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\": continue if not node_annotation is\",\n \"= a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats =\",\n \"= target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event\",\n \"= 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] =\",\n \"\\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state\",\n \"zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0:\",\n \"binary features for labeling the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color =\",\n \"= get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping,\",\n \"# Medium Gray # The following don't work well for people with defective\",\n \"add misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index: tf\",\n \"i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for\",\n \"= node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if\",\n \"\\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\",\",\n \"rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'orange') else: rf\",\n \"top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event =\",\n \"pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder'))\",\n \"- val) / (recon_max - recon_min) v_new = v - (v * (scale_factor))\",\n \"pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class =\",\n \"n in pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\": continue if not\",\n \"sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in\",\n \"Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, #\",\n \"i, position = \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in node2name: n.name\",\n \"= [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for\",\n \"gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3\",\n \"ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name\",\n \"threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype]\",\n \"\\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if using continuous features\",\n \"if sample was misclassified and add misclassified label if not miscl is None:\",\n \"gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf)\",\n \"labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: #\",\n \"if value == 0: rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample\",\n \"0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow\",\n \"for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon\",\n \"top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss\",\n \"style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] =\",\n \"features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain\",\n \"#if pf_acc: # labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i\",\n \"if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf\",\n \"pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node\",\n \"top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf\",\n \"] def my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face\",\n \"= 'green' if not n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\" +\",\n \"# labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc:\",\n \"Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light\",\n \"max_feats = 10, miscl = None, node_annotation = None): #read target feats feats\",\n \"%s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels\",\n \"size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name face to the image\",\n \"# Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish\",\n \"None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col = 0,\",\n \"= pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse(): #ignore\",\n \"\\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold\",\n \"= 8, style = \\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius =\",\n \"style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor']\",\n \"= \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\",\",\n \"type = int, default = 10, help = \\\"visualize at most max_feats features\\\")\",\n \"a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype,\",\n \"figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list,\",\n \"node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False,\",\n \"people with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish\",\n \"column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in\",\n \"mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help =\",\n \"= False ts.show_scale = True ts.force_topology = False # Use my custom layout\",\n \"layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color\",\n \"node, draws its name name_face = AttrFace(\\\"name\\\") else: # If internal node, draws\",\n \"if not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf,\",\n \"style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold:\",\n \"features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help =\",\n \"Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange\",\n \"top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\")\",\n \"not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i\",\n \"loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf)\",\n \"gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]),\",\n \"call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\",\",\n \"6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color\",\n \"Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, #\",\n \"= 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style)\",\n \"face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def\",\n \"= None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation =\",\n \"= \\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style =\",\n \"< 0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius =\",\n \"def get_style(): ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name\",\n \"ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color =\",\n \"in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the style\",\n \"labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\",\",\n \"threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name == \\\"N1\\\":\",\n \"= a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list =\",\n \"a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold,\",\n \"Grayish Yellow 0x817066, # Medium Gray # The following don't work well for\",\n \"names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use\",\n \"[\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node is\",\n \"is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value =\",\n \"\\\"branch-right\\\") #set species name instead of tax id if n.name in sample_mapping.index: node2name[n.name]\",\n \"state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help\",\n \"matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon,\",\n \"= AttrFace(\\\"name\\\") else: # If internal node, draws label with smaller font size\",\n \"0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray #\",\n \"node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set()\",\n \"is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0,\",\n \"probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range of\",\n \"# Adds the name face to the image at the preferred position faces.add_face_to_node(name_face,\",\n \"= 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if\",\n \"0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8,\",\n \"as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab\",\n \"threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else:\",\n \"== \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help\",\n \"include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if\",\n \"ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation,\",\n \"events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf =\",\n \"kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] >\",\n \"i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon =\",\n \"= \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids\",\n \"Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800,\",\n \"node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name =\",\n \"my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype =\",\n \"return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc =\",\n \"and add misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index:\",\n \"losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf =\",\n \"of the branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,]\",\n \"import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75,\",\n \"target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style())\",\n \"events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\")\",\n \"parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if using continuous features with a\",\n \"= \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target =\",\n \"= 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read node\",\n \"= 'orange') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"% (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index] #labels=\",\n \"its name name_face = AttrFace(\\\"name\\\") else: # If internal node, draws label with\",\n \"def my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face =\",\n \"(v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() #\",\n \"frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend\",\n \"= 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample\",\n \"abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0:\",\n \"style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns\",\n \"= parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon\",\n \"argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\")\",\n \"- recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new])))\",\n \"nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous\",\n \"features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set\",\n \"pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i\",\n \"> threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features\",\n \"features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to\",\n \"style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for\",\n \"\\\"table of binary features for labeling the nodes\\\") a = parser.parse_args() pt_tree, feats,\",\n \"= 10, miscl = None, node_annotation = None): #read target feats feats =\",\n \"8, style = \\\"circle\\\", color = 'red') elif value == 2: rf =\",\n \"= \\\"branch-right\\\") #set species name instead of tax id if n.name in sample_mapping.index:\",\n \"pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from\",\n \"= 'grey') n.add_face(rf, column = i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype]\",\n \"between sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type =\",\n \"val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in continuous reconstruction value\\\"\\\"\\\" h,\",\n \"feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s\",\n \"[\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels\",\n \"labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out\",\n \"= 'preorder')) pfams_with_event = set() pfam2color = {} #set the style of the\",\n \"with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name face\",\n \"= 1 - (recon_max - val) / (recon_max - recon_min) v_new = v\",\n \"[\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i],\",\n \"ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state reconstruction\\\")\",\n \"= a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node,\",\n \"(recon_max - val) / (recon_max - recon_min) v_new = v - (v *\",\n \"s, v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf names\",\n \"Adds the name face to the image at the preferred position faces.add_face_to_node(name_face, node,\",\n \"Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16,\",\n \"color = 'orange') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def\",\n \"= 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\")\",\n \"Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00,\",\n \"help = \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events\",\n \"misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for labeling the nodes\\\")\",\n \"import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors\",\n \"'black' n.set_style(style) #check if sample was misclassified and add misclassified label if not\",\n \"= \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i\",\n \"column = i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle()\",\n \"\\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help = \\\"visualize at\",\n \"= 10, help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table\",\n \"1]) for i in range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\" %\",\n \"a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats, a.out, pf2color)\",\n \"feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping\",\n \"= a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold =\",\n \"= \\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius = 8, style =\",\n \"Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D,\",\n \"val) / (recon_max - recon_min) v_new = v - (v * (scale_factor)) return\",\n \"= my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype\",\n \"pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\",\",\n \"0) for n in pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\": continue\",\n \"hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, #\",\n \"tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color\",\n \"= a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl =\",\n \"[i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if\",\n \"in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels=\",\n \"events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\")\",\n \"the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max\",\n \"\\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name\",\n \"not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0)\",\n \"style = \\\"circle\\\", color = 'red') elif value == 2: rf = faces.CircleFace(radius\",\n \"filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] -\",\n \"8, style = \\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius = 8,\",\n \"scale_factor = 1 - (recon_max - val) / (recon_max - recon_min) v_new =\",\n \"Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very\",\n \"= False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend =\",\n \"events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help =\",\n \"'orange') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'green')\",\n \"# Grayish Yellow 0x817066, # Medium Gray # The following don't work well\",\n \"0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue\",\n \"style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] >\",\n \"state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help\",\n \"tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None,\",\n \"= 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep =\",\n \"#ignore the root if n.name == \\\"N1\\\": continue if not node_annotation is None:\",\n \"are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m\",\n \"position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in continuous\",\n \"= \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is\",\n \"elif value == 2: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"\\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\")\",\n \"the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the\",\n \"# Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262,\",\n \"miscl is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for\",\n \"faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]]\",\n \"= \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for\",\n \"AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex =\",\n \"style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name == \\\"N1\\\": branch_id\",\n \"adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id,\",\n \"= faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style =\",\n \"= 'store_true', help = \\\"set if using continuous features with a discrete phenotype\\\")\",\n \"target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats\",\n \"8, style = \\\"circle\\\", color = 'grey') n.add_face(rf, column = i, position =\",\n \"to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get\",\n \"0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange\",\n \"i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print\",\n \"numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out +\",\n \"Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong\",\n \"target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree =\",\n \"#filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node\",\n \"target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\",\",\n \"== 0: rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\", color = 'red')\",\n \"loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype\",\n \"gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep =\",\n \"if not node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns,\",\n \"%s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale =\",\n \"get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node =\",\n \"def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list =\",\n \"The following don't work well for people with defective color vision 0x007D34, #\",\n \"= i, position = \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in node2name:\",\n \"parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list\",\n \"pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i\",\n \"1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color\",\n \"n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set species name instead of tax\",\n \"v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style():\",\n \"defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A,\",\n \"target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat\",\n \"pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf\",\n \"\\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse(): #ignore the root if n.name\",\n \"pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column =\",\n \"node annotation return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse parser\",\n \"= a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node =\",\n \"values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if\",\n \"for labeling the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon =\",\n \"recon_max): \\\"\\\"\\\"set brightness according to change in continuous reconstruction value\\\"\\\"\\\" h, s, v\",\n \"pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read node and edge reconstruction matrices\",\n \"for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if\",\n \"= 8, style = \\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius =\",\n \"feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target\",\n \"#add majority feature gains and losses events = [] for i in range(top10_feats.shape[0]):\",\n \"= kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf\",\n \"pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return\",\n \"according to change in continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor\",\n \"feats, pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node\",\n \"markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out +\",\n \"if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None:\",\n \"to change in continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor =\",\n \"0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid\",\n \"for n in pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\": continue if\",\n \"\\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] =\",\n \"#if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))]\",\n \"0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target node\",\n \"= argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state\",\n \"'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if\",\n \"%(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:,\",\n \"[\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col\",\n \"are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree,\",\n \"1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] =\",\n \"rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'grey') n.add_face(rf, column\",\n \"position = \\\"branch-right\\\") #set species name instead of tax id if n.name in\",\n \"10, help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of\",\n \"faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set species name instead of\",\n \"node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep\",\n \"parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target\",\n \"node, draws label with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds\",\n \"posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range\",\n \"[\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: #\",\n \"file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help = \\\"visualize at most\",\n \"Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315,\",\n \"target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style\",\n \"is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for\",\n \"i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] =\",\n \"Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If\",\n \"n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams\",\n \"of continuous feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype:\",\n \"Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A,\",\n \"gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep\",\n \"\\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune\",\n \"vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong\",\n \"discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to call genotype/phenotype events\\\")\",\n \"def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class = False):\",\n \"sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read node and edge\",\n \"reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon =\",\n \"= node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids\",\n \"#target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out,\",\n \"continue if not node_annotation is None: if n.name in node_table.index: for attr,i in\",\n \"value == 0: rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"in range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\") for n in pt_tree.traverse():\",\n \"#tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else:\",\n \"Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, #\",\n \"tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc =\",\n \"None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for n in\",\n \"style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] =\",\n \"events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf =\",\n \"annotation return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse parser =\",\n \"help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral\",\n \"i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index\",\n \"i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {}\",\n \"argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral\",\n \"in pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\": continue if not node_annotation\",\n \"get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree\",\n \"\\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help =\",\n \"- (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle()\",\n \"pf_desc = False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend\",\n \"= False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def\",\n \"recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m =\",\n \"= faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'grey') n.add_face(rf, column =\",\n \"= adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i],\",\n \"in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False)\",\n \"from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color,\",\n \"pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns):\",\n \"def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node\",\n \"< threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not n.name ==\",\n \"0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep\",\n \"i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1\",\n \"= faces.CircleFace(radius = 8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]\",\n \"leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False #\",\n \"'green' if not n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name\",\n \"= \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]]\",\n \"phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype\",\n \"pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints =\",\n \"n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event),\",\n \"def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in continuous reconstruction\",\n \"list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help\",\n \"True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6))\",\n \"8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf)\",\n \"node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids =\",\n \"filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__\",\n \"[0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i\",\n \"name face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\")\",\n \"style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if\",\n \"events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]])\",\n \"help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if using\",\n \"= 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1\",\n \"feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is\",\n \"color = 'green') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"False, max_feats = 10, miscl = None, node_annotation = None): #read target feats\",\n \"gains and losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype:\",\n \"= \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if using continuous\",\n \"Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, #\",\n \"Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow\",\n \"= \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal\",\n \"help = \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\")\",\n \"[] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8,\",\n \"= TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale\",\n \"[\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale\",\n \"= ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table =\",\n \"nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon =\",\n \"column = 0, position = \\\"branch-right\\\") #set species name instead of tax id\",\n \"faces.CircleFace(radius = 8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i])\",\n \"using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help =\",\n \"of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help =\",\n \"image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min,\",\n \"x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0]\",\n \"\\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character\",\n \"target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True,\",\n \"parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if\",\n \"features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]])\",\n \"node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change\",\n \"\\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help\",\n \"\\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type\",\n \"= sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for i\",\n \"style = \\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius = 8, style\",\n \"Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, #\",\n \"v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts =\",\n \"not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column\",\n \"kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf =\",\n \"to the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color,\",\n \"target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name)\",\n \"# Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink\",\n \"n.set_style(style) #check if sample was misclassified and add misclassified label if not miscl\",\n \"# Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C,\",\n \"most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help =\",\n \"internal node, draws label with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) #\",\n \"= gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl,\",\n \"reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature\",\n \"following don't work well for people with defective color vision 0x007D34, # Vivid\",\n \"0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following don't work\",\n \"ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\",\",\n \"#set the style of the branches and nodes according to the posterior probability\",\n \"Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium\",\n \"Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf():\",\n \"= a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype =\",\n \"figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout()\",\n \"Gray # The following don't work well for people with defective color vision\",\n \"pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node =\",\n \"Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851,\",\n \"smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name face to\",\n \"ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10\",\n \"ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif\",\n \"#set species name instead of tax id if n.name in sample_mapping.index: node2name[n.name] =\",\n \"pf_desc: # labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in\",\n \"draws label with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the\",\n \"visualization of continuous feature get the range of values for each feature if\",\n \"in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids\",\n \"rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800,\",\n \"value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val)\",\n \"edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon\",\n \"value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf =\",\n \"= pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy =\",\n \"if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else:\",\n \"Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out):\",\n \"% (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5,\",\n \"for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams =\",\n \"return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize\",\n \"parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character\",\n \"index_col = 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize()\",\n \"= 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon,\",\n \"= True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0]\",\n \"miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column =\",\n \"fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" %\",\n \"\\\"\\\"\\\"set brightness according to change in continuous reconstruction value\\\"\\\"\\\" h, s, v =\",\n \"#read target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree\",\n \"n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda\",\n \"else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'green') else:\",\n \"with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink\",\n \"help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of\",\n \"Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws its\",\n \"# Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The\",\n \"help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features\",\n \"# If terminal node, draws its name name_face = AttrFace(\\\"name\\\") else: # If\",\n \"rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'green') else: rf\",\n \"max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table\",\n \"not node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))):\",\n \"sep = \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col\",\n \"n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if\",\n \"'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check\",\n \"\\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] =\",\n \"in range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1])\",\n \"None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position\",\n \"Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, #\",\n \"in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index =\",\n \"{} #set the style of the branches and nodes according to the posterior\",\n \"= kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]]\",\n \"AttrFace(\\\"name\\\", fsize=10) # Adds the name face to the image at the preferred\",\n \"adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] <\",\n \"= \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name]\",\n \"list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event\",\n \"a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping,\",\n \"and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0)\",\n \"reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max -\",\n \"draws its name name_face = AttrFace(\\\"name\\\") else: # If internal node, draws label\",\n \"for i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i])\",\n \"feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\"\",\n \"feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping\",\n \"pandas as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import\",\n \"None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\")\",\n \"= rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max - recon_min)\",\n \"'preorder')) pfams_with_event = set() pfam2color = {} #set the style of the branches\",\n \"get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min)\",\n \"style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0\",\n \"\\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams\",\n \"threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif\",\n \"pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None: node_table\",\n \"n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events\",\n \"%s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels =\",\n \"the style of the branches and nodes according to the posterior probability top10_feats\",\n \"0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out\",\n \"species name instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0]\",\n \"sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10,\",\n \"= get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def\",\n \"parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help\",\n \"Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green\",\n \"node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius =\",\n \"add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False\",\n \"= [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node\",\n \"the image at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val,\",\n \"(scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not\",\n \"sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int,\",\n \"pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, #\",\n \"'darkred' else: style['fgcolor'] = 'green' if not n.name == \\\"N1\\\": branch_id = n.name\",\n \"= pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for\",\n \"events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]\",\n \"= 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"]\",\n \"ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use my custom\",\n \"#loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\")\",\n \"(9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o',\",\n \"pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping =\",\n \"tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id,\",\n \"If terminal node, draws its name name_face = AttrFace(\\\"name\\\") else: # If internal\",\n \"parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\")\",\n \"= [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))]\",\n \"filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process\",\n \"format = 1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep\",\n \"help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default =\",\n \"node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping\",\n \"= gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\",\",\n \"'_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc\",\n \"#fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree,\",\n \"color = 'red') elif value == 2: rf = faces.CircleFace(radius = 8, style\",\n \"# Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, #\",\n \"return rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not add\",\n \"miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set species\",\n \"#process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse\",\n \"\\\"N1\\\": continue if not node_annotation is None: if n.name in node_table.index: for attr,i\",\n \"= \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help = \\\"visualize\",\n \"out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat =\",\n \"= fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\"\",\n \"# Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish\",\n \"pfam2color if __name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of\",\n \"Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown\",\n \"== \\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] >\",\n \"Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, #\",\n \"misclassified label if not miscl is None: if node2name[n.name] in miscl_m.index: tf =\",\n \"set() pfam2color = {} #set the style of the branches and nodes according\",\n \"phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor']\",\n \"of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature\",\n \"pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position =\",\n \"loss_recon.index.values] #prune to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name\",\n \"a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree,\",\n \"get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats,\",\n \"are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain\",\n \"range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max =\",\n \"> threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"]\",\n \"events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state\",\n \"attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8,\",\n \"TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale =\",\n \"in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values)\",\n \"pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style\",\n \"terminal node, draws its name name_face = AttrFace(\\\"name\\\") else: # If internal node,\",\n \"color vision 0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, #\",\n \"Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws\",\n \"= ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey'\",\n \"loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target node if\",\n \"= a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation =\",\n \"\\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i],\",\n \"+ \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping,\",\n \"None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation = None):\",\n \"if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col =\",\n \"ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name = False\",\n \"recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in continuous reconstruction value\\\"\\\"\\\" h, s,\",\n \"at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help\",\n \"with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to call\",\n \"not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius = 8, style =\",\n \"root if n.name == \\\"N1\\\": continue if not node_annotation is None: if n.name\",\n \"state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help =\",\n \"target node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name\",\n \"miscl = None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col\",\n \"\\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\",\",\n \"Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, #\",\n \"loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf =\",\n \"in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300,\",\n \"a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node,\",\n \"pf_acc: # labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in\",\n \"= pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png',\",\n \"labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines,\",\n \"my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree,\",\n \"get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc\",\n \"pf2color, pf_desc = False, pf_acc = True, include_class = False): fig = pylab.figure()\",\n \"= False # Use my custom layout ts.layout_fn = my_layout return ts def\",\n \"= pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col =\",\n \"#filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams,\",\n \"id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and\",\n \"phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else:\",\n \"\\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes\",\n \"miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse():\",\n \"threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl\",\n \"(pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\\\"%s\\\"\",\n \"= float, help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping\",\n \"events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\") for\",\n \"color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i\",\n \"else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"= \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true',\",\n \"tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set species name\",\n \"font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name face to the\",\n \"internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of\",\n \"from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid\",\n \"character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help\",\n \"ete2 from ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import\",\n \"0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish\",\n \"tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold:\",\n \"label if not miscl is None: if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\")\",\n \"\\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0)\",\n \"0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal\",\n \"'grey') n.add_face(rf, column = i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style\",\n \"of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for labeling the\",\n \"instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority\",\n \"faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"== 2: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'orange')\",\n \"node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\\\"name\\\") else: #\",\n \"i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target node if target_node is not\",\n \"sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation\",\n \"= faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]]\",\n \"get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist\",\n \"= phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist =\",\n \"are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation = None): #read\",\n \"faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'green') else: rf = faces.CircleFace(radius\",\n \"1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1,\",\n \"= faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'green') else: rf =\",\n \"sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = []\",\n \"type = float, help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help =\",\n \"Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following\",\n \"tree = a.tree, feat_list = a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold\",\n \"0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node):\",\n \"Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green\",\n \"labels= [i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])]\",\n \"0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None,\",\n \"elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green' if not\",\n \"faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf =\",\n \"feature gains and losses events = [] for i in range(top10_feats.shape[0]): if not\",\n \"action = 'store_true', help = \\\"set if using continuous features with a discrete\",\n \"if not miscl is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col =\",\n \"0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not\",\n \"% (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels =\",\n \"help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action =\",\n \"v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf names automatically\",\n \"and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of\",\n \"#top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ ==\",\n \"a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats,\",\n \"0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was misclassified and add misclassified\",\n \"ts.show_scale = True ts.force_topology = False # Use my custom layout ts.layout_fn =\",\n \"= \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col =\",\n \"plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\",\",\n \"rgb2hex(hsv_to_rgb(pd.np.array([h, s, v_new]))) def get_style(): ts = TreeStyle() # Do not add leaf\",\n \"= filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0]\",\n \"= False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax =\",\n \"target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return\",\n \"Green ] def my_layout(node): if node.is_leaf(): # If terminal node, draws its name\",\n \"2: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'orange') else:\",\n \"sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for i in\",\n \"of binary features for labeling the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color\",\n \"range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color\",\n \"False # Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree,\",\n \"= \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep =\",\n \"recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\")\",\n \"= 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style())\",\n \"automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology = False # Use my\",\n \"= 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was misclassified and add\",\n \"not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = kelly_colors_hex[i])\",\n \"pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style =\",\n \"= 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index =\",\n \"majority feature gains and losses events = [] for i in range(top10_feats.shape[0]): if\",\n \"pfam2color = {} #set the style of the branches and nodes according to\",\n \"a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out)\",\n \"= \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size']\",\n \"else: # If internal node, draws label with smaller font size name_face =\",\n \"= 8, style = \\\"circle\\\", color = 'grey') n.add_face(rf, column = i, position\",\n \"n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"]\",\n \"continuous feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min\",\n \"= [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid\",\n \"top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list\",\n \"tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style\",\n \"top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import\",\n \"0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red 0xF4C800, # Vivid\",\n \"= None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list, index_col =\",\n \"help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help =\",\n \"0x817066, # Medium Gray # The following don't work well for people with\",\n \"\\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon\",\n \"+ \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon,\",\n \"'_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target, feats,\",\n \"pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0,\",\n \"range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for\",\n \"loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1]))\",\n \"in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value ==\",\n \"\\\"circle\\\", color = 'grey') n.add_face(rf, column = i, position = \\\"aligned\\\") ns =\",\n \"rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong\",\n \"preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness\",\n \"i in loss_recon.index.values] #prune to target node if target_node is not None: pt_tree\",\n \"parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree\",\n \"pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i\",\n \"__name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\",\",\n \"gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype\",\n \"'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"]\",\n \"elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] =\",\n \"= None): #read target feats feats = pd.read_csv(feat_list, index_col = 0, sep =\",\n \"color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i]\",\n \"figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5,\",\n \"= pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format =\",\n \"= set() pfam2color = {} #set the style of the branches and nodes\",\n \"style of the branches and nodes according to the posterior probability top10_feats =\",\n \"# Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive\",\n \"\\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help\",\n \"# Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish\",\n \"labeling the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon,\",\n \"ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout return ts\",\n \"# Strong Violet 0xFF8E00, # Vivid Orange Yellow 0xB32851, # Strong Purplish Red\",\n \"= \\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep =\",\n \"value == 2: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"for i in loss_recon.index.values] #prune to target node if target_node is not None:\",\n \"# Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish\",\n \"+ '_tree.png', tree_style = get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color,\",\n \"0: rf = ete2.CircleFace(radius = 8, style = \\\"circle\\\", color = 'red') elif\",\n \"'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold:\",\n \"= 0, position = \\\"branch-right\\\") #set species name instead of tax id if\",\n \"= \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in\",\n \"None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping,\",\n \"10, miscl = None, node_annotation = None): #read target feats feats = pd.read_csv(feat_list,\",\n \"index_col = 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to\",\n \"sep = \\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse(): #ignore the root\",\n \"n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype]\",\n \"Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex\",\n \"0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7,\",\n \"style = \\\"circle\\\", color = 'grey') n.add_face(rf, column = i, position = \\\"aligned\\\")\",\n \"gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list\",\n \"\\\"class\\\"].iloc[i]) for i in range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\" %\",\n \"a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list,\",\n \"well for people with defective color vision 0x007D34, # Vivid Green 0xF6768E, #\",\n \"in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i\",\n \"pf_acc = True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize =\",\n \"= [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc:\",\n \"index_col = 0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index\",\n \"8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]] >\",\n \"\\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\",\",\n \"= a.feat_list, phenotype = a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping =\",\n \"pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x,\",\n \"= pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation\",\n \"False ts.show_scale = True ts.force_topology = False # Use my custom layout ts.layout_fn\",\n \"[ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, # Vivid Orange\",\n \"= 0) loss_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in loss_recon.index.values] #prune to target\",\n \"0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet\",\n \"lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in\",\n \"style['fgcolor'] = 'green' if not n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\"\",\n \"n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1\",\n \"index_col = 0, sep = \\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon\",\n \"help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified\",\n \"index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read\",\n \"= 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold:\",\n \"if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses\",\n \"of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help\",\n \"the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set\",\n \"True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out\",\n \"#pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True,\",\n \"i in range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i],\",\n \"for i in range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\" % (labels[i],\",\n \"= a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon, pfam_mapping = a.pfam_mapping, tree =\",\n \"target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event =\",\n \"position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square'\",\n \"\\\"set if using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float,\",\n \"node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\")\",\n \"None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name,\",\n \"target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out +\",\n \"sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation\",\n \"= True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9,\",\n \"ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype, feat_list\",\n \"i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style =\",\n \"figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon,\",\n \"Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13,\",\n \"return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats, pf2color = get_tree(phenotype = phenotype,\",\n \"the branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for\",\n \"node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col\",\n \"return figlegend def get_tree(phenotype, tree, gain_recon, loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold =\",\n \"= 'red') elif value == 2: rf = faces.CircleFace(radius = 8, style =\",\n \"\\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"# Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish\",\n \"s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max\",\n \"if using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help\",\n \"work well for people with defective color vision 0x007D34, # Vivid Green 0xF6768E,\",\n \"# Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066,\",\n \"target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl = None, node_annotation\",\n \"branches and nodes according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization\",\n \"= [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\",\",\n \"for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max) if not\",\n \"#gain events if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\")\",\n \"range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\") for n in pt_tree.traverse(): if\",\n \"\\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action\",\n \"pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse(): #ignore the\",\n \"custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node, out): #pt_tree, feats,\",\n \"= faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'orange') else: rf =\",\n \"= 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns <\",\n \"phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0\",\n \"phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif\",\n \"if __name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\")\",\n \"Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ]\",\n \"for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col = 0)\",\n \"sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in\",\n \"= n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] =\",\n \"a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl,\",\n \"help = \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\",\",\n \"= a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats, a.out,\",\n \"import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target list of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node\",\n \"= 2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\")\",\n \"= {} #set the style of the branches and nodes according to the\",\n \"= \\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help\",\n \"= False, max_feats = 10, miscl = None, node_annotation = None): #read target\",\n \"# Strong Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020,\",\n \"v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max -\",\n \"fsize=10) # Adds the name face to the image at the preferred position\",\n \"continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max\",\n \"Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, #\",\n \"= \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values]\",\n \"Green 0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, #\",\n \"0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read node and\",\n \"= \\\"\\\\t\\\", index_col = 0) for n in pt_tree.traverse(): #ignore the root if\",\n \"None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in\",\n \"#if pf_desc: # labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i\",\n \"a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation)\",\n \"range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints = 1, frameon = False) #fig.show()\",\n \"pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\": import argparse parser = argparse.ArgumentParser(\\\"\\\"\\\"visualize target\",\n \"for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set\",\n \"figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines\",\n \"#check if sample was misclassified and add misclassified label if not miscl is\",\n \"= pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep =\",\n \"1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"]\",\n \"= faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set species name instead\",\n \"Yellow 0x817066, # Medium Gray # The following don't work well for people\",\n \"i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep = \\\"\\\\t\\\", index_col =\",\n \"= \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\",\",\n \"top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous\",\n \"import pandas as pd import ete2 from ete2 import faces, Tree, AttrFace, TreeStyle\",\n \"range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i in\",\n \"= 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats = 10, miscl =\",\n \"AttrFace(\\\"name\\\") else: # If internal node, draws label with smaller font size name_face\",\n \"Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray\",\n \"else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was misclassified\",\n \"# Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid Orange Yellow\",\n \"with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list\",\n \"# labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))]\",\n \"1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return\",\n \"if n.name == \\\"N1\\\": continue if not node_annotation is None: if n.name in\",\n \"threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] =\",\n \"phenotype\\\") parser.add_argument(\\\"--are_continuous_features_with_discrete_phenotype\\\", action = 'store_true', help = \\\"set if using continuous features with\",\n \"= \\\"circle\\\", color = 'grey') n.add_face(rf, column = i, position = \\\"aligned\\\") ns\",\n \"node_annotation is None: if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value\",\n \"a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon,\",\n \"style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] =\",\n \"Purple 0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid\",\n \"= i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style = ete2.NodeStyle() style[\\\"shape\\\"]\",\n \"name instead of tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add\",\n \"else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if\",\n \"color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)):\",\n \"0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish\",\n \"= [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius =\",\n \"get_style(): ts = TreeStyle() # Do not add leaf names automatically ts.show_leaf_name =\",\n \"False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111)\",\n \"fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype, tree, gain_recon,\",\n \"i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: #\",\n \"= AttrFace(\\\"name\\\", fsize=10) # Adds the name face to the image at the\",\n \"node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events = [] for\",\n \"and losses events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf\",\n \"to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and names\\\")\",\n \"target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out +\",\n \"loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3\",\n \"and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default =\",\n \"parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for labeling the nodes\\\") a =\",\n \"(recon_max - recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h, s,\",\n \"include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax\",\n \"reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help =\",\n \"tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"= top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if __name__ == \\\"__main__\\\":\",\n \"for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class:\",\n \"# Use my custom layout ts.layout_fn = my_layout return ts def plot_tree(pt_tree, target_node,\",\n \"\\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and\",\n \"faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] =\",\n \"parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss\",\n \"sep = \\\"\\\\t\\\", index_col = 0) sample_mapping = pd.read_csv(sample_mapping, index_col = 0, sep\",\n \"#print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats, pfam2color if\",\n \"don't work well for people with defective color vision 0x007D34, # Vivid Green\",\n \"= 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor'] = 'green'\",\n \"not n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id,\",\n \"if gain_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i])\",\n \"0x593315, # Deep Yellowish Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark\",\n \"#continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id,\",\n \"of features\\\"\\\"\\\") parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help =\",\n \"adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column\",\n \"= kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color =\",\n \"position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according\",\n \"plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class = False): fig\",\n \"Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown 0xF13A13, #\",\n \"Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology\",\n \"names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10,\",\n \"genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help\",\n \"= 3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red'\",\n \"tax id if n.name in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains\",\n \"= 'darkred' else: style['fgcolor'] = 'green' if not n.name == \\\"N1\\\": branch_id =\",\n \"0xFF6800, # Vivid Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red\",\n \"of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min = gain_recon.abs().apply(pd.np.min) recon_max = gain_recon.abs().apply(pd.np.max)\",\n \"the root if n.name == \\\"N1\\\": continue if not node_annotation is None: if\",\n \"in sample_mapping.index: node2name[n.name] = sample_mapping.loc[n.name,][0] #add majority feature gains and losses events =\",\n \"== \\\"N1\\\": continue if not node_annotation is None: if n.name in node_table.index: for\",\n \"8, style = \\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius = 8,\",\n \"# Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00,\",\n \"pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\"\",\n \"2.5, numpoints = 1, frameon = False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out\",\n \"in continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 -\",\n \"= \\\"set if using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type =\",\n \"help = \\\"table of binary features for labeling the nodes\\\") a = parser.parse_args()\",\n \"float, help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between\",\n \"parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help\",\n \"(labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if pf_acc: # labels = [\\\"%s\",\n \"0xB32851, # Strong Purplish Red 0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong\",\n \"# If internal node, draws label with smaller font size name_face = AttrFace(\\\"name\\\",\",\n \"True ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout return\",\n \"according to the posterior probability top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature\",\n \"'green') else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'grey')\",\n \"in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not\",\n \"faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius\",\n \"If internal node, draws label with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10)\",\n \"0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree,\",\n \"\\\"circle\\\", color = 'red') elif value == 2: rf = faces.CircleFace(radius = 8,\",\n \"top10_feats.index[i]] < 0: tf = faces.TextFace(\\\"-\\\") else: tf = faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius\",\n \"feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values for each\",\n \"= \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\",\",\n \"a.phenotype, target_node = a.target_node, threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype,\",\n \"pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype = False, max_feats\",\n \"gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1]))\",\n \"1) pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\",\",\n \"n.add_face(events[i], column = i, position = \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name\",\n \"i in range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\") for n in\",\n \"if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] =\",\n \"(labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] figlegend.legend(lines, labels, markerscale = 2.5, numpoints\",\n \"Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, #\",\n \"10 if pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred'\",\n \"Brown 0xF13A13, # Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def\",\n \"continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold\",\n \"Red 0xCEA262, # Grayish Yellow 0x817066, # Medium Gray # The following don't\",\n \"= \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character\",\n \"top10_feats = feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values\",\n \"parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary\",\n \"parser.add_argument(\\\"--max_feats\\\", type = int, default = 10, help = \\\"visualize at most max_feats\",\n \"TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [\",\n \"help = \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help = \\\"tree with\",\n \"i in range(len(labels))] #if pf_desc: # labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i],\",\n \"features for labeling the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon\",\n \"style = ete2.NodeStyle() style[\\\"shape\\\"] = 'square' style['size'] = 10 if pd.isnull(ns): style['fgcolor'] =\",\n \"False, pf_acc = True, include_class = False): fig = pylab.figure() figlegend = pylab.figure(figsize\",\n \"0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon = pd.read_csv(loss_recon, sep\",\n \"= dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color\",\n \"= 'black' n.set_style(style) #check if sample was misclassified and add misclassified label if\",\n \"elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i])\",\n \"character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\",\",\n \"if node2name[n.name] in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position =\",\n \"cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] =\",\n \"= \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral\",\n \"= node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf = ete2.CircleFace(radius\",\n \"attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value\",\n \"hsv_to_rgb, rgb_to_hsv kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple\",\n \"\\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] =\",\n \"3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was\",\n \"\\\"circle\\\", color = adjusted_color) pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in\",\n \"max_feats = a.max_feats, miscl = a.miscl, node_annotation = a.node_annotation) plot_tree(pt_tree, a.target_node, a.out) plot_legend(feats,\",\n \"faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb, rgb_to_hsv\",\n \"# Do not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True\",\n \"character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state reconstruction\\\") parser.add_argument(\\\"loss_recon\\\",\",\n \"Orange 0xA6BDD7, # Very Light Blue 0xC10020, # Vivid Red 0xCEA262, # Grayish\",\n \"/ (recon_max - recon_min) v_new = v - (v * (scale_factor)) return rgb2hex(hsv_to_rgb(pd.np.array([h,\",\n \"for i in range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\" % (labels[i],\",\n \"3 elif loss_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"]\",\n \"= adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]]\",\n \"node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value):\",\n \"sample was misclassified and add misclassified label if not miscl is None: if\",\n \"the name face to the image at the preferred position faces.add_face_to_node(name_face, node, column=0,\",\n \"= pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1] lines =\",\n \"int, default = 10, help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help\",\n \"= kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i,\",\n \"kelly_colors_hex[i] pfams_with_event.add(node_recon.index[i]) events.append(cf) events.append(tf) for i in range(len(events)): n.add_face(events[i], column = i, position\",\n \"my_layout(node): if node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\\\"name\\\")\",\n \"feature get the range of values for each feature if are_continuous_features_with_discrete_phenotype: recon_min =\",\n \"pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color\",\n \"not add leaf names automatically ts.show_leaf_name = False ts.show_scale = True ts.force_topology =\",\n \"ete2.CircleFace(radius = 8, style = \\\"circle\\\", color = 'red') elif value == 2:\",\n \"\\\"tree with internal nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help =\",\n \"= pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x =\",\n \"pfams_with_event = set() pfam2color = {} #set the style of the branches and\",\n \"\\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i for i in feats.index]\",\n \"= faces.CircleFace(radius = 8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if\",\n \"= get_style()) return target, feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False,\",\n \"events = [] for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius\",\n \"parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon =\",\n \"phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to call genotype/phenotype events\\\") parser.add_argument(\\\"sample_mapping\\\",\",\n \"feats feats = pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree,\",\n \"for i in range(len(events)): n.add_face(events[i], column = i, position = \\\"branch-top\\\") for n\",\n \"help = \\\"set if using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type\",\n \"False) #fig.show() fig.tight_layout() figlegend.savefig(out + \\\"_legend.svg\\\") figlegend.savefig(out + \\\"_legend.png\\\") return figlegend def get_tree(phenotype,\",\n \"= faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]),\",\n \"feats, pf2color def plot_legend(feats, out, pf2color, pf_desc = False, pf_acc = True, include_class\",\n \"at the preferred position faces.add_face_to_node(name_face, node, column=0, position=\\\"branch-right\\\") def adjust_kelly_brightness(hex_color, val, recon_min, recon_max):\",\n \"for i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i])\",\n \"pd.isnull(ns): style['fgcolor'] = 'grey' elif ns < threshold: style['fgcolor'] = 'darkred' else: style['fgcolor']\",\n \"0x007D34, # Vivid Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue\",\n \"faces.CircleFace(radius = 8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id,\",\n \"pt_tree.ladderize() if not node_annotation is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col\",\n \"brightness according to change in continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color)))\",\n \"- (recon_max - val) / (recon_max - recon_min) v_new = v - (v\",\n \"= target_node)[0] target.render(out + '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style =\",\n \"import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex, hsv_to_rgb,\",\n \"was misclassified and add misclassified label if not miscl is None: if node2name[n.name]\",\n \"features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of\",\n \"in loss_recon.index.values] #prune to target node if target_node is not None: pt_tree =\",\n \"is_ml_plus_phypat = True, target_node = target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name =\",\n \"\\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format = 1) pt_tree.ladderize() if not node_annotation is None:\",\n \"Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong\",\n \"out, pf2color, pf_desc = False, pf_acc = True, include_class = False): fig =\",\n \"is None: node_table = pd.read_csv(node_annotation, sep = \\\"\\\\t\\\", index_col = 0) sample_mapping =\",\n \"dict((i.name, i.name) for i in pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color =\",\n \"nodes labeled\\\") parser.add_argument(\\\"pfam_mapping\\\", help = \\\"feature mapping/list\\\") parser.add_argument(\\\"feat_list\\\", help = \\\"list of features\\\")\",\n \"range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for\",\n \"- 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,] #process node annotation return pt_tree, top10_feats,\",\n \"= \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help = \\\"table of misclassified samples\\\")\",\n \"the nodes\\\") a = parser.parse_args() pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon\",\n \"rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1 - (recon_max - val) / (recon_max - recon_min) v_new\",\n \"threshold = a.threshold, sample_mapping = a.sample_mapping, are_continuous_features_with_discrete_phenotype = a.are_continuous_features_with_discrete_phenotype, max_feats = a.max_feats, miscl\",\n \"name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name face to the image at\",\n \"misclassified and add misclassified label if not miscl is None: if node2name[n.name] in\",\n \"= int, default = 10, help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\",\",\n \"node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col =\",\n \"if not n.name == \\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name if\",\n \"# labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))]\",\n \"index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for i in gain_recon.index.values] loss_recon =\",\n \"cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events\",\n \"faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]],\",\n \"'store_true', help = \\\"set if using continuous features with a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\",\",\n \"color = 'grey') n.add_face(rf, column = i, position = \\\"aligned\\\") ns = node_recon.loc[n.name,\",\n \"kelly_colors_hex = [ 0xFFB300, # Vivid Yellow 0x803E75, # Strong Purple 0xFF6800, #\",\n \"ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output file\\\") parser.add_argument(\\\"--max_feats\\\", type = int, default\",\n \"Green 0xF6768E, # Strong Purplish Pink 0x00538A, # Strong Blue 0xFF7A5C, # Strong\",\n \"parser.add_argument(\\\"node_recon\\\", help = \\\"node ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events\",\n \"#labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\"\",\n \"= 0) for n in pt_tree.traverse(): #ignore the root if n.name == \\\"N1\\\":\",\n \"pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x = [0,1]\",\n \"feats = pd.read_csv(feat_list, index_col = 0, sep = \\\"\\\\t\\\") pt_tree = ete2.Tree(tree, format\",\n \"faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'grey') n.add_face(rf, column = i,\",\n \"position = \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in node2name: n.name =\",\n \"events elif loss_recon.loc[branch_id, top10_feats.index[i]] > threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf)\",\n \"reconstruction\\\") parser.add_argument(\\\"loss_recon\\\", help = \\\"loss events ancestral character state reconstruction\\\") parser.add_argument(\\\"tree\\\", help =\",\n \"in range(feats.shape[0])] #if include_class: # labels= [\\\"%s %s\\\" %(labels[i], feats.loc[:, \\\"class\\\"].iloc[i]) for i\",\n \"+ n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green'\",\n \"name_face = AttrFace(\\\"name\\\") else: # If internal node, draws label with smaller font\",\n \"samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for labeling the nodes\\\") a\",\n \"0) gain_recon = pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]),\",\n \"adjust_kelly_brightness(hex_color, val, recon_min, recon_max): \\\"\\\"\\\"set brightness according to change in continuous reconstruction value\\\"\\\"\\\"\",\n \"= 8, style = \\\"circle\\\", color = 'red') elif value == 2: rf\",\n \"index_col = 0) for n in pt_tree.traverse(): #ignore the root if n.name ==\",\n \"= pd.read_csv(gain_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon.index = [\\\"_\\\".join((\\\"_\\\".join(i.split(\\\"_\\\")[:-1]), i.split(\\\"_\\\")[-1])) for\",\n \"n.add_face(rf, column = i, position = \\\"aligned\\\") ns = node_recon.loc[n.name, phenotype] style =\",\n \"= get_tree(phenotype = phenotype, feat_list = \\\"top_cor\\\", is_ml_plus_phypat = True, target_node = target_node)\",\n \"for i in range(top10_feats.shape[0]): if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style\",\n \"sep = \\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep\",\n \"# Vivid Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if\",\n \"branch_id = n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"]\",\n \"pfams_with_event.add(node_recon.index[i]) events.append(cf) #continuous features else: adjusted_color = adjust_kelly_brightness(kelly_colors_hex[i], abs(loss_recon.loc[branch_id, top10_feats.index[i]]), recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf\",\n \"ancestral character state reconstruction\\\") parser.add_argument(\\\"gain_recon\\\", help = \\\"gain events ancestral character state reconstruction\\\")\",\n \"node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col = 0) gain_recon = pd.read_csv(gain_recon, sep\",\n \"#prune to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name =\",\n \"if node.is_leaf(): # If terminal node, draws its name name_face = AttrFace(\\\"name\\\") else:\",\n \"'red') elif value == 2: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\",\",\n \"change in continuous reconstruction value\\\"\\\"\\\" h, s, v = rgb_to_hsv(hex2color('#{0:06X}'.format(hex_color))) scale_factor = 1\",\n \"label with smaller font size name_face = AttrFace(\\\"name\\\", fsize=10) # Adds the name\",\n \"\\\"table of misclassified samples\\\") parser.add_argument(\\\"--node_annotation\\\", help = \\\"table of binary features for labeling\",\n \"for people with defective color vision 0x007D34, # Vivid Green 0xF6768E, # Strong\",\n \"= target_node) pt_tree.dist = 0 target = pt_tree.search_nodes(name = target_node)[0] target.render(out + '_tree.pdf',\",\n \"0xF4C800, # Vivid Greenish Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid\",\n \"labels = [\\\"%s %s\\\" % (labels[i], pf2short_desc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1]) for i in range(len(labels))] #if\",\n \"n.name == \\\"N1\\\": continue if not node_annotation is None: if n.name in node_table.index:\",\n \"else: rf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = 'grey') n.add_face(rf,\",\n \"+ \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"]\",\n \"Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, # Deep Yellowish Brown\",\n \"= True ts.force_topology = False # Use my custom layout ts.layout_fn = my_layout\",\n \"1 - (recon_max - val) / (recon_max - recon_min) v_new = v -\",\n \"style[\\\"hz_line_color\\\"] = 'red' style[\\\"hz_line_width\\\"] = 3 else: style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black'\",\n \"i in range(len(pf2color))] labels= [i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for\",\n \"= feats.iloc[:max_feats,] #for visualization of continuous feature get the range of values for\",\n \"loss_recon, node_recon, pfam_mapping, feat_list, sample_mapping, threshold = 0.5, target_node = None, are_continuous_features_with_discrete_phenotype =\",\n \"is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0) for n\",\n \"parser.add_argument(\\\"sample_mapping\\\", help = \\\"mapping between sample ids and names\\\") parser.add_argument(\\\"out\\\", help = \\\"output\",\n \"#read node and edge reconstruction matrices node_recon = pd.read_csv(node_recon, sep = \\\"\\\\t\\\", index_col\",\n \"ete2 import faces, Tree, AttrFace, TreeStyle import pylab from matplotlib.colors import hex2color, rgb2hex,\",\n \"if n.name in node_table.index: for attr,i in zip(node_table.columns, range(len(node_table.columns))): value = node_table.loc[n.name, attr]\",\n \"in miscl_m.index: tf = faces.TextFace(\\\"misclassified\\\") n.add_face(tf, column = 0, position = \\\"branch-right\\\") #set\",\n \"default = 10, help = \\\"visualize at most max_feats features\\\") parser.add_argument(\\\"--miscl\\\", help =\",\n \"Reddish Orange 0x232C16, # Dark Olive Green ] def my_layout(node): if node.is_leaf(): #\",\n \"= ete2.CircleFace(radius = 8, style = \\\"circle\\\", color = 'red') elif value ==\",\n \"= \\\"list of features\\\") parser.add_argument(\\\"--target_node\\\", default = \\\"N1\\\", help = \\\"list of features\\\")\",\n \"\\\"N1\\\": branch_id = n.name + \\\"_\\\" + n.up.name if gain_recon.loc[branch_id, phenotype] > threshold:\",\n \"in pt_tree.traverse(): if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i:\",\n \"# The following don't work well for people with defective color vision 0x007D34,\",\n \"Medium Gray # The following don't work well for people with defective color\",\n \"> threshold: pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events\",\n \"recon_min.loc[top10_feats.index[i]], recon_max.loc[top10_feats.index[i]]) #tf = faces.TextFace(gain_recon.loc[branch_id, top10_feats.index[i]]) if loss_recon.loc[branch_id, top10_feats.index[i]] < 0: tf =\",\n \"#print filtered_pfams #filtered_ids = pt_gt2id.loc[filtered_pfams, 0] - 1 #print filtered_ids #top10_feats_with_event = top10_feats.loc[filtered_ids,]\",\n \"0, position = \\\"branch-right\\\") #set species name instead of tax id if n.name\",\n \"a discrete phenotype\\\") parser.add_argument(\\\"threshold\\\", type = float, help = \\\"threshold to call genotype/phenotype\",\n \"# Dark Olive Green ] def my_layout(node): if node.is_leaf(): # If terminal node,\",\n \"in range(len(labels))] #if pf_acc: # labels = [\\\"%s %s\\\" % (labels[i], pf2acc.loc[feats.loc[:,\\\"Pfam_acc\\\"].iloc[i], 1])\",\n \"range(len(node_table.columns))): value = node_table.loc[n.name, attr] if not pd.isnull(value): if value == 0: rf\",\n \"= a.loss_recon, pfam_mapping = a.pfam_mapping, tree = a.tree, feat_list = a.feat_list, phenotype =\",\n \"range(len(pf2color))] labels= [i for i in feats.index] #labels= [\\\"%s\\\" %(feats.loc[:,\\\"Pfam_acc\\\"].iloc[i]) for i in\",\n \"not miscl is None: miscl_m = pd.read_csv(miscl, sep = \\\"\\\\t\\\", index_col = 0)\",\n \"[ax.plot(x, pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels=\",\n \"style = \\\"circle\\\", color = 'orange') else: rf = faces.CircleFace(radius = 8, style\",\n \"pd.np.ones(len(x)), 'o', color = \\\"#%06x\\\" % (pf2color[feats.index[i]]))[0] for i in range(len(pf2color))] labels= [i\",\n \"= \\\"circle\\\", color = 'red') elif value == 2: rf = faces.CircleFace(radius =\",\n \"= 8, style = \\\"circle\\\", color = kelly_colors_hex[i]) #gain events if gain_recon.loc[branch_id, top10_feats.index[i]]\",\n \"fig = pylab.figure() figlegend = pylab.figure(figsize = (9, 6)) ax = fig.add_subplot(111) x\",\n \"if n.name in node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in\",\n \"to target node if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0]\",\n \"node2name: n.name = node2name[n.name] #filtered_pfams = filter(lambda i: i in list(pfams_with_event), top10_feats.loc[:,\\\"Pfam_acc\\\"].values) #print\",\n \"default = \\\"N1\\\", help = \\\"list of features\\\") parser.add_argument(\\\"phenotype\\\", help = \\\"target phenotype\\\")\",\n \"+ '_tree.pdf', tree_style = get_style()) #target.render(out + '_tree.png', tree_style = get_style()) return target,\",\n \"if not are_continuous_features_with_discrete_phenotype: cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color =\",\n \"recon_max = gain_recon.abs().apply(pd.np.max) if not miscl is None: miscl_m = pd.read_csv(miscl, sep =\",\n \"= \\\"table of binary features for labeling the nodes\\\") a = parser.parse_args() pt_tree,\",\n \"pt_tree.traverse(strategy = 'preorder')) pfams_with_event = set() pfam2color = {} #set the style of\",\n \"= pd.read_csv(sample_mapping, index_col = 0, sep = \\\"\\\\t\\\") #read node and edge reconstruction\",\n \"Yellow 0x7F180D, # Strong Reddish Brown 0x93AA00, # Vivid Yellowish Green 0x593315, #\",\n \"style[\\\"hz_line_type\\\"] = 0 style[\\\"hz_line_color\\\"] = 'black' n.set_style(style) #check if sample was misclassified and\",\n \"column = i, position = \\\"branch-top\\\") for n in pt_tree.traverse(): if n.name in\",\n \"if target_node is not None: pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name,\",\n \"Blue 0xFF7A5C, # Strong Yellowish Pink 0x53377A, # Strong Violet 0xFF8E00, # Vivid\",\n \"pt_tree, feats, pf2color = get_tree(node_recon = a.node_recon, gain_recon = a.gain_recon, loss_recon = a.loss_recon,\",\n \"pfam2color[top10_feats.index[i]] = kelly_colors_hex[i] tf = faces.TextFace(\\\"-\\\") events.append(tf) pfams_with_event.add(node_recon.index[i]) events.append(cf) #loss events elif loss_recon.loc[branch_id,\",\n \"> threshold: style[\\\"hz_line_type\\\"] = 1 style[\\\"hz_line_color\\\"] = 'green' style[\\\"hz_line_width\\\"] = 3 elif loss_recon.loc[branch_id,\",\n \"= 0, sep = \\\"\\\\t\\\") #read node and edge reconstruction matrices node_recon =\",\n \"pt_tree = pt_tree.search_nodes(name = target_node)[0] node2name = dict((i.name, i.name) for i in pt_tree.traverse(strategy\",\n \"= faces.TextFace(\\\"+\\\") cf = faces.CircleFace(radius = 8, style = \\\"circle\\\", color = adjusted_color)\"\n]","isConversational":false}}},{"rowIdx":2198,"cells":{"ngram":{"kind":"list like","value":["= CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) *","/ singular_values.min() else: raise NotImplementedError(\"The required method for condition number estimation is currently","singular_values.max() / singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK)","dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) *","= attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a","'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,","use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\",","# delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta","q(\"+\") * dS # a += delta_1(\"+\") * dot(u_hat, n) * q *","csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with","trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed matrix for single","+= (p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n)))","else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W","pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space","results) ** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n))","(dot(u, v) - div(v) * p - q * div(u)) * dx #","grad(q)) * dx L = f * q * dx # DG edge","- lambda_h) * n # HDG classical form a = -dot(u, grad(q)) *","solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in solvers_options: #","Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name,","test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities","n)) * ds # Weakly imposed BC from hybridization # a += mu_h","pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V =","BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2","currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V","a plot of a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form","delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4 = delta","* div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx #","= LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat =","avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms","= A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib()","# Average cell size and mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\"))","ds - dot(grad(p), q * n) * ds a += beta * p","h # method B in the Badia-Codina paper # eta_p = 1 #","= \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)","parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 =","= h / L0 # method B in the Badia-Codina paper eta_u_bc =","= delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4 =","Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1)","plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([])","** The terms below are based on ASGS Badia-Codina (2010), it is not","== \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values","* dS # a += delta_5 * (dot(u, n) - dot(u_hat, n)) *","convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS","# (inner(u, v) - q * div(u) - p * div(v) + inner(grad(p),","n) * q * ds # a += delta_1(\"+\") * lambda_h(\"+\") * jump(v,","% name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \"cg\":","* dS # Weakly imposed BC a += dot(u_hat, n) * q *","+= 0.5 * div(u) * div(v) * dx a += 0.5 * inner(curl(u),","a += delta_1 * div(u) * div(v) * dx a += delta_2 *","sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and mesh dependent stabilization h_avg =","p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h =","is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms","import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap)","L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1,","+= delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS a += delta_1 *","= delta delta_1 = delta delta_2 = delta delta_3 = 1 / h","dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge","* jump(v, n=n) * dS # a += delta_1 * lambda_h * dot(v,","L += 0.5 * h * h * f * div(v) * dx","parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h)","method for condition number estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1,","ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat =","div(v) * p + delta_0 * q * div(u)) * dx L =","Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0)","h # delta = Constant(1) # delta = h delta_0 = delta delta_1","= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\")","f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier","a += (beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\"))","\"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree","# Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q)","numpy array. :param array: Array with real and complex numbers. :param imag_threshold: Threshold",":param imag_threshold: Threshold to cut off imaginary part in complex number. :return: Filtered","+ last_degree, quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N,","ds a += dot(u_hat, n) * q * ds F = a -","v) - div(v) * p) * dx + lambda_h(\"+\") * jump(v, n) *","'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and","def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'","form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc(","sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f","in a numpy array. :param array: Array with real and complex numbers. :param","* inner(u + grad(p), v + grad(q)) * dx # a += 0.5","import SLEPc import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg')","* dx # Classical mixed Darcy eq. first-order terms as stabilizing terms a","n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a += delta_5 * (dot(u, n)","# Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0 =","result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result","space declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial and test functions u","* dx # Edge stabilizing terms # ** Badia-Codina based (better results) **","- p_boundaries) * mu_h * ds F = a - L a_form =","* p - q * div(u)) * dx # Stabilizing terms a +=","* p * ds - q * dot(u, n) * ds a +=","1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp =","nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): #","array with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array","ds # How to set this bc?? # a += (beta / h)","0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0]","the solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence study","* ds # may decrease convergente rates # ** The terms below are","n), jump(q, n)) * dS # ** Mesh independent (original) # a +=","tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form","exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) #","div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") #","* div(u)) * dx # Stabilizing terms a += -0.5 * inner((u +","parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p = L0","= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta","q(\"+\") * dS L = f * q * dx # Transmission condition","Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads","else 'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V =","= copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs,","* jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v, n)","as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from","return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads","u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities","plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0,","f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp","# is this necessary? L += s * dot(grad(q), n) * p_boundaries *","# beta = beta_0 # Numerical flux trace u_hat = u + beta","= TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form","results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" %","FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"]","of a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size","mu_h * (lambda_h - p_boundaries) * ds # a += mu_h * lambda_h","* delta_1 # * dx # ) # # These terms below are","* (lambda_h - p_boundaries) * ds # a += mu_h * lambda_h *","* dx a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a,","**kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5,","U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U","delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER / h","* dS # Volumetric stabilizing terms # a += 0.5 * h *","elif backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST)","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh,","solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U","beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 #","size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs =","v_hat = v + beta * (q - mu_h) * n # Flux","\"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend:","plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed","* dS # Weakly imposed BC a += lambda_h * dot(v, n) *","color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a","u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n =","method B in the Badia-Codina paper # eta_p = 1 # eta_p =","'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family,","= inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle","'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree)","* y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression =","jump(u_hat, n) * q(\"+\") * dS L = f * q * dx","* (lambda_h - p_boundaries) * ds # ) # maybe this is not","mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","* n # HDG classical form a = -dot(u, grad(q)) * dx +","stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing parameters based","curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based (better results)","= calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric","= assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp","q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)","= {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size =","2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 =","bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() #","degree) W = U * V * T # Trial and test functions","solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads:","the Badia-Codina paper # eta_p = 1 # eta_p = L0 * L0","a += jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed BC a","beta = beta_0 / h # beta = beta_0 # Numerical flux trace","solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg,","def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())","condensed hybrid-mixed matrix for single scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A","{name} - degree = {degree} - N = {n}\") mesh = UnitSquareMesh(n, n,","= delta_0 * inner(u + grad(p), v + grad(q)) * dx a +=","def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\" fig, ax =","+ grad(q)) * dx a += delta_1 * div(u) * div(v) * dx",":] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0,","\"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if","dx # Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) -","= 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V =","csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) -","based on Badia-Codina stabilized dG method # L0 = 1 # eta_p =","number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def","* dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3 *","q * n) * ds # may decrease convergente rates # ** Classical","(dot(u, v) - div(v) * p) * dx a += delta_1(\"+\") * lambda_h(\"+\")","name=name ) # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) #","= petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz","+= delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f *","# a += 0.5 * h * h * inner(curl(u), curl(v)) * dx","(p - lambda_h) * n v_hat = v + beta * (q -","result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =","n), jump(q, n)) * dS # Volumetric stabilizing terms # a += 0.5","velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters","trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx,","* dot(v, n) * ds a += dot(u_hat, n) * q * ds","singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values): singular_value =","method=\"geometric\") # Average cell size and mesh dependent stabilization h_avg = (h(\"+\") +","dot(u_hat, n) * q * ds F = a - L a_form =","= Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number","(eta_p / h) * dot(u, n) * dot(v, n) * ds a +=","15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\": list(), \"Number","= mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n","velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet","mu_h * lambda_h * ds # ### # a += ( # (mu_h","pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object):","is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function","bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space","/ h) * dot(u, n) * dot(v, n) * ds a += (eta_u","* dx + jump(u_hat, n) * q(\"+\") * dS L = f *","decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v,","= attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form,","delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4","= 1 # eta_p = L0 * h_avg # method B in the","# Stabilizing terms a += delta_1 * inner(u + grad(p), v + grad(q))","(original) # a += jump(u, n) * jump(v, n) * dS # not","and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float =","\"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree)","- 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\")","dx # a += 0.5 * h * h * div(u) * div(v)","* dot(u_projected, n) * q * ds # a += delta_1(\"+\") * lambda_h(\"+\")","= plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize()","DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 /","- 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form,","* h * inner(curl(u), curl(v)) * dx # L += 0.5 * h","terms a += delta_1 * (dot(u, v) - div(v) * p) * dx","if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh,","= (dot(u, v) - div(v) * p - q * div(u)) * dx","* p_boundaries * ds F = a - L a_form = lhs(F) _A","classical terms a = (dot(u, v) - div(v) * p + delta_0 *","necessary? L += s * dot(grad(q), n) * p_boundaries * ds F =","n)(\"+\")) * dS # a += delta_5 * (dot(u, n) - dot(u_hat, n))","L = f * q * dx # DG edge terms a +=","avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L = f","terms a += beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS # Weak","* div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx #","0.5 * h * h * f * div(v) * dx # a","= 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W","sin(2 * pi * x) * sin(2 * pi * y) exact_solution =","= Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and","= lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2] -","values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of","a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\")","* dx # ** Badia-Codina based a += -eta_u * inner(u + grad(p),","def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function to filter","Average cell size and mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\")) /","ds # a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS #","grad(p)), v + grad(q)) * dx # a += 0.5 * h *","singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \"slepc\": S","rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","of a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form,","= avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L =","Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs)","# Trial and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W)","flux trace u = -grad(p) u_hat = u + beta * (p -","ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0]","0.5 * div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v))","bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","q * div(u)) * dx # Stabilizing terms a += -0.5 * inner((u","* ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","+ f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot","TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y","dx a += delta_1 * div(u) * div(v) * dx a += delta_2","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads","TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") #","solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True,","dS # ** Mesh independent terms # a += jump(u, n) * jump(v,","C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V * T #","its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree,","terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\"+\")","Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx =","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh,","based on Badia-Codina stabilized dG method L0 = 1 eta_p = L0 *","L0 * h_avg # method B in the Badia-Codina paper eta_p = 1","* ds # # L = delta_1 * p_exact * dot(v, n) *","ds # a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v,","real and complex numbers. :param imag_threshold: Threshold to cut off imaginary part in","return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads","* p + q * div(u)) * dx # DG terms a +=","1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","1e-5 ): backend = backend.lower() if backend == \"scipy\": size = A.getSize() Mnp","= A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat","Constant(1.0e0) beta = beta_0 / h # beta = beta_0 # Numerical flux","beta * (q - mu_h) * n # Flux least-squares # a =","# a += ( # (mu_h - q) * (lambda_h - p_boundaries) *","ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] -","on Badia-Codina stabilized dG method L0 = 1 eta_p = L0 * h","for condition number estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False):","* sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e","n) * p * ds - q * dot(u, n) * ds #","TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =","u_hat = u + beta * (p - lambda_h) * n v_hat =","'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family","Weakly imposed BC a += lambda_h * dot(v, n) * ds a +=","assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =","= beta_0 / h # beta = beta_0 # Numerical flux trace u","Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") #","pressure_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" V =","use_quads=False): # Function space declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial and","Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()","* dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\")","h # beta = beta_0 # Numerical flux trace u = -grad(p) u_hat","** Badia-Codina based a += -eta_u * inner(u + grad(p), v + grad(q))","n) * ds # may decrease convergente rates # ** Classical Nitsche #","values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0]","* dx # a += -0.5 * inner(u + grad(p), v + grad(q))","a += eta_p * div(u) * div(v) * dx a += eta_p *","resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\")","= np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am =","# solution = Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h","How to set this bc?? # a += (beta / h) * (p-","result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # #","* dot(v, n) * ds # # L = delta_1 * p_exact *","+ beta * (q - mu_h) * n # Flux least-squares # a","Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp","* dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS a += beta(\"+\") *","in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\")","n) * p_boundaries * ds F = a - L a_form = lhs(F)","f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp","lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # Boundary terms # a +=","* n) * ds # may decrease convergente rates # ** Classical Nitsche","# ** Mesh independent terms # a += jump(u, n) * jump(v, n)","mu_h(\"+\") * jump(u_hat, n=n) * dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\"))","result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","* div(u) * div(v) * dx # a += 0.5 * h *","u_hat = u + beta * (p - lambda_h) * n # HDG","method D in the Badia-Codina paper eta_u = h / L0 # method","plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot","n) - dot(v_hat, n)) * ds # Weakly imposed BC from hybridization #","= 1e-5 ): backend = backend.lower() if backend == \"scipy\": size = A.getSize()","2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0","delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta =","- q * div(u) - p * div(v) + inner(grad(p), grad(q))) # *","dot(v_hat, n)) * ds # Weakly imposed BC from hybridization # a +=","as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form","VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\",","* dot(u, n) * ds # ** The terms below are based on","mu_h(\"+\") * dS # Weakly imposed BC a += dot(u_hat, n) * q","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): #","Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors =","pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form =","in the Badia-Codina paper eta_u = h / L0 # method B in","functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V,","q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution","* f * div(v) * dx # Irrotational least-squares a += delta_3 *","Badia-Codina paper # eta_p = L0 * L0 # method D in the","use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv","Badia-Codina paper # eta_u = h_avg / L0 # method B in the","# method B in the Badia-Codina paper # eta_u = 1 # Nitsche's","solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","curl(v)) * dx # Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n) *","div(u)) * dx # Stabilizing terms a += -0.5 * inner((u + grad(p)),","= split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) #","s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS","+= delta_1 * div(u) * div(v) * dx a += delta_2 * inner(curl(u),","symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') #","str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ'","backend.lower() if backend == \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","ds - q * dot(u, n) * ds # ** The terms below","25), quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\": list(), \"Number of Elements\":","singular_values.min() else: raise NotImplementedError(\"The required method for condition number estimation is currently unavailable.\")","beta_0 # Numerical flux trace u_hat = u + beta * (p -","# Mixed classical terms a = (dot(u, v) - div(v) * p -","Mixed classical terms a = (dot(u, v) - div(v) * p + q","cell size and mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0","or NIPG (1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p),","grad(q)) * ds - dot(grad(p), q * n) * ds a += beta","-eta_u * inner(u + grad(p), v + grad(q)) * dx a += eta_p","a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS a += delta_1","result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\",","/ h # Mixed classical terms a = (dot(u, v) - div(v) *","delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\"+\") *","+= mu_h * (lambda_h - p_boundaries) * ds # a += mu_h *","= Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W)",") return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration","backend == \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse:","= delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 =","f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp =","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration","* y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity')","eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a +=","* mu_h(\"+\") * dS # Weakly imposed BC a += dot(u_hat, n) *","convergente rates # ** Classical Nitsche # a += beta * p *","beta_0 / h(\"+\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 =","-delta_1 * dot(u_projected, n) * q * ds # a += delta_1(\"+\") *","ds a += (eta_u / h) * dot(p * n, q * n)","h delta_5 = delta # Numerical flux trace u_hat = u + beta","\"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar:","necessary _A = Tensor(a) A = _A.blocks S = A[2, 2] - A[2,","* h * h delta_2 = Constant(0.5) * h * h delta_3 =","def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())","p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h =","+= 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a +=","tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib()","# Below there is the spy alternative # plot = plt.spy(Am, **kwargs) #","parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta = beta_0","# a += 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5","ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot","Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q) -","csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False,","and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh","to set this bc?? # a += (beta / h) * (p- p_boundaries)","* dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) *","is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads =","# method B in the Badia-Codina paper # Mixed classical terms a =","dx # DG edge terms a += s * dot(jump(p, n), avg(grad(q))) *","trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout()","slepc4py import SLEPc import pandas as pd from tqdm import tqdm import os","+= (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS # **","s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx","= p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18)","= plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S","TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h","else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = V","degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family","= _A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv *","return df_cond_number # Solver options solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\":","= TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\")","= Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2),","functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h","u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W)","Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\")","dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","# maybe this is not a good way to impose BC, but this","result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =","n v_hat = v + beta * (q - mu_h) * n #","terms a = delta_1 * inner(u + grad(p), v + grad(q)) * dx","# \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, #","Least-squares terms a = delta_0 * inner(u + grad(p), v + grad(q)) *","functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh)","dx a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 *","* (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # Boundary terms","ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float","convergente rates # ** The terms below are based on ASGS Badia-Codina (2010),","a += -eta_u * inner(u + grad(p), v + grad(q)) * dx a","condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \"CG\",","np.ndarray: \"\"\"Utility function to filter real part in a numpy array. :param array:","VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V","return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family","S = A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1]","cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form,","* h * div(u) * div(v) * dx # a += 0.5 *","lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1,","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1,","'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U =","Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0)","* lambda_h * dot(v, n) * ds # Mass balance least-square a +=","delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5)","delta_2 = delta delta_3 = 1 / h delta_4 = 1 / h","matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize()","classical terms a = (dot(u, v) - div(v) * p + q *","grad(q)) * dx # a += 0.5 * div(u) * div(v) * dx","\"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values =","# ** Mesh independent ** # a += jump(u, n) * jump(v, n)","Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)","return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form,","# L = -delta_1 * dot(u_projected, n) * q * ds # a","h_avg) * (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) /","BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s","* ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size","Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta =","Weakly imposed boundary conditions a += dot(v, n) * p * ds -","= V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =","convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N","Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero entries","# \"sipg\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in","S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values): singular_value","* inner(curl(u), curl(v)) * dx # L += 0.5 * f * div(v)","= beta_0 / h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1)","= \"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous:","# Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx","0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides","dependent stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing parameters","dS # a += delta_1(\"+\") * dot(u_hat, n) * q * ds #","mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0,","based (better results) ** a += eta_u * avg(delta_3) * (jump(u, n) *","least-square a += delta_2 * div(u) * div(v) * dx # L =","else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\"","q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,","delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = 1 /","return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads","Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms","(p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) *","-dot(vel_projected, n) * v * ds # How to set this bc?? #","n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y =","dot(p * n, q * n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat","**kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed matrix for single scale problems.\"\"\"","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration","+= beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS #","grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","B in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term","= trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv *","a = inner(grad(p), grad(q)) * dx L = f * q * dx","* dx # L = delta_2 * f * div(v) * dx #","Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0 = 1","color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\")","div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L","= Constant(1.0) # delta = h delta_0 = delta delta_1 = delta delta_2","matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix","L += 0.5 * f * div(v) * dx A = assemble(a, bcs=bcs,","flux trace u_hat = u + beta * (p - lambda_h) * n","= TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) #","inner(grad(p), grad(q)) * dx L = f * q * dx # DG","bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig, ax =","Mesh independent ** # a += jump(u, n) * jump(v, n) * dS","function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0)","= Constant(1.0e0) beta = beta_0 / h # beta = beta_0 # Numerical","q * ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\")","= attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a","# Edge stabilizing terms # ** Badia-Codina based (better results) ** a +=","the Badia-Codina paper eta_p = 1 # eta_p = L0 * L0 #","= SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list","mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,","is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family =","Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0","+= delta_1 * inner(u + grad(p), v + grad(q)) * dx a +=","= (dot(u, v) - div(v) * p) * dx + lambda_h(\"+\") * jump(v,","maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False)","= delta delta_2 = delta delta_3 = delta delta_4 = delta # delta_4","* jump(v, n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat,","n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") * (q(\"+\")","# plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap)","# DG terms a += jump(v, n) * avg(p) * dS - avg(q)","* dot(u, n) * ds a += beta * p * q *","solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls,","scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py","n) * ds a += dot(u_hat, n) * q * ds F =","def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse: bool = False, zero_tol:","# L = delta_1 * p_exact * dot(v, n) * ds # Flux","+= 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat","real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str","h) * dot(p * n, q * n) * ds A = assemble(a,","inner(u + grad(p), v + grad(q)) * dx a += delta_1 * div(u)","T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family,","Mesh independent (original) # a += jump(u, n) * jump(v, n) * dS","div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx","= 1e-5) -> np.ndarray: \"\"\"Utility function to filter real part in a numpy","* ds # a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) *","# Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h *","with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def","NotImplementedError(\"The required method for condition number estimation is currently unavailable.\") return condition_number def","* div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v)) *","(dot(u, v) - div(v) * p + delta_0 * q * div(u)) *","N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size","* dot(v, n) * ds # Mass balance least-square a += delta_2 *","if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family","n) * mu_h(\"+\") * dS # Weakly imposed BC a += dot(u_hat, n)","* dx # Transmission condition a += jump(u_hat, n) * mu_h(\"+\") * dS","v) - div(v) * p) * dx a += delta_1(\"+\") * lambda_h(\"+\") *","solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc,","bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20,","= delta_0 * inner(u + grad(p), v + grad(q)) * dx # Classical","'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family,","dot(u, n) * ds a += beta * p * q * ds","V * T # Trial and test functions # solution = Function(W) #","velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs","* div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx # )","div(v) * p) * dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n)","= 1 for current_solver in solvers_options: # Setting the output file name name","f\"{current_solver}\" # Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing","Stabilizing terms a += -0.5 * inner((u + grad(p)), v + grad(q)) *","use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ'","delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina","# Numerical flux trace u = -grad(p) u_hat = u + beta *","A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat","beta = beta_0 / h # Mixed classical terms a = (dot(u, v)","delta_3 * p * q * ds # may decrease convergente rates a","in the Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0","curl(v)) * dx # L += 0.5 * h * h * f","use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' V","numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors,","dS # a += dot(jump(p, n), jump(q, n)) * dS # a +=","# plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([])","condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric","p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed BC a +=","= 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U","imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function to filter real part in","a += delta_2 * div(u) * div(v) * dx a += delta_3 *","* inner(u + grad(p), grad(q) - v) * dx a += eta_p *","form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh(","result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =","mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if","based a += eta_u * inner(u + grad(p), grad(q) - v) * dx","primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T","0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) #","Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(","h / L0 # method B in the Badia-Codina paper # eta_u =","is not a classical Nitsche's method a += (eta_p / h) * dot(u,","dot(u, n) * ds # ** The terms below are based on ASGS","Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2","term. Choose if the method is SIPG (-1) or NIPG (1) s =","* ds # Flux Least-squares as in DG a = delta_0 * inner(u","grad(q)) * dx a += delta_2 * div(u) * div(v) * dx a","as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from","jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina based a","based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) *","dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a +=","stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p =","div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx","str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family","inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v) * dx","\"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\":","+= (eta_p / h) * dot(u, n) * dot(v, n) * ds a","ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a","* dS a += eta_u_bc * delta_3 * p * q * ds","# Mass balance least-square a += delta_2 * div(u) * div(v) * dx","# Symmetry term. Choose if the method is SIPG (-1) or NIPG (1)","'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,","\"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(), } element_kind = \"Quad\"","= S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values):","exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f","dx # Stabilizing terms a += -0.5 * inner((u + grad(p)), v +","p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y =","* dx # ** Badia-Codina based a += eta_u * inner(u + grad(p),","* dx L = delta_0 * f * q * dx # Stabilizing","lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh","ds # Weakly imposed BC from hybridization # a += mu_h * (lambda_h","min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 # mesh","n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") -","svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd","quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\": list(), \"Number of Elements\": list(),","def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'","import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class","= U * V # Trial and test functions u, p = TrialFunctions(W)","n) * ds a += (eta_u / h) * dot(p * n, q","* q * ds # # L = -delta_1 * dot(u_projected, n) *","+= delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat,","FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T =","= beta_0 / h(\"+\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh,","hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat","# Weakly imposed boundary conditions a += dot(v, n) * p * ds","# Boundary terms # a += -dot(vel_projected, n) * v * ds #","div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx","assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver,","pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} - N","terms # a += -dot(vel_projected, n) * v * ds # How to","raise NotImplementedError(\"The required method for condition number estimation is currently unavailable.\") return condition_number","def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family","delta_2 * f * div(v) * dx # Hybridization terms a += lambda_h(\"+\")","- avg(q) * jump(u, n) * dS # Edge stabilizing terms # **","result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) #","p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh)","dx # ** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q)","boundary conditions a += s * dot(p * n, grad(q)) * ds -","* dx # ) # # These terms below are unsymmetric # a","functions # solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h","inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n)","the Badia-Codina paper # eta_u = h_avg / L0 # method B in","q * ds F = a - L a_form = lhs(F) _A =","petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form,","method a += (eta_p / h) * dot(u, n) * dot(v, n) *","* q * ds # # a += delta_1 * dot(u, n) *","= Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1] *","A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()","ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size =","result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V","dx # a += 0.5 * div(u) * div(v) * dx # a","h_avg = (h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing parameters based on","jump(v, n=n) * dS # a += delta_1 * lambda_h * dot(v, n)","jump(q, n)) * dS # Edge stabilizing terms a += beta(\"+\") * dot(jump(p,","* dot(u, n) * dot(v, n) * ds # ** Mesh independent **","solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family =","entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi","= UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not","degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family","h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms","jump(q, n)) * dS # a += p * q * ds A","n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds a +=","# plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap)","dx # a += 0.5 * inner(curl(u), curl(v)) * dx # L +=","singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method","if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,","# a += delta_1 * dot(u, n) * q * ds # #","dot(v_hat, n)(\"+\")) * dS # a += delta_5 * (dot(u, n) - dot(u_hat,","petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors","import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function","* p + delta_0 * q * div(u)) * dx L = delta_0","* q * ds F = a - L a_form = lhs(F) _A","ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot = plt.spy(Am,","FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W","= plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return","- p * div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx","= Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx L","# Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 =","pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing","return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads","to cut off imaginary part in complex number. :return: Filtered array with only","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family","dx # Classical mixed Darcy eq. first-order terms as stabilizing terms a +=","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1):","sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs","= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh)","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False","* dx # DG terms a += jump(v, n) * avg(p) * dS","= Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 =","dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a += beta(\"+\") *","T = FunctionSpace(mesh, trace_family, degree) W = U * V * T #","div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx A =","and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :]","- A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs)","n) * q * ds F = a - L a_form = lhs(F)","* h * f * div(v) * dx a += 0.5 * div(u)","delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\"))","f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs =","rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[...,","q * dx # Stabilizing terms a += delta_1 * inner(u + grad(p),","a += mu_h * lambda_h * ds # ### # a += (","= delta_2 * f * div(v) * dx # Irrotational least-squares a +=","form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat","(dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC from hybridization","-dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS L =","= TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y","dx L = f * q * dx # Hybridization terms a +=","* n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True)","n), jump(q, n)) * dS # Weak boundary conditions a += s *","n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds #","+= jump(u, n) * jump(v, n) * dS # a += dot(jump(p, n),","bool = False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend","UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') #","h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original)","# my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # #","to filter real part in a numpy array. :param array: Array with real","dot(p * n, q * n) * ds # may decrease convergente rates","h delta_3 = Constant(0.5) * h * h # Mixed classical terms a","# Numerical flux trace u_hat = u + beta * (p - lambda_h)","n)) * dS a += eta_u_bc * delta_3 * p * q *","\"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values =","dot(u, n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\")","Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs","the original paper # a += dot(jump(p, n), jump(q, n)) * dS #","# Edge stabilizing terms a += beta(\"+\") * dot(jump(p, n), jump(q, n)) *","** # a += jump(u, n) * jump(v, n) * dS # a","condition number estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): #","assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp","\"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT'","# eta_u_bc = h / L0 # method B in the Badia-Codina paper","use_quads else 'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if","Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed","- 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp,","a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size =","# print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') #","= 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family,",":idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size =","and its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver,","p_exact = sin(2 * pi * x) * sin(2 * pi * y)","sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs","stabilizing terms a += delta_1 * (dot(u, v) - div(v) * p) *","* lambda_h * ds # ### # a += ( # (mu_h -","# Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh)","q(\"+\")) * dS # Weakly imposed BC a += (p_boundaries * dot(v, n)","assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size","_A = Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx]","bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta","size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate","* p + q * div(u)) * dx # Stabilizing terms a +=","= 1 / h delta_4 = 1 / h # Least-squares terms a","= _A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv *","- lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # Boundary terms # a","= beta_0 / h # beta = beta_0 # Numerical flux trace u_hat","= 1 last_degree = 1 for current_solver in solvers_options: # Setting the output","on Badia-Codina stabilized dG method # L0 = 1 # eta_p = L0","# Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h #","list(), \"Condition Number\": list(), } element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar","name = f\"{current_solver}\" # Selecting the solver and its kwargs solver = solvers_options[current_solver]","* avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a += eta_p","solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U","h) * (p- p_boundaries) * q * ds # is this necessary? L","q * div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1","def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed matrix","- div(v) * p - q * div(u)) * dx # Stabilizing terms","= U * V * T # Trial and test functions # solution","numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\":","* dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed","v) * dx a += eta_p * div(u) * div(v) * dx #","Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name )","sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0],","dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed","* h # Mixed classical terms a = (dot(u, v) - div(v) *","# BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0)","# L += 0.5 * f * div(v) * dx A = assemble(a,","= np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else:","= Constant(-0.5) * h * h delta_2 = Constant(0.5) * h * h","* q * dx # Stabilizing terms a += delta_1 * inner(u +","a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a","v) - div(v) * p + delta_0 * q * div(u)) * dx","in DG a = delta_0 * inner(u + grad(p), v + grad(q)) *","(dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a += delta_5 * (dot(u,","+= 0.5 * h * h * f * div(v) * dx a","+ grad(p), v + grad(q)) * dx # Classical mixed Darcy eq. first-order","fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx =","# Solver options solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, #","# Mixed classical terms a = (dot(u, v) - div(v) * p +","\"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh)","L0 # method B in the Badia-Codina paper # eta_u = 1 #","= DirichletBC(V, 0.0, \"on_boundary\") # Variational form a = inner(grad(u), grad(v)) * dx","# Trial and test functions # solution = Function(W) # u, p, lambda_h","Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact,","dx + jump(u_hat, n) * q(\"+\") * dS L = f * q","p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n","complex number. :return: Filtered array with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag)","* ds # may decrease convergente rates a += eta_u_bc * delta_4 *","a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a","grad(q)) * dx a += delta_1 * div(u) * div(v) * dx a","# \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1","mu_h * ds F = a - L a_form = lhs(F) _A =","v) - div(v) * p + q * div(u)) * dx # Stabilizing","terms # a += 0.5 * inner(u + grad(p), grad(q) - v) *","mu_h) * n # Flux least-squares # a = ( # (inner(u, v)","(avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS a +=","* (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) *","scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm",") # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result","terms a = (dot(u, v) - div(v) * p + delta_0 * q","sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing","p * ds - q * dot(u, n) * ds a += beta","assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =","ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray:","Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs","# method D in the Badia-Codina paper eta_u = h / L0 #","# a += delta_4 * (p - lambda_h) * (q - mu_h) *","path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options =","n))) * ds a += beta * (lambda_h - p_boundaries) * mu_h *","0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #","n), jump(q, n)) * dS a += eta_u_bc * delta_3 * p *","L0 # method D in the Badia-Codina paper eta_u = h / L0","bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and mesh dependent","= L0 * L0 # method D in the Badia-Codina paper # eta_u","the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) #","dx a += eta_p * div(u) * div(v) * dx a += eta_p","a += (beta / h) * (p- p_boundaries) * q * ds #","q * dot(u, n) * ds # ** The terms below are based","declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family =","* f * div(v) * dx # a += -0.5 * inner(u +","h # method B in the Badia-Codina paper # eta_p = L0 *","bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space","= 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh,","* dS # Boundary terms # a += -dot(vel_projected, n) * v *","= delta # Numerical flux trace u_hat = u + beta * (p","q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12)","is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space","dot(u, n) * dot(v, n) * ds # ** Mesh independent ** #","DirichletBC(V, 0.0, \"on_boundary\") # Variational form a = inner(grad(u), grad(v)) * dx A","Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") #","eta_u = h / L0 # method B in the Badia-Codina paper #","= csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000,","max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} - degree","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration","* h # method B in the Badia-Codina paper # eta_p = 1","# delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) #","plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\" fig, ax","= FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family","(p- p_boundaries) * q * ds # is this necessary? L += s","= assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size =","jump(q, n)) * dS a += eta_u_bc * delta_3 * p * q","= 1 eta_p = L0 * h # method B in the Badia-Codina","beta = beta_0 # Numerical flux trace u = -grad(p) u_hat = u","'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" U","+= s * dot(p * n, grad(q)) * ds - dot(grad(p), q *","# HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n)","Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy","n) * dS # a += dot(jump(p, n), jump(q, n)) * dS #","* sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") #","h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = 1","2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2,","axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold:","dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\")","Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0)","+= s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) *","trace_family, degree) W = V * T # Trial and test functions #","# delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) #","q * dot(u, n) * ds a += beta * p * q","/ h # Symmetry term. Choose if the method is SIPG (-1) or","inner((u + grad(p)), v + grad(q)) * dx # a += 0.5 *","\"Condition Number\": list(), } element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar =","* div(u) * div(v) * dx # a += 0.5 * inner(curl(u), curl(v))","parameters based on Badia-Codina stabilized dG method # L0 = 1 # eta_p","pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh,","matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form,","DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 /","Constant(1) # delta = h delta_0 = delta delta_1 = delta delta_2 =","A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz","n=n) * q(\"+\") * dS # a += delta_1(\"+\") * dot(u_hat, n) *","# Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) #","A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric","* (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed","bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta =","* dx # a += 0.5 * inner(curl(u), curl(v)) * dx # **","result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","dx # Weakly imposed boundary conditions a += dot(v, n) * p *","n) * mu_h(\"+\") * dS # Weakly imposed BC a += lambda_h *","this necessary _A = Tensor(a) A = _A.blocks S = A[2, 2] -","\"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1)","* dx # Stabilizing terms a += -0.5 * inner((u + grad(p)), v","dx # L = delta_2 * f * div(v) * dx # Irrotational","hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U *","Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p),","exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs #","svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc","* q * dx # Transmission condition a += jump(u_hat, n) * mu_h(\"+\")","h # beta = beta_0 # Numerical flux trace u_hat = u +","0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx]","* dx # L += 0.5 * f * div(v) * dx A","* p) * dx + lambda_h(\"+\") * jump(v, n) * dS a +=","LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat = u","\"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\":","def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell())","tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} -","method # L0 = 1 # eta_p = L0 * h_avg # method","* ds # is this necessary? L += s * dot(grad(q), n) *","else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p","if the method is SIPG (-1) or NIPG (1) s = Constant(-1) #","= a - L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks","DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0","ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot","v + grad(q)) * dx a += delta_2 * div(u) * div(v) *","* q * dx # Hybridization terms a += s * dot(grad(q), n)(\"+\")","delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS a += delta_1 * lambda_h","os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number =","\"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(), } element_kind","div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx A =","+ beta * (p - lambda_h) * n v_hat = v + beta","a = (dot(u, v) - div(v) * p + q * div(u)) *","# \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\":","dx # L += 0.5 * f * div(v) * dx A =","\"scipy\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend = backend.lower()","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space","Numerical flux trace u = -grad(p) u_hat = u + beta * (p","- q * dot(u, n) * ds a += beta * p *","solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg,","h beta_avg = beta_0 / h(\"+\") # Stabilizing parameter # delta_0 = Constant(1)","matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative #","= DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a = (dot(u, v) -","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function","Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0 # method","test functions # solution = Function(W) # u, p, lambda_h = split(solution) u,","- lambda_h) * (q - mu_h) * ds # a += delta_5 *","Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =","beta0 = Constant(1e1) beta = beta0 / h # Symmetry term. Choose if","delta_1 # * dx # ) # # These terms below are unsymmetric","pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) #","terms as stabilizing terms a += delta_1 * (dot(u, v) - div(v) *","div(v) * dx # L = delta_2 * f * div(v) * dx","= 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1)","# delta = Constant(1) # delta = h delta_0 = delta delta_1 =","dS # ** Mesh independent (original) # a += jump(u, n) * jump(v,","dx # Edge stabilizing terms # ** Badia-Codina based (better results) ** a","petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True","# mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is","= assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =","idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S,","(better results) ** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v,","= FunctionSpace(mesh, trace_family, degree) W = U * V * T # Trial","div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx L +=","if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for","* n, grad(q)) * ds - dot(grad(p), q * n) * ds a","Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree)","( # (inner(u, v) - q * div(u) - p * div(v) +","class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator =","inner(grad(p), grad(q))) # * delta_1 # * dx # ) # # These","+= s * dot(grad(q), n) * p_boundaries * ds F = a -","ds # ### # a += ( # (mu_h - q) * (lambda_h","from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib()","\"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\":","* (dot(u, v) - div(v) * p) * dx a += delta_1(\"+\") *","return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a condensed","list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(), }","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space","solution = Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h =","inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a += dot(v, n)","V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(","delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3","a += (eta_p / h) * dot(u, n) * dot(v, n) * ds","a += 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 *","and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n","delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # a","jump(u, n) * jump(v, n) * dS # a += dot(jump(p, n), jump(q,","import * import numpy as np import matplotlib.pyplot as plt import matplotlib from","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ):","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh,","L += 0.5 * f * div(v) * dx # ** Badia-Codina based","# ** The terms below are based on ASGS Badia-Codina (2010), it is","B in the Badia-Codina paper # eta_p = L0 * L0 # method","solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict","lambda_h) * n # HDG classical form a = -dot(u, grad(q)) * dx","beta_0 # Numerical flux trace u = -grad(p) u_hat = u + beta","inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based (better","# # These terms below are unsymmetric # a += delta_1 * jump(u_hat,","int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number,","+= -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS L","a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1]","stabilizing terms # ** Badia-Codina based (better results) ** a += eta_u *","n) * ds a += beta * p * q * ds A","L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2,","num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise","assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh,","dot(u_projected, n) * q * ds # a += delta_1(\"+\") * lambda_h(\"+\") *","quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1","+= dot(u_hat, n) * q * ds F = a - L a_form","has not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number =","lambda_h * dot(v, n) * ds a += dot(u_hat, n) * q *","= str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' V =","p_boundaries * ds F = a - L a_form = lhs(F) _A =","= TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h","dx # Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n) * dS a","np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function to filter real part","= Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,","petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and","h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = delta","dot(grad(p), q * n) * ds a += beta * p * q","num_of_factors, backend: str = \"scipy\", use_sparse: bool = False, zero_tol: float = 1e-5","= singular_values.max() / singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A)","delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares as in","div(v) * p) * dx + lambda_h(\"+\") * jump(v, n) * dS a","dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds a","* ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm(","delta_1 * lambda_h * dot(v, n) * ds # # L = delta_1","+= eta_p * div(u) * div(v) * dx # Weakly imposed boundary conditions","* q * div(u)) * dx L = delta_0 * f * q","x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi *","Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n))","q * ds # is this necessary? L += s * dot(grad(q), n)","A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,","full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\")","p = TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h","- dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries) *","# u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q,","f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1)","ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size","+= beta * p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat","div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge","/ h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent","**kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def","matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") #","below are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\"+\") *","= assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p), v +","+ grad(q)) * dx # a += 0.5 * div(u) * div(v) *","from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from","result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads =","* lambda_h(\"+\") * jump(v, n=n) * dS # a += delta_1 * lambda_h","* ds # ** The terms below are based on ASGS Badia-Codina (2010),","a += delta_1 * lambda_h * dot(v, n) * ds # Mass balance","* ds - q * dot(u, n) * ds a += beta *","A = _A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv","mu_h(\"+\")) * dS # Boundary terms # a += -dot(vel_projected, n) * v","the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and","pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression","delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat","stabilized dG method L0 = 1 eta_p = L0 * h # method","# delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) #","+ grad(q)) * dx # Classical mixed Darcy eq. first-order terms as stabilizing","Hybridization terms a += s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) *","terms a = (dot(u, v) - div(v) * p - q * div(u))","/ n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size)","* dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS #","jump(q, n)) * dS # Weak boundary conditions a += s * dot(p","= \"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in","S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for","functions # solution = Function(W) # u, p, lambda_h = split(solution) u, p,","decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, #","- A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier)","delta_5 = delta # Numerical flux trace u_hat = u + beta *","a classical Nitsche's method a += (eta_p / h) * dot(u, n) *","= A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat","else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family,","form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg(","Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values","= TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact","* div(v) * dx # Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\")","- mu_h(\"+\")) * dS # Boundary terms # a += -dot(vel_projected, n) *","1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial","(mu_h - q) * (lambda_h - p_boundaries) * ds # ) # maybe","# may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle",":return: Filtered array with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5]","if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement =","solution p_exact = sin(2 * pi * x) * sin(2 * pi *","+ grad(p), v + grad(q)) * dx a += delta_1 * div(u) *","= Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2] *","v + grad(q)) * dx a += eta_p * div(u) * div(v) *","results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\"","# Stabilizing terms a += -0.5 * inner((u + grad(p)), v + grad(q))","assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","= (dot(u, v) - div(v) * p + q * div(u)) * dx",") return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25),","n # HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat,","grad(p), v + grad(q)) * dx a += delta_2 * div(u) * div(v)","mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","\"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h","h(\"+\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) #","0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there","* h * h * inner(curl(u), curl(v)) * dx # L += 0.5","= V * T # Trial and test functions # solution = Function(W)","Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy",") return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())","nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number =","def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family","# # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\"sparse_pattern.png\") #","* (q - mu_h) * n # Flux least-squares # a = (","\"on_boundary\") # Mixed classical terms a = (dot(u, v) - div(v) * p","numbers. :param imag_threshold: Threshold to cut off imaginary part in complex number. :return:","* p * q * ds # may decrease convergente rates (Nitsche) A","\"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree)","div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta =","(dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds","= assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()","sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 =","{ # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm,","# a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based","div(v) * p - q * div(u)) * dx # Stabilizing terms a","- 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot","* ds a += dot(u_hat, n) * q * ds F = a","ds # # L = delta_1 * p_exact * dot(v, n) * ds","\"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT,","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ):","div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs","= 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh,","beta_0 / h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1","hybrid-mixed matrix for single scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A =","(dot(u, v) - div(v) * p + q * div(u)) * dx #","mu_h(\"+\")) * dS a += (beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) *","* (p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") -","delta delta_3 = 1 / h delta_4 = 1 / h # Least-squares","- A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs)","complex numbers. :param imag_threshold: Threshold to cut off imaginary part in complex number.","+= delta_1 * lambda_h * dot(v, n) * ds # Mass balance least-square","(p - lambda_h) * n # HDG classical form a = (dot(u, v)","\"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = U * V *","\"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms,","+= beta * p * q * ds # may decrease convergente rates","u + beta * (p - lambda_h) * n v_hat = v +","grad(p), v + grad(q)) * dx # a += 0.5 * div(u) *",") # maybe this is not a good way to impose BC, but","parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0 /","FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and test functions","velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V","trace u = -grad(p) u_hat = u + beta * (p - lambda_h)","p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) #","lambda_h(\"+\") * jump(v, n) * dS a += -dot(u, grad(q)) * dx +","dS # a += p * q * ds A = assemble(a, mat_type=\"aij\")","* (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC from","# Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\")","n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number","Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #","+ grad(p), grad(q) - v) * dx # a += 0.5 * h","# a += (beta / h) * (p- p_boundaries) * q * ds","in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} -","filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function to filter real","dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a +=","use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M","BC a += lambda_h * dot(v, n) * ds a += dot(u_hat, n)","impose BC, but this necessary _A = Tensor(a) A = _A.blocks S =","# * dx # ) # # These terms below are unsymmetric #","name name = f\"{current_solver}\" # Selecting the solver and its kwargs solver =","= DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0","= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\")","in solvers_options: # Setting the output file name name = f\"{current_solver}\" # Selecting","values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return","h * f * div(v) * dx # a += -0.5 * inner(u","# a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS # a","# Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization","degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial","n, q * n) * ds # may decrease convergente rates # **","(p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # Boundary terms #","= Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) *","grad(q)) * dx L = f * q * dx # Hybridization terms","condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz,","Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry","= (dot(u, v) - div(v) * p + delta_0 * q * div(u))","* ds # How to set this bc?? # a += (beta /","p + delta_0 * q * div(u)) * dx L = delta_0 *","-0.5 * inner((u + grad(p)), v + grad(q)) * dx # a +=","+ grad(p), v + grad(q)) * dx # a += 0.5 * div(u)","\"sipg\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in solvers_options:","# Hybridization terms a += s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\"))","as stabilizing terms a += delta_1 * (dot(u, v) - div(v) * p)","list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(), } element_kind = \"Quad\" if","+= delta_1 * dot(u, n) * q * ds # # L =","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1,","* (p - lambda_h) * (q - mu_h) * ds # a +=","use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions","if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test","bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize()","= Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2] *","/ h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 =","trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = U *","f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization","Weakly imposed BC a += dot(u_hat, n) * q * ds F =","color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] +","import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number","A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values =","= TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y","zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method for condition","u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y","# \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\":","n) * dS # not considered in the original paper # a +=","Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\",","+= delta_4 * (p - lambda_h) * (q - mu_h) * ds #","A = _A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx]","fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle","V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and","\"dofs\": list(), \"h\": list(), \"Condition Number\": list(), } element_kind = \"Quad\" if quadrilateral","result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =","v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational","LARGE_NUMBER = Constant(1e0) delta = h * h # delta = Constant(1) #","solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver","solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U","Constant(-0.5) * h * h delta_2 = Constant(0.5) * h * h delta_3","color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a","a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms #","* (p- p_boundaries) * q * ds # is this necessary? L +=","# Least-squares terms a = delta_0 * inner(u + grad(p), v + grad(q))","= str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family =","* pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U,","method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta","n=n) * dS # a += delta_1 * lambda_h * dot(v, n) *","mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver","Filtered array with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return","q * n) * ds a += beta * p * q *","axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am,","s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p),","- dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly","velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") #","A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat","# Setting the output file name name = f\"{current_solver}\" # Selecting the solver","grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS L = f","degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family","array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\",","* inner(u + grad(p), v + grad(q)) * dx a += eta_p *","axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides","matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib()","n) - dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries)","nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number =","calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs","* ds - dot(grad(p), q * n) * ds a += beta *","mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size","Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors =","dot(v, n) * ds a += dot(u_hat, n) * q * ds F","= svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() /","= u + beta * (p - lambda_h) * n v_hat = v","paper eta_p = 1 # eta_p = L0 * L0 # method D","term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical terms","A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat =","= beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h","curl(v)) * dx # L += 0.5 * f * div(v) * dx","paper # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing","and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot","a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize()","= FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and test","a += 0.5 * div(u) * div(v) * dx a += 0.5 *","+= dot(jump(p, n), jump(q, n)) * dS # a += p * q","name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number","plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed matrix for","= A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks","a += eta_u_bc * delta_3 * p * q * ds # may","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function","{result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg')","= FunctionSpace(mesh, \"CG\", degree) # Trial and test functions u = TrialFunction(V) v",":2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat =","\"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { #","import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as","B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta =","* jump(u_hat, n=n) * dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) *","conditions a += dot(v, n) * p * ds - q * dot(u,","not a classical Nitsche's method a += (eta_p / h) * dot(u, n)","Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size","a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n))","# a += mu_h * lambda_h * ds # ### # a +=","dS # Weakly imposed BC a += lambda_h * dot(v, n) * ds","= 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V","Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a =","off imaginary part in complex number. :return: Filtered array with only real numbers.","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration","10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\": list(),","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads","ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) - v) *","= ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0]","+= 0.5 * h * h * f * div(v) * dx #","sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 =","/ 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method L0","nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides","f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact,","* div(v) * dx # ** Badia-Codina based a += eta_u * inner(u","1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs,","beta * p * q * ds # may decrease convergente rates (Nitsche)","A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric","delta_0 * q * div(u)) * dx L = delta_0 * f *","solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree +","v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)","a += eta_p * div(u) * div(v) * dx # Weakly imposed boundary","* div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","lambda_h * ds # ### # a += ( # (mu_h - q)","# ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) *","L0 * L0 # method D in the Badia-Codina paper # eta_u =","# delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h","* inner(u + grad(p), v + grad(q)) * dx # Classical mixed Darcy","print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import","inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u * inner(u","= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h","A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","volumetric terms a = inner(grad(p), grad(q)) * dx L = f * q","method B in the Badia-Codina paper # Mixed classical terms a = (dot(u,","ds F = a - L a_form = lhs(F) _A = Tensor(a_form) A","dx # Stabilizing terms a += delta_1 * inner(u + grad(p), v +","= Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 =","entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp =","TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =","to impose BC, but this necessary _A = Tensor(a) A = _A.blocks S","numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} - N = {n}\") mesh =","attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\"","* mu_h(\"+\") * dS # Weakly imposed BC a += lambda_h * dot(v,","# delta = h delta_0 = delta delta_1 = delta delta_2 = delta","else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values >","L0 = 1 eta_p = L0 * h # method B in the","a += jump(u, n) * jump(v, n) * dS # not considered in","- lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) *","(p - lambda_h) * n # HDG classical form a = -dot(u, grad(q))","# a += delta_1 * lambda_h * dot(v, n) * ds # #","= { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\":","alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0)","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration","q * ds # may decrease convergente rates # ** The terms below","** Mesh independent terms # a += jump(u, n) * jump(v, n) *","results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict)","assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp =","+= jump(v, n) * avg(p) * dS - avg(q) * jump(u, n) *","q * n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","div(u) * div(v) * dx # L = delta_2 * f * div(v)","* div(v) * dx # a += 0.5 * div(u) * div(v) *","= L0 * h # method B in the Badia-Codina paper # eta_p",":1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric =","= A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp,","delta = h * h # delta = Constant(1) # delta = h","delta_2 = delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER","Trace\" T = FunctionSpace(mesh, trace_family, degree) W = U * V * T","0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 * f *","): results_dict = { \"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\":","+= delta_2 * div(u) * div(v) * dx a += delta_3 * inner(curl(u),","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family","* h * f * div(v) * dx # a += -0.5 *","dx + lambda_h(\"+\") * jump(v, n) * dS a += -dot(u, grad(q)) *","= assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()","# delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) #","/ h # Least-squares terms a = delta_0 * inner(u + grad(p), v","n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS #","a += s * dot(p * n, grad(q)) * ds - dot(grad(p), q","( # (mu_h - q) * (lambda_h - p_boundaries) * ds # )","= -grad(p) u_hat = u + beta * (p - lambda_h) * n","A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv","terms # a += jump(u, n) * jump(v, n) * dS # a","= lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1] -","A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat","delta_0 * inner(u + grad(p), v + grad(q)) * dx # Classical mixed","Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx] - A[idx,","a += ( # (mu_h - q) * (lambda_h - p_boundaries) * ds","* (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a += delta_5 *","# Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 =","* (p - lambda_h) * n v_hat = v + beta * (q","# LARGE_NUMBER = Constant(1e0) delta = h * h # delta = Constant(1)","f * div(v) * dx # a += 0.5 * div(u) * div(v)","delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h #","ds a += beta * p * q * ds A = assemble(a,","calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse: bool = False, zero_tol: float","h * h * f * div(v) * dx # a += 0.5","dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") *","pressure_family, degree) W = U * V # Trial and test functions u,","form a = (dot(u, v) - div(v) * p) * dx + lambda_h(\"+\")","function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries","= Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\")","# BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\")","** Classical Nitsche # a += beta * p * q * ds","solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 #","+ q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat =","= calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric,","# Transmission condition a += jump(u_hat, n) * mu_h(\"+\") * dS # Weakly","paper eta_u = 1 # eta_u_bc = h / L0 # method B","- q * div(u)) * dx # DG terms a += jump(v, n)","solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1 last_degree =","* q * ds # may decrease convergente rates a += eta_u_bc *","beta_0 / h # beta = beta_0 # Numerical flux trace u =","Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u,","FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U * V","inner(u + grad(p), v + grad(q)) * dx # Classical mixed Darcy eq.","a += delta_4 * (p - lambda_h) * (q - mu_h) * ds","dS a += delta_1 * lambda_h * dot(v, n) * ds # Mass","os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results)","solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') #","singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values =","stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q) - v)","== \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values","dx # L += 0.5 * h * h * f * div(v)","return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh,","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh,","* A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","\"\"\"Provides a plot of a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat","S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for i","* q * dx # DG edge terms a += s * dot(jump(p,","Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp,","is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement)","dot(u_hat, n) * q * ds # # a += delta_1 * dot(u,","beta_0 / h beta_avg = beta_0 / h(\"+\") # Stabilizing parameter # delta_0","Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" )","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1,","a = (dot(u, v) - div(v) * p) * dx + lambda_h(\"+\") *","* dS a += delta_1 * lambda_h * dot(v, n) * ds #","results_dict = { \"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(),","* dot(u, n) * dot(v, n) * ds a += (eta_u / h)","= singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required","jump(v, n) * dS # a += dot(jump(p, n), jump(q, n)) * dS","dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary","attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig, ax =","> zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method for","* f * div(v) * dx # a += 0.5 * div(u) *","in the Badia-Codina paper eta_p = 1 # eta_p = L0 * L0","import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form =","/ h delta_5 = delta # Numerical flux trace u_hat = u +","hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ):","lambda_h) * n v_hat = v + beta * (q - mu_h) *","(dot(u, n) - dot(u_projected, n))) * ds a += beta * (lambda_h -","dx # Hybridization terms a += s * dot(grad(q), n)(\"+\") * (p(\"+\") -","Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function","beta = beta_0 / h beta_avg = beta_0 / h(\"+\") # Stabilizing parameter","# print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number:","\"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0],","Boundary terms # a += -dot(vel_projected, n) * v * ds # How","grad(q) - v) * dx # a += 0.5 * h * h","is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz =","terms a += -0.5 * inner((u + grad(p)), v + grad(q)) * dx","delta # Numerical flux trace u_hat = u + beta * (p -","copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) #","return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values","delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) * h *","+= ( # (mu_h - q) * (lambda_h - p_boundaries) * ds #","= DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0","* V * T # Trial and test functions # solution = Function(W)","sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0","ds # is this necessary? L += s * dot(grad(q), n) * p_boundaries","are based on ASGS Badia-Codina (2010), it is not a classical Nitsche's method","a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS # a +=","delta delta_2 = delta delta_3 = 1 / h delta_4 = 1 /","matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp","== 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13)","0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5,","num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier )","- mu_h(\"+\")) * dS # a += delta_4 * (p - lambda_h) *","v + grad(q)) * dx # Classical mixed Darcy eq. first-order terms as","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration","terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n)","dS # a += delta_1 * lambda_h * dot(v, n) * ds #","B in the Badia-Codina paper # eta_p = 1 # eta_p = L0","* delta_4 * dot(u, n) * dot(v, n) * ds # ** Mesh","= Constant(0.5) * h * h # Mixed classical terms a = (dot(u,","# beta = beta_0 # Numerical flux trace u = -grad(p) u_hat =","+= delta_1(\"+\") * dot(u_hat, n) * q * ds # # a +=","imag_threshold: Threshold to cut off imaginary part in complex number. :return: Filtered array","n) * q(\"+\") * dS L = f * q * dx #","* x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\",","mu_h * (dot(u, n) - dot(u_projected, n))) * ds a += beta *","attr from firedrake import * import numpy as np import matplotlib.pyplot as plt","mu_h(\"+\") * dot(u, n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\"))","real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A,","= list() if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i)","* div(v) * dx # a += 0.5 * h * h *","a = ( # (inner(u, v) - q * div(u) - p *","B in the Badia-Codina paper eta_u = 1 # eta_u_bc = h /","a += delta_1 * inner(u + grad(p), v + grad(q)) * dx a","* div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v))","= FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and test","= DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1)","h * h delta_3 = Constant(0.5) * h * h # Mixed classical","Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 *","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): #","sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1","fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp =","V = FunctionSpace(mesh, \"CG\", degree) # Trial and test functions u = TrialFunction(V)","a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected,","delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5","= CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 *",") return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG'","inner(curl(u), curl(v)) * dx # L += 0.5 * f * div(v) *","= FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement =","a += beta * (lambda_h - p_boundaries) * mu_h * ds F =","# print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix","print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix #","Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form a = inner(grad(u),","* inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a += dot(v,","= assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp","(inner(u, v) - q * div(u) - p * div(v) + inner(grad(p), grad(q)))","(p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\"))","dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms # a +=","part in complex number. :return: Filtered array with only real numbers. \"\"\" real_part_array","(lambda_h - p_boundaries) * ds # a += mu_h * lambda_h * ds","test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs =","* n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function","list(), \"h\": list(), \"Condition Number\": list(), } element_kind = \"Quad\" if quadrilateral else","int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number,","} degree = 1 last_degree = 1 for current_solver in solvers_options: # Setting","= -delta_1 * dot(u_projected, n) * q * ds # a += delta_1(\"+\")","n) * dot(v, n) * ds # ** Mesh independent ** # a","may decrease convergente rates # ** Classical Nitsche # a += beta *","* h * f * div(v) * dx # a += 0.5 *","# method B in the Badia-Codina paper eta_u = 1 # eta_u_bc =","\"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(),","dx a += delta_2 * div(u) * div(v) * dx a += delta_3","# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\")","- N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree)","* dot(p * n, grad(q)) * ds - dot(grad(p), q * n) *","# a += -dot(vel_projected, n) * v * ds # How to set","assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size","# eta_p = L0 * h_avg # method B in the Badia-Codina paper","Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p","* q * ds # a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n)","ds # may decrease convergente rates # ** The terms below are based","* (jump(u, n) * jump(v, n)) * dS a += eta_p * avg(delta_4)","paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta =","is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family =","lambda_h) * n # HDG classical form a = (dot(u, v) - div(v)","# Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 =","in the Badia-Codina paper # eta_p = 1 # eta_p = L0 *","a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\")","BC a += dot(u_hat, n) * q * ds F = a -","- div(v) * p + q * div(u)) * dx # Stabilizing terms","plot of a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size","grad(q)) * dx a += eta_p * div(u) * div(v) * dx a","dx # DG terms a += jump(v, n) * avg(p) * dS -","S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values","'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree)","* dx a += delta_2 * div(u) * div(v) * dx a +=","assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()","(eta_u / h) * dot(p * n, q * n) * ds #","ds # may decrease convergente rates # ** Classical Nitsche # a +=","result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return","Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()","Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg =","1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse: bool","a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds","list() if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value)","h) * dot(u, n) * dot(v, n) * ds a += (eta_u /","plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot","imposed BC a += lambda_h * dot(v, n) * ds a += dot(u_hat,","# HDG classical form a = (dot(u, v) - div(v) * p) *","+= beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions","grad(p), v + grad(q)) * dx a += eta_p * div(u) * div(v)","/ h # beta = beta_0 # Numerical flux trace u_hat = u","n) * q * ds # # a += delta_1 * dot(u, n)","Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs):","FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and test functions","# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries","Badia-Codina paper # eta_p = 1 # eta_p = L0 * L0 #","\"\"\"Provides a plot of a condensed hybrid-mixed matrix for single scale problems.\"\"\" fig,","f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries =","# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier","+= lambda_h * dot(v, n) * ds a += dot(u_hat, n) * q","* A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","lambda_h * dot(v, n) * ds # Mass balance least-square a += delta_2","= Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h # delta","/ h_avg) * (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u)","S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values","* dS # a += p * q * ds A = assemble(a,","* L0 # method D in the Badia-Codina paper eta_u = h /","q * div(u)) * dx # DG terms a += jump(v, n) *","= sin(2 * pi * x) * sin(2 * pi * y) exact_solution","use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family =","# DG edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS","there is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis","V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V)","# L = delta_2 * f * div(v) * dx # Irrotational least-squares","# Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function","a += delta_1 * (dot(u, v) - div(v) * p) * dx a","% name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return","= TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)","div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds(","# ) # # These terms below are unsymmetric # a += delta_1","q * dx # Hybridization terms a += s * dot(grad(q), n)(\"+\") *","grad(p), grad(q) - v) * dx a += eta_p * div(u) * div(v)","solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","= h * h # delta = Constant(1) # delta = h delta_0","= Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx] -","(Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:","s * dot(grad(q), n) * p_boundaries * ds F = a - L","# Flux Least-squares as in DG a = delta_0 * inner(u + grad(p),","# Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a","= delta delta_3 = 1 / h delta_4 = 1 / h #","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1,","def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \"CG\", degree)","* dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\"))","eq. first-order terms as stabilizing terms a += delta_1 * (dot(u, v) -","delta_1 * dot(u, n) * q * ds # # L = -delta_1","ASGS Badia-Codina (2010), it is not a classical Nitsche's method a += (eta_p","+= eta_u_bc * delta_3 * p * q * ds # may decrease","= solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree,","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function","* dx # a += 0.5 * inner(curl(u), curl(v)) * dx # L","S = A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2]","= v + beta * (q - mu_h) * n # Flux least-squares","+ grad(q)) * dx # a += 0.5 * h * h *","Constant(1e0) delta = h * h # delta = Constant(1) # delta =","{result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting","* dx a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2","- mu_h) * ds # a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat,","else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in","terms # ** Badia-Codina based (better results) ** a += eta_u * avg(delta_3)","pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge","# \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, }","# method B in the Badia-Codina paper # eta_p = 1 # eta_p","# Classical volumetric terms a = inner(grad(p), grad(q)) * dx L = f","# Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2","+= delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS #","declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V","1 for current_solver in solvers_options: # Setting the output file name name =","a += beta * p * q * ds A = assemble(a, mat_type=\"aij\")","= UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}')","for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has","grad(q)) * dx # a += 0.5 * h * h * div(u)","n)) * dS # Edge stabilizing terms a += beta(\"+\") * dot(jump(p, n),","dx # Transmission condition a += jump(u_hat, n) * mu_h(\"+\") * dS #","BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a = (dot(u,","** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) *","number. :return: Filtered array with only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) <","h * h * f * div(v) * dx a += 0.5 *","a += delta_1 * lambda_h * dot(v, n) * ds # # L","+ delta_0 * q * div(u)) * dx L = delta_0 * f","1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp","= 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG'","terms a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u,","petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","Badia-Codina based a += -eta_u * inner(u + grad(p), v + grad(q)) *","1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs)","SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list =","in the Badia-Codina paper # eta_p = L0 * L0 # method D","* dS # a += dot(jump(p, n), jump(q, n)) * dS # a","T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V","dS a += eta_u_bc * delta_3 * p * q * ds #","# a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n)","DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a = (dot(u, v) - div(v)","a = delta_1 * inner(u + grad(p), v + grad(q)) * dx a","else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = U","cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\"sparse_pattern.png\")","* dx a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a,","h / L0 # method B in the Badia-Codina paper eta_u_bc = 1","1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S,","independent ** # a += jump(u, n) * jump(v, n) * dS #","+= mu_h * lambda_h * ds # ### # a += ( #","v + grad(q)) * dx # a += 0.5 * h * h","jump(u, n) * jump(v, n) * dS # not considered in the original","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ):","n)) * dS # Volumetric stabilizing terms # a += 0.5 * inner(u","color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full","bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms a = (dot(u, v)","term a = dot(grad(p), grad(q)) * dx L = f * q *","# Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution","= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim()","in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0","n) * ds # ** The terms below are based on ASGS Badia-Codina","+= dot(v, n) * p * ds - q * dot(u, n) *","+ f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides","stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry term.","Badia-Codina paper eta_p = 1 # eta_p = L0 * L0 # method","= Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =","HDG classical form a = (dot(u, v) - div(v) * p) * dx","result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return","# # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2,","ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility","calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier","* (q(\"+\") - mu_h(\"+\")) * dS a += (beta / h_avg) * (p(\"+\")","function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1),","p_boundaries) * mu_h * ds F = a - L a_form = lhs(F)","bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta =","/ h) * (p- p_boundaries) * q * ds # is this necessary?","# \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, #","n) * ds a += beta * p * q * ds #","delta_3 = Constant(0.5) * h * h # Mixed classical terms a =","= FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V *","exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) #","* A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle","* dot(u, n) * q * ds # # L = -delta_1 *","# a += 0.5 * inner(u + grad(p), grad(q) - v) * dx","* lambda_h(\"+\") * jump(v, n=n) * dS a += delta_1 * lambda_h *","the Badia-Codina paper # eta_p = L0 * L0 # method D in","= 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial","in the original paper # a += dot(jump(p, n), jump(q, n)) * dS","singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max()","h # Symmetry term. Choose if the method is SIPG (-1) or NIPG","= str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' trace_family =","ds a += beta * p * q * ds # may decrease","(mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed BC a += (p_boundaries *","HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n) *","delta_1 * jump(u_hat, n=n) * q(\"+\") * dS # a += delta_1(\"+\") *","p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh,","with real and complex numbers. :param imag_threshold: Threshold to cut off imaginary part","avg(p) * dS - avg(q) * jump(u, n) * dS # Edge stabilizing","+= 0.5 * div(u) * div(v) * dx # a += 0.5 *","function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 =","a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f","0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides","Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads","Symmetry term. Choose if the method is SIPG (-1) or NIPG (1) s","- mu_h(\"+\")) * dS a += (beta / h_avg) * (p(\"+\") - lambda_h(\"+\"))","good way to impose BC, but this necessary _A = Tensor(a) A =","# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array:","- L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S =","(dot(u, v) - div(v) * p) * dx + lambda_h(\"+\") * jump(v, n)","* div(u)) * dx # DG terms a += jump(v, n) * avg(p)","result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =","petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","dot(u, n) * dot(v, n) * ds a += (eta_u / h) *","= DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0","backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve()","/ singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors)","# may decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n)","delta # delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux","name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) #","= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs)","Badia-Codina based (better results) ** a += eta_u * avg(delta_3) * (jump(u, n)","* dot(v, n) * ds # Flux Least-squares as in DG a =","= \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = U * V","= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x,","dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS a +=","eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc","div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) * dx #","dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS","* ds a += beta * p * q * ds # may","Constant(0.5) * h * h # Mixed classical terms a = (dot(u, v)","terms a += delta_1 * inner(u + grad(p), v + grad(q)) * dx","= backend.lower() if backend == \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size)","ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib()","* dx # DG edge terms a += s * dot(jump(p, n), avg(grad(q)))","beta_0 / h # Mixed classical terms a = (dot(u, v) - div(v)","a += delta_1 * jump(u_hat, n=n) * q(\"+\") * dS # a +=","use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family","import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import","solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation(","is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of","the output file name name = f\"{current_solver}\" # Selecting the solver and its","results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results =","terms a = delta_0 * inner(u + grad(p), v + grad(q)) * dx","else: raise NotImplementedError(\"The required method for condition number estimation is currently unavailable.\") return","a += beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary","numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd","1 / h # Least-squares terms a = delta_0 * inner(u + grad(p),","bc?? # a += (beta / h) * (p- p_boundaries) * q *","Flux Least-squares as in DG a = delta_0 * inner(u + grad(p), v","a condensed hybrid-mixed matrix for single scale problems.\"\"\" fig, ax = plt.subplots(1, 1)","for single scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A","p + q * div(u)) * dx # Stabilizing terms a += 0.5","plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \"\"\"Utility function to","D in the Badia-Codina paper eta_u = h / L0 # method B","axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot","# Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell","= np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) #","plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","= 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 /","* f * div(v) * dx # ** Badia-Codina based a += eta_u","n), jump(q, n)) * dS # a += p * q * ds","use_quads else 'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V","scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas","- dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a += beta(\"+\")","singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend ==","a += 0.5 * div(u) * div(v) * dx # a += 0.5","dot(v, n) * ds # Flux Least-squares as in DG a = delta_0","# eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta =","dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms","color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[],","# Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta","* (mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed BC a += (p_boundaries","# Least-squares terms a = delta_1 * inner(u + grad(p), v + grad(q))","Array with real and complex numbers. :param imag_threshold: Threshold to cut off imaginary","S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0:","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): #","delta_0 * inner(u + grad(p), v + grad(q)) * dx a += delta_1","assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #","rates # ** The terms below are based on ASGS Badia-Codina (2010), it","attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs):","# bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and mesh","q * ds # # a += delta_1 * dot(u, n) * q","= h / L0 # method B in the Badia-Codina paper # Mixed","dS - avg(q) * jump(u, n) * dS # Edge stabilizing terms #","def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family","zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp","delta_1 * (dot(u, v) - div(v) * p) * dx a += delta_1(\"+\")","compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif","f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a","color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full","div(v) * dx # a += -0.5 * inner(u + grad(p), v +","These terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n) *","is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads =","eta_u * inner(u + grad(p), grad(q) - v) * dx a += eta_p","n in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} - N = {n}\")","ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs):","(beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS","* jump(v, n) * dS # not considered in the original paper #","_A = Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2]","* ds # Weakly imposed BC from hybridization # a += mu_h *","bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space","q * ds # # L = -delta_1 * dot(u_projected, n) * q","+= eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a +=",":param array: Array with real and complex numbers. :param imag_threshold: Threshold to cut","= array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str =","-> np.ndarray: \"\"\"Utility function to filter real part in a numpy array. :param","B in the Badia-Codina paper # Mixed classical terms a = (dot(u, v)","assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh,","-grad(p) u_hat = u + beta * (p - lambda_h) * n #","= attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig, ax","* inner(curl(u), curl(v)) * dx # L += 0.5 * h * h","solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values =","Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u +","* dx L = f * q * dx # DG edge terms","eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta = h","+ jump(u_hat, n) * q(\"+\") * dS L = f * q *","* n v_hat = v + beta * (q - mu_h) * n","hybridization # a += mu_h * (lambda_h - p_boundaries) * ds # a","* h * h * div(u) * div(v) * dx # a +=",":2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric =","p) * dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS","= LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T =","# ** Badia-Codina based (better results) ** a += eta_u * avg(delta_3) *","quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n","study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N =","singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000)","Mesh independent terms # a += jump(u, n) * jump(v, n) * dS","dS # Edge stabilizing terms a += beta(\"+\") * dot(jump(p, n), jump(q, n))","= -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS L","== \"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family,","dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries) * mu_h","v + beta * (q - mu_h) * n # Flux least-squares #","my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form,","= False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend ==","* inner(u + grad(p), v + grad(q)) * dx a += delta_2 *","v) - q * div(u) - p * div(v) + inner(grad(p), grad(q))) #","a += dot(v, n) * p * ds - q * dot(u, n)","(q(\"+\") - mu_h(\"+\")) * dS # Boundary terms # a += -dot(vel_projected, n)","RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol]","Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number","+= p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle","entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact =","exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs =","if use_quads else 'DG' trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree)","* (q - mu_h) * ds # a += delta_5 * (dot(u, n)(\"+\")","= f * q * dx # Transmission condition a += jump(u_hat, n)","= W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =","import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import","inner(u + grad(p), v + grad(q)) * dx a += eta_p * div(u)","* jump(v, n) * dS # a += dot(jump(p, n), jump(q, n)) *","delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4","Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1)","list(), } element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree))","stabilizing terms # a += 0.5 * h * h * div(u) *","= assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =",") return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG'","* inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u *","/ h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS #","condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method for condition number","fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh,","* n # Flux least-squares # a = ( # (inner(u, v) -","* h * h delta_3 = Constant(0.5) * h * h # Mixed","B in the Badia-Codina paper eta_p = 1 # eta_p = L0 *","= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact","_A = Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1]","for degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} - degree =","+ grad(p)), v + grad(q)) * dx # a += 0.5 * h","dx a += 0.5 * div(u) * div(v) * dx a += 0.5","= 1 / h # Least-squares terms a = delta_0 * inner(u +","* div(u) * div(v) * dx # L = delta_2 * f *","* (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # a +=","0.5 * div(u) * div(v) * dx # a += 0.5 * inner(curl(u),","n) * jump(v, n) * dS # a += dot(jump(p, n), jump(q, n))","dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) *","* dx # Weakly imposed boundary conditions a += dot(v, n) * p","h * h * div(u) * div(v) * dx # a += 0.5","div(v) * dx # ** Badia-Codina based a += eta_u * inner(u +","eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0","a plot of a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle","a good way to impose BC, but this necessary _A = Tensor(a) A","ds a += beta * (lambda_h - p_boundaries) * mu_h * ds F","FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V * T","# a += mu_h * (lambda_h - p_boundaries) * ds # a +=","current_solver in solvers_options: # Setting the output file name name = f\"{current_solver}\" #","h * inner(curl(u), curl(v)) * dx # L += 0.5 * h *","bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =","\"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh,","from hybridization # a += mu_h * (lambda_h - p_boundaries) * ds #","cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # #","sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average","def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if",") return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())","assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize()","dx # a += -0.5 * inner(u + grad(p), v + grad(q)) *","paper eta_u = h / L0 # method B in the Badia-Codina paper","eta_p = 1 # eta_p = L0 * L0 # method D in",") return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())","+= dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms # a","jump(v, n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat, n)","the Badia-Codina paper eta_u = h / L0 # method B in the","a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2]","grad(q)) * dx # Classical mixed Darcy eq. first-order terms as stabilizing terms","Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\"+\") # Stabilizing","* dS # ** Mesh independent terms # a += jump(u, n) *","as in DG a = delta_0 * inner(u + grad(p), v + grad(q))","lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # a += delta_4 * (p","0.5 * f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat","= delta # delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical","the method is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical","* L0 # method D in the Badia-Codina paper # eta_u = h_avg","+= mu_h(\"+\") * jump(u_hat, n=n) * dS a += delta_4(\"+\") * (p(\"+\") -","n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else","* delta_3 * p * q * ds # may decrease convergente rates","is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family =","- div(v) * p - q * div(u)) * dx # DG terms","### # a += ( # (mu_h - q) * (lambda_h - p_boundaries)","= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs)","- q * dot(u, n) * ds # ** The terms below are","W = U * V # Trial and test functions u, p =","a += lambda_h * dot(v, n) * ds a += dot(u_hat, n) *","Number\": list(), } element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree,","* dx # a += 0.5 * div(u) * div(v) * dx #","- div(v) * p + delta_0 * q * div(u)) * dx L","* dot(v, n) * ds a += (eta_u / h) * dot(p *","p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h","p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h","# # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap","jump(v, n) * avg(p) * dS - avg(q) * jump(u, n) * dS","return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","# Weak boundary conditions a += s * dot(p * n, grad(q)) *","# # L = -delta_1 * dot(u_projected, n) * q * ds #","def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig, ax = plt.subplots(1,","S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values >","v) - div(v) * p + q * div(u)) * dx # DG","is not a good way to impose BC, but this necessary _A =","grad(p), grad(q) - v) * dx # a += 0.5 * h *","* dot(p * n, q * n) * ds # may decrease convergente","if use_quads else 'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree)","array: Array with real and complex numbers. :param imag_threshold: Threshold to cut off","hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5","is this necessary? L += s * dot(grad(q), n) * p_boundaries * ds","* dot(p * n, q * n) * ds A = assemble(a, mat_type=\"aij\")","n) * jump(v, n) * dS # not considered in the original paper","= beta_0 / h beta_avg = beta_0 / h(\"+\") # Stabilizing parameter #","= attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz =","# * delta_1 # * dx # ) # # These terms below","= Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u","dG method # L0 = 1 # eta_p = L0 * h_avg #","* pi * x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact)","L += delta_2 * f * div(v) * dx # Hybridization terms a","BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n) -","Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0]","jump(v, n) * dS # not considered in the original paper # a","(q(\"+\") - mu_h(\"+\")) * dS # a += delta_4 * (p - lambda_h)","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): #","inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","* h * h # Mixed classical terms a = (dot(u, v) -","- dot(grad(p), q * n) * ds a += beta * p *","Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term","Classical Nitsche # a += beta * p * q * ds #","delta_4 * (p - lambda_h) * (q - mu_h) * ds # a","/ h) * dot(p * n, q * n) * ds A =","* p * q * ds # may decrease convergente rates a +=","exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression","\"\"\"Utility function to filter real part in a numpy array. :param array: Array","**kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1,","form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh(","cut off imaginary part in complex number. :return: Filtered array with only real","* n, q * n) * ds # may decrease convergente rates #","{n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min()","dG method L0 = 1 eta_p = L0 * h # method B","plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([])","solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \"CG\", degree) #","* dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a +=","* dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing","# Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta","* q * ds # may decrease convergente rates (Nitsche) A = assemble(a,","dS # Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h","\"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing","/ L0 # method B in the Badia-Codina paper eta_u_bc = 1 #","dS # Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p),","plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig, ax = plt.subplots(1, 1)","bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form a = inner(grad(u), grad(v)) *","pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression =","csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm import tqdm","/ L0 # method B in the Badia-Codina paper eta_u = 1 #","plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) #","file name name = f\"{current_solver}\" # Selecting the solver and its kwargs solver","q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","part in a numpy array. :param array: Array with real and complex numbers.","grad(p), v + grad(q)) * dx # Classical mixed Darcy eq. first-order terms","\"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1)","SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x) * sin(2","Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) *","* dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS a","dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") *","def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'","zero_tol: float = 1e-5 ): backend = backend.lower() if backend == \"scipy\": size","+= jump(u, n) * jump(v, n) * dS # not considered in the","W = U * V * T # Trial and test functions #","2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S,","delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5","n)) * dS # Weak boundary conditions a += s * dot(p *","# Weakly imposed BC from hybridization # a += mu_h * (lambda_h -","NIPG (1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q))","* dS # Edge stabilizing terms a += beta(\"+\") * dot(jump(p, n), jump(q,","u + beta * (p - lambda_h) * n # HDG classical form","* div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) * dx","p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) #","# Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n) * dS a +=","> 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc","a += delta_1 * dot(u, n) * q * ds # # L","L = delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares","# import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs,","+ lambda_h(\"+\") * jump(v, n) * dS a += -dot(u, grad(q)) * dx","jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v, n) *","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1):","plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap)","y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact))","= FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T","else: T = FunctionSpace(mesh, trace_family, degree) W = U * V * T","= f\"{current_solver}\" # Selecting the solver and its kwargs solver = solvers_options[current_solver] #","n) * ds # ** Mesh independent ** # a += jump(u, n)","p * div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx #","a += mu_h * (lambda_h - p_boundaries) * ds # a += mu_h","- 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs):","= Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter","solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name","ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return","= 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family,","- p_boundaries) * ds # ) # maybe this is not a good","h / L0 # method B in the Badia-Codina paper # Mixed classical","functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x,","name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\")","avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a += eta_p *","a plot of a condensed hybrid-mixed matrix for single scale problems.\"\"\" fig, ax","* q * ds # is this necessary? L += s * dot(grad(q),","delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 = delta","current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of","real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse: bool = False,","* A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W)","- 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] -","np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot","dS # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing",") return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration","\"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1 last_degree","DG edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS -","+= delta_2 * div(u) * div(v) * dx # L = delta_2 *","- q(\"+\")) * dS # Weakly imposed BC a += (p_boundaries * dot(v,","f * div(v) * dx a += 0.5 * div(u) * div(v) *","= L0 * h_avg # method B in the Badia-Codina paper eta_p =","f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization","plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse","* p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle","assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh,","* p_exact * dot(v, n) * ds # Flux Least-squares as in DG","* dS # a += delta_1 * lambda_h * dot(v, n) * ds","{result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy #","terms a += mu_h(\"+\") * jump(u_hat, n=n) * dS a += delta_4(\"+\") *","in complex number. :return: Filtered array with only real numbers. \"\"\" real_part_array =","* dx # a += 0.5 * h * h * div(u) *","1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h","inner(u + grad(p), grad(q) - v) * dx a += eta_p * div(u)","bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2 =","): backend = backend.lower() if backend == \"scipy\": size = A.getSize() Mnp =","* div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v)) *","condition_number = singular_values.max() / singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create()","W = V * T # Trial and test functions # solution =","parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1)","from firedrake import * import numpy as np import matplotlib.pyplot as plt import","Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\") *","grad(q))) # * delta_1 # * dx # ) # # These terms","Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx L =","mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric?","TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact","sigma_e, \"on_boundary\") # Mixed classical terms a = (dot(u, v) - div(v) *","* ds - q * dot(u, n) * ds # ** The terms","* div(u)) * dx L = delta_0 * f * q * dx","= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim()","- mu_h) * n # Flux least-squares # a = ( # (inner(u,","* inner((u + grad(p)), v + grad(q)) * dx # a += 0.5","V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement","dot(grad(q), n) * p_boundaries * ds F = a - L a_form =","eta_p = L0 * h_avg # method B in the Badia-Codina paper eta_p","* mu_h * ds F = a - L a_form = lhs(F) _A","inner(curl(u), curl(v)) * dx # L += 0.5 * h * h *","U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W","* T # Trial and test functions # solution = Function(W) # u,","assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()","DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3","# print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting","lambda_h(\"+\") * jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v,","a += -0.5 * inner((u + grad(p)), v + grad(q)) * dx #","* (dot(u, n) - dot(u_projected, n))) * ds a += beta * (lambda_h","last_degree, quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N, N,","+= delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat,","velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V #","L0 # method B in the Badia-Codina paper eta_u = 1 # eta_u_bc","5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) #","= FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U *","parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta = beta_0","independent terms # a += jump(u, n) * jump(v, n) * dS #","* ds a += beta * (lambda_h - p_boundaries) * mu_h * ds","a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a +=","Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads","= Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0)","* n, q * n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat =","div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx","ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5,","= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled","dS # not considered in the original paper # a += dot(jump(p, n),","solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family =","solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family =","delta_0 * f * q * dx # Stabilizing terms a += delta_1","* div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","L = delta_2 * f * div(v) * dx # Irrotational least-squares a","ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[],","beta = beta_0 # Numerical flux trace u_hat = u + beta *","else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree +","{result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # #","= beta0 / h # Symmetry term. Choose if the method is SIPG","pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else","Function space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family,","max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 # mesh =","FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U","Threshold to cut off imaginary part in complex number. :return: Filtered array with","= delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER /","in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} - N = {n}\") mesh","firedrake import * import numpy as np import matplotlib.pyplot as plt import matplotlib","Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta =","= ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result","0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u","\"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h #","solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W)","\"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree)","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1):","solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","- degree = {degree} - N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral)","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space","- q * div(u)) * dx # Stabilizing terms a += -0.5 *","Solver options solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\":","use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)","* inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v) *","u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization","may decrease convergente rates # ** The terms below are based on ASGS","p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 /","# a += delta_1 * jump(u_hat, n=n) * q(\"+\") * dS # a","(jump(u, n) * jump(v, n)) * dS a += eta_p * avg(delta_4) *","and test functions # solution = Function(W) # u, p, lambda_h = split(solution)","plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig, ax","TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact =","* A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle","- div(v) * p) * dx + lambda_h(\"+\") * jump(v, n) * dS","# a += p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat","# Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h #","based on ASGS Badia-Codina (2010), it is not a classical Nitsche's method a","Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)","(lambda_h - p_boundaries) * ds # ) # maybe this is not a","f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot def","# Classical term a = dot(grad(p), grad(q)) * dx L = f *","delta_1(\"+\") * dot(u_hat, n) * q * ds # # a += delta_1","- dot(v_hat, n)) * ds # Weakly imposed BC from hybridization # a","Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression)","degree) W = V * T # Trial and test functions # solution","== \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family,","scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks","classical form a = (dot(u, v) - div(v) * p) * dx +","f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact,","= Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q,","number estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function","ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a","* dS # Weak boundary conditions a += s * dot(p * n,","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1):","dS # Weakly imposed BC a += dot(u_hat, n) * q * ds","a += s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a","delta delta_2 = delta delta_3 = delta delta_4 = delta # delta_4 =","1e-5) -> np.ndarray: \"\"\"Utility function to filter real part in a numpy array.","BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form a = inner(grad(u), grad(v))","div(u)) * dx # DG terms a += jump(v, n) * avg(p) *","n), jump(q, n)) * dS # ** Mesh independent terms # a +=","= delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares as","a += dot(u_hat, n) * q * ds F = a - L","0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a","dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms # a","solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh,","a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs,","* p * q * ds # may decrease convergente rates # **","may decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n) *","'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree","# (mu_h - q) * (lambda_h - p_boundaries) * ds # ) #","q * dx # Transmission condition a += jump(u_hat, n) * mu_h(\"+\") *","BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") #","= LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W","delta_4 = 1 / h # Least-squares terms a = delta_0 * inner(u","* h delta_3 = Constant(0.5) * h * h # Mixed classical terms","Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0","size and mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0 #","idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp =","- div(v) * p) * dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v,",") # # These terms below are unsymmetric # a += delta_1 *","name=\"\", **kwargs ): results_dict = { \"Element\": list(), \"Number of Elements\": list(), \"Degree\":","**kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat","is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function","* ds # Mass balance least-square a += delta_2 * div(u) * div(v)","= petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz","plot of a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh,","rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n) *","converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() /","use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\" V","least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a","F = a - L a_form = lhs(F) _A = Tensor(a_form) A =","Trial and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities","\"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs","* dx # a += 0.5 * h * h * inner(curl(u), curl(v))","= DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta =","required method for condition number estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh,","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1):","else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz)","Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence","= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e,","pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet","\"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs =","= ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot =","classical Nitsche's method a += (eta_p / h) * dot(u, n) * dot(v,","# Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family =","beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a","Flux least-squares # a = ( # (inner(u, v) - q * div(u)","# method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights","+= eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a","U * V # Trial and test functions u, p = TrialFunctions(W) v,","np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp,","delta_1 * div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v))","singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values =","and mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0 # Jump","# may decrease convergente rates # ** The terms below are based on","# method B in the Badia-Codina paper # eta_p = L0 * L0","= Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\"+\") #","n) * jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p,","# eta_p = 1 # eta_p = L0 * L0 # method D","Constant(1.0) beta = beta_0 / h # Mixed classical terms a = (dot(u,","paper # eta_u = h_avg / L0 # method B in the Badia-Codina","+= lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\") *","= pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options","div(u)) * dx # Stabilizing terms a += 0.5 * inner(u + grad(p),","# Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap =","number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result","a = (dot(u, v) - div(v) * p - q * div(u)) *","'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V =","use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh,","declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG'","delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER","N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh,","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads","Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier =","= DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0)","1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs,","first-order terms as stabilizing terms a += delta_1 * (dot(u, v) - div(v)","\"h\": list(), \"Condition Number\": list(), } element_kind = \"Quad\" if quadrilateral else \"Tri\"","mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n *","Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray,","div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly","delta_1 = delta delta_2 = delta delta_3 = delta delta_4 = delta #","dx # a += 0.5 * h * h * inner(curl(u), curl(v)) *","dx # ** Badia-Codina based a += -eta_u * inner(u + grad(p), v","- div(v) * p + q * div(u)) * dx # DG terms","classical terms a = (dot(u, v) - div(v) * p - q *","* dS - avg(q) * jump(u, n) * dS # Edge stabilizing terms","plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\"","lambda_h) * (q - mu_h) * ds # a += delta_5 * (dot(u,","* dS # Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p)","degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U =","bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta =","0.5 * h * h * inner(curl(u), curl(v)) * dx # L +=","L0 # method D in the Badia-Codina paper # eta_u = h_avg /","* A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle","declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial and test functions u =","rates # ** Classical Nitsche # a += beta * p * q",":2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric =","/ h(\"+\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1)","# Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg","= beta_0 / h # Mixed classical terms a = (dot(u, v) -","== \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\"","** Mesh independent (original) # a += jump(u, n) * jump(v, n) *","(lambda_h - p_boundaries) * mu_h * ds F = a - L a_form","plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) #","'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,",":1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat =","n) * ds # Mass balance least-square a += delta_2 * div(u) *","petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors","ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def","Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1","= 1 # eta_u_bc = h / L0 # method B in the","dx a += eta_p * div(u) * div(v) * dx # Weakly imposed","degree) # Trial and test functions p = TrialFunction(V) q = TestFunction(V) #","in the Badia-Codina paper # eta_u = h_avg / L0 # method B","is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function","A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size","1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #","pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options","degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ'","paper # eta_p = 1 # eta_p = L0 * L0 # method","/ h beta_avg = beta_0 / h(\"+\") # Stabilizing parameter # delta_0 =","n) * dot(v, n) * ds a += (eta_u / h) * dot(p","is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads =","= Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs","= int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A,","solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree =","0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat =","* q(\"+\") * dS L = f * q * dx # Transmission","a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS","* dS # a += delta_4 * (p - lambda_h) * (q -","eta_p = L0 * L0 # method D in the Badia-Codina paper eta_u","result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads =","not a good way to impose BC, but this necessary _A = Tensor(a)","ds # may decrease convergente rates a += eta_u_bc * delta_4 * dot(u,","sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\")","solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, #","* div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1 #","LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh,","delta_2 * div(u) * div(v) * dx # L = delta_2 * f","# \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, #","Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number =","function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2),","Classical volumetric terms a = inner(grad(p), grad(q)) * dx L = f *","eta_p = L0 * L0 # method D in the Badia-Codina paper #","which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False,","= inner(grad(p), grad(q)) * dx L = f * q * dx #","beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h *","# ) # maybe this is not a good way to impose BC,","avg(q) * jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina","Constant(1e1) beta = beta0 / h # Symmetry term. Choose if the method","1 / h delta_4 = 1 / h # Least-squares terms a =","(beta / h) * (p- p_boundaries) * q * ds # is this","a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") -","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): #","0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2,","stabilizing parameters based on Badia-Codina stabilized dG method # L0 = 1 #","s * dot(p * n, grad(q)) * ds - dot(grad(p), q * n)","* dx # Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n) * dS","h_avg # method B in the Badia-Codina paper eta_p = 1 # eta_p","div(v) * dx # a += 0.5 * h * h * inner(curl(u),","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ):","dS # Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) /","* import numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): #","weights delta = Constant(1.0) # delta = h delta_0 = delta delta_1 =","q * div(u)) * dx L = delta_0 * f * q *","ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def","def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family","Stabilizing terms a += delta_1 * inner(u + grad(p), v + grad(q)) *","* f * div(v) * dx a += 0.5 * div(u) * div(v)","return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads","# Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg =","- q) * (lambda_h - p_boundaries) * ds # ) # maybe this","method B in the Badia-Codina paper eta_u = 1 # eta_u_bc = h","dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3 * p","div(v) * dx a += 0.5 * div(u) * div(v) * dx a","degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree}","considered in the original paper # a += dot(jump(p, n), jump(q, n)) *","= Constant(0.5) * h * h delta_3 = Constant(0.5) * h * h","condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz,","my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form,","is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15,","* div(v) * dx a += 0.5 * div(u) * div(v) * dx","n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution","terms a = inner(grad(p), grad(q)) * dx L = f * q *","curl(v)) * dx # Weakly imposed boundary conditions a += dot(v, n) *","div(v) * p - q * div(u)) * dx # DG terms a","result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =","results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\"","* h_avg # method B in the Badia-Codina paper eta_p = 1 #","* dS # not considered in the original paper # a += dot(jump(p,","TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y =","max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict = {","result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs:","Transmission condition a += jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed","**kwargs ): results_dict = { \"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(),","= _A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx] *","/ 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method #","shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1","+ q * div(u)) * dx # DG terms a += jump(v, n)","else: T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial","= f * q * dx # Hybridization terms a += s *","delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS # a += delta_1 *","\"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\":","form a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") *","a numpy array. :param array: Array with real and complex numbers. :param imag_threshold:","ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh,","Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact","+= 0.5 * inner(u + grad(p), grad(q) - v) * dx # a","ds # Flux Least-squares as in DG a = delta_0 * inner(u +","1 eta_p = L0 * h # method B in the Badia-Codina paper","* div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v)) *","beta_avg = beta_0 / h(\"+\") # Stabilizing parameter # delta_0 = Constant(1) #","y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x)","div(v) * dx # Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\") *","Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\"))","x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\")","Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors =","Below there is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove","of a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size =","array. :param array: Array with real and complex numbers. :param imag_threshold: Threshold to","# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter","1 last_degree = 1 for current_solver in solvers_options: # Setting the output file","# Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h","shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1","= int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat,","1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx,","= singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \"slepc\":","* div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx A","= Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp =","delta delta_1 = delta delta_2 = delta delta_3 = 1 / h delta_4","dot(u, n) * q * ds # # L = -delta_1 * dot(u_projected,","+= -0.5 * inner((u + grad(p)), v + grad(q)) * dx # a","n) * q * ds # # L = -delta_1 * dot(u_projected, n)","Least-squares as in DG a = delta_0 * inner(u + grad(p), v +","= attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs =","way to impose BC, but this necessary _A = Tensor(a) A = _A.blocks","FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact =","div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1),","# \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\":","# Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg)","* dx L += delta_2 * f * div(v) * dx # Hybridization","+= s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a +=","dx # Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS +","from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd from","a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions","* dS L = f * q * dx # Transmission condition a","a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS","Mass balance least-square a += delta_2 * div(u) * div(v) * dx #",") return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration","eta_u = 1 # eta_u_bc = h / L0 # method B in","print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the","* div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx L","name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter","solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, #","solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls,","f * div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u),","may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric","mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","* dx # Hybridization terms a += s * dot(grad(q), n)(\"+\") * (p(\"+\")","f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact,","/ h delta_4 = 1 / h # Least-squares terms a = delta_0","# solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h =","space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree)","/ L0 # method B in the Badia-Codina paper # Mixed classical terms","- v) * dx a += eta_p * div(u) * div(v) * dx","f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0","np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove","plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp","* q(\"+\") * dS # a += delta_1(\"+\") * dot(u_hat, n) * q","pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh,","N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz:","+= 0.5 * f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\")","n)) * dS # ** Mesh independent (original) # a += jump(u, n)","= tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\"Processing {name}","n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition","delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 = delta #","def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig,","(avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh","# L0 = 1 # eta_p = L0 * h_avg # method B","DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 /","= VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U *","'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement","# # L = delta_1 * p_exact * dot(v, n) * ds #","y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact))","petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs","if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U =","edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)),","# method B in the Badia-Codina paper eta_p = 1 # eta_p =","dS # a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v,","# a += jump(u, n) * jump(v, n) * dS # not considered","name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization","trace u_hat = u + beta * (p - lambda_h) * n #","return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family","div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) *","= Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical","return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True,","the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0 =","plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S =","# Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is the","* dS # a += delta_1(\"+\") * dot(u_hat, n) * q * ds","** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) - v)","'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U","\"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads","not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max()","petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs","calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric )","= Constant(1) # delta = h delta_0 = delta delta_1 = delta delta_2","(p - lambda_h) * (q - mu_h) * ds # a += delta_5","petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =","\"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1 last_degree = 1 for","dx # a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina","np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below","jump(u_hat, n=n) * q(\"+\") * dS # a += delta_1(\"+\") * dot(u_hat, n)","# ** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) -","lambda_h * dot(v, n) * ds # # L = delta_1 * p_exact","Choose if the method is SIPG (-1) or NIPG (1) s = Constant(-1)","+= (beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) *","is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads =","* dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space","Exact solution p_exact = sin(2 * pi * x) * sin(2 * pi","= Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 =","'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W =","* dot(u, n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) *","(eta_u / h) * dot(p * n, q * n) * ds A","estimation is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space","* ds # ** Mesh independent ** # a += jump(u, n) *","# L += 0.5 * f * div(v) * dx # ** Badia-Codina","dx L = f * q * dx # DG edge terms a","f * q * dx # DG edge terms a += s *","'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p =","v) - div(v) * p - q * div(u)) * dx # DG","import csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm import","* dx # L += 0.5 * h * h * f *","* dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) *","pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs =","avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3","degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T =","= Constant(1e0) delta = h * h # delta = Constant(1) # delta","# u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h =","balance least-square a += delta_2 * div(u) * div(v) * dx # L","num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs )","): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ'","ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form,","- p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed BC a","lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS","and complex numbers. :param imag_threshold: Threshold to cut off imaginary part in complex","= u + beta * (p - lambda_h) * n # HDG classical","+ grad(p), v + grad(q)) * dx a += delta_2 * div(u) *","of a condensed hybrid-mixed matrix for single scale problems.\"\"\" fig, ax = plt.subplots(1,","Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) #","degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family,","+= -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a += (beta /","= SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x) *","= ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return","a = delta_0 * inner(u + grad(p), v + grad(q)) * dx #","method B in the Badia-Codina paper # eta_p = L0 * L0 #","Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1] * A[:1,","# a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms","* dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original) #","the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0)","+= eta_p * div(u) * div(v) * dx a += eta_p * inner(curl(u),","svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min()","bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot of a matrix.\"\"\" fig,","str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv","= { \"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\":","* dot(grad(q), n) * p_boundaries * ds F = a - L a_form","n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) *","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False","# Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing the","* dS a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a","v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact","eta_u = h_avg / L0 # method B in the Badia-Codina paper eta_u","= Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h","a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\") * dS","a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms #","+= delta_1 * lambda_h * dot(v, n) * ds # # L =","- dot(v_hat, n)(\"+\")) * dS # a += delta_5 * (dot(u, n) -","= ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result","float = 1e-5) -> np.ndarray: \"\"\"Utility function to filter real part in a","nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): #","SLEPc import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s","* dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS a += -dot(grad(p), n)(\"+\")","i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not","a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS","Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF'","ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def","- div(v) * p + q * div(u)) * dx A = assemble(a,","a += p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat =","idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am","Badia-Codina stabilized dG method L0 = 1 eta_p = L0 * h #","dS # Boundary terms # a += -dot(vel_projected, n) * v * ds","False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend == \"scipy\":","= DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and mesh dependent stabilization","(dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS","Weakly imposed BC from hybridization # a += mu_h * (lambda_h - p_boundaries)","DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h","use_sparse: bool = False, zero_tol: float = 1e-5 ): backend = backend.lower() if","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space","* ds # a += mu_h * lambda_h * ds # ### #","imposed BC a += dot(u_hat, n) * q * ds F = a","lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n =","is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function","beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS # Weakly","\"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\":","p - q * div(u)) * dx # Stabilizing terms a += -0.5","= sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter","0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD","ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5,","v) - div(v) * p - q * div(u)) * dx # Stabilizing","a += 0.5 * h * h * div(u) * div(v) * dx","lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2,","the matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative","-dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a += (beta / h_avg)","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration","Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h # delta =","\"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg","q) * (lambda_h - p_boundaries) * ds # ) # maybe this is","+= -eta_u * inner(u + grad(p), v + grad(q)) * dx a +=","h * h # delta = Constant(1) # delta = h delta_0 =","p + q * div(u)) * dx # DG terms a += jump(v,","velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0","n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS a += beta(\"+\")","(lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\")) * dS # Weakly imposed BC","(2010), it is not a classical Nitsche's method a += (eta_p / h)","+ mu_h * (dot(u, n) - dot(u_projected, n))) * ds a += beta","sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e,","v + grad(q)) * dx # a += 0.5 * div(u) * div(v)","paper # Mixed classical terms a = (dot(u, v) - div(v) * p","TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y =","* dx # L += 0.5 * f * div(v) * dx #","pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs","* (q(\"+\") - mu_h(\"+\")) * dS # Boundary terms # a += -dot(vel_projected,","eta_p * div(u) * div(v) * dx # Weakly imposed boundary conditions a","singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method for condition number estimation is","beta0 / h # Symmetry term. Choose if the method is SIPG (-1)","2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =","\"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy:","backend: str = \"scipy\", use_sparse: bool = False, zero_tol: float = 1e-5 ):","bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp","delta_1 * inner(u + grad(p), v + grad(q)) * dx a += delta_2","plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig, ax","results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number)","* q * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 =","exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number #","div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1 # *","return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family","bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp","delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat =","degree) W = U * V # Trial and test functions u, p","terms a = (dot(u, v) - div(v) * p + q * div(u))","= Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,","ds # a += mu_h * lambda_h * ds # ### # a","trace u_hat = u + beta * (p - lambda_h) * n v_hat",":2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat =","h # Mixed classical terms a = (dot(u, v) - div(v) * p","V # Trial and test functions u, p = TrialFunctions(W) v, q =","div(u) * div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) *","method L0 = 1 eta_p = L0 * h # method B in","attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib()","ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot","Volumetric stabilizing terms # a += 0.5 * h * h * div(u)","form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs","dS # Weak boundary conditions a += s * dot(p * n, grad(q))","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space","CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi","* div(u) * div(v) * dx # Weakly imposed boundary conditions a +=","lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS","petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size =","num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return","velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(),","eta_u_bc * delta_3 * p * q * ds # may decrease convergente","a += (eta_u / h) * dot(p * n, q * n) *","* dx # Stabilizing terms a += delta_1 * inner(u + grad(p), v","terms below are based on ASGS Badia-Codina (2010), it is not a classical","= plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","* ds F = a - L a_form = lhs(F) _A = Tensor(a_form)","dot(v, n) * ds # Mass balance least-square a += delta_2 * div(u)","from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import","+= 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 * f","# a += dot(jump(p, n), jump(q, n)) * dS # a += p","plot of a condensed hybrid-mixed matrix for single scale problems.\"\"\" fig, ax =","* h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) *","* ds # ### # a += ( # (mu_h - q) *","{degree} - N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh,","Edge stabilizing terms a += beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS","delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) * h *","\"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" V","* (p - lambda_h) * n # HDG classical form a = (dot(u,","delta_2 * f * div(v) * dx # Irrotational least-squares a += delta_3","= f * q * dx # DG edge terms a += s","Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s = Constant(-1.0) beta","jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed BC a += lambda_h","if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family =","dx a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs,","+= 0.5 * h * h * inner(curl(u), curl(v)) * dx # L","the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) #","mu_h(\"+\")) * dS # a += delta_4 * (p - lambda_h) * (q","test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n =","Trial and test functions # solution = Function(W) # u, p, lambda_h =","ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed","bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],","ds # ** The terms below are based on ASGS Badia-Codina (2010), it","in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0)","* f * q * dx # Stabilizing terms a += delta_1 *","classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\"+\")","v + grad(q)) * dx a += delta_1 * div(u) * div(v) *","eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds # **","== \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if","ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] + f1_size[0]","= dot(grad(p), grad(q)) * dx L = f * q * dx #","Constant(0.5) * h * h delta_3 = Constant(0.5) * h * h #","condition a += jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed BC","only real numbers. \"\"\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number(","h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # Boundary","Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz","Nitsche # a += beta * p * q * ds # may","+= eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds #","* (p - lambda_h) * n # HDG classical form a = -dot(u,","delta_1 * lambda_h * dot(v, n) * ds # Mass balance least-square a","h * f * div(v) * dx # a += 0.5 * div(u)","ds # Mass balance least-square a += delta_2 * div(u) * div(v) *","df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \"cg\": solve_poisson_cg, #","# Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs,","delta = Constant(1.0) # delta = h delta_0 = delta delta_1 = delta","h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function","* p - q * div(u)) * dx # DG terms a +=","= str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\" V =","# \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, #","+ inner(grad(p), grad(q))) # * delta_1 # * dx # ) # #","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): #","# # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form,","curl(v)) * dx L += delta_2 * f * div(v) * dx #","L += s * dot(grad(q), n) * p_boundaries * ds F = a","= Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function f_expression = div(-grad(p_exact)) f =","a += jump(u, n) * jump(v, n) * dS # a += dot(jump(p,","is currently unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration","mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv","beta = beta_0 / h beta = beta_0 # Stabilization parameters delta_0 =","FunctionSpace(mesh, trace_family, degree) W = U * V * T # Trial and","n) * ds # # L = delta_1 * p_exact * dot(v, n)","Badia-Codina stabilized dG method # L0 = 1 # eta_p = L0 *","primal_family = \"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if","* dS # Volumetric stabilizing terms # a += 0.5 * inner(u +","is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family =","h(\"-\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method","# Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2","p * ds - q * dot(u, n) * ds # ** The","stabilized dG method # L0 = 1 # eta_p = L0 * h_avg","pi * x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact","\"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement","beta * p * q * ds # may decrease convergente rates #","+ q * div(u)) * dx # Stabilizing terms a += 0.5 *","p_exact * dot(v, n) * ds # Flux Least-squares as in DG a","* p) * dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) *","ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index","jump(q, n)) * dS # Volumetric stabilizing terms # a += 0.5 *","axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] -","csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) -","= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows","* dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx","beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\"+\")","# Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if","n) * v * ds # How to set this bc?? # a","bcs=[], **kwargs): \"\"\"Provides a plot of a condensed hybrid-mixed matrix for single scale","hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1)","h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) * h","terms a += 0.5 * inner(u + grad(p), grad(q) - v) * dx","Hybridization terms a += mu_h(\"+\") * jump(u_hat, n=n) * dS a += delta_4(\"+\")","idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot =","n) * avg(p) * dS - avg(q) * jump(u, n) * dS #","= delta_0 * f * q * dx # Stabilizing terms a +=","beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) *","T # Trial and test functions # solution = Function(W) # u, p,","functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n","grad(p), v + grad(q)) * dx a += delta_1 * div(u) * div(v)","* inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\"+\") * jump(u_hat,","Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 # Stabilization parameters","velocity_family = 'DQ' if use_quads else 'DG' trace_family = \"HDiv Trace\" U =","Least-Squares weights delta = Constant(1.0) # delta = h delta_0 = delta delta_1","L = f * q * dx # Hybridization terms a += s","lambda_h(\"+\") * jump(v, n=n) * dS # a += delta_1 * lambda_h *","L0 = 1 # eta_p = L0 * h_avg # method B in","= (h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina","Darcy eq. first-order terms as stabilizing terms a += delta_1 * (dot(u, v)","delta_1 = delta delta_2 = delta delta_3 = 1 / h delta_4 =","= Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()","Least-squares terms a = delta_1 * inner(u + grad(p), v + grad(q)) *","solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","options solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls,","CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) * dx","trace_family = \"HDiv Trace\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,","Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Stabilization parameters delta_1 = Constant(1)","trace_family, degree) W = U * V * T # Trial and test","_A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx,","pressure_family, degree) # Trial and test functions p = TrialFunction(V) q = TestFunction(V)","n, grad(q)) * ds - dot(grad(p), q * n) * ds a +=","# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected =","a = (dot(u, v) - div(v) * p + delta_0 * q *","0.5 * f * div(v) * dx # ** Badia-Codina based a +=","(q(\"+\") - mu_h(\"+\")) * dS a += (beta / h_avg) * (p(\"+\") -","U * V * T # Trial and test functions # solution =","\"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms,","= L0 * L0 # method D in the Badia-Codina paper eta_u =","nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number =","Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)","a += -0.5 * inner(u + grad(p), v + grad(q)) * dx #","The terms below are based on ASGS Badia-Codina (2010), it is not a","mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp =","assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp","q * div(u)) * dx # Stabilizing terms a += 0.5 * inner(u","- 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs):","# Exact solution p_exact = sin(2 * pi * x) * sin(2 *","dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original) # a","* p * ds - q * dot(u, n) * ds # **","least-squares # a = ( # (inner(u, v) - q * div(u) -","+= delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\"+\")","list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(), } element_kind =","u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h =","this is not a good way to impose BC, but this necessary _A","# ** Badia-Codina based a += -eta_u * inner(u + grad(p), v +","svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values","petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =","check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend","= assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp","= assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize()","Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries =","Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter","div(v) * dx # a += 0.5 * div(u) * div(v) * dx","csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) -","T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and","\"on_boundary\") # Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a,","split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh","h) * dot(p * n, q * n) * ds # may decrease","* inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based","ds # # a += delta_1 * dot(u, n) * q * ds","dS # a += delta_4 * (p - lambda_h) * (q - mu_h)","S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list()","ds # ) # maybe this is not a good way to impose","1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize()","ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\"","* V # Trial and test functions u, p = TrialFunctions(W) v, q","= FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W =","V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement","Classical mixed Darcy eq. first-order terms as stabilizing terms a += delta_1 *","attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list())","(q - mu_h) * n # Flux least-squares # a = ( #","np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg","= Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 # Stabilization","+= eta_u * inner(u + grad(p), grad(q) - v) * dx a +=","kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree","ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) ->","= ( # (inner(u, v) - q * div(u) - p * div(v)","else 'DG' trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:","bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta =","div(v) * p + q * div(u)) * dx # Stabilizing terms a","Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h *","a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS","quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}')","mesh dependent stabilization h_avg = (h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing","mixed Darcy eq. first-order terms as stabilizing terms a += delta_1 * (dot(u,","space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family","# Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name","1 # Least-Squares weights delta = Constant(1.0) # delta = h delta_0 =","Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization","* dx # Hybridization terms a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS","degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\"","# Flux least-squares # a = ( # (inner(u, v) - q *","p * q * ds # may decrease convergente rates # ** The","* dS # ** Mesh independent (original) # a += jump(u, n) *","jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed BC a += dot(u_hat,","matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([])","= FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact","_A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2,","+= (eta_u / h) * dot(p * n, q * n) * ds","plot of a full hybrid-mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form =","beta_0 / h # beta = beta_0 # Numerical flux trace u_hat =","a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) -","quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric)","jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q,","(q - mu_h) * ds # a += delta_5 * (dot(u, n)(\"+\") -","= FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement =","+ mu_h(\"+\") * dot(u, n)(\"+\") * dS a += beta(\"+\") * (lambda_h(\"+\") -","# Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and","# a = ( # (inner(u, v) - q * div(u) - p","# not considered in the original paper # a += dot(jump(p, n), jump(q,","declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\"","decrease convergente rates # ** The terms below are based on ASGS Badia-Codina","sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected","attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \"\"\"Provides a plot","\"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact))","of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(),","and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\")","n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n))","* div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx A","* dS # Weakly imposed BC a += (p_boundaries * dot(v, n) +","+= (beta / h) * (p- p_boundaries) * q * ds # is","imaginary part in complex number. :return: Filtered array with only real numbers. \"\"\"","- p_boundaries) * ds # a += mu_h * lambda_h * ds #","# ### # a += ( # (mu_h - q) * (lambda_h -","curl(v)) * dx # ** Badia-Codina based a += -eta_u * inner(u +","BC, but this necessary _A = Tensor(a) A = _A.blocks S = A[2,","color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a","# Trial and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh","degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number","Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression)","Trial and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) #","range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values","+= eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a","nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10,","q * ds # a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) *","str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh,","return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse: bool =","plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\" fig, ax = plt.subplots(1,","div(u) * div(v) * dx # Weakly imposed boundary conditions a += dot(v,","**kwargs) # Below there is the spy alternative # plot = plt.spy(Am, **kwargs)","n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC","f * q * dx # Stabilizing terms a += delta_1 * inner(u","= solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n","matrix.\"\"\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat =","p_boundaries) * q * ds # is this necessary? L += s *","+ 1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V #","dx # ) # # These terms below are unsymmetric # a +=","degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree)","M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol]","Classical term a = dot(grad(p), grad(q)) * dx L = f * q","(dot(u, v) - div(v) * p + q * div(u)) * dx A","# eta_p = L0 * L0 # method D in the Badia-Codina paper","h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5)","imposed boundary conditions a += dot(v, n) * p * ds - q","= Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER =","of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" %","= h_avg / L0 # method B in the Badia-Codina paper eta_u =","zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD()","Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h","A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize()","f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle","+ grad(p), v + grad(q)) * dx a += eta_p * div(u) *","raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values >","Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v,","# a += 0.5 * div(u) * div(v) * dx # a +=","} element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for","* f * div(v) * dx # Hybridization terms a += lambda_h(\"+\") *","velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)","Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) - v) *","- lambda_h) * n v_hat = v + beta * (q - mu_h)","matrix for single scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form)","dot(jump(p, n), jump(q, n)) * dS # a += p * q *","Function space declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial and test functions","_A = Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2]","delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 *","a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat =","n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a += (beta / h_avg) *","not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n) results_dict[\"Degree\"].append(degree)","= assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size =","assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1,","** Mesh independent ** # a += jump(u, n) * jump(v, n) *",") return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration","attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib()","dx # L += 0.5 * f * div(v) * dx # **","Constant(1.0) # delta = h delta_0 = delta delta_1 = delta delta_2 =","h * h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5)","n) * dS # Edge stabilizing terms # ** Badia-Codina based a +=","# Classical mixed Darcy eq. first-order terms as stabilizing terms a += delta_1","beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a","= Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p), v","# L += 0.5 * h * h * f * div(v) *","\"on_boundary\", method=\"geometric\") # Average cell size and mesh dependent stabilization h_avg = (h(\"+\")","div(v) * p + q * div(u)) * dx # DG terms a","solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, #","last_degree = 1 for current_solver in solvers_options: # Setting the output file name","== \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family =","# a += jump(u, n) * jump(v, n) * dS # a +=","0.5 * h * h * div(u) * div(v) * dx # a","# eta_u = h_avg / L0 # method B in the Badia-Codina paper","are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\"+\") * dS","n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a += delta_5","inner(u + grad(p), v + grad(q)) * dx # a += 0.5 *","= FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V) q","if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind) results_dict[\"Number of Elements\"].append(n * n)","- lambda_h) * n # HDG classical form a = (dot(u, v) -","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration","Weak boundary conditions a += s * dot(p * n, grad(q)) * ds","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1,","= Constant(1.0) beta = beta_0 / h # Mixed classical terms a =","terms a += s * dot(grad(q), n)(\"+\") * (p(\"+\") - lambda_h(\"+\")) * dS","Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0","* A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize()","* jump(u_hat, n=n) * q(\"+\") * dS # a += delta_1(\"+\") * dot(u_hat,","delta_2 * div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v))","plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\"","h * div(u) * div(v) * dx # a += 0.5 * h","independent (original) # a += jump(u, n) * jump(v, n) * dS #","delta = h delta_0 = delta delta_1 = delta delta_2 = delta delta_3","# Function space declaration V = FunctionSpace(mesh, \"CG\", degree) # Trial and test","A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat =","p + q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat","# \"ldgc\": solve_poisson_ldgc, # \"sipg\": solve_poisson_sipg, } degree = 1 last_degree = 1","# Weakly imposed BC a += dot(u_hat, n) * q * ds F","= plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\"aij\") petsc_mat = assembled_form.M.handle f0_size =","div(u)) * dx L = delta_0 * f * q * dx #","degree) # Trial and test functions u = TrialFunction(V) v = TestFunction(V) #","* jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n),","+ grad(q)) * dx a += eta_p * div(u) * div(v) * dx","1 # eta_p = L0 * h_avg # method B in the Badia-Codina","matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds","dx L += delta_2 * f * div(v) * dx # Hybridization terms","+ grad(p), grad(q) - v) * dx a += eta_p * div(u) *","L = f * q * dx # Transmission condition a += jump(u_hat,","= split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities","n # Flux least-squares # a = ( # (inner(u, v) - q","* dot(u_hat, n) * q * ds # # a += delta_1 *","ds # # L = -delta_1 * dot(u_projected, n) * q * ds","BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta","* h * h * f * div(v) * dx a += 0.5","jump(q, n)) * dS # ** Mesh independent terms # a += jump(u,","problems.\"\"\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx","p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)","a += beta * p * q * ds # may decrease convergente","* dx + lambda_h(\"+\") * jump(v, n) * dS a += -dot(u, grad(q))","import attr from firedrake import * import numpy as np import matplotlib.pyplot as","beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical terms a","if backend == \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if","= Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx,","v * ds # How to set this bc?? # a += (beta","delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p),","str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads else \"DG\" V = FunctionSpace(mesh,","* f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat =","p * q * ds # may decrease convergente rates a += eta_u_bc","* h # method B in the Badia-Codina paper # eta_p = L0","eta_p = L0 * h # method B in the Badia-Codina paper #","- v) * dx # a += 0.5 * h * h *","= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier =","= Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a =","* lambda_h * dot(v, n) * ds # # L = delta_1 *","paper # eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta","* inner(u + grad(p), grad(q) - v) * dx # a += 0.5","= assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","= delta delta_2 = delta delta_3 = 1 / h delta_4 = 1","f * div(v) * dx # Hybridization terms a += lambda_h(\"+\") * dot(v,",") return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())","* (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg)","# How to set this bc?? # a += (beta / h) *","** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v,","mu_h) * ds # a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\"))","div(v) * p + q * div(u)) * dx A = assemble(a, bcs=bcs,","dot(v, n) * ds a += (eta_u / h) * dot(p * n,","a += -dot(grad(p), n)(\"+\") * (q(\"+\") - mu_h(\"+\")) * dS a += (beta","degree = {degree} - N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result","p_boundaries) * ds # ) # maybe this is not a good way","assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz","eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a +=","0.5, color=\"k\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \"\"\"Provides a plot of","ds - q * dot(u, n) * ds a += beta * p","A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric","# result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') #","petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors","# beta = beta_0 / h beta = beta_0 # Stabilization parameters delta_0","Trial and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs","Nitsche's method a += (eta_p / h) * dot(u, n) * dot(v, n)","a = delta_0 * inner(u + grad(p), v + grad(q)) * dx a","entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact","+= delta_1 * (dot(u, v) - div(v) * p) * dx a +=","= Constant(1e1) beta = beta0 / h # Symmetry term. Choose if the","float = 1e-5 ): backend = backend.lower() if backend == \"scipy\": size =","S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged()","* h * h * f * div(v) * dx # a +=","q * dx # DG edge terms a += s * dot(jump(p, n),","results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results","* dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms #","* div(v) * dx # a += -0.5 * inner(u + grad(p), v","rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks","the Badia-Codina paper # Mixed classical terms a = (dot(u, v) - div(v)","unavailable.\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V =","= 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,","0.5 * inner(u + grad(p), grad(q) - v) * dx # a +=","spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and values","* h # delta = Constant(1) # delta = h delta_0 = delta","0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a","= delta_1 * inner(u + grad(p), v + grad(q)) * dx a +=","result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 /","h * h # Mixed classical terms a = (dot(u, v) - div(v)","# Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1","div(v) * dx # Weakly imposed boundary conditions a += dot(v, n) *","sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e =","\"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, # \"sipg\":","Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns","* avg(p) * dS - avg(q) * jump(u, n) * dS # Edge","eta_p * div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v))","a += 0.5 * h * h * inner(curl(u), curl(v)) * dx #","\"CG\", degree) # Trial and test functions u = TrialFunction(V) v = TestFunction(V)","split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n","* div(v) * dx # Weakly imposed boundary conditions a += dot(v, n)","paper # eta_p = L0 * L0 # method D in the Badia-Codina","dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a +=","* ds # # a += delta_1 * dot(u, n) * q *","# Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization","imposed BC from hybridization # a += mu_h * (lambda_h - p_boundaries) *","pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T","q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh)","real part in a numpy array. :param array: Array with real and complex","* dS # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric","dS a += (beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") -","* ds # # L = -delta_1 * dot(u_projected, n) * q *",") return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function","C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree)","method D in the Badia-Codina paper # eta_u = h_avg / L0 #","def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())","p - q * div(u)) * dx # DG terms a += jump(v,","singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The","L0 * h # method B in the Badia-Codina paper # eta_p =","ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot","return plot def plot_matrix_mixed(assembled_form, **kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\" fig,","* pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") # Forcing function","W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(","# Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) - v)","h * h * f * div(v) * dx # a += -0.5","Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number","L = -delta_1 * dot(u_projected, n) * q * ds # a +=","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1):","Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition","number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result","Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares","a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS #","sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical terms","space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else","nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): #","stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u,","* dx L = f * q * dx # Hybridization terms a","- 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of","ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[],","num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a,","this bc?? # a += (beta / h) * (p- p_boundaries) * q","@attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator","degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial","h_avg / L0 # method B in the Badia-Codina paper eta_u = 1","SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms a","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False","element_kind = \"Quad\" if quadrilateral else \"Tri\" pbar = tqdm(range(min_degree, max_degree)) for degree",") return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration","+= 0.5 * f * div(v) * dx # ** Badia-Codina based a","Badia-Codina paper # Mixed classical terms a = (dot(u, v) - div(v) *","delta_3 = 1 / h delta_4 = 1 / h # Least-squares terms","delta = Constant(1) # delta = h delta_0 = delta delta_1 = delta","v) * dx # a += 0.5 * h * h * div(u)","solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\"Element\"].append(element_kind)","beta * (p - lambda_h) * n v_hat = v + beta *","exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs","dx a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs,","= \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\",","+= -dot(vel_projected, n) * v * ds # How to set this bc??","# \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\": solve_poisson_cgh, # \"ldgc\": solve_poisson_ldgc, #","* dS a += (beta / h_avg) * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\")","# Volumetric stabilizing terms # a += 0.5 * h * h *","BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0) beta","singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min()","results_dict[\"Degree\"].append(degree) results_dict[\"Symmetric\"].append(result.is_operator_symmetric) results_dict[\"nnz\"].append(result.nnz) results_dict[\"dofs\"].append(result.number_of_dofs) results_dict[\"h\"].append(current_cell_size) results_dict[\"Condition Number\"].append(result.condition_number) os.makedirs(\"./cond_number_results/results_%s\" % name, exist_ok=True) df_cond_number =","A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","* n # HDG classical form a = (dot(u, v) - div(v) *","+= jump(u_hat, n) * mu_h(\"+\") * dS # Weakly imposed BC a +=","FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V) q =","unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\"+\") * dS #","= \"./cond_number_results/results_%s/cond_numbers.csv\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = {","\"\"\"Provides a plot of a matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat =","* inner(u + grad(p), v + grad(q)) * dx a += delta_1 *","= beta_0 # Numerical flux trace u_hat = u + beta * (p","in the Badia-Codina paper # Mixed classical terms a = (dot(u, v) -","f * q * dx # Hybridization terms a += s * dot(grad(q),","Badia-Codina (2010), it is not a classical Nitsche's method a += (eta_p /","method B in the Badia-Codina paper eta_p = 1 # eta_p = L0","delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n))","pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2),","result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\"","method B in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing","= h / L0 # method B in the Badia-Codina paper # eta_u","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function","function to filter real part in a numpy array. :param array: Array with","Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h","** Badia-Codina based (better results) ** a += eta_u * avg(delta_3) * (jump(u,","C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U * V *","(1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) *","# ** Mesh independent (original) # a += jump(u, n) * jump(v, n)","* n) * ds a += beta * p * q * ds","\"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, # \"hdg\": solve_poisson_hdg, # \"cgh\":","Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\",","= FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree)","* (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) *","dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a += s","backend = backend.lower() if backend == \"scipy\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1],","+= delta_1 * jump(u_hat, n=n) * q(\"+\") * dS # a += delta_1(\"+\")","parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry term. Choose","list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(),","nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads","* A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle","Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1)","h * h * inner(curl(u), curl(v)) * dx # L += 0.5 *","a plot of a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1) petsc_mat =","+ h(\"-\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG","+= delta_2 * f * div(v) * dx # Hybridization terms a +=","mu_h(\"+\") * dS # Weakly imposed BC a += lambda_h * dot(v, n)","Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 =","n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing","(p(\"+\") - lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # a += delta_4","20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict = { \"Element\": list(), \"Number of","A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat","= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\")","v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =","p * q * ds # may decrease convergente rates (Nitsche) A =","= h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 =","(jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg) *","else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if","dS L = f * q * dx # Transmission condition a +=","Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0","q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle","+= (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS a","= Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet","set this bc?? # a += (beta / h) * (p- p_boundaries) *",") return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG'","* dx a += eta_p * div(u) * div(v) * dx # Weakly","= np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix","Numerical flux trace u_hat = u + beta * (p - lambda_h) *","return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed","dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS","a += delta_2 * div(u) * div(v) * dx # L = delta_2","pbar.set_description(f\"Processing {name} - degree = {degree} - N = {n}\") mesh = UnitSquareMesh(n,","dot(v, n) * ds # ** Mesh independent ** # a += jump(u,","u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h","< 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \"scipy\", use_sparse:","n, q * n) * ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle","delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v)","**kwargs): \"\"\"Provides a plot of a mixed matrix.\"\"\" fig, ax = plt.subplots(1, 1)","A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors,","TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)","1 # eta_p = L0 * L0 # method D in the Badia-Codina","single scale problems.\"\"\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A =","jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q,","> zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \"slepc\": S =","a += mu_h(\"+\") * jump(u_hat, n=n) * dS a += delta_4(\"+\") * (p(\"+\")","output file name name = f\"{current_solver}\" # Selecting the solver and its kwargs","L0 * L0 # method D in the Badia-Codina paper eta_u = h","= solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}')","FunctionSpace(mesh, \"CG\", degree) # Trial and test functions u = TrialFunction(V) v =","div(u) * div(v) * dx # a += 0.5 * h * h","else \"DG\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(),","* jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina based","* (lambda_h - p_boundaries) * mu_h * ds F = a - L","method is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False","Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e","= svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray()","h = CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q))","def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs","_A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1,","+= 0.5 * h * h * div(u) * div(v) * dx #","solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family =","p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n =","= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs)","Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) * h","test functions # solution = Function(W) # u, p, lambda_h = split(solution) p,","# delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux trace","values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5)","n) * ds # Flux Least-squares as in DG a = delta_0 *","/ h # beta = beta_0 # Numerical flux trace u = -grad(p)","# Weakly imposed BC a += lambda_h * dot(v, n) * ds a","Mixed classical terms a = (dot(u, v) - div(v) * p + delta_0","not considered in the original paper # a += dot(jump(p, n), jump(q, n))","\"cgls\": solve_poisson_cgls, # \"dgls\": solve_poisson_dgls, # \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\":","= TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h =","quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True)","beta * (lambda_h - p_boundaries) * mu_h * ds F = a -","ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] + f1_size[0] -","* dx a += delta_1 * div(u) * div(v) * dx a +=","ds # ** Mesh independent ** # a += jump(u, n) * jump(v,","# These terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n)","and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs","+= -0.5 * inner(u + grad(p), v + grad(q)) * dx # a","Setting the output file name name = f\"{current_solver}\" # Selecting the solver and","= VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement =","num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in","* div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v)) *","columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] ==","\"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\": list(),","Mixed classical terms a = (dot(u, v) - div(v) * p - q","/ L0 # method B in the Badia-Codina paper # eta_u = 1","# plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs,","* dS a += beta(\"+\") * (lambda_h(\"+\") - p(\"+\")) * (mu_h(\"+\") - q(\"+\"))","div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx # ) #","L = delta_0 * f * q * dx # Stabilizing terms a","= assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp","(h(\"+\") + h(\"-\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized","s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) #","# Least-Squares weights delta = Constant(1.0) # delta = h delta_0 = delta","def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed matrix.\"\"\" fig,","= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f =","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5,","form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation(","shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero","a += delta_1(\"+\") * dot(u_hat, n) * q * ds # # a","str = \"scipy\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend","A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size","= attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric =","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1):","np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise","df_cond_number # Solver options solvers_options = { # \"cg\": solve_poisson_cg, # \"cgls\": solve_poisson_cgls,","f * q * dx # Transmission condition a += jump(u_hat, n) *","boundary conditions a += dot(v, n) * p * ds - q *","0.0, \"on_boundary\") # Variational form a = inner(grad(u), grad(v)) * dx A =","= 1 # Least-Squares weights delta = Constant(1.0) # delta = h delta_0","# a += 0.5 * h * h * div(u) * div(v) *","C0TraceElement) else: trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W =","# my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) #","original paper # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric","degree = 1 last_degree = 1 for current_solver in solvers_options: # Setting the","beta * p * q * ds A = assemble(a, mat_type=\"aij\") petsc_mat =","rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is","Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)]","(-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms a =","print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}')","but this necessary _A = Tensor(a) A = _A.blocks S = A[2, 2]","- 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp,","# a += delta_1(\"+\") * dot(u_hat, n) * q * ds # #","k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M = Mnp.toarray() singular_values = svd(M,","copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap)","'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:","{ \"Element\": list(), \"Number of Elements\": list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(),","n) * p * ds - q * dot(u, n) * ds a","BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0) beta","0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis","q * ds # may decrease convergente rates a += eta_u_bc * delta_4","* (q(\"+\") - mu_h(\"+\")) * dS # a += delta_4 * (p -","+ grad(q)) * dx a += delta_2 * div(u) * div(v) * dx","a += eta_u * inner(u + grad(p), grad(q) - v) * dx a","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): #","+= delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat","- lambda_h(\"+\")) * (q(\"+\") - mu_h(\"+\")) * dS # a += delta_4 *","Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter beta_0 = Constant(1.0e0)","\"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact,","= A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat","= ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def","= 1 # eta_p = L0 * L0 # method D in the","/ h) * dot(p * n, q * n) * ds # may","n)) * dS # a += p * q * ds A =","jump(q, n)) * dS # ** Mesh independent (original) # a += jump(u,","= \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = V * T","= \"scipy\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend =","dot(grad(p), grad(q)) * dx L = f * q * dx # Hybridization","a += lambda_h(\"+\") * dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\")","jump(u_hat, n=n) * dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\")","terms # a += 0.5 * h * h * div(u) * div(v)","degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition","return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1,","condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function","if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else:","* p + q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\")","BC from hybridization # a += mu_h * (lambda_h - p_boundaries) * ds","for current_solver in solvers_options: # Setting the output file name name = f\"{current_solver}\"","filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0,","p) * dx + lambda_h(\"+\") * jump(v, n) * dS a += -dot(u,","= Function(V).interpolate(p_exact) exact_solution.rename(\"Exact pressure\", \"label\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing","f * div(v) * dx # ** Badia-Codina based a += eta_u *","dot(v, n) * p * ds - q * dot(u, n) * ds","+= delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS # a += delta_1","a += dot(jump(p, n), jump(q, n)) * dS # a += p *","+ beta * (p - lambda_h) * n # HDG classical form a","TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) #","penalizing term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical","= S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list)","ds A = assemble(a, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size =","assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): #","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space","DirichletBC(W[0], sigma_e, \"on_boundary\", method=\"geometric\") # Average cell size and mesh dependent stabilization h_avg","dx a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS a +=","number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ):","# Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form a =","A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp","decrease convergente rates # ** Classical Nitsche # a += beta * p","U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement","div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") #","# matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) # my_cmap.set_bad(color=\"lightgray\") # #","= attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs =","* dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)","f * div(v) * dx # a += -0.5 * inner(u + grad(p),","from slepc4py import SLEPc import pandas as pd from tqdm import tqdm import","form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1):","n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n))","bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space","* jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n),","min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\"\", **kwargs ): results_dict =","inner(u + grad(p), grad(q) - v) * dx # a += 0.5 *","eta_u_bc = h / L0 # method B in the Badia-Codina paper eta_u_bc","LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else:","a += 0.5 * inner(u + grad(p), grad(q) - v) * dx #","assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()","* dx a += 0.5 * div(u) * div(v) * dx a +=","n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p,","dS # Volumetric stabilizing terms # a += 0.5 * h * h","- dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\") - dot(v_hat, n)(\"+\")) * dS # a","dx L = delta_0 * f * q * dx # Stabilizing terms","= beta_0 # Numerical flux trace u = -grad(p) u_hat = u +",":idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat =","= {degree} - N = {n}\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result =","* ds # ) # maybe this is not a good way to","delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a","L0 # method B in the Badia-Codina paper # Mixed classical terms a","= Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter","L0 # method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares","mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh,","Trace\" T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial","use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" pressure_family = 'DQ' if use_quads else 'DG' trace_family","return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","inner(u + grad(p), v + grad(q)) * dx a += delta_2 * div(u)","solvers_options: # Setting the output file name name = f\"{current_solver}\" # Selecting the","n)) * dS # Volumetric stabilizing terms # a += 0.5 * h","# Function space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh,","dot(v, n) * ds # # L = delta_1 * p_exact * dot(v,","sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4,","0.5 * h * h * f * div(v) * dx a +=","= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0]","beta * (p - lambda_h) * n # HDG classical form a =","FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family =","* div(u)) * dx # Stabilizing terms a += 0.5 * inner(u +","A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat =","terms a += jump(v, n) * avg(p) * dS - avg(q) * jump(u,","else: raise RuntimeError(\"SLEPc SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values","SVD has not converged.\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number","# ** Classical Nitsche # a += beta * p * q *","a - L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S","assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh,","trace_family = \"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = V *","curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle is_symmetric =","p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \"on_boundary\") # Hybridization parameter s =","on ASGS Badia-Codina (2010), it is not a classical Nitsche's method a +=","TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \"on_boundary\") # Variational form a","# # a += delta_1 * dot(u, n) * q * ds #","# a += beta * p * q * ds # may decrease","# a += -0.5 * inner(u + grad(p), v + grad(q)) * dx","= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \"on_boundary\") #","Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number","# \"sdhm\": solve_poisson_sdhm, # \"ls\": solve_poisson_ls, # \"dls\": solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\":","= Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a =","= str(mesh.ufl_cell()) == \"quadrilateral\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else:","it is not a classical Nitsche's method a += (eta_p / h) *","if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else:","= assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result","u = -grad(p) u_hat = u + beta * (p - lambda_h) *","* v * ds # How to set this bc?? # a +=","= singular_values.max() / singular_values.min() else: raise NotImplementedError(\"The required method for condition number estimation","a += -dot(vel_projected, n) * v * ds # How to set this","name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \"cg\": solve_poisson_cg,","D in the Badia-Codina paper # eta_u = h_avg / L0 # method","filter real part in a numpy array. :param array: Array with real and","* inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\"aij\") petsc_mat = A.M.handle","* dx # Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q)","+= delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms # **","convergente rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n)","return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of a full hybrid-mixed","A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz","the Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0 #","for n in numel_xy: pbar.set_description(f\"Processing {name} - degree = {degree} - N =","* dot(v, n) * ds # ** Mesh independent ** # a +=","Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta","this necessary? L += s * dot(grad(q), n) * p_boundaries * ds F","trace_family = \"HDiv Trace\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement =","): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\"","# N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result =","assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)","T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U","+= beta * (lambda_h - p_boundaries) * mu_h * ds F = a","* div(v) * dx # L = delta_2 * f * div(v) *","dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx #","n=n) * dS a += delta_4(\"+\") * (p(\"+\") - lambda_h(\"+\")) * (q(\"+\") -","h # Least-squares terms a = delta_0 * inner(u + grad(p), v +","h * f * div(v) * dx a += 0.5 * div(u) *","beta = beta0 / h # Symmetry term. Choose if the method is","delta_4 * dot(u, n) * dot(v, n) * ds # ** Mesh independent","DG a = delta_0 * inner(u + grad(p), v + grad(q)) * dx","singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\" ) else: M =","* avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc *","* dot(v, n)(\"+\") * dS + mu_h(\"+\") * dot(u, n)(\"+\") * dS a","n # HDG classical form a = (dot(u, v) - div(v) * p)","n)) * dS # ** Mesh independent terms # a += jump(u, n)","ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\"k\") ax.axvline(x=f0_size[0] - 0.5, color=\"k\") ax.axhline(y=f0_size[0] +","# may decrease convergente rates # ** Classical Nitsche # a += beta","n=n) * dS a += delta_1 * lambda_h * dot(v, n) * ds","1 # eta_u_bc = h / L0 # method B in the Badia-Codina","* ds a += (eta_u / h) * dot(p * n, q *","name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed","def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())","nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): #","# a += delta_5 * (dot(u, n)(\"+\") - dot(u_hat, n)(\"+\")) * (dot(v, n)(\"+\")","hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG'","beta_0 = Constant(1.0e0) beta = beta_0 / h # beta = beta_0 #","a += jump(v, n) * avg(p) * dS - avg(q) * jump(u, n)","return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==","imposed BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n)","with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0))","grad(q) - v) * dx a += eta_p * div(u) * div(v) *","* ds # may decrease convergente rates # ** Classical Nitsche # a","DG terms a += jump(v, n) * avg(p) * dS - avg(q) *","S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if","* ds # a += delta_1(\"+\") * lambda_h(\"+\") * jump(v, n=n) * dS","dot(p * n, grad(q)) * ds - dot(grad(p), q * n) * ds","velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \"on_boundary\") # Mixed classical","conditions a += s * dot(p * n, grad(q)) * ds - dot(grad(p),","h delta_4 = 1 / h # Least-squares terms a = delta_0 *","FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\"facet\"]","shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1","assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =","* dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds","C0TraceElement = LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \"HDiv Trace\" T","UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral","- A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier)","maybe this is not a good way to impose BC, but this necessary","Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2,","a = dot(grad(p), grad(q)) * dx L = f * q * dx","A, num_of_factors, backend: str = \"scipy\", use_sparse: bool = False, zero_tol: float =",") else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values","Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1)","# Trial and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet","\"HDiv Trace\" T = FunctionSpace(mesh, trace_family, degree) W = V * T #","based a += -eta_u * inner(u + grad(p), v + grad(q)) * dx","# plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\"sparse_pattern.png\") # plt.show()","# method D in the Badia-Codina paper # eta_u = h_avg / L0","parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms","'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W =","= Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,","* q * ds # may decrease convergente rates # ** The terms","- 0.5, color=\"k\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \"\"\"Provides a plot of","-0.5 * inner(u + grad(p), v + grad(q)) * dx # a +=","Edge stabilizing terms # ** Badia-Codina based (better results) ** a += eta_u","* dx a += eta_p * div(u) * div(v) * dx a +=","u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0,","* ds a += beta * p * q * ds A =","import numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import","shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\"LM\", maxiter=5000, return_singular_vectors=False, solver=\"lobpcg\"","= Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 /","terms a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q,","LagrangeElement[\"facet\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W =","lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)","is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family =","Badia-Codina paper eta_u = h / L0 # method B in the Badia-Codina","idx = trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv","p_boundaries) * ds # a += mu_h * lambda_h * ds # ###","stabilizing terms a += beta(\"+\") * dot(jump(p, n), jump(q, n)) * dS #","space declaration use_quads = str(mesh.ufl_cell()) == \"quadrilateral\" primal_family = \"DQ\" if use_quads else","list(), \"Degree\": list(), \"Symmetric\": list(), \"nnz\": list(), \"dofs\": list(), \"h\": list(), \"Condition Number\":","solve_poisson_dls, \"lsh\": solve_poisson_lsh, # \"vms\": solve_poisson_vms, # \"dvms\": solve_poisson_dvms, # \"mixed_RT\": solve_poisson_mixed_RT, #","below are based on ASGS Badia-Codina (2010), it is not a classical Nitsche's","v) - div(v) * p + q * div(u)) * dx A =","is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz =","the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\"winter\")) #"],"string":"[\n \"= CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) *\",\n \"/ singular_values.min() else: raise NotImplementedError(\\\"The required method for condition number estimation is currently\",\n \"singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK)\",\n \"dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) *\",\n \"= attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a\",\n \"'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,\",\n \"use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\",\",\n \"# delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta\",\n \"q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") * dot(u_hat, n) * q *\",\n \"csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with\",\n \"trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix for single\",\n \"+= (p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n)))\",\n \"else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W\",\n \"pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space\",\n \"results) ** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n))\",\n \"(dot(u, v) - div(v) * p - q * div(u)) * dx #\",\n \"grad(q)) * dx L = f * q * dx # DG edge\",\n \"- lambda_h) * n # HDG classical form a = -dot(u, grad(q)) *\",\n \"solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in solvers_options: #\",\n \"Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name,\",\n \"test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities\",\n \"n)) * ds # Weakly imposed BC from hybridization # a += mu_h\",\n \"pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V =\",\n \"BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2\",\n \"currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V\",\n \"a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form\",\n \"delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4 = delta\",\n \"* div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx #\",\n \"= LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat =\",\n \"avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms\",\n \"= A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib()\",\n \"# Average cell size and mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\"))\",\n \"ds - dot(grad(p), q * n) * ds a += beta * p\",\n \"h # method B in the Badia-Codina paper # eta_p = 1 #\",\n \"= \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)\",\n \"parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 =\",\n \"= h / L0 # method B in the Badia-Codina paper eta_u_bc =\",\n \"= delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4 =\",\n \"Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1)\",\n \"plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([])\",\n \"** The terms below are based on ASGS Badia-Codina (2010), it is not\",\n \"== \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values\",\n \"* dS # a += delta_5 * (dot(u, n) - dot(u_hat, n)) *\",\n \"convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS\",\n \"# (inner(u, v) - q * div(u) - p * div(v) + inner(grad(p),\",\n \"n) * q * ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v,\",\n \"% name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \\\"cg\\\":\",\n \"* dS # Weakly imposed BC a += dot(u_hat, n) * q *\",\n \"+= 0.5 * div(u) * div(v) * dx a += 0.5 * inner(curl(u),\",\n \"a += delta_1 * div(u) * div(v) * dx a += delta_2 *\",\n \"sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh dependent stabilization h_avg =\",\n \"p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h =\",\n \"is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms\",\n \"import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap)\",\n \"L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1,\",\n \"+= delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS a += delta_1 *\",\n \"= delta delta_1 = delta delta_2 = delta delta_3 = 1 / h\",\n \"dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge\",\n \"* jump(v, n=n) * dS # a += delta_1 * lambda_h * dot(v,\",\n \"L += 0.5 * h * h * f * div(v) * dx\",\n \"parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h)\",\n \"method for condition number estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1,\",\n \"ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat =\",\n \"div(v) * p + delta_0 * q * div(u)) * dx L =\",\n \"Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0)\",\n \"h # delta = Constant(1) # delta = h delta_0 = delta delta_1\",\n \"= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\")\",\n \"f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier\",\n \"a += (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\"))\",\n \"\\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree\",\n \"# Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q)\",\n \"numpy array. :param array: Array with real and complex numbers. :param imag_threshold: Threshold\",\n \":param imag_threshold: Threshold to cut off imaginary part in complex number. :return: Filtered\",\n \"+ last_degree, quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N,\",\n \"ds a += dot(u_hat, n) * q * ds F = a -\",\n \"v) - div(v) * p) * dx + lambda_h(\\\"+\\\") * jump(v, n) *\",\n \"'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and\",\n \"def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'\",\n \"form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc(\",\n \"sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f\",\n \"in a numpy array. :param array: Array with real and complex numbers. :param\",\n \"* inner(u + grad(p), v + grad(q)) * dx # a += 0.5\",\n \"import SLEPc import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg')\",\n \"* dx # Classical mixed Darcy eq. first-order terms as stabilizing terms a\",\n \"n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5 * (dot(u, n)\",\n \"# Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0 =\",\n \"result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result\",\n \"space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions u\",\n \"* dx # Edge stabilizing terms # ** Badia-Codina based (better results) **\",\n \"- p_boundaries) * mu_h * ds F = a - L a_form =\",\n \"* p - q * div(u)) * dx # Stabilizing terms a +=\",\n \"* p * ds - q * dot(u, n) * ds a +=\",\n \"1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp =\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): #\",\n \"array with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array\",\n \"ds # How to set this bc?? # a += (beta / h)\",\n \"0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0]\",\n \"the solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence study\",\n \"* ds # may decrease convergente rates # ** The terms below are\",\n \"n), jump(q, n)) * dS # ** Mesh independent (original) # a +=\",\n \"tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form\",\n \"exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) #\",\n \"div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") #\",\n \"* div(u)) * dx # Stabilizing terms a += -0.5 * inner((u +\",\n \"parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p = L0\",\n \"= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta\",\n \"q(\\\"+\\\") * dS L = f * q * dx # Transmission condition\",\n \"Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads\",\n \"else 'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V =\",\n \"= copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs,\",\n \"* jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v, n)\",\n \"as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from\",\n \"return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads\",\n \"u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities\",\n \"plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0,\",\n \"f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp\",\n \"# is this necessary? L += s * dot(grad(q), n) * p_boundaries *\",\n \"# beta = beta_0 # Numerical flux trace u_hat = u + beta\",\n \"= TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form\",\n \"results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" %\",\n \"FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"]\",\n \"of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size\",\n \"mu_h * (lambda_h - p_boundaries) * ds # a += mu_h * lambda_h\",\n \"* delta_1 # * dx # ) # # These terms below are\",\n \"* (lambda_h - p_boundaries) * ds # a += mu_h * lambda_h *\",\n \"* dx a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a,\",\n \"**kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5,\",\n \"U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U\",\n \"delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER / h\",\n \"* dS # Volumetric stabilizing terms # a += 0.5 * h *\",\n \"elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST)\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh,\",\n \"solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U\",\n \"beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 #\",\n \"size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs =\",\n \"v_hat = v + beta * (q - mu_h) * n # Flux\",\n \"\\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend:\",\n \"plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed\",\n \"* dS # Weakly imposed BC a += lambda_h * dot(v, n) *\",\n \"color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a\",\n \"u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n =\",\n \"method B in the Badia-Codina paper # eta_p = 1 # eta_p =\",\n \"'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family,\",\n \"= inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree)\",\n \"* y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression =\",\n \"jump(u_hat, n) * q(\\\"+\\\") * dS L = f * q * dx\",\n \"* (lambda_h - p_boundaries) * ds # ) # maybe this is not\",\n \"mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"* n # HDG classical form a = -dot(u, grad(q)) * dx +\",\n \"stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based\",\n \"curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based (better results)\",\n \"= calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric\",\n \"= assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp\",\n \"q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)\",\n \"= {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size =\",\n \"2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 =\",\n \"bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() #\",\n \"degree) W = U * V * T # Trial and test functions\",\n \"solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads:\",\n \"the Badia-Codina paper # eta_p = 1 # eta_p = L0 * L0\",\n \"a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a\",\n \"beta = beta_0 / h # beta = beta_0 # Numerical flux trace\",\n \"solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg,\",\n \"def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A\",\n \"{name} - degree = {degree} - N = {n}\\\") mesh = UnitSquareMesh(n, n,\",\n \"= delta_0 * inner(u + grad(p), v + grad(q)) * dx a +=\",\n \"def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig, ax =\",\n \"+ grad(q)) * dx a += delta_1 * div(u) * div(v) * dx\",\n \":] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0,\",\n \"\\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if\",\n \"dx # Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) -\",\n \"= 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V =\",\n \"csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) -\",\n \"based on Badia-Codina stabilized dG method # L0 = 1 # eta_p =\",\n \"number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def\",\n \"* dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3 *\",\n \"q * n) * ds # may decrease convergente rates # ** Classical\",\n \"(dot(u, v) - div(v) * p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\")\",\n \"name=name ) # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) #\",\n \"= petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz\",\n \"+= delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f *\",\n \"# a += 0.5 * h * h * inner(curl(u), curl(v)) * dx\",\n \"(p - lambda_h) * n v_hat = v + beta * (q -\",\n \"result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =\",\n \"n), jump(q, n)) * dS # Volumetric stabilizing terms # a += 0.5\",\n \"velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters\",\n \"trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx,\",\n \"* dot(v, n) * ds a += dot(u_hat, n) * q * ds\",\n \"singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values): singular_value =\",\n \"method=\\\"geometric\\\") # Average cell size and mesh dependent stabilization h_avg = (h(\\\"+\\\") +\",\n \"dot(u_hat, n) * q * ds F = a - L a_form =\",\n \"= Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number\",\n \"(eta_p / h) * dot(u, n) * dot(v, n) * ds a +=\",\n \"15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number\",\n \"= mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n\",\n \"velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet\",\n \"mu_h * lambda_h * ds # ### # a += ( # (mu_h\",\n \"pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object):\",\n \"is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function\",\n \"bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space\",\n \"/ h) * dot(u, n) * dot(v, n) * ds a += (eta_u\",\n \"* dx + jump(u_hat, n) * q(\\\"+\\\") * dS L = f *\",\n \"decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v,\",\n \"= attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form,\",\n \"delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = delta delta_4\",\n \"= 1 # eta_p = L0 * h_avg # method B in the\",\n \"# Stabilizing terms a += delta_1 * inner(u + grad(p), v + grad(q))\",\n \"(original) # a += jump(u, n) * jump(v, n) * dS # not\",\n \"and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float =\",\n \"\\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree)\",\n \"- 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\")\",\n \"dx # a += 0.5 * h * h * div(u) * div(v)\",\n \"* dot(u_projected, n) * q * ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\")\",\n \"= plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize()\",\n \"DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 /\",\n \"- 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form,\",\n \"* h * inner(curl(u), curl(v)) * dx # L += 0.5 * h\",\n \"terms a += delta_1 * (dot(u, v) - div(v) * p) * dx\",\n \"if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh,\",\n \"= (dot(u, v) - div(v) * p - q * div(u)) * dx\",\n \"* p_boundaries * ds F = a - L a_form = lhs(F) _A\",\n \"classical terms a = (dot(u, v) - div(v) * p + delta_0 *\",\n \"necessary? L += s * dot(grad(q), n) * p_boundaries * ds F =\",\n \"n)(\\\"+\\\")) * dS # a += delta_5 * (dot(u, n) - dot(u_hat, n))\",\n \"L = f * q * dx # DG edge terms a +=\",\n \"avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L = f\",\n \"terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS # Weak\",\n \"* div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx #\",\n \"0.5 * h * h * f * div(v) * dx # a\",\n \"= 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W\",\n \"sin(2 * pi * x) * sin(2 * pi * y) exact_solution =\",\n \"= Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and\",\n \"= lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2] -\",\n \"values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of\",\n \"a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"* dx # ** Badia-Codina based a += -eta_u * inner(u + grad(p),\",\n \"def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter\",\n \"Average cell size and mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) /\",\n \"ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS #\",\n \"grad(p)), v + grad(q)) * dx # a += 0.5 * h *\",\n \"singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S\",\n \"rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form,\",\n \"= avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L =\",\n \"Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs)\",\n \"# Trial and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W)\",\n \"flux trace u = -grad(p) u_hat = u + beta * (p -\",\n \"ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0]\",\n \"0.5 * div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v))\",\n \"bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"q * div(u)) * dx # Stabilizing terms a += -0.5 * inner((u\",\n \"* ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"+ f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot\",\n \"TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y\",\n \"dx a += delta_1 * div(u) * div(v) * dx a += delta_2\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads\",\n \"TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") #\",\n \"solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True,\",\n \"dS # ** Mesh independent terms # a += jump(u, n) * jump(v,\",\n \"C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V * T #\",\n \"its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree,\",\n \"terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\")\",\n \"Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx =\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh,\",\n \"based on Badia-Codina stabilized dG method L0 = 1 eta_p = L0 *\",\n \"L0 * h_avg # method B in the Badia-Codina paper eta_p = 1\",\n \"* ds # # L = delta_1 * p_exact * dot(v, n) *\",\n \"ds # a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v,\",\n \"real and complex numbers. :param imag_threshold: Threshold to cut off imaginary part in\",\n \"return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads\",\n \"* p + q * div(u)) * dx # DG terms a +=\",\n \"1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"1e-5 ): backend = backend.lower() if backend == \\\"scipy\\\": size = A.getSize() Mnp\",\n \"= A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat\",\n \"Constant(1.0e0) beta = beta_0 / h # beta = beta_0 # Numerical flux\",\n \"beta * (q - mu_h) * n # Flux least-squares # a =\",\n \"# a += ( # (mu_h - q) * (lambda_h - p_boundaries) *\",\n \"ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] -\",\n \"on Badia-Codina stabilized dG method L0 = 1 eta_p = L0 * h\",\n \"for condition number estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False):\",\n \"* sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e\",\n \"n) * p * ds - q * dot(u, n) * ds #\",\n \"TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =\",\n \"u_hat = u + beta * (p - lambda_h) * n v_hat =\",\n \"'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family\",\n \"Weakly imposed BC a += lambda_h * dot(v, n) * ds a +=\",\n \"assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =\",\n \"= beta_0 / h # beta = beta_0 # Numerical flux trace u\",\n \"Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") #\",\n \"pressure_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V =\",\n \"use_quads=False): # Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and\",\n \"Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()\",\n \"* dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\")\",\n \"h # beta = beta_0 # Numerical flux trace u = -grad(p) u_hat\",\n \"** Badia-Codina based a += -eta_u * inner(u + grad(p), v + grad(q))\",\n \"n) * ds # may decrease convergente rates # ** Classical Nitsche #\",\n \"values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0]\",\n \"* dx # a += -0.5 * inner(u + grad(p), v + grad(q))\",\n \"a += eta_p * div(u) * div(v) * dx a += eta_p *\",\n \"resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\")\",\n \"= np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am =\",\n \"# solution = Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h\",\n \"How to set this bc?? # a += (beta / h) * (p-\",\n \"result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # #\",\n \"* dot(v, n) * ds # # L = delta_1 * p_exact *\",\n \"+ beta * (q - mu_h) * n # Flux least-squares # a\",\n \"Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp\",\n \"* dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") *\",\n \"in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\")\",\n \"n) * p_boundaries * ds F = a - L a_form = lhs(F)\",\n \"f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp\",\n \"lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms # a +=\",\n \"* n) * ds # may decrease convergente rates # ** Classical Nitsche\",\n \"# ** Mesh independent terms # a += jump(u, n) * jump(v, n)\",\n \"mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\"))\",\n \"result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"* div(u) * div(v) * dx # a += 0.5 * h *\",\n \"u_hat = u + beta * (p - lambda_h) * n # HDG\",\n \"method D in the Badia-Codina paper eta_u = h / L0 # method\",\n \"plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot\",\n \"n) - dot(v_hat, n)) * ds # Weakly imposed BC from hybridization #\",\n \"= 1e-5 ): backend = backend.lower() if backend == \\\"scipy\\\": size = A.getSize()\",\n \"2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0\",\n \"delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta =\",\n \"- q * div(u) - p * div(v) + inner(grad(p), grad(q))) # *\",\n \"dot(v_hat, n)) * ds # Weakly imposed BC from hybridization # a +=\",\n \"as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form\",\n \"VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\",\",\n \"* dot(u, n) * ds # ** The terms below are based on\",\n \"mu_h(\\\"+\\\") * dS # Weakly imposed BC a += dot(u_hat, n) * q\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): #\",\n \"Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors =\",\n \"pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form =\",\n \"in the Badia-Codina paper eta_u = h / L0 # method B in\",\n \"functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V,\",\n \"q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution\",\n \"* f * div(v) * dx # Irrotational least-squares a += delta_3 *\",\n \"Badia-Codina paper # eta_p = L0 * L0 # method D in the\",\n \"use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv\",\n \"Badia-Codina paper # eta_u = h_avg / L0 # method B in the\",\n \"# method B in the Badia-Codina paper # eta_u = 1 # Nitsche's\",\n \"solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"curl(v)) * dx # Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) *\",\n \"div(u)) * dx # Stabilizing terms a += -0.5 * inner((u + grad(p)),\",\n \"= split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) #\",\n \"s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS\",\n \"+= delta_1 * div(u) * div(v) * dx a += delta_2 * inner(curl(u),\",\n \"symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') #\",\n \"str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ'\",\n \"backend.lower() if backend == \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"ds - q * dot(u, n) * ds # ** The terms below\",\n \"25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\":\",\n \"singular_values.min() else: raise NotImplementedError(\\\"The required method for condition number estimation is currently unavailable.\\\")\",\n \"beta_0 # Numerical flux trace u_hat = u + beta * (p -\",\n \"# Mixed classical terms a = (dot(u, v) - div(v) * p -\",\n \"Mixed classical terms a = (dot(u, v) - div(v) * p + q\",\n \"cell size and mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0\",\n \"or NIPG (1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p),\",\n \"grad(q)) * ds - dot(grad(p), q * n) * ds a += beta\",\n \"-eta_u * inner(u + grad(p), v + grad(q)) * dx a += eta_p\",\n \"a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS a += delta_1\",\n \"result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\",\",\n \"/ h # Mixed classical terms a = (dot(u, v) - div(v) *\",\n \"delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\\\"+\\\") *\",\n \"+= mu_h * (lambda_h - p_boundaries) * ds # a += mu_h *\",\n \"= Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W)\",\n \") return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration\",\n \"backend == \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse:\",\n \"= delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 =\",\n \"f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp =\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration\",\n \"* y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity')\",\n \"eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a +=\",\n \"* mu_h(\\\"+\\\") * dS # Weakly imposed BC a += dot(u_hat, n) *\",\n \"convergente rates # ** Classical Nitsche # a += beta * p *\",\n \"beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 =\",\n \"-delta_1 * dot(u_projected, n) * q * ds # a += delta_1(\\\"+\\\") *\",\n \"ds a += (eta_u / h) * dot(p * n, q * n)\",\n \"h delta_5 = delta # Numerical flux trace u_hat = u + beta\",\n \"\\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar:\",\n \"necessary _A = Tensor(a) A = _A.blocks S = A[2, 2] - A[2,\",\n \"* h * h delta_2 = Constant(0.5) * h * h delta_3 =\",\n \"def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h =\",\n \"+= 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a +=\",\n \"tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib()\",\n \"# Below there is the spy alternative # plot = plt.spy(Am, **kwargs) #\",\n \"parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta = beta_0\",\n \"# a += 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5\",\n \"ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot\",\n \"Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q) -\",\n \"csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False,\",\n \"and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh\",\n \"to set this bc?? # a += (beta / h) * (p- p_boundaries)\",\n \"* dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) *\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads =\",\n \"# method B in the Badia-Codina paper # Mixed classical terms a =\",\n \"dx # DG edge terms a += s * dot(jump(p, n), avg(grad(q))) *\",\n \"trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout()\",\n \"slepc4py import SLEPc import pandas as pd from tqdm import tqdm import os\",\n \"+= (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS # **\",\n \"s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx\",\n \"= p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18)\",\n \"= plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S\",\n \"TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h\",\n \"else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = V\",\n \"degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family\",\n \"= _A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv *\",\n \"return df_cond_number # Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\":\",\n \"= TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\")\",\n \"= Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2),\",\n \"functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h\",\n \"u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W)\",\n \"Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"# maybe this is not a good way to impose BC, but this\",\n \"result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =\",\n \"n v_hat = v + beta * (q - mu_h) * n #\",\n \"terms a = delta_1 * inner(u + grad(p), v + grad(q)) * dx\",\n \"# \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, #\",\n \"Least-squares terms a = delta_0 * inner(u + grad(p), v + grad(q)) *\",\n \"functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh)\",\n \"dx a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 *\",\n \"* (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms\",\n \"ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float\",\n \"convergente rates # ** The terms below are based on ASGS Badia-Codina (2010),\",\n \"a += -eta_u * inner(u + grad(p), v + grad(q)) * dx a\",\n \"condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \\\"CG\\\",\",\n \"np.ndarray: \\\"\\\"\\\"Utility function to filter real part in a numpy array. :param array:\",\n \"VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V\",\n \"return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family\",\n \"S = A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1]\",\n \"cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form,\",\n \"* h * div(u) * div(v) * dx # a += 0.5 *\",\n \"lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1,\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1,\",\n \"'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U =\",\n \"Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0)\",\n \"* lambda_h * dot(v, n) * ds # Mass balance least-square a +=\",\n \"delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5)\",\n \"delta_2 = delta delta_3 = 1 / h delta_4 = 1 / h\",\n \"matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize()\",\n \"classical terms a = (dot(u, v) - div(v) * p + q *\",\n \"grad(q)) * dx # a += 0.5 * div(u) * div(v) * dx\",\n \"\\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values =\",\n \"# ** Mesh independent ** # a += jump(u, n) * jump(v, n)\",\n \"Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)\",\n \"return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form,\",\n \"# L = -delta_1 * dot(u_projected, n) * q * ds # a\",\n \"h_avg) * (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) /\",\n \"BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s\",\n \"* ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size\",\n \"Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta =\",\n \"Weakly imposed boundary conditions a += dot(v, n) * p * ds -\",\n \"= V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =\",\n \"convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N\",\n \"Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero entries\",\n \"# \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in\",\n \"S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values): singular_value\",\n \"* inner(curl(u), curl(v)) * dx # L += 0.5 * f * div(v)\",\n \"= beta_0 / h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1)\",\n \"= \\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous:\",\n \"# Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx\",\n \"0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides\",\n \"dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters\",\n \"dS # a += delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds #\",\n \"mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0,\",\n \"based (better results) ** a += eta_u * avg(delta_3) * (jump(u, n) *\",\n \"least-square a += delta_2 * div(u) * div(v) * dx # L =\",\n \"else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\"\",\n \"q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,\",\n \"delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = 1 /\",\n \"return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads\",\n \"Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms\",\n \"(p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) *\",\n \"-dot(vel_projected, n) * v * ds # How to set this bc?? #\",\n \"n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y =\",\n \"dot(p * n, q * n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat\",\n \"**kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\"\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration\",\n \"+= beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS #\",\n \"grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"B in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term\",\n \"= trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv *\",\n \"a = inner(grad(p), grad(q)) * dx L = f * q * dx\",\n \"* dx # L = delta_2 * f * div(v) * dx #\",\n \"Jump stabilizing parameters based on Badia-Codina stabilized dG method # L0 = 1\",\n \"color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\")\",\n \"div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) * dx L\",\n \"= Constant(1.0) # delta = h delta_0 = delta delta_1 = delta delta_2\",\n \"matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix\",\n \"L += 0.5 * f * div(v) * dx A = assemble(a, bcs=bcs,\",\n \"flux trace u_hat = u + beta * (p - lambda_h) * n\",\n \"= TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) #\",\n \"inner(grad(p), grad(q)) * dx L = f * q * dx # DG\",\n \"bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax =\",\n \"Mesh independent ** # a += jump(u, n) * jump(v, n) * dS\",\n \"function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0)\",\n \"= Constant(1.0e0) beta = beta_0 / h # beta = beta_0 # Numerical\",\n \"q * ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\")\",\n \"= attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a\",\n \"# Edge stabilizing terms # ** Badia-Codina based (better results) ** a +=\",\n \"the Badia-Codina paper eta_p = 1 # eta_p = L0 * L0 #\",\n \"= SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list\",\n \"mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,\",\n \"is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family =\",\n \"Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0\",\n \"+= delta_1 * inner(u + grad(p), v + grad(q)) * dx a +=\",\n \"= (dot(u, v) - div(v) * p) * dx + lambda_h(\\\"+\\\") * jump(v,\",\n \"maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False)\",\n \"= delta delta_2 = delta delta_3 = delta delta_4 = delta # delta_4\",\n \"* jump(v, n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat,\",\n \"n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\")\",\n \"# plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap)\",\n \"# DG terms a += jump(v, n) * avg(p) * dS - avg(q)\",\n \"* dot(u, n) * ds a += beta * p * q *\",\n \"solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls,\",\n \"scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py\",\n \"n) * ds a += dot(u_hat, n) * q * ds F =\",\n \"def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool = False, zero_tol:\",\n \"# L = delta_1 * p_exact * dot(v, n) * ds # Flux\",\n \"+= 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str\",\n \"h) * dot(p * n, q * n) * ds A = assemble(a,\",\n \"inner(u + grad(p), v + grad(q)) * dx a += delta_1 * div(u)\",\n \"T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family,\",\n \"Mesh independent (original) # a += jump(u, n) * jump(v, n) * dS\",\n \"div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx\",\n \"= 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real part in a numpy\",\n \"* ds # a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) *\",\n \"# Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h *\",\n \"with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def\",\n \"NotImplementedError(\\\"The required method for condition number estimation is currently unavailable.\\\") return condition_number def\",\n \"* div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v)) *\",\n \"(dot(u, v) - div(v) * p + delta_0 * q * div(u)) *\",\n \"N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size\",\n \"* dot(v, n) * ds # Mass balance least-square a += delta_2 *\",\n \"if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family\",\n \"n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a += dot(u_hat, n)\",\n \"* dx # Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS\",\n \"v) - div(v) * p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") *\",\n \"solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc,\",\n \"bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20,\",\n \"= delta_0 * inner(u + grad(p), v + grad(q)) * dx # Classical\",\n \"'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family,\",\n \"dot(u, n) * ds a += beta * p * q * ds\",\n \"V * T # Trial and test functions # solution = Function(W) #\",\n \"velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs\",\n \"* div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx # )\",\n \"div(v) * p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n)\",\n \"= 1 for current_solver in solvers_options: # Setting the output file name name\",\n \"f\\\"{current_solver}\\\" # Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing\",\n \"Stabilizing terms a += -0.5 * inner((u + grad(p)), v + grad(q)) *\",\n \"use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ'\",\n \"delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina\",\n \"# Numerical flux trace u = -grad(p) u_hat = u + beta *\",\n \"A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat\",\n \"beta = beta_0 / h # Mixed classical terms a = (dot(u, v)\",\n \"delta_3 * p * q * ds # may decrease convergente rates a\",\n \"in the Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0\",\n \"curl(v)) * dx # L += 0.5 * h * h * f\",\n \"use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' V\",\n \"numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors,\",\n \"dS # a += dot(jump(p, n), jump(q, n)) * dS # a +=\",\n \"# plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([])\",\n \"condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric\",\n \"p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed BC a +=\",\n \"= 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U\",\n \"imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real part in\",\n \"a += delta_2 * div(u) * div(v) * dx a += delta_3 *\",\n \"* inner(u + grad(p), grad(q) - v) * dx a += eta_p *\",\n \"form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh(\",\n \"result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =\",\n \"mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if\",\n \"based a += eta_u * inner(u + grad(p), grad(q) - v) * dx\",\n \"primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T\",\n \"0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) #\",\n \"Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(\",\n \"h / L0 # method B in the Badia-Codina paper # eta_u =\",\n \"is not a classical Nitsche's method a += (eta_p / h) * dot(u,\",\n \"dot(u, n) * ds # ** The terms below are based on ASGS\",\n \"Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2\",\n \"term. Choose if the method is SIPG (-1) or NIPG (1) s =\",\n \"* ds # Flux Least-squares as in DG a = delta_0 * inner(u\",\n \"grad(q)) * dx a += delta_2 * div(u) * div(v) * dx a\",\n \"as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from\",\n \"jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina based a\",\n \"based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) *\",\n \"dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a +=\",\n \"stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p =\",\n \"div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx\",\n \"str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family\",\n \"inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v) * dx\",\n \"\\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\":\",\n \"+= (eta_p / h) * dot(u, n) * dot(v, n) * ds a\",\n \"ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a\",\n \"* dS a += eta_u_bc * delta_3 * p * q * ds\",\n \"# Mass balance least-square a += delta_2 * div(u) * div(v) * dx\",\n \"# Symmetry term. Choose if the method is SIPG (-1) or NIPG (1)\",\n \"'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,\",\n \"\\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\"\",\n \"= S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in range(num_converged_values):\",\n \"exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f\",\n \"dx # Stabilizing terms a += -0.5 * inner((u + grad(p)), v +\",\n \"p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y =\",\n \"* dx # ** Badia-Codina based a += eta_u * inner(u + grad(p),\",\n \"* dx L = delta_0 * f * q * dx # Stabilizing\",\n \"lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh\",\n \"ds # Weakly imposed BC from hybridization # a += mu_h * (lambda_h\",\n \"min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 # mesh\",\n \"n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") -\",\n \"svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd\",\n \"quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(),\",\n \"def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'\",\n \"import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class\",\n \"= U * V # Trial and test functions u, p = TrialFunctions(W)\",\n \"n) * ds a += (eta_u / h) * dot(p * n, q\",\n \"* q * ds # # L = -delta_1 * dot(u_projected, n) *\",\n \"+= delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat,\",\n \"FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T =\",\n \"= beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh,\",\n \"hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"# Weakly imposed boundary conditions a += dot(v, n) * p * ds\",\n \"# Boundary terms # a += -dot(vel_projected, n) * v * ds #\",\n \"div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx\",\n \"assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver,\",\n \"pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} - N\",\n \"terms # a += -dot(vel_projected, n) * v * ds # How to\",\n \"raise NotImplementedError(\\\"The required method for condition number estimation is currently unavailable.\\\") return condition_number\",\n \"def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family\",\n \"delta_2 * f * div(v) * dx # Hybridization terms a += lambda_h(\\\"+\\\")\",\n \"- avg(q) * jump(u, n) * dS # Edge stabilizing terms # **\",\n \"result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) #\",\n \"p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh)\",\n \"dx # ** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q)\",\n \"boundary conditions a += s * dot(p * n, grad(q)) * ds -\",\n \"* dx # ) # # These terms below are unsymmetric # a\",\n \"functions # solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h\",\n \"inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n)\",\n \"the Badia-Codina paper # eta_u = h_avg / L0 # method B in\",\n \"q * ds F = a - L a_form = lhs(F) _A =\",\n \"petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form,\",\n \"method a += (eta_p / h) * dot(u, n) * dot(v, n) *\",\n \"* q * ds # # a += delta_1 * dot(u, n) *\",\n \"= Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1] *\",\n \"A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()\",\n \"ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size =\",\n \"result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V\",\n \"dx # a += 0.5 * div(u) * div(v) * dx # a\",\n \"h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based on\",\n \"jump(v, n=n) * dS # a += delta_1 * lambda_h * dot(v, n)\",\n \"jump(q, n)) * dS # Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p,\",\n \"* dot(u, n) * dot(v, n) * ds # ** Mesh independent **\",\n \"solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family =\",\n \"entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi\",\n \"= UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not\",\n \"degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family\",\n \"h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms\",\n \"jump(q, n)) * dS # a += p * q * ds A\",\n \"n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds a +=\",\n \"# plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap)\",\n \"dx # a += 0.5 * inner(curl(u), curl(v)) * dx # L +=\",\n \"singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method\",\n \"if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,\",\n \"# a += delta_1 * dot(u, n) * q * ds # #\",\n \"dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5 * (dot(u, n) - dot(u_hat,\",\n \"petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors\",\n \"import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function\",\n \"* p + delta_0 * q * div(u)) * dx L = delta_0\",\n \"* q * ds F = a - L a_form = lhs(F) _A\",\n \"ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot = plt.spy(Am,\",\n \"FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W\",\n \"= plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return\",\n \"- p * div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx\",\n \"= Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx L\",\n \"# Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 =\",\n \"pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing\",\n \"return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads\",\n \"to cut off imaginary part in complex number. :return: Filtered array with only\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family\",\n \"dx # Classical mixed Darcy eq. first-order terms as stabilizing terms a +=\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1):\",\n \"sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs\",\n \"= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh)\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False\",\n \"* dx # DG terms a += jump(v, n) * avg(p) * dS\",\n \"= Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 =\",\n \"dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a += beta(\\\"+\\\") *\",\n \"T = FunctionSpace(mesh, trace_family, degree) W = U * V * T #\",\n \"div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx A =\",\n \"and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :]\",\n \"- A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs)\",\n \"n) * q * ds F = a - L a_form = lhs(F)\",\n \"* h * f * div(v) * dx a += 0.5 * div(u)\",\n \"delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\"))\",\n \"f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs =\",\n \"rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[...,\",\n \"q * dx # Stabilizing terms a += delta_1 * inner(u + grad(p),\",\n \"a += mu_h * lambda_h * ds # ### # a += (\",\n \"= delta_2 * f * div(v) * dx # Irrotational least-squares a +=\",\n \"form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"(dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC from hybridization\",\n \"-dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS L =\",\n \"= TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y\",\n \"dx L = f * q * dx # Hybridization terms a +=\",\n \"* n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True)\",\n \"n), jump(q, n)) * dS # Weak boundary conditions a += s *\",\n \"n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds #\",\n \"+= jump(u, n) * jump(v, n) * dS # a += dot(jump(p, n),\",\n \"bool = False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend\",\n \"UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') #\",\n \"h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original)\",\n \"# my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # #\",\n \"to filter real part in a numpy array. :param array: Array with real\",\n \"dot(p * n, q * n) * ds # may decrease convergente rates\",\n \"h delta_3 = Constant(0.5) * h * h # Mixed classical terms a\",\n \"# Numerical flux trace u_hat = u + beta * (p - lambda_h)\",\n \"n)) * dS a += eta_u_bc * delta_3 * p * q *\",\n \"\\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values =\",\n \"dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\")\",\n \"Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs\",\n \"the original paper # a += dot(jump(p, n), jump(q, n)) * dS #\",\n \"# Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) *\",\n \"** # a += jump(u, n) * jump(v, n) * dS # a\",\n \"condition number estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): #\",\n \"assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp\",\n \"\\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT'\",\n \"# eta_u_bc = h / L0 # method B in the Badia-Codina paper\",\n \"use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if\",\n \"Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed\",\n \"- 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp,\",\n \"a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size =\",\n \"# print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') #\",\n \"= 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family,\",\n \":idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size =\",\n \"and its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver,\",\n \"p_exact = sin(2 * pi * x) * sin(2 * pi * y)\",\n \"sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs\",\n \"stabilizing terms a += delta_1 * (dot(u, v) - div(v) * p) *\",\n \"* lambda_h * ds # ### # a += ( # (mu_h -\",\n \"# Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh)\",\n \"q(\\\"+\\\")) * dS # Weakly imposed BC a += (p_boundaries * dot(v, n)\",\n \"assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size\",\n \"_A = Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx]\",\n \"bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta\",\n \"size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate\",\n \"* p + q * div(u)) * dx # Stabilizing terms a +=\",\n \"= 1 / h delta_4 = 1 / h # Least-squares terms a\",\n \"= _A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv *\",\n \"- lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms # a\",\n \"= beta_0 / h # beta = beta_0 # Numerical flux trace u_hat\",\n \"= 1 last_degree = 1 for current_solver in solvers_options: # Setting the output\",\n \"on Badia-Codina stabilized dG method # L0 = 1 # eta_p = L0\",\n \"# Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h #\",\n \"list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar\",\n \"name = f\\\"{current_solver}\\\" # Selecting the solver and its kwargs solver = solvers_options[current_solver]\",\n \"* avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a += eta_p\",\n \"solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U\",\n \"h) * (p- p_boundaries) * q * ds # is this necessary? L\",\n \"q * div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1\",\n \"def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix\",\n \"- div(v) * p - q * div(u)) * dx # Stabilizing terms\",\n \"= U * V * T # Trial and test functions # solution\",\n \"numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\":\",\n \"* dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed\",\n \"v) * dx a += eta_p * div(u) * div(v) * dx #\",\n \"Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name )\",\n \"sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0],\",\n \"dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed\",\n \"* h # Mixed classical terms a = (dot(u, v) - div(v) *\",\n \"# BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0)\",\n \"# L += 0.5 * f * div(v) * dx A = assemble(a,\",\n \"= np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else:\",\n \"= Constant(-0.5) * h * h delta_2 = Constant(0.5) * h * h\",\n \"* q * dx # Stabilizing terms a += delta_1 * inner(u +\",\n \"a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a\",\n \"v) - div(v) * p + delta_0 * q * div(u)) * dx\",\n \"in DG a = delta_0 * inner(u + grad(p), v + grad(q)) *\",\n \"(dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5 * (dot(u,\",\n \"+= 0.5 * h * h * f * div(v) * dx a\",\n \"+ grad(p), v + grad(q)) * dx # Classical mixed Darcy eq. first-order\",\n \"fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx =\",\n \"# Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, #\",\n \"# Mixed classical terms a = (dot(u, v) - div(v) * p +\",\n \"\\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh)\",\n \"L0 # method B in the Badia-Codina paper # eta_u = 1 #\",\n \"= DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v)) * dx\",\n \"# Trial and test functions # solution = Function(W) # u, p, lambda_h\",\n \"Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact,\",\n \"dx + jump(u_hat, n) * q(\\\"+\\\") * dS L = f * q\",\n \"p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n\",\n \"complex number. :return: Filtered array with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag)\",\n \"* ds # may decrease convergente rates a += eta_u_bc * delta_4 *\",\n \"a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a\",\n \"grad(q)) * dx a += delta_1 * div(u) * div(v) * dx a\",\n \"# \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1\",\n \"mu_h * ds F = a - L a_form = lhs(F) _A =\",\n \"v) - div(v) * p + q * div(u)) * dx # Stabilizing\",\n \"terms # a += 0.5 * inner(u + grad(p), grad(q) - v) *\",\n \"mu_h) * n # Flux least-squares # a = ( # (inner(u, v)\",\n \"(avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS a +=\",\n \"* (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) *\",\n \"scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm\",\n \") # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result\",\n \"terms a = (dot(u, v) - div(v) * p + delta_0 * q\",\n \"sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing\",\n \"p * ds - q * dot(u, n) * ds a += beta\",\n \"assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =\",\n \"ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray:\",\n \"Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs\",\n \"# method D in the Badia-Codina paper eta_u = h / L0 #\",\n \"# a += delta_4 * (p - lambda_h) * (q - mu_h) *\",\n \"path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options =\",\n \"n))) * ds a += beta * (lambda_h - p_boundaries) * mu_h *\",\n \"0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #\",\n \"n), jump(q, n)) * dS a += eta_u_bc * delta_3 * p *\",\n \"L0 # method D in the Badia-Codina paper eta_u = h / L0\",\n \"bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh dependent\",\n \"= L0 * L0 # method D in the Badia-Codina paper # eta_u\",\n \"the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) #\",\n \"dx a += eta_p * div(u) * div(v) * dx a += eta_p\",\n \"a += (beta / h) * (p- p_boundaries) * q * ds #\",\n \"q * dot(u, n) * ds # ** The terms below are based\",\n \"declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family =\",\n \"* f * div(v) * dx # a += -0.5 * inner(u +\",\n \"h # method B in the Badia-Codina paper # eta_p = L0 *\",\n \"bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space\",\n \"= 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh,\",\n \"* dS # Boundary terms # a += -dot(vel_projected, n) * v *\",\n \"= delta # Numerical flux trace u_hat = u + beta * (p\",\n \"q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12)\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space\",\n \"dot(u, n) * dot(v, n) * ds # ** Mesh independent ** #\",\n \"DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v)) * dx A\",\n \"Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") #\",\n \"eta_u = h / L0 # method B in the Badia-Codina paper #\",\n \"= csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000,\",\n \"max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration\",\n \"* h # method B in the Badia-Codina paper # eta_p = 1\",\n \"# delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) #\",\n \"plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig, ax\",\n \"= FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family\",\n \"(p- p_boundaries) * q * ds # is this necessary? L += s\",\n \"= assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size =\",\n \"jump(q, n)) * dS a += eta_u_bc * delta_3 * p * q\",\n \"= 1 eta_p = L0 * h # method B in the Badia-Codina\",\n \"beta = beta_0 # Numerical flux trace u = -grad(p) u_hat = u\",\n \"'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U\",\n \"+= s * dot(p * n, grad(q)) * ds - dot(grad(p), q *\",\n \"# HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n)\",\n \"Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy\",\n \"n) * dS # a += dot(jump(p, n), jump(q, n)) * dS #\",\n \"* sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") #\",\n \"h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = 1\",\n \"2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2,\",\n \"axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold:\",\n \"dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\")\",\n \"Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0)\",\n \"+= s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) *\",\n \"trace_family, degree) W = V * T # Trial and test functions #\",\n \"# delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) #\",\n \"q * dot(u, n) * ds a += beta * p * q\",\n \"/ h # Symmetry term. Choose if the method is SIPG (-1) or\",\n \"inner((u + grad(p)), v + grad(q)) * dx # a += 0.5 *\",\n \"\\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar =\",\n \"* div(u) * div(v) * dx # a += 0.5 * inner(curl(u), curl(v))\",\n \"parameters based on Badia-Codina stabilized dG method # L0 = 1 # eta_p\",\n \"pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh,\",\n \"matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form,\",\n \"DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 /\",\n \"Constant(1) # delta = h delta_0 = delta delta_1 = delta delta_2 =\",\n \"A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz\",\n \"n=n) * q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") * dot(u_hat, n) *\",\n \"# Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) #\",\n \"A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric\",\n \"* (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed\",\n \"bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta =\",\n \"* dx # a += 0.5 * inner(curl(u), curl(v)) * dx # **\",\n \"result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"dx # Weakly imposed boundary conditions a += dot(v, n) * p *\",\n \"n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a += lambda_h *\",\n \"this necessary _A = Tensor(a) A = _A.blocks S = A[2, 2] -\",\n \"\\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1)\",\n \"* dx # Stabilizing terms a += -0.5 * inner((u + grad(p)), v\",\n \"dx # L = delta_2 * f * div(v) * dx # Irrotational\",\n \"hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U *\",\n \"Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p),\",\n \"exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs #\",\n \"svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc\",\n \"* q * dx # Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\")\",\n \"h # beta = beta_0 # Numerical flux trace u_hat = u +\",\n \"0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx]\",\n \"* dx # L += 0.5 * f * div(v) * dx A\",\n \"* p) * dx + lambda_h(\\\"+\\\") * jump(v, n) * dS a +=\",\n \"LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat = u\",\n \"\\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\":\",\n \"def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} -\",\n \"method # L0 = 1 # eta_p = L0 * h_avg # method\",\n \"* ds # is this necessary? L += s * dot(grad(q), n) *\",\n \"else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p\",\n \"if the method is SIPG (-1) or NIPG (1) s = Constant(-1) #\",\n \"= a - L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks\",\n \"DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0\",\n \"ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot\",\n \"v + grad(q)) * dx a += delta_2 * div(u) * div(v) *\",\n \"* q * dx # Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\")\",\n \"delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS a += delta_1 * lambda_h\",\n \"os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number =\",\n \"\\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind\",\n \"div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx A =\",\n \"+ beta * (p - lambda_h) * n v_hat = v + beta\",\n \"a = (dot(u, v) - div(v) * p + q * div(u)) *\",\n \"# \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\":\",\n \"dx # L += 0.5 * f * div(v) * dx A =\",\n \"\\\"scipy\\\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend = backend.lower()\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space\",\n \"Numerical flux trace u = -grad(p) u_hat = u + beta * (p\",\n \"- q * dot(u, n) * ds a += beta * p *\",\n \"solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg,\",\n \"h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1)\",\n \"matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative #\",\n \"= DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a = (dot(u, v) -\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function\",\n \"Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0 # method\",\n \"test functions # solution = Function(W) # u, p, lambda_h = split(solution) u,\",\n \"- lambda_h) * (q - mu_h) * ds # a += delta_5 *\",\n \"Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =\",\n \"beta0 = Constant(1e1) beta = beta0 / h # Symmetry term. Choose if\",\n \"delta_1 # * dx # ) # # These terms below are unsymmetric\",\n \"pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) #\",\n \"terms as stabilizing terms a += delta_1 * (dot(u, v) - div(v) *\",\n \"div(v) * dx # L = delta_2 * f * div(v) * dx\",\n \"= 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1)\",\n \"# delta = Constant(1) # delta = h delta_0 = delta delta_1 =\",\n \"dS # ** Mesh independent (original) # a += jump(u, n) * jump(v,\",\n \"dx # Edge stabilizing terms # ** Badia-Codina based (better results) ** a\",\n \"petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True\",\n \"# mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is\",\n \"= assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =\",\n \"idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S,\",\n \"(better results) ** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v,\",\n \"= FunctionSpace(mesh, trace_family, degree) W = U * V * T # Trial\",\n \"div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx L +=\",\n \"if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for\",\n \"* n, grad(q)) * ds - dot(grad(p), q * n) * ds a\",\n \"Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree)\",\n \"( # (inner(u, v) - q * div(u) - p * div(v) +\",\n \"class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator =\",\n \"inner(grad(p), grad(q))) # * delta_1 # * dx # ) # # These\",\n \"+= s * dot(grad(q), n) * p_boundaries * ds F = a -\",\n \"ds # ### # a += ( # (mu_h - q) * (lambda_h\",\n \"from tqdm import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib()\",\n \"\\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\":\",\n \"* (dot(u, v) - div(v) * p) * dx a += delta_1(\\\"+\\\") *\",\n \"return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed\",\n \"list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), }\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space\",\n \"solution = Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h =\",\n \"inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a += dot(v, n)\",\n \"V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(\",\n \"delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3\",\n \"a += (eta_p / h) * dot(u, n) * dot(v, n) * ds\",\n \"a += 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 *\",\n \"and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n\",\n \"delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a\",\n \"jump(u, n) * jump(v, n) * dS # a += dot(jump(p, n), jump(q,\",\n \"import * import numpy as np import matplotlib.pyplot as plt import matplotlib from\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ):\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh,\",\n \"L += 0.5 * f * div(v) * dx # ** Badia-Codina based\",\n \"# ** The terms below are based on ASGS Badia-Codina (2010), it is\",\n \"B in the Badia-Codina paper # eta_p = L0 * L0 # method\",\n \"solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict\",\n \"lambda_h) * n # HDG classical form a = -dot(u, grad(q)) * dx\",\n \"beta_0 # Numerical flux trace u = -grad(p) u_hat = u + beta\",\n \"inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based (better\",\n \"# # These terms below are unsymmetric # a += delta_1 * jump(u_hat,\",\n \"int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number,\",\n \"+= -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS L\",\n \"a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1]\",\n \"stabilizing terms # ** Badia-Codina based (better results) ** a += eta_u *\",\n \"n) * ds a += beta * p * q * ds A\",\n \"L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2,\",\n \"num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise\",\n \"assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh,\",\n \"dot(u_projected, n) * q * ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") *\",\n \"quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1\",\n \"+= dot(u_hat, n) * q * ds F = a - L a_form\",\n \"has not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number =\",\n \"lambda_h * dot(v, n) * ds a += dot(u_hat, n) * q *\",\n \"= str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' V =\",\n \"p_boundaries * ds F = a - L a_form = lhs(F) _A =\",\n \"= TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h\",\n \"dx # Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a\",\n \"np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real part\",\n \"= Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,\",\n \"petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and\",\n \"h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 = delta\",\n \"dot(grad(p), q * n) * ds a += beta * p * q\",\n \"num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool = False, zero_tol: float = 1e-5\",\n \"= singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A)\",\n \"delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares as in\",\n \"div(v) * p) * dx + lambda_h(\\\"+\\\") * jump(v, n) * dS a\",\n \"dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds a\",\n \"* ds # may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm(\",\n \"delta_1 * lambda_h * dot(v, n) * ds # # L = delta_1\",\n \"+= eta_p * div(u) * div(v) * dx # Weakly imposed boundary conditions\",\n \"* q * div(u)) * dx L = delta_0 * f * q\",\n \"x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi *\",\n \"Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n))\",\n \"q * ds # is this necessary? L += s * dot(grad(q), n)\",\n \"A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x,\",\n \"full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"p = TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h\",\n \"- dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries) *\",\n \"# u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q,\",\n \"f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1)\",\n \"ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size\",\n \"+= beta * p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat\",\n \"div(v) * dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge\",\n \"/ h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent\",\n \"**kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def\",\n \"matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") #\",\n \"below are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") *\",\n \"= assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p), v +\",\n \"+ grad(q)) * dx # a += 0.5 * div(u) * div(v) *\",\n \"from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from\",\n \"result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads =\",\n \"* lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a += delta_1 * lambda_h\",\n \"* ds # ** The terms below are based on ASGS Badia-Codina (2010),\",\n \"a += delta_1 * lambda_h * dot(v, n) * ds # Mass balance\",\n \"* ds - q * dot(u, n) * ds a += beta *\",\n \"A = _A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv\",\n \"mu_h(\\\"+\\\")) * dS # Boundary terms # a += -dot(vel_projected, n) * v\",\n \"the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and\",\n \"pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression\",\n \"delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux trace u_hat\",\n \"stabilized dG method L0 = 1 eta_p = L0 * h # method\",\n \"# delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) #\",\n \"+ grad(q)) * dx # Classical mixed Darcy eq. first-order terms as stabilizing\",\n \"Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) *\",\n \"terms a = (dot(u, v) - div(v) * p - q * div(u))\",\n \"/ n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size)\",\n \"* dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS #\",\n \"jump(q, n)) * dS # Weak boundary conditions a += s * dot(p\",\n \"= \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in\",\n \"S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for\",\n \"functions # solution = Function(W) # u, p, lambda_h = split(solution) u, p,\",\n \"decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, #\",\n \"- A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier)\",\n \"delta_5 = delta # Numerical flux trace u_hat = u + beta *\",\n \"a classical Nitsche's method a += (eta_p / h) * dot(u, n) *\",\n \"= A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat\",\n \"else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family,\",\n \"form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg(\",\n \"Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values\",\n \"= TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact\",\n \"* div(v) * dx # Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\")\",\n \"- mu_h(\\\"+\\\")) * dS # Boundary terms # a += -dot(vel_projected, n) *\",\n \"1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial\",\n \"(mu_h - q) * (lambda_h - p_boundaries) * ds # ) # maybe\",\n \"# may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \":return: Filtered array with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5]\",\n \"if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement =\",\n \"solution p_exact = sin(2 * pi * x) * sin(2 * pi *\",\n \"+ grad(p), v + grad(q)) * dx a += delta_1 * div(u) *\",\n \"= Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2] *\",\n \"v + grad(q)) * dx a += eta_p * div(u) * div(v) *\",\n \"results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\"\",\n \"# Stabilizing terms a += -0.5 * inner((u + grad(p)), v + grad(q))\",\n \"assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"= (dot(u, v) - div(v) * p + q * div(u)) * dx\",\n \") return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25),\",\n \"n # HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat,\",\n \"grad(p), v + grad(q)) * dx a += delta_2 * div(u) * div(v)\",\n \"mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"\\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h\",\n \"h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) #\",\n \"0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there\",\n \"* h * h * inner(curl(u), curl(v)) * dx # L += 0.5\",\n \"= V * T # Trial and test functions # solution = Function(W)\",\n \"Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy\",\n \") return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number =\",\n \"def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family\",\n \"# # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\\\"sparse_pattern.png\\\") #\",\n \"* (q - mu_h) * n # Flux least-squares # a = (\",\n \"\\\"on_boundary\\\") # Mixed classical terms a = (dot(u, v) - div(v) * p\",\n \"numbers. :param imag_threshold: Threshold to cut off imaginary part in complex number. :return:\",\n \"* p * q * ds # may decrease convergente rates (Nitsche) A\",\n \"\\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree)\",\n \"div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta =\",\n \"(dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds\",\n \"= assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()\",\n \"sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 =\",\n \"{ # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm,\",\n \"# a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based\",\n \"div(v) * p - q * div(u)) * dx # Stabilizing terms a\",\n \"- 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot\",\n \"* ds a += dot(u_hat, n) * q * ds F = a\",\n \"ds # # L = delta_1 * p_exact * dot(v, n) * ds\",\n \"\\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT,\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ):\",\n \"div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact bcs\",\n \"= 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh,\",\n \"beta_0 / h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1\",\n \"hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A =\",\n \"(dot(u, v) - div(v) * p + q * div(u)) * dx #\",\n \"mu_h(\\\"+\\\")) * dS a += (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) *\",\n \"* (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") -\",\n \"delta delta_3 = 1 / h delta_4 = 1 / h # Least-squares\",\n \"- A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs)\",\n \"complex numbers. :param imag_threshold: Threshold to cut off imaginary part in complex number.\",\n \"+= delta_1 * lambda_h * dot(v, n) * ds # Mass balance least-square\",\n \"(p - lambda_h) * n # HDG classical form a = (dot(u, v)\",\n \"\\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = U * V *\",\n \"\\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms,\",\n \"+= beta * p * q * ds # may decrease convergente rates\",\n \"u + beta * (p - lambda_h) * n v_hat = v +\",\n \"grad(p), v + grad(q)) * dx # a += 0.5 * div(u) *\",\n \") # maybe this is not a good way to impose BC, but\",\n \"parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0 /\",\n \"FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and test functions\",\n \"velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V\",\n \"trace u = -grad(p) u_hat = u + beta * (p - lambda_h)\",\n \"p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) #\",\n \"lambda_h(\\\"+\\\") * jump(v, n) * dS a += -dot(u, grad(q)) * dx +\",\n \"dS # a += p * q * ds A = assemble(a, mat_type=\\\"aij\\\")\",\n \"* (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC from\",\n \"# Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\")\",\n \"n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number\",\n \"Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #\",\n \"+ grad(p), grad(q) - v) * dx # a += 0.5 * h\",\n \"# a += (beta / h) * (p- p_boundaries) * q * ds\",\n \"in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} -\",\n \"filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real\",\n \"dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a +=\",\n \"use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M\",\n \"BC a += lambda_h * dot(v, n) * ds a += dot(u_hat, n)\",\n \"impose BC, but this necessary _A = Tensor(a) A = _A.blocks S =\",\n \"# * dx # ) # # These terms below are unsymmetric #\",\n \"name name = f\\\"{current_solver}\\\" # Selecting the solver and its kwargs solver =\",\n \"= DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0\",\n \"= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\")\",\n \"in solvers_options: # Setting the output file name name = f\\\"{current_solver}\\\" # Selecting\",\n \"values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return\",\n \"h * f * div(v) * dx # a += -0.5 * inner(u\",\n \"# a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a\",\n \"# Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization\",\n \"degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial\",\n \"n, q * n) * ds # may decrease convergente rates # **\",\n \"(p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms #\",\n \"= Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) *\",\n \"grad(q)) * dx L = f * q * dx # Hybridization terms\",\n \"condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz,\",\n \"Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry\",\n \"= (dot(u, v) - div(v) * p + delta_0 * q * div(u))\",\n \"* ds # How to set this bc?? # a += (beta /\",\n \"p + delta_0 * q * div(u)) * dx L = delta_0 *\",\n \"-0.5 * inner((u + grad(p)), v + grad(q)) * dx # a +=\",\n \"+ grad(p), v + grad(q)) * dx # a += 0.5 * div(u)\",\n \"\\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver in solvers_options:\",\n \"# Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\"))\",\n \"as stabilizing terms a += delta_1 * (dot(u, v) - div(v) * p)\",\n \"list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if\",\n \"+= delta_1 * dot(u, n) * q * ds # # L =\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1,\",\n \"* (p - lambda_h) * (q - mu_h) * ds # a +=\",\n \"use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions\",\n \"if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test\",\n \"bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize()\",\n \"= Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2] *\",\n \"/ h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 =\",\n \"trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = U *\",\n \"f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization\",\n \"Weakly imposed BC a += dot(u_hat, n) * q * ds F =\",\n \"color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] +\",\n \"import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number\",\n \"A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values =\",\n \"= TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y\",\n \"zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method for condition\",\n \"u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y\",\n \"# \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\":\",\n \"n) * dS # not considered in the original paper # a +=\",\n \"Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\",\",\n \"+= delta_4 * (p - lambda_h) * (q - mu_h) * ds #\",\n \"A = _A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx]\",\n \"fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle\",\n \"V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and\",\n \"\\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral\",\n \"result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads =\",\n \"v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational\",\n \"LARGE_NUMBER = Constant(1e0) delta = h * h # delta = Constant(1) #\",\n \"solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree = 1 for current_solver\",\n \"solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U\",\n \"Constant(-0.5) * h * h delta_2 = Constant(0.5) * h * h delta_3\",\n \"color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a\",\n \"a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms #\",\n \"* (p- p_boundaries) * q * ds # is this necessary? L +=\",\n \"# Least-squares terms a = delta_0 * inner(u + grad(p), v + grad(q))\",\n \"= str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family =\",\n \"* pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U,\",\n \"method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta\",\n \"n=n) * dS # a += delta_1 * lambda_h * dot(v, n) *\",\n \"mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver\",\n \"Filtered array with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return\",\n \"q * n) * ds a += beta * p * q *\",\n \"axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am,\",\n \"s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p),\",\n \"- dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly\",\n \"velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") #\",\n \"A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat\",\n \"# Setting the output file name name = f\\\"{current_solver}\\\" # Selecting the solver\",\n \"grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS L = f\",\n \"degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family\",\n \"array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\",\",\n \"* inner(u + grad(p), v + grad(q)) * dx a += eta_p *\",\n \"axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides\",\n \"matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib()\",\n \"n) - dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries)\",\n \"nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number =\",\n \"calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs\",\n \"* ds - dot(grad(p), q * n) * ds a += beta *\",\n \"mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size\",\n \"Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors =\",\n \"dot(v, n) * ds a += dot(u_hat, n) * q * ds F\",\n \"= svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() /\",\n \"= u + beta * (p - lambda_h) * n v_hat = v\",\n \"paper eta_p = 1 # eta_p = L0 * L0 # method D\",\n \"term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical terms\",\n \"A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat =\",\n \"= beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h\",\n \"curl(v)) * dx # L += 0.5 * f * div(v) * dx\",\n \"paper # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing\",\n \"and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot\",\n \"a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize()\",\n \"= FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial and test\",\n \"a += 0.5 * div(u) * div(v) * dx a += 0.5 *\",\n \"+= dot(jump(p, n), jump(q, n)) * dS # a += p * q\",\n \"name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number\",\n \"plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix for\",\n \"= A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks\",\n \"a += eta_u_bc * delta_3 * p * q * ds # may\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function\",\n \"{result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg')\",\n \"= FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions u = TrialFunction(V) v\",\n \":2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat =\",\n \"\\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { #\",\n \"import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as\",\n \"B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta =\",\n \"* jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) *\",\n \"conditions a += dot(v, n) * p * ds - q * dot(u,\",\n \"not a classical Nitsche's method a += (eta_p / h) * dot(u, n)\",\n \"Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size\",\n \"a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n))\",\n \"# a += mu_h * lambda_h * ds # ### # a +=\",\n \"dS # Weakly imposed BC a += lambda_h * dot(v, n) * ds\",\n \"= 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V\",\n \"Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a =\",\n \"off imaginary part in complex number. :return: Filtered array with only real numbers.\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration\",\n \"10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(),\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads\",\n \"ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) - v) *\",\n \"= ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0]\",\n \"+= 0.5 * h * h * f * div(v) * dx #\",\n \"sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 =\",\n \"/ 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method L0\",\n \"nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides\",\n \"f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact,\",\n \"* div(v) * dx # ** Badia-Codina based a += eta_u * inner(u\",\n \"1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs,\",\n \"beta * p * q * ds # may decrease convergente rates (Nitsche)\",\n \"A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric\",\n \"delta_0 * q * div(u)) * dx L = delta_0 * f *\",\n \"solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree +\",\n \"v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)\",\n \"a += eta_p * div(u) * div(v) * dx # Weakly imposed boundary\",\n \"* div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"lambda_h * ds # ### # a += ( # (mu_h - q)\",\n \"# ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) *\",\n \"L0 * L0 # method D in the Badia-Codina paper # eta_u =\",\n \"# delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h\",\n \"* inner(u + grad(p), v + grad(q)) * dx # Classical mixed Darcy\",\n \"print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import\",\n \"inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u * inner(u\",\n \"= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h\",\n \"A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"volumetric terms a = inner(grad(p), grad(q)) * dx L = f * q\",\n \"method B in the Badia-Codina paper # Mixed classical terms a = (dot(u,\",\n \"ds F = a - L a_form = lhs(F) _A = Tensor(a_form) A\",\n \"dx # Stabilizing terms a += delta_1 * inner(u + grad(p), v +\",\n \"= Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 =\",\n \"entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp =\",\n \"TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =\",\n \"to impose BC, but this necessary _A = Tensor(a) A = _A.blocks S\",\n \"numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} - N = {n}\\\") mesh =\",\n \"attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\"\",\n \"* mu_h(\\\"+\\\") * dS # Weakly imposed BC a += lambda_h * dot(v,\",\n \"# delta = h delta_0 = delta delta_1 = delta delta_2 = delta\",\n \"else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values >\",\n \"L0 = 1 eta_p = L0 * h # method B in the\",\n \"a += jump(u, n) * jump(v, n) * dS # not considered in\",\n \"- lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) *\",\n \"(p - lambda_h) * n # HDG classical form a = -dot(u, grad(q))\",\n \"# a += delta_1 * lambda_h * dot(v, n) * ds # #\",\n \"= { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\":\",\n \"alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0)\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration\",\n \"q * ds # may decrease convergente rates # ** The terms below\",\n \"** Mesh independent terms # a += jump(u, n) * jump(v, n) *\",\n \"results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict)\",\n \"assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp =\",\n \"+= jump(v, n) * avg(p) * dS - avg(q) * jump(u, n) *\",\n \"q * n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"div(u) * div(v) * dx # L = delta_2 * f * div(v)\",\n \"* div(v) * dx # a += 0.5 * div(u) * div(v) *\",\n \"= L0 * h # method B in the Badia-Codina paper # eta_p\",\n \":1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric =\",\n \"= A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp,\",\n \"delta = h * h # delta = Constant(1) # delta = h\",\n \"delta_2 = delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER\",\n \"Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = U * V * T\",\n \"0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 * f *\",\n \"): results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\":\",\n \"+= delta_2 * div(u) * div(v) * dx a += delta_3 * inner(curl(u),\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family\",\n \"* h * f * div(v) * dx # a += -0.5 *\",\n \"dx + lambda_h(\\\"+\\\") * jump(v, n) * dS a += -dot(u, grad(q)) *\",\n \"= assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()\",\n \"# delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) #\",\n \"/ h # Least-squares terms a = delta_0 * inner(u + grad(p), v\",\n \"n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS #\",\n \"a += s * dot(p * n, grad(q)) * ds - dot(grad(p), q\",\n \"( # (mu_h - q) * (lambda_h - p_boundaries) * ds # )\",\n \"= -grad(p) u_hat = u + beta * (p - lambda_h) * n\",\n \"A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv\",\n \"terms # a += jump(u, n) * jump(v, n) * dS # a\",\n \"= lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[1, 1] -\",\n \"A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat\",\n \"delta_0 * inner(u + grad(p), v + grad(q)) * dx # Classical mixed\",\n \"Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx] - A[idx,\",\n \"a += ( # (mu_h - q) * (lambda_h - p_boundaries) * ds\",\n \"* (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5 *\",\n \"# Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 =\",\n \"* (p - lambda_h) * n v_hat = v + beta * (q\",\n \"# LARGE_NUMBER = Constant(1e0) delta = h * h # delta = Constant(1)\",\n \"f * div(v) * dx # a += 0.5 * div(u) * div(v)\",\n \"delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h #\",\n \"ds a += beta * p * q * ds A = assemble(a,\",\n \"calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool = False, zero_tol: float\",\n \"h * h * f * div(v) * dx # a += 0.5\",\n \"dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") *\",\n \"pressure_family, degree) W = U * V # Trial and test functions u,\",\n \"form a = (dot(u, v) - div(v) * p) * dx + lambda_h(\\\"+\\\")\",\n \"function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries\",\n \"= Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\")\",\n \"# BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\")\",\n \"** Classical Nitsche # a += beta * p * q * ds\",\n \"solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 #\",\n \"+ q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"= calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric,\",\n \"# Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly\",\n \"paper eta_u = 1 # eta_u_bc = h / L0 # method B\",\n \"- q * div(u)) * dx # DG terms a += jump(v, n)\",\n \"solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree =\",\n \"* q * ds # may decrease convergente rates a += eta_u_bc *\",\n \"beta_0 / h # beta = beta_0 # Numerical flux trace u =\",\n \"Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u,\",\n \"FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U * V\",\n \"inner(u + grad(p), v + grad(q)) * dx # Classical mixed Darcy eq.\",\n \"a += delta_4 * (p - lambda_h) * (q - mu_h) * ds\",\n \"dS a += delta_1 * lambda_h * dot(v, n) * ds # Mass\",\n \"os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results)\",\n \"solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') #\",\n \"singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values =\",\n \"stabilizing terms # a += 0.5 * inner(u + grad(p), grad(q) - v)\",\n \"== \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values\",\n \"dx # L += 0.5 * h * h * f * div(v)\",\n \"return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh,\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh,\",\n \"* A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"\\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat\",\n \"S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for i\",\n \"* q * dx # DG edge terms a += s * dot(jump(p,\",\n \"Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp,\",\n \"is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement)\",\n \"dot(u_hat, n) * q * ds # # a += delta_1 * dot(u,\",\n \"beta_0 / h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0\",\n \"Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" )\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1,\",\n \"a = (dot(u, v) - div(v) * p) * dx + lambda_h(\\\"+\\\") *\",\n \"* dS a += delta_1 * lambda_h * dot(v, n) * ds #\",\n \"results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(),\",\n \"* dot(u, n) * dot(v, n) * ds a += (eta_u / h)\",\n \"= singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required\",\n \"jump(v, n) * dS # a += dot(jump(p, n), jump(q, n)) * dS\",\n \"dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary\",\n \"attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax =\",\n \"> zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method for\",\n \"* f * div(v) * dx # a += 0.5 * div(u) *\",\n \"in the Badia-Codina paper eta_p = 1 # eta_p = L0 * L0\",\n \"import tqdm import os matplotlib.use('Agg') @attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form =\",\n \"/ h delta_5 = delta # Numerical flux trace u_hat = u +\",\n \"hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ):\",\n \"lambda_h) * n v_hat = v + beta * (q - mu_h) *\",\n \"(dot(u, n) - dot(u_projected, n))) * ds a += beta * (lambda_h -\",\n \"dx # Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") -\",\n \"Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function\",\n \"beta = beta_0 / h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter\",\n \"# print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number:\",\n \"\\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0],\",\n \"Boundary terms # a += -dot(vel_projected, n) * v * ds # How\",\n \"grad(q) - v) * dx # a += 0.5 * h * h\",\n \"is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz =\",\n \"terms a += -0.5 * inner((u + grad(p)), v + grad(q)) * dx\",\n \"delta # Numerical flux trace u_hat = u + beta * (p -\",\n \"copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) #\",\n \"return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values\",\n \"delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) * h *\",\n \"+= ( # (mu_h - q) * (lambda_h - p_boundaries) * ds #\",\n \"= DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0\",\n \"* V * T # Trial and test functions # solution = Function(W)\",\n \"sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0\",\n \"ds # is this necessary? L += s * dot(grad(q), n) * p_boundaries\",\n \"are based on ASGS Badia-Codina (2010), it is not a classical Nitsche's method\",\n \"a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a +=\",\n \"delta delta_2 = delta delta_3 = 1 / h delta_4 = 1 /\",\n \"matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp\",\n \"== 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13)\",\n \"0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5,\",\n \"num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier )\",\n \"- mu_h(\\\"+\\\")) * dS # a += delta_4 * (p - lambda_h) *\",\n \"v + grad(q)) * dx # Classical mixed Darcy eq. first-order terms as\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration\",\n \"terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n)\",\n \"dS # a += delta_1 * lambda_h * dot(v, n) * ds #\",\n \"B in the Badia-Codina paper # eta_p = 1 # eta_p = L0\",\n \"* delta_4 * dot(u, n) * dot(v, n) * ds # ** Mesh\",\n \"= Constant(0.5) * h * h # Mixed classical terms a = (dot(u,\",\n \"# beta = beta_0 # Numerical flux trace u = -grad(p) u_hat =\",\n \"+= delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds # # a +=\",\n \"imag_threshold: Threshold to cut off imaginary part in complex number. :return: Filtered array\",\n \"n) * q(\\\"+\\\") * dS L = f * q * dx #\",\n \"* x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\",\",\n \"mu_h * (dot(u, n) - dot(u_projected, n))) * ds a += beta *\",\n \"attr from firedrake import * import numpy as np import matplotlib.pyplot as plt\",\n \"mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\"))\",\n \"real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A,\",\n \"= list() if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i)\",\n \"* div(v) * dx # a += 0.5 * h * h *\",\n \"a = ( # (inner(u, v) - q * div(u) - p *\",\n \"B in the Badia-Codina paper eta_u = 1 # eta_u_bc = h /\",\n \"a += delta_1 * inner(u + grad(p), v + grad(q)) * dx a\",\n \"* div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v))\",\n \"= FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and test\",\n \"= DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1)\",\n \"h * h delta_3 = Constant(0.5) * h * h # Mixed classical\",\n \"Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 *\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): #\",\n \"sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1\",\n \"fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp =\",\n \"V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions u = TrialFunction(V)\",\n \"a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n) - dot(u_projected,\",\n \"delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5\",\n \"= CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 *\",\n \") return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG'\",\n \"inner(curl(u), curl(v)) * dx # L += 0.5 * f * div(v) *\",\n \"= FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement =\",\n \"a += beta * (lambda_h - p_boundaries) * mu_h * ds F =\",\n \"# print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix\",\n \"print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting matrix #\",\n \"Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u),\",\n \"* inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a += dot(v,\",\n \"= assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp\",\n \"(inner(u, v) - q * div(u) - p * div(v) + inner(grad(p), grad(q)))\",\n \"(p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\"))\",\n \"dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms # a +=\",\n \"part in complex number. :return: Filtered array with only real numbers. \\\"\\\"\\\" real_part_array\",\n \"(lambda_h - p_boundaries) * ds # a += mu_h * lambda_h * ds\",\n \"test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs =\",\n \"* n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function\",\n \"list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral else\",\n \"int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number,\",\n \"} degree = 1 last_degree = 1 for current_solver in solvers_options: # Setting\",\n \"= -delta_1 * dot(u_projected, n) * q * ds # a += delta_1(\\\"+\\\")\",\n \"n) * dot(v, n) * ds # ** Mesh independent ** # a\",\n \"may decrease convergente rates # ** Classical Nitsche # a += beta *\",\n \"* h * f * div(v) * dx # a += 0.5 *\",\n \"# method B in the Badia-Codina paper eta_u = 1 # eta_u_bc =\",\n \"\\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(),\",\n \"dx a += delta_2 * div(u) * div(v) * dx a += delta_3\",\n \"# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\")\",\n \"- N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree)\",\n \"* dot(p * n, grad(q)) * ds - dot(grad(p), q * n) *\",\n \"# a += -dot(vel_projected, n) * v * ds # How to set\",\n \"assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size\",\n \"# eta_p = L0 * h_avg # method B in the Badia-Codina paper\",\n \"Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1 eta_p\",\n \"* q * ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n)\",\n \"ds # may decrease convergente rates # ** The terms below are based\",\n \"* (jump(u, n) * jump(v, n)) * dS a += eta_p * avg(delta_4)\",\n \"paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta =\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family =\",\n \"lambda_h) * n # HDG classical form a = (dot(u, v) - div(v)\",\n \"# Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 =\",\n \"in the Badia-Codina paper # eta_p = 1 # eta_p = L0 *\",\n \"a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"BC a += dot(u_hat, n) * q * ds F = a -\",\n \"- div(v) * p + q * div(u)) * dx # Stabilizing terms\",\n \"plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size\",\n \"grad(q)) * dx a += eta_p * div(u) * div(v) * dx a\",\n \"dx # DG terms a += jump(v, n) * avg(p) * dS -\",\n \"S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values\",\n \"'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree)\",\n \"* dx a += delta_2 * div(u) * div(v) * dx a +=\",\n \"assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()\",\n \"(eta_u / h) * dot(p * n, q * n) * ds #\",\n \"ds # may decrease convergente rates # ** Classical Nitsche # a +=\",\n \"result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return\",\n \"Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize()\",\n \"Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg =\",\n \"1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool\",\n \"a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds\",\n \"list() if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value)\",\n \"h) * dot(u, n) * dot(v, n) * ds a += (eta_u /\",\n \"plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot\",\n \"imposed BC a += lambda_h * dot(v, n) * ds a += dot(u_hat,\",\n \"# HDG classical form a = (dot(u, v) - div(v) * p) *\",\n \"+= beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions\",\n \"grad(p), v + grad(q)) * dx a += eta_p * div(u) * div(v)\",\n \"/ h # beta = beta_0 # Numerical flux trace u_hat = u\",\n \"n) * q * ds # # a += delta_1 * dot(u, n)\",\n \"Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs):\",\n \"FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and test functions\",\n \"# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries\",\n \"Badia-Codina paper # eta_p = 1 # eta_p = L0 * L0 #\",\n \"\\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig,\",\n \"f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries =\",\n \"# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier\",\n \"+= lambda_h * dot(v, n) * ds a += dot(u_hat, n) * q\",\n \"* A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"lambda_h * dot(v, n) * ds # Mass balance least-square a += delta_2\",\n \"= Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h # delta\",\n \"/ h_avg) * (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u)\",\n \"S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values\",\n \"* dS # a += p * q * ds A = assemble(a,\",\n \"* L0 # method D in the Badia-Codina paper eta_u = h /\",\n \"q * div(u)) * dx # DG terms a += jump(v, n) *\",\n \"= sin(2 * pi * x) * sin(2 * pi * y) exact_solution\",\n \"use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family =\",\n \"# DG edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS\",\n \"there is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis\",\n \"V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V)\",\n \"# L = delta_2 * f * div(v) * dx # Irrotational least-squares\",\n \"# Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function\",\n \"a += delta_1 * (dot(u, v) - div(v) * p) * dx a\",\n \"% name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return\",\n \"= TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)\",\n \"div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds(\",\n \"# ) # # These terms below are unsymmetric # a += delta_1\",\n \"q * dx # Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\") *\",\n \"grad(p), grad(q) - v) * dx a += eta_p * div(u) * div(v)\",\n \"solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"= h * h # delta = Constant(1) # delta = h delta_0\",\n \"= Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx, idx] -\",\n \"(Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:\",\n \"s * dot(grad(q), n) * p_boundaries * ds F = a - L\",\n \"# Flux Least-squares as in DG a = delta_0 * inner(u + grad(p),\",\n \"# Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a\",\n \"= delta delta_3 = 1 / h delta_4 = 1 / h #\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1,\",\n \"def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree)\",\n \"* dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\"))\",\n \"eq. first-order terms as stabilizing terms a += delta_1 * (dot(u, v) -\",\n \"delta_1 * dot(u, n) * q * ds # # L = -delta_1\",\n \"ASGS Badia-Codina (2010), it is not a classical Nitsche's method a += (eta_p\",\n \"+= eta_u_bc * delta_3 * p * q * ds # may decrease\",\n \"= solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree,\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function\",\n \"* dx # a += 0.5 * inner(curl(u), curl(v)) * dx # L\",\n \"S = A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2]\",\n \"= v + beta * (q - mu_h) * n # Flux least-squares\",\n \"+ grad(q)) * dx # a += 0.5 * h * h *\",\n \"Constant(1e0) delta = h * h # delta = Constant(1) # delta =\",\n \"{result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the resulting\",\n \"* dx a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2\",\n \"- mu_h) * ds # a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat,\",\n \"else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in\",\n \"terms # ** Badia-Codina based (better results) ** a += eta_u * avg(delta_3)\",\n \"pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge\",\n \"# \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, }\",\n \"# method B in the Badia-Codina paper # eta_p = 1 # eta_p\",\n \"# Classical volumetric terms a = inner(grad(p), grad(q)) * dx L = f\",\n \"# Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2\",\n \"+= delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS #\",\n \"declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V\",\n \"1 for current_solver in solvers_options: # Setting the output file name name =\",\n \"a += beta * p * q * ds A = assemble(a, mat_type=\\\"aij\\\")\",\n \"= UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}')\",\n \"for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has\",\n \"grad(q)) * dx # a += 0.5 * h * h * div(u)\",\n \"n)) * dS # Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n),\",\n \"dx # Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS #\",\n \"BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a = (dot(u,\",\n \"** a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) *\",\n \"number. :return: Filtered array with only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) <\",\n \"h * h * f * div(v) * dx a += 0.5 *\",\n \"a += delta_1 * lambda_h * dot(v, n) * ds # # L\",\n \"+ delta_0 * q * div(u)) * dx L = delta_0 * f\",\n \"1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp\",\n \"= 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG'\",\n \"terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u,\",\n \"petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"Badia-Codina based a += -eta_u * inner(u + grad(p), v + grad(q)) *\",\n \"1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs)\",\n \"SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list =\",\n \"in the Badia-Codina paper # eta_p = L0 * L0 # method D\",\n \"* dS # a += dot(jump(p, n), jump(q, n)) * dS # a\",\n \"T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V\",\n \"dS a += eta_u_bc * delta_3 * p * q * ds #\",\n \"# a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n)\",\n \"DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a = (dot(u, v) - div(v)\",\n \"a = delta_1 * inner(u + grad(p), v + grad(q)) * dx a\",\n \"else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = U\",\n \"cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\\\"sparse_pattern.png\\\")\",\n \"* dx a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a,\",\n \"h / L0 # method B in the Badia-Codina paper eta_u_bc = 1\",\n \"1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S,\",\n \"independent ** # a += jump(u, n) * jump(v, n) * dS #\",\n \"+= mu_h * lambda_h * ds # ### # a += ( #\",\n \"v + grad(q)) * dx # a += 0.5 * h * h\",\n \"jump(u, n) * jump(v, n) * dS # not considered in the original\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ):\",\n \"n)) * dS # Volumetric stabilizing terms # a += 0.5 * inner(u\",\n \"color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full\",\n \"bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms a = (dot(u, v)\",\n \"term a = dot(grad(p), grad(q)) * dx L = f * q *\",\n \"# Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution\",\n \"= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim()\",\n \"in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0\",\n \"n) * ds # ** The terms below are based on ASGS Badia-Codina\",\n \"+= dot(v, n) * p * ds - q * dot(u, n) *\",\n \"+ f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides\",\n \"stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry term.\",\n \"Badia-Codina paper eta_p = 1 # eta_p = L0 * L0 # method\",\n \"= Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =\",\n \"HDG classical form a = (dot(u, v) - div(v) * p) * dx\",\n \"result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return\",\n \"# # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2,\",\n \"ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility\",\n \"calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier\",\n \"* (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta / h_avg) * (p(\\\"+\\\")\",\n \"function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1),\",\n \"p_boundaries) * mu_h * ds F = a - L a_form = lhs(F)\",\n \"bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta =\",\n \"/ h) * (p- p_boundaries) * q * ds # is this necessary?\",\n \"# \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, #\",\n \"n) * ds a += beta * p * q * ds #\",\n \"delta_3 = Constant(0.5) * h * h # Mixed classical terms a =\",\n \"= FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V *\",\n \"exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) #\",\n \"* A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle\",\n \"* dot(u, n) * q * ds # # L = -delta_1 *\",\n \"# a += 0.5 * inner(u + grad(p), grad(q) - v) * dx\",\n \"* lambda_h(\\\"+\\\") * jump(v, n=n) * dS a += delta_1 * lambda_h *\",\n \"the Badia-Codina paper # eta_p = L0 * L0 # method D in\",\n \"= 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial\",\n \"in the original paper # a += dot(jump(p, n), jump(q, n)) * dS\",\n \"singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max()\",\n \"h # Symmetry term. Choose if the method is SIPG (-1) or NIPG\",\n \"= str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' trace_family =\",\n \"ds a += beta * p * q * ds # may decrease\",\n \"(mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed BC a += (p_boundaries *\",\n \"HDG classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n) *\",\n \"delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") *\",\n \"p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh,\",\n \"with real and complex numbers. :param imag_threshold: Threshold to cut off imaginary part\",\n \"avg(p) * dS - avg(q) * jump(u, n) * dS # Edge stabilizing\",\n \"+= 0.5 * div(u) * div(v) * dx # a += 0.5 *\",\n \"function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 =\",\n \"a += delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f\",\n \"0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides\",\n \"Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads\",\n \"Symmetry term. Choose if the method is SIPG (-1) or NIPG (1) s\",\n \"- mu_h(\\\"+\\\")) * dS a += (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\"))\",\n \"good way to impose BC, but this necessary _A = Tensor(a) A =\",\n \"# Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array:\",\n \"- L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S =\",\n \"(dot(u, v) - div(v) * p) * dx + lambda_h(\\\"+\\\") * jump(v, n)\",\n \"* div(u)) * dx # DG terms a += jump(v, n) * avg(p)\",\n \"result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =\",\n \"petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"dot(u, n) * dot(v, n) * ds a += (eta_u / h) *\",\n \"= DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0\",\n \"backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve()\",\n \"/ singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors)\",\n \"# may decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n)\",\n \"delta # delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux\",\n \"name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) #\",\n \"= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs)\",\n \"Badia-Codina based (better results) ** a += eta_u * avg(delta_3) * (jump(u, n)\",\n \"* dot(v, n) * ds # Flux Least-squares as in DG a =\",\n \"= \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = U * V\",\n \"= TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x,\",\n \"dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a +=\",\n \"eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc\",\n \"div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) * dx #\",\n \"dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS\",\n \"* ds a += beta * p * q * ds # may\",\n \"Constant(0.5) * h * h # Mixed classical terms a = (dot(u, v)\",\n \"terms a += delta_1 * inner(u + grad(p), v + grad(q)) * dx\",\n \"= backend.lower() if backend == \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size)\",\n \"ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib()\",\n \"* dx # DG edge terms a += s * dot(jump(p, n), avg(grad(q)))\",\n \"beta_0 / h # Mixed classical terms a = (dot(u, v) - div(v)\",\n \"a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS # a +=\",\n \"use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family\",\n \"import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import\",\n \"solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation(\",\n \"is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of\",\n \"the output file name name = f\\\"{current_solver}\\\" # Selecting the solver and its\",\n \"results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results =\",\n \"terms a = delta_0 * inner(u + grad(p), v + grad(q)) * dx\",\n \"else: raise NotImplementedError(\\\"The required method for condition number estimation is currently unavailable.\\\") return\",\n \"a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary\",\n \"numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd\",\n \"1 / h # Least-squares terms a = delta_0 * inner(u + grad(p),\",\n \"bc?? # a += (beta / h) * (p- p_boundaries) * q *\",\n \"Flux Least-squares as in DG a = delta_0 * inner(u + grad(p), v\",\n \"a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1)\",\n \"for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A\",\n \"p + q * div(u)) * dx # Stabilizing terms a += 0.5\",\n \"plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to\",\n \"D in the Badia-Codina paper eta_u = h / L0 # method B\",\n \"axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot\",\n \"# Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell\",\n \"= np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) #\",\n \"plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"= 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 /\",\n \"* f * div(v) * dx # ** Badia-Codina based a += eta_u\",\n \"n), jump(q, n)) * dS # a += p * q * ds\",\n \"use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V\",\n \"scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas\",\n \"- dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a += beta(\\\"+\\\")\",\n \"singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend ==\",\n \"a += 0.5 * div(u) * div(v) * dx # a += 0.5\",\n \"dot(v, n) * ds # Flux Least-squares as in DG a = delta_0\",\n \"# eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta =\",\n \"dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms\",\n \"color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[],\",\n \"# Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 / h beta\",\n \"* (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed BC a += (p_boundaries\",\n \"# Least-squares terms a = delta_1 * inner(u + grad(p), v + grad(q))\",\n \"Array with real and complex numbers. :param imag_threshold: Threshold to cut off imaginary\",\n \"S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0:\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): #\",\n \"delta_0 * inner(u + grad(p), v + grad(q)) * dx a += delta_1\",\n \"assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #\",\n \"rates # ** The terms below are based on ASGS Badia-Codina (2010), it\",\n \"attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs):\",\n \"# bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh\",\n \"q * ds # # a += delta_1 * dot(u, n) * q\",\n \"= h / L0 # method B in the Badia-Codina paper # Mixed\",\n \"dS - avg(q) * jump(u, n) * dS # Edge stabilizing terms #\",\n \"def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family\",\n \"zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp\",\n \"delta_1 * (dot(u, v) - div(v) * p) * dx a += delta_1(\\\"+\\\")\",\n \"compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif\",\n \"f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a\",\n \"color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full\",\n \"div(v) * dx # a += -0.5 * inner(u + grad(p), v +\",\n \"These terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n) *\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads =\",\n \"eta_u * inner(u + grad(p), grad(q) - v) * dx a += eta_p\",\n \"n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} - N = {n}\\\")\",\n \"ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs):\",\n \"(beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS\",\n \"* jump(v, n) * dS # not considered in the original paper #\",\n \"_A = Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2]\",\n \"* ds # Weakly imposed BC from hybridization # a += mu_h *\",\n \"bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space\",\n \"q * ds # # L = -delta_1 * dot(u_projected, n) * q\",\n \"+= eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a +=\",\n \":param array: Array with real and complex numbers. :param imag_threshold: Threshold to cut\",\n \"= array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str =\",\n \"-> np.ndarray: \\\"\\\"\\\"Utility function to filter real part in a numpy array. :param\",\n \"B in the Badia-Codina paper # Mixed classical terms a = (dot(u, v)\",\n \"assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh,\",\n \"-grad(p) u_hat = u + beta * (p - lambda_h) * n #\",\n \"= attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax\",\n \"* inner(curl(u), curl(v)) * dx # L += 0.5 * h * h\",\n \"solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values =\",\n \"Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u +\",\n \"* dx L = f * q * dx # DG edge terms\",\n \"eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta = h\",\n \"+ jump(u_hat, n) * q(\\\"+\\\") * dS L = f * q *\",\n \"* n v_hat = v + beta * (q - mu_h) * n\",\n \"hybridization # a += mu_h * (lambda_h - p_boundaries) * ds # a\",\n \"* h * h * div(u) * div(v) * dx # a +=\",\n \":2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric =\",\n \"p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS\",\n \"= LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T =\",\n \"# ** Badia-Codina based (better results) ** a += eta_u * avg(delta_3) *\",\n \"quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n\",\n \"study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N =\",\n \"singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000)\",\n \"Mesh independent terms # a += jump(u, n) * jump(v, n) * dS\",\n \"dS # Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n))\",\n \"= -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS L\",\n \"== \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family,\",\n \"dot(u_projected, n))) * ds a += beta * (lambda_h - p_boundaries) * mu_h\",\n \"v + beta * (q - mu_h) * n # Flux least-squares #\",\n \"my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form,\",\n \"= False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend ==\",\n \"* inner(u + grad(p), v + grad(q)) * dx a += delta_2 *\",\n \"v) - q * div(u) - p * div(v) + inner(grad(p), grad(q))) #\",\n \"a += dot(v, n) * p * ds - q * dot(u, n)\",\n \"(q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms # a += -dot(vel_projected, n)\",\n \"RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol]\",\n \"Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number\",\n \"+= p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact =\",\n \"exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs =\",\n \"if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree)\",\n \"* (q - mu_h) * ds # a += delta_5 * (dot(u, n)(\\\"+\\\")\",\n \"= f * q * dx # Transmission condition a += jump(u_hat, n)\",\n \"= W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result =\",\n \"import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse import\",\n \"inner(u + grad(p), v + grad(q)) * dx a += eta_p * div(u)\",\n \"* jump(v, n) * dS # a += dot(jump(p, n), jump(q, n)) *\",\n \"delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4\",\n \"Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1)\",\n \"list(), } element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree))\",\n \"stabilizing terms # a += 0.5 * h * h * div(u) *\",\n \"= assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =\",\n \") return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG'\",\n \"* inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u *\",\n \"/ h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS #\",\n \"condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method for condition number\",\n \"fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh,\",\n \"* n # Flux least-squares # a = ( # (inner(u, v) -\",\n \"* h * h delta_3 = Constant(0.5) * h * h # Mixed\",\n \"B in the Badia-Codina paper eta_p = 1 # eta_p = L0 *\",\n \"= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e p_boundaries = p_exact\",\n \"_A = Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1]\",\n \"for degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree =\",\n \"+ grad(p)), v + grad(q)) * dx # a += 0.5 * h\",\n \"dx a += 0.5 * div(u) * div(v) * dx a += 0.5\",\n \"= 1 / h # Least-squares terms a = delta_0 * inner(u +\",\n \"* div(u) * div(v) * dx # L = delta_2 * f *\",\n \"* (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a +=\",\n \"0.5 * div(u) * div(v) * dx # a += 0.5 * inner(curl(u),\",\n \"n) * jump(v, n) * dS # a += dot(jump(p, n), jump(q, n))\",\n \"dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) *\",\n \"* dx # Weakly imposed boundary conditions a += dot(v, n) * p\",\n \"h * h * div(u) * div(v) * dx # a += 0.5\",\n \"div(v) * dx # ** Badia-Codina based a += eta_u * inner(u +\",\n \"eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0\",\n \"a plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle\",\n \"a good way to impose BC, but this necessary _A = Tensor(a) A\",\n \"ds a += beta * (lambda_h - p_boundaries) * mu_h * ds F\",\n \"FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = V * T\",\n \"# a += mu_h * (lambda_h - p_boundaries) * ds # a +=\",\n \"current_solver in solvers_options: # Setting the output file name name = f\\\"{current_solver}\\\" #\",\n \"h * inner(curl(u), curl(v)) * dx # L += 0.5 * h *\",\n \"bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =\",\n \"\\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh,\",\n \"from hybridization # a += mu_h * (lambda_h - p_boundaries) * ds #\",\n \"cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # #\",\n \"sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average\",\n \"def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if\",\n \") return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize()\",\n \"dx # a += -0.5 * inner(u + grad(p), v + grad(q)) *\",\n \"paper eta_u = h / L0 # method B in the Badia-Codina paper\",\n \"eta_p = 1 # eta_p = L0 * L0 # method D in\",\n \") return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"+= dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms # a\",\n \"jump(v, n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat, n)\",\n \"the Badia-Codina paper eta_u = h / L0 # method B in the\",\n \"a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2]\",\n \"grad(q)) * dx # Classical mixed Darcy eq. first-order terms as stabilizing terms\",\n \"Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing\",\n \"* dS # ** Mesh independent terms # a += jump(u, n) *\",\n \"as in DG a = delta_0 * inner(u + grad(p), v + grad(q))\",\n \"lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a += delta_4 * (p\",\n \"0.5 * f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"= delta # delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical\",\n \"the method is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical\",\n \"* L0 # method D in the Badia-Codina paper # eta_u = h_avg\",\n \"+= mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") -\",\n \"n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else\",\n \"* delta_3 * p * q * ds # may decrease convergente rates\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family =\",\n \"- div(v) * p - q * div(u)) * dx # DG terms\",\n \"### # a += ( # (mu_h - q) * (lambda_h - p_boundaries)\",\n \"= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs)\",\n \"- q * dot(u, n) * ds # ** The terms below are\",\n \"W = U * V # Trial and test functions u, p =\",\n \"a += lambda_h * dot(v, n) * ds a += dot(u_hat, n) *\",\n \"Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree,\",\n \"* dx # a += 0.5 * div(u) * div(v) * dx #\",\n \"- div(v) * p + delta_0 * q * div(u)) * dx L\",\n \"* dot(v, n) * ds a += (eta_u / h) * dot(p *\",\n \"p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h\",\n \"p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h\",\n \"# # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap\",\n \"jump(v, n) * avg(p) * dS - avg(q) * jump(u, n) * dS\",\n \"return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"# Weak boundary conditions a += s * dot(p * n, grad(q)) *\",\n \"# # L = -delta_1 * dot(u_projected, n) * q * ds #\",\n \"def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1,\",\n \"S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values >\",\n \"v) - div(v) * p + q * div(u)) * dx # DG\",\n \"is not a good way to impose BC, but this necessary _A =\",\n \"grad(p), grad(q) - v) * dx # a += 0.5 * h *\",\n \"* dot(p * n, q * n) * ds # may decrease convergente\",\n \"if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree)\",\n \"array: Array with real and complex numbers. :param imag_threshold: Threshold to cut off\",\n \"hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5\",\n \"is this necessary? L += s * dot(grad(q), n) * p_boundaries * ds\",\n \"* dot(p * n, q * n) * ds A = assemble(a, mat_type=\\\"aij\\\")\",\n \"n) * jump(v, n) * dS # not considered in the original paper\",\n \"= beta_0 / h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter #\",\n \"= attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz =\",\n \"# * delta_1 # * dx # ) # # These terms below\",\n \"= Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u\",\n \"dG method # L0 = 1 # eta_p = L0 * h_avg #\",\n \"* pi * x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact)\",\n \"L += delta_2 * f * div(v) * dx # Hybridization terms a\",\n \"BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n) -\",\n \"Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0]\",\n \"jump(v, n) * dS # not considered in the original paper # a\",\n \"(q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a += delta_4 * (p - lambda_h)\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): #\",\n \"inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"* h * h # Mixed classical terms a = (dot(u, v) -\",\n \"- dot(grad(p), q * n) * ds a += beta * p *\",\n \"Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term\",\n \"Classical Nitsche # a += beta * p * q * ds #\",\n \"delta_4 * (p - lambda_h) * (q - mu_h) * ds # a\",\n \"/ h) * dot(p * n, q * n) * ds A =\",\n \"* p * q * ds # may decrease convergente rates a +=\",\n \"exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression\",\n \"\\\"\\\"\\\"Utility function to filter real part in a numpy array. :param array: Array\",\n \"**kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1,\",\n \"form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh(\",\n \"cut off imaginary part in complex number. :return: Filtered array with only real\",\n \"* n, q * n) * ds # may decrease convergente rates #\",\n \"{n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min()\",\n \"dG method L0 = 1 eta_p = L0 * h # method B\",\n \"plot = plt.spy(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([])\",\n \"solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) #\",\n \"* dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a +=\",\n \"* dx a += delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing\",\n \"# Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta\",\n \"* q * ds # may decrease convergente rates (Nitsche) A = assemble(a,\",\n \"dS # Weakly imposed BC a += (p_boundaries * dot(v, n) + mu_h\",\n \"\\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing\",\n \"/ L0 # method B in the Badia-Codina paper eta_u_bc = 1 #\",\n \"dS # Volumetric stabilizing terms # a += 0.5 * inner(u + grad(p),\",\n \"plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1)\",\n \"bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v)) *\",\n \"pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression =\",\n \"csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm import tqdm\",\n \"/ L0 # method B in the Badia-Codina paper eta_u = 1 #\",\n \"plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) #\",\n \"file name name = f\\\"{current_solver}\\\" # Selecting the solver and its kwargs solver\",\n \"q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"part in a numpy array. :param array: Array with real and complex numbers.\",\n \"grad(p), v + grad(q)) * dx # Classical mixed Darcy eq. first-order terms\",\n \"\\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1)\",\n \"SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x) * sin(2\",\n \"Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) *\",\n \"* dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS a\",\n \"dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") *\",\n \"def solve_poisson_vms(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG'\",\n \"zero_tol: float = 1e-5 ): backend = backend.lower() if backend == \\\"scipy\\\": size\",\n \"+= jump(u, n) * jump(v, n) * dS # not considered in the\",\n \"W = U * V * T # Trial and test functions #\",\n \"2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S,\",\n \"delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5\",\n \"n)) * dS # Weak boundary conditions a += s * dot(p *\",\n \"# Weakly imposed BC from hybridization # a += mu_h * (lambda_h -\",\n \"NIPG (1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q))\",\n \"* dS # Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q,\",\n \"u + beta * (p - lambda_h) * n # HDG classical form\",\n \"* div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) * dx\",\n \"p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) #\",\n \"# Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a +=\",\n \"> 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc\",\n \"a += delta_1 * dot(u, n) * q * ds # # L\",\n \"L = delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares\",\n \"# import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs,\",\n \"+ lambda_h(\\\"+\\\") * jump(v, n) * dS a += -dot(u, grad(q)) * dx\",\n \"jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v, n) *\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1):\",\n \"plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap)\",\n \"y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact))\",\n \"= FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T\",\n \"else: T = FunctionSpace(mesh, trace_family, degree) W = U * V * T\",\n \"= f\\\"{current_solver}\\\" # Selecting the solver and its kwargs solver = solvers_options[current_solver] #\",\n \"n) * ds # ** Mesh independent ** # a += jump(u, n)\",\n \"p * div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx #\",\n \"a += mu_h * (lambda_h - p_boundaries) * ds # a += mu_h\",\n \"- 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs):\",\n \"= Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter\",\n \"solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name\",\n \"ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return\",\n \"= 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family,\",\n \"- p_boundaries) * ds # ) # maybe this is not a good\",\n \"h / L0 # method B in the Badia-Codina paper # Mixed classical\",\n \"functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x,\",\n \"name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\")\",\n \"avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a += eta_p *\",\n \"a plot of a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax\",\n \"* q * ds # is this necessary? L += s * dot(grad(q),\",\n \"delta delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 = delta\",\n \"current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of\",\n \"real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool = False,\",\n \"* A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W)\",\n \"- 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] -\",\n \"np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot\",\n \"dS # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing\",\n \") return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration\",\n \"\\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree\",\n \"DG edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS -\",\n \"+= delta_2 * div(u) * div(v) * dx # L = delta_2 *\",\n \"- q(\\\"+\\\")) * dS # Weakly imposed BC a += (p_boundaries * dot(v,\",\n \"f * div(v) * dx a += 0.5 * div(u) * div(v) *\",\n \"= L0 * h_avg # method B in the Badia-Codina paper eta_p =\",\n \"f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization\",\n \"plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds from scipy.sparse\",\n \"* p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh,\",\n \"* p_exact * dot(v, n) * ds # Flux Least-squares as in DG\",\n \"* dS # a += delta_1 * lambda_h * dot(v, n) * ds\",\n \"{result.condition_number}') # # Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy #\",\n \"terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") *\",\n \"in complex number. :return: Filtered array with only real numbers. \\\"\\\"\\\" real_part_array =\",\n \"* dx # a += 0.5 * h * h * div(u) *\",\n \"1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h\",\n \"inner(u + grad(p), grad(q) - v) * dx a += eta_p * div(u)\",\n \"bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2 =\",\n \"): backend = backend.lower() if backend == \\\"scipy\\\": size = A.getSize() Mnp =\",\n \"* div(u) * div(v) * dx a += 0.5 * inner(curl(u), curl(v)) *\",\n \"condition_number = singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD() S.create()\",\n \"W = V * T # Trial and test functions # solution =\",\n \"parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1)\",\n \"from firedrake import * import numpy as np import matplotlib.pyplot as plt import\",\n \"Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") *\",\n \"grad(q))) # * delta_1 # * dx # ) # # These terms\",\n \"Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) * dx L =\",\n \"mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric?\",\n \"TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact\",\n \"sigma_e, \\\"on_boundary\\\") # Mixed classical terms a = (dot(u, v) - div(v) *\",\n \"* ds - q * dot(u, n) * ds # ** The terms\",\n \"* div(u)) * dx L = delta_0 * f * q * dx\",\n \"= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim()\",\n \"- mu_h) * n # Flux least-squares # a = ( # (inner(u,\",\n \"* inner((u + grad(p)), v + grad(q)) * dx # a += 0.5\",\n \"V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement\",\n \"dot(grad(q), n) * p_boundaries * ds F = a - L a_form =\",\n \"eta_p = L0 * h_avg # method B in the Badia-Codina paper eta_p\",\n \"* mu_h * ds F = a - L a_form = lhs(F) _A\",\n \"inner(curl(u), curl(v)) * dx # L += 0.5 * h * h *\",\n \"U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W\",\n \"* T # Trial and test functions # solution = Function(W) # u,\",\n \"assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros()\",\n \"DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3\",\n \"# print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting\",\n \"lambda_h(\\\"+\\\") * jump(v, n=n) * dS a += delta_1 * lambda_h * dot(v,\",\n \"a += -0.5 * inner((u + grad(p)), v + grad(q)) * dx #\",\n \"* (dot(u, n) - dot(u_projected, n))) * ds a += beta * (lambda_h\",\n \"last_degree, quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N, N,\",\n \"+= delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat,\",\n \"velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V #\",\n \"L0 # method B in the Badia-Codina paper eta_u = 1 # eta_u_bc\",\n \"5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) #\",\n \"= FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U *\",\n \"parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta = beta_0\",\n \"independent terms # a += jump(u, n) * jump(v, n) * dS #\",\n \"* ds a += beta * (lambda_h - p_boundaries) * mu_h * ds\",\n \"a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a +=\",\n \"Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads\",\n \"= Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0)\",\n \"* n, q * n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat =\",\n \"div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx\",\n \"ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5,\",\n \"= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled\",\n \"dS # not considered in the original paper # a += dot(jump(p, n),\",\n \"solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family =\",\n \"solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family =\",\n \"delta_0 * f * q * dx # Stabilizing terms a += delta_1\",\n \"* div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"L = delta_2 * f * div(v) * dx # Irrotational least-squares a\",\n \"ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[],\",\n \"beta = beta_0 # Numerical flux trace u_hat = u + beta *\",\n \"else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree +\",\n \"{result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # #\",\n \"= beta0 / h # Symmetry term. Choose if the method is SIPG\",\n \"pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else\",\n \"Function space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family,\",\n \"max_degree=degree + last_degree, quadrilateral=True, name=name ) # N = 5 # mesh =\",\n \"FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W = U\",\n \"Threshold to cut off imaginary part in complex number. :return: Filtered array with\",\n \"= delta delta_3 = delta delta_4 = delta # delta_4 = LARGE_NUMBER /\",\n \"in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} - N = {n}\\\") mesh\",\n \"firedrake import * import numpy as np import matplotlib.pyplot as plt import matplotlib\",\n \"Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h # beta =\",\n \"= ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result\",\n \"0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina based a += -eta_u\",\n \"\\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 / h #\",\n \"solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W)\",\n \"\\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree)\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1):\",\n \"solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"- degree = {degree} - N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral)\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space\",\n \"- q * div(u)) * dx # Stabilizing terms a += -0.5 *\",\n \"Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\":\",\n \"use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)\",\n \"* inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v) *\",\n \"u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization\",\n \"may decrease convergente rates # ** The terms below are based on ASGS\",\n \"p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta = beta_0 /\",\n \"# a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS # a\",\n \"(jump(u, n) * jump(v, n)) * dS a += eta_p * avg(delta_4) *\",\n \"and test functions # solution = Function(W) # u, p, lambda_h = split(solution)\",\n \"plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax\",\n \"TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact solution p_exact =\",\n \"* A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle\",\n \"- div(v) * p) * dx + lambda_h(\\\"+\\\") * jump(v, n) * dS\",\n \"# a += p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat\",\n \"# Nitsche's penalizing term beta_0 = Constant(1.0) beta = beta_0 / h #\",\n \"based on ASGS Badia-Codina (2010), it is not a classical Nitsche's method a\",\n \"Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)\",\n \"(lambda_h - p_boundaries) * ds # ) # maybe this is not a\",\n \"f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot def\",\n \"# Classical term a = dot(grad(p), grad(q)) * dx L = f *\",\n \"delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds # # a += delta_1\",\n \"- dot(v_hat, n)) * ds # Weakly imposed BC from hybridization # a\",\n \"Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression)\",\n \"degree) W = V * T # Trial and test functions # solution\",\n \"== \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh, pressure_family,\",\n \"scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks\",\n \"classical form a = (dot(u, v) - div(v) * p) * dx +\",\n \"f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact,\",\n \"= Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q,\",\n \"number estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function\",\n \"ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a\",\n \"* dS # Weak boundary conditions a += s * dot(p * n,\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1):\",\n \"dS # Weakly imposed BC a += dot(u_hat, n) * q * ds\",\n \"a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a\",\n \"delta delta_2 = delta delta_3 = delta delta_4 = delta # delta_4 =\",\n \"1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real part in a numpy array.\",\n \"BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v))\",\n \"div(u)) * dx # DG terms a += jump(v, n) * avg(p) *\",\n \"n), jump(q, n)) * dS # ** Mesh independent terms # a +=\",\n \"= delta_1 * p_exact * dot(v, n) * ds # Flux Least-squares as\",\n \"a += dot(u_hat, n) * q * ds F = a - L\",\n \"0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a\",\n \"dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms # a\",\n \"solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh,\",\n \"a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs,\",\n \"* p * q * ds # may decrease convergente rates # **\",\n \"may decrease convergente rates a += eta_u_bc * delta_4 * dot(u, n) *\",\n \"'DQ' else: hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree\",\n \"# (mu_h - q) * (lambda_h - p_boundaries) * ds # ) #\",\n \"q * dx # Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\") *\",\n \"BCs u_projected = sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") #\",\n \"= LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W\",\n \"delta_4 = 1 / h # Least-squares terms a = delta_0 * inner(u\",\n \"* h delta_3 = Constant(0.5) * h * h # Mixed classical terms\",\n \"Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0\",\n \"size and mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 #\",\n \"idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp =\",\n \"- div(v) * p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v,\",\n \") # # These terms below are unsymmetric # a += delta_1 *\",\n \"name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\":\",\n \"**kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat\",\n \"is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): # Function\",\n \"* ds # Mass balance least-square a += delta_2 * div(u) * div(v)\",\n \"= petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz\",\n \"plot of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh,\",\n \"rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n) *\",\n \"converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() /\",\n \"use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V\",\n \"least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a\",\n \"F = a - L a_form = lhs(F) _A = Tensor(a_form) A =\",\n \"Trial and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities\",\n \"\\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs\",\n \"* dx # a += 0.5 * h * h * inner(curl(u), curl(v))\",\n \"= DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e-18) # beta =\",\n \"required method for condition number estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh,\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1):\",\n \"else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz)\",\n \"Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing the convergence\",\n \"= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs = DirichletBC(W[0], sigma_e,\",\n \"pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet\",\n \"\\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs =\",\n \"= ax.matshow(Am, **kwargs) # Below there is the spy alternative # plot =\",\n \"classical Nitsche's method a += (eta_p / h) * dot(u, n) * dot(v,\",\n \"# Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family =\",\n \"beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a\",\n \"Flux least-squares # a = ( # (inner(u, v) - q * div(u)\",\n \"# method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights\",\n \"+= eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS a\",\n \"U * V # Trial and test functions u, p = TrialFunctions(W) v,\",\n \"np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp,\",\n \"delta_1 * div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v))\",\n \"singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values =\",\n \"and mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump\",\n \"# may decrease convergente rates # ** The terms below are based on\",\n \"# method B in the Badia-Codina paper # eta_p = L0 * L0\",\n \"= Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\\\"+\\\") #\",\n \"n) * jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p,\",\n \"# eta_p = 1 # eta_p = L0 * L0 # method D\",\n \"Constant(1.0) beta = beta_0 / h # Mixed classical terms a = (dot(u,\",\n \"paper # eta_u = h_avg / L0 # method B in the Badia-Codina\",\n \"+= lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") *\",\n \"= pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options\",\n \"div(u)) * dx # Stabilizing terms a += 0.5 * inner(u + grad(p),\",\n \"# Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap =\",\n \"number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result\",\n \"a = (dot(u, v) - div(v) * p - q * div(u)) *\",\n \"'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V =\",\n \"use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh,\",\n \"declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG'\",\n \"delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER\",\n \"N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result = solve_poisson_lsh(mesh,\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads\",\n \"Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier =\",\n \"= DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0)\",\n \"1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs,\",\n \"first-order terms as stabilizing terms a += delta_1 * (dot(u, v) - div(v)\",\n \"\\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\"\",\n \"mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n *\",\n \"Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray,\",\n \"div(v) * dx a += eta_p * inner(curl(u), curl(v)) * dx # Weakly\",\n \"delta_1 = delta delta_2 = delta delta_3 = delta delta_4 = delta #\",\n \"dx # a += 0.5 * h * h * inner(curl(u), curl(v)) *\",\n \"dx # ** Badia-Codina based a += -eta_u * inner(u + grad(p), v\",\n \"- div(v) * p + q * div(u)) * dx # DG terms\",\n \"classical terms a = (dot(u, v) - div(v) * p - q *\",\n \"* dS - avg(q) * jump(u, n) * dS # Edge stabilizing terms\",\n \"plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\"\",\n \"lambda_h) * (q - mu_h) * ds # a += delta_5 * (dot(u,\",\n \"* dS # Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p)\",\n \"degree=1): # Function space declaration pressure_family = 'CG' velocity_family = 'CG' U =\",\n \"bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta =\",\n \"0.5 * h * h * inner(curl(u), curl(v)) * dx # L +=\",\n \"L0 # method D in the Badia-Codina paper # eta_u = h_avg /\",\n \"* A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle\",\n \"declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions u =\",\n \"rates # ** Classical Nitsche # a += beta * p * q\",\n \":2].inv * A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric =\",\n \"/ h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1)\",\n \"# Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg\",\n \"= beta_0 / h # Mixed classical terms a = (dot(u, v) -\",\n \"== \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\"\",\n \"** Mesh independent (original) # a += jump(u, n) * jump(v, n) *\",\n \"(lambda_h - p_boundaries) * mu_h * ds F = a - L a_form\",\n \"plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) #\",\n \"'DQ' if use_quads else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,\",\n \":1] * A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat =\",\n \"n) * ds # Mass balance least-square a += delta_2 * div(u) *\",\n \"petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors\",\n \"ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def\",\n \"Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1\",\n \"= 1 # eta_u_bc = h / L0 # method B in the\",\n \"dx a += eta_p * div(u) * div(v) * dx # Weakly imposed\",\n \"degree) # Trial and test functions p = TrialFunction(V) q = TestFunction(V) #\",\n \"in the Badia-Codina paper # eta_u = h_avg / L0 # method B\",\n \"is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function\",\n \"A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size\",\n \"1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() #\",\n \"pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options\",\n \"degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ'\",\n \"paper # eta_p = 1 # eta_p = L0 * L0 # method\",\n \"/ h beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0 =\",\n \"n) * dot(v, n) * ds a += (eta_u / h) * dot(p\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads =\",\n \"= Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs\",\n \"= int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A,\",\n \"solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree =\",\n \"0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"* q(\\\"+\\\") * dS L = f * q * dx # Transmission\",\n \"a += -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS\",\n \"* dS # a += delta_4 * (p - lambda_h) * (q -\",\n \"eta_p = L0 * L0 # method D in the Badia-Codina paper eta_u\",\n \"result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function space declaration use_quads =\",\n \"not a good way to impose BC, but this necessary _A = Tensor(a)\",\n \"ds # may decrease convergente rates a += eta_u_bc * delta_4 * dot(u,\",\n \"sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\")\",\n \"solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, #\",\n \"* div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1 #\",\n \"LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh,\",\n \"delta_2 * div(u) * div(v) * dx # L = delta_2 * f\",\n \"# \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, #\",\n \"Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number =\",\n \"function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2),\",\n \"Classical volumetric terms a = inner(grad(p), grad(q)) * dx L = f *\",\n \"eta_p = L0 * L0 # method D in the Badia-Codina paper #\",\n \"which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False,\",\n \"= inner(grad(p), grad(q)) * dx L = f * q * dx #\",\n \"beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h *\",\n \"# ) # maybe this is not a good way to impose BC,\",\n \"avg(q) * jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina\",\n \"Constant(1e1) beta = beta0 / h # Symmetry term. Choose if the method\",\n \"1 / h delta_4 = 1 / h # Least-squares terms a =\",\n \"(beta / h) * (p- p_boundaries) * q * ds # is this\",\n \"a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") -\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): #\",\n \"0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2,\",\n \"stabilizing parameters based on Badia-Codina stabilized dG method # L0 = 1 #\",\n \"s * dot(p * n, grad(q)) * ds - dot(grad(p), q * n)\",\n \"* dx # Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS\",\n \"h_avg # method B in the Badia-Codina paper eta_p = 1 # eta_p\",\n \"div(v) * dx # a += 0.5 * h * h * inner(curl(u),\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ):\",\n \"dS # Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) /\",\n \"* import numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): #\",\n \"weights delta = Constant(1.0) # delta = h delta_0 = delta delta_1 =\",\n \"q * div(u)) * dx L = delta_0 * f * q *\",\n \"ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def\",\n \"def solve_poisson_dgls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family\",\n \"Stabilizing terms a += delta_1 * inner(u + grad(p), v + grad(q)) *\",\n \"* f * div(v) * dx a += 0.5 * div(u) * div(v)\",\n \"return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads\",\n \"# Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg =\",\n \"- q) * (lambda_h - p_boundaries) * ds # ) # maybe this\",\n \"method B in the Badia-Codina paper eta_u = 1 # eta_u_bc = h\",\n \"dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3 * p\",\n \"div(v) * dx a += 0.5 * div(u) * div(v) * dx a\",\n \"degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree}\",\n \"considered in the original paper # a += dot(jump(p, n), jump(q, n)) *\",\n \"= Constant(0.5) * h * h delta_3 = Constant(0.5) * h * h\",\n \"condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz,\",\n \"my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form,\",\n \"is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15,\",\n \"* div(v) * dx a += 0.5 * div(u) * div(v) * dx\",\n \"n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution\",\n \"terms a = inner(grad(p), grad(q)) * dx L = f * q *\",\n \"curl(v)) * dx # Weakly imposed boundary conditions a += dot(v, n) *\",\n \"div(v) * p - q * div(u)) * dx # DG terms a\",\n \"result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family =\",\n \"results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\"\",\n \"* h_avg # method B in the Badia-Codina paper eta_p = 1 #\",\n \"* dS # not considered in the original paper # a += dot(jump(p,\",\n \"TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y =\",\n \"max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = {\",\n \"result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs:\",\n \"Transmission condition a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed\",\n \"**kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(),\",\n \"= _A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx] *\",\n \"/ 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method #\",\n \"shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1\",\n \"+ q * div(u)) * dx # DG terms a += jump(v, n)\",\n \"else: T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial\",\n \"= f * q * dx # Hybridization terms a += s *\",\n \"delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a += delta_1 *\",\n \"\\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\":\",\n \"form a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") *\",\n \"a numpy array. :param array: Array with real and complex numbers. :param imag_threshold:\",\n \"ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh,\",\n \"Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact\",\n \"+= 0.5 * inner(u + grad(p), grad(q) - v) * dx # a\",\n \"ds # Flux Least-squares as in DG a = delta_0 * inner(u +\",\n \"1 eta_p = L0 * h # method B in the Badia-Codina paper\",\n \"* div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v)) *\",\n \"beta_avg = beta_0 / h(\\\"+\\\") # Stabilizing parameter # delta_0 = Constant(1) #\",\n \"y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x)\",\n \"div(v) * dx # Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") *\",\n \"Plotting the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\"))\",\n \"x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\")\",\n \"Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors =\",\n \"Below there is the spy alternative # plot = plt.spy(Am, **kwargs) # Remove\",\n \"of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size =\",\n \"array. :param array: Array with real and complex numbers. :param imag_threshold: Threshold to\",\n \"# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Edge stabilizing parameter\",\n \"1 last_degree = 1 for current_solver in solvers_options: # Setting the output file\",\n \"# Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) * h * h\",\n \"shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1\",\n \"= int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat,\",\n \"1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S = A[idx,\",\n \"= singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\":\",\n \"* div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx A\",\n \"= Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp =\",\n \"delta delta_1 = delta delta_2 = delta delta_3 = 1 / h delta_4\",\n \"dot(u, n) * q * ds # # L = -delta_1 * dot(u_projected,\",\n \"+= -0.5 * inner((u + grad(p)), v + grad(q)) * dx # a\",\n \"n) * q * ds # # L = -delta_1 * dot(u_projected, n)\",\n \"Least-squares as in DG a = delta_0 * inner(u + grad(p), v +\",\n \"= attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs =\",\n \"way to impose BC, but this necessary _A = Tensor(a) A = _A.blocks\",\n \"FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact =\",\n \"div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1),\",\n \"# \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\":\",\n \"# Edge stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg)\",\n \"* dx L += delta_2 * f * div(v) * dx # Hybridization\",\n \"+= s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a +=\",\n \"dx # Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS +\",\n \"from scipy.sparse import csr_matrix from slepc4py import SLEPc import pandas as pd from\",\n \"a += eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions\",\n \"* dS L = f * q * dx # Transmission condition a\",\n \"a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS\",\n \"Mass balance least-square a += delta_2 * div(u) * div(v) * dx #\",\n \") return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration\",\n \"eta_u = 1 # eta_u_bc = h / L0 # method B in\",\n \"print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}') # # Plotting the\",\n \"* div(v) * dx a += delta_3 * inner(curl(u), curl(v)) * dx L\",\n \"name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter\",\n \"solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, #\",\n \"solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls,\",\n \"f * div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u),\",\n \"may decrease convergente rates (Nitsche) A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric\",\n \"mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"* dx # Hybridization terms a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\")\",\n \"f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact,\",\n \"/ h delta_4 = 1 / h # Least-squares terms a = delta_0\",\n \"# solution = Function(W) # u, p, lambda_h = split(solution) p, lambda_h =\",\n \"space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree)\",\n \"/ L0 # method B in the Badia-Codina paper # Mixed classical terms\",\n \"- v) * dx a += eta_p * div(u) * div(v) * dx\",\n \"f = Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0\",\n \"np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove\",\n \"plt.subplots(1, 1) petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp\",\n \"* q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") * dot(u_hat, n) * q\",\n \"pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh,\",\n \"N, quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz:\",\n \"+= 0.5 * f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"n)) * dS # ** Mesh independent (original) # a += jump(u, n)\",\n \"= tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy: pbar.set_description(f\\\"Processing {name}\",\n \"n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition\",\n \"delta_4 = delta # delta_4 = LARGE_NUMBER / h delta_5 = delta #\",\n \"def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig,\",\n \"(avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS # ** Mesh\",\n \"# L0 = 1 # eta_p = L0 * h_avg # method B\",\n \"DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta = beta_0 /\",\n \"= VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W = U *\",\n \"'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement\",\n \"# # L = delta_1 * p_exact * dot(v, n) * ds #\",\n \"y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact))\",\n \"petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs\",\n \"if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U =\",\n \"edge terms a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)),\",\n \"# method B in the Badia-Codina paper eta_p = 1 # eta_p =\",\n \"dS # a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v,\",\n \"# a += jump(u, n) * jump(v, n) * dS # not considered\",\n \"name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization\",\n \"trace u_hat = u + beta * (p - lambda_h) * n #\",\n \"return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family\",\n \"div(v) * dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) *\",\n \"= Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical\",\n \"return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True,\",\n \"the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing term beta_0 =\",\n \"plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index S =\",\n \"# Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is the\",\n \"* dS # a += delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds\",\n \"** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) - v)\",\n \"'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' U\",\n \"\\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if use_quads\",\n \"not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max()\",\n \"petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs\",\n \"calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric )\",\n \"= Constant(1) # delta = h delta_0 = delta delta_1 = delta delta_2\",\n \"(p - lambda_h) * (q - mu_h) * ds # a += delta_5\",\n \"petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =\",\n \"\\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree = 1 for\",\n \"dx # a += 0.5 * inner(curl(u), curl(v)) * dx # ** Badia-Codina\",\n \"np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below\",\n \"jump(u_hat, n=n) * q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") * dot(u_hat, n)\",\n \"# ** Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) -\",\n \"lambda_h * dot(v, n) * ds # # L = delta_1 * p_exact\",\n \"Choose if the method is SIPG (-1) or NIPG (1) s = Constant(-1)\",\n \"+= (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) *\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads =\",\n \"* dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space\",\n \"Exact solution p_exact = sin(2 * pi * x) * sin(2 * pi\",\n \"= Constant(1) # delta_3 = Constant(1) # delta_4 = Constant(1) # delta_5 =\",\n \"'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W =\",\n \"* dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) *\",\n \"(eta_u / h) * dot(p * n, q * n) * ds A\",\n \"estimation is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space\",\n \"* ds # ** Mesh independent ** # a += jump(u, n) *\",\n \"# L += 0.5 * f * div(v) * dx # ** Badia-Codina\",\n \"dx L = f * q * dx # DG edge terms a\",\n \"f * q * dx # DG edge terms a += s *\",\n \"'DG' V = FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p =\",\n \"v) - div(v) * p - q * div(u)) * dx # DG\",\n \"import csr_matrix from slepc4py import SLEPc import pandas as pd from tqdm import\",\n \"* dx # L += 0.5 * h * h * f *\",\n \"* dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) *\",\n \"pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs =\",\n \"avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc * delta_3\",\n \"degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T =\",\n \"= Constant(1e0) delta = h * h # delta = Constant(1) # delta\",\n \"# u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h =\",\n \"balance least-square a += delta_2 * div(u) * div(v) * dx # L\",\n \"num_of_factors) result = ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs )\",\n \"): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ'\",\n \"ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form,\",\n \"- p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed BC a\",\n \"lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS\",\n \"and complex numbers. :param imag_threshold: Threshold to cut off imaginary part in complex\",\n \"= u + beta * (p - lambda_h) * n # HDG classical\",\n \"+ grad(p), v + grad(q)) * dx a += delta_2 * div(u) *\",\n \"of a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1,\",\n \"Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) #\",\n \"degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family,\",\n \"+= -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta /\",\n \"= SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi * x) *\",\n \"= ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return\",\n \"a = delta_0 * inner(u + grad(p), v + grad(q)) * dx #\",\n \"method B in the Badia-Codina paper # eta_p = L0 * L0 #\",\n \"Tensor(a_form) A = _A.blocks S = A[1, 1] - A[1, :1] * A[:1,\",\n \"# a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms\",\n \"* dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original) #\",\n \"the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0)\",\n \"+= eta_p * div(u) * div(v) * dx a += eta_p * inner(curl(u),\",\n \"svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min()\",\n \"bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig,\",\n \"str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv\",\n \"= { \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\":\",\n \"* dot(grad(q), n) * p_boundaries * ds F = a - L a_form\",\n \"n) * dS a += -dot(u, grad(q)) * dx + jump(u_hat, n) *\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False\",\n \"# Selecting the solver and its kwargs solver = solvers_options[current_solver] # Performing the\",\n \"* dS a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a\",\n \"v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) # Exact\",\n \"eta_u = h_avg / L0 # method B in the Badia-Codina paper eta_u\",\n \"= Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h\",\n \"a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\") * dS\",\n \"a += dot(jump(p, n), jump(q, n)) * dS # Volumetric stabilizing terms #\",\n \"+= delta_1 * lambda_h * dot(v, n) * ds # # L =\",\n \"- dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5 * (dot(u, n) -\",\n \"= ConditionNumberResult( form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result\",\n \"float = 1e-5) -> np.ndarray: \\\"\\\"\\\"Utility function to filter real part in a\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): #\",\n \"SLEPc import pandas as pd from tqdm import tqdm import os matplotlib.use('Agg') @attr.s\",\n \"* dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS a += -dot(grad(p), n)(\\\"+\\\")\",\n \"i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not\",\n \"a += eta_u * avg(delta_3) * (jump(u, n) * jump(v, n)) * dS\",\n \"Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF'\",\n \"ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def\",\n \"- div(v) * p + q * div(u)) * dx A = assemble(a,\",\n \"a += p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat =\",\n \"idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1) Am\",\n \"Badia-Codina stabilized dG method L0 = 1 eta_p = L0 * h #\",\n \"dS # Boundary terms # a += -dot(vel_projected, n) * v * ds\",\n \"False, zero_tol: float = 1e-5 ): backend = backend.lower() if backend == \\\"scipy\\\":\",\n \"= DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh dependent stabilization\",\n \"(dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS\",\n \"Weakly imposed BC from hybridization # a += mu_h * (lambda_h - p_boundaries)\",\n \"DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta = Constant(32.0) h\",\n \"use_sparse: bool = False, zero_tol: float = 1e-5 ): backend = backend.lower() if\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dvms(mesh, degree=1): # Function space\",\n \"* ds # a += mu_h * lambda_h * ds # ### #\",\n \"imposed BC a += dot(u_hat, n) * q * ds F = a\",\n \"lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh entities n =\",\n \"is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_ldgc( mesh, degree=1, is_multiplier_continuous=True ): # Function\",\n \"beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly\",\n \"\\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\":\",\n \"p - q * div(u)) * dx # Stabilizing terms a += -0.5\",\n \"= sigma_e p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter\",\n \"0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD\",\n \"ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5,\",\n \"v) - div(v) * p - q * div(u)) * dx # Stabilizing\",\n \"a += 0.5 * h * h * div(u) * div(v) * dx\",\n \"lhs(F) _A = Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2,\",\n \"the matrix plot = ax.matshow(Am, **kwargs) # Below there is the spy alternative\",\n \"-dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta / h_avg)\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration\",\n \"Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h * h # delta =\",\n \"\\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg\",\n \"q) * (lambda_h - p_boundaries) * ds # ) # maybe this is\",\n \"+= -eta_u * inner(u + grad(p), v + grad(q)) * dx a +=\",\n \"h * h # delta = Constant(1) # delta = h delta_0 =\",\n \"p + q * div(u)) * dx # DG terms a += jump(v,\",\n \"velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0\",\n \"n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\")\",\n \"(lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\")) * dS # Weakly imposed BC\",\n \"(2010), it is not a classical Nitsche's method a += (eta_p / h)\",\n \"+ mu_h * (dot(u, n) - dot(u_projected, n))) * ds a += beta\",\n \"sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e,\",\n \"v + grad(q)) * dx # a += 0.5 * div(u) * div(v)\",\n \"paper # Mixed classical terms a = (dot(u, v) - div(v) * p\",\n \"TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y =\",\n \"* dx # L += 0.5 * f * div(v) * dx #\",\n \"pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs # bcs\",\n \"* (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary terms # a += -dot(vel_projected,\",\n \"eta_p * div(u) * div(v) * dx # Weakly imposed boundary conditions a\",\n \"singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method for condition number estimation is\",\n \"beta0 / h # Symmetry term. Choose if the method is SIPG (-1)\",\n \"2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size =\",\n \"\\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree in pbar: for n in numel_xy:\",\n \"backend: str = \\\"scipy\\\", use_sparse: bool = False, zero_tol: float = 1e-5 ):\",\n \"bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp\",\n \"delta_1 * inner(u + grad(p), v + grad(q)) * dx a += delta_2\",\n \"plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax\",\n \"results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number)\",\n \"* q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"p_boundaries = p_exact bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 =\",\n \"exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results = \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number #\",\n \"div(u) - p * div(v) + inner(grad(p), grad(q))) # * delta_1 # *\",\n \"return result def solve_poisson_ls(mesh, degree=1): # Function space declaration pressure_family = 'CG' velocity_family\",\n \"bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp\",\n \"delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"degree) W = U * V # Trial and test functions u, p\",\n \"terms a = (dot(u, v) - div(v) * p + q * div(u))\",\n \"= Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,\",\n \"ds # a += mu_h * lambda_h * ds # ### # a\",\n \"trace u_hat = u + beta * (p - lambda_h) * n v_hat\",\n \":2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat =\",\n \"h # Mixed classical terms a = (dot(u, v) - div(v) * p\",\n \"V # Trial and test functions u, p = TrialFunctions(W) v, q =\",\n \"div(u) * div(v) * dx # a += 0.5 * inner(curl(u), curl(v)) *\",\n \"method L0 = 1 eta_p = L0 * h # method B in\",\n \"attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib()\",\n \"ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot\",\n \"Volumetric stabilizing terms # a += 0.5 * h * h * div(u)\",\n \"form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs\",\n \"dS # Weak boundary conditions a += s * dot(p * n, grad(q))\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space\",\n \"CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2 * pi\",\n \"* div(u) * div(v) * dx # Weakly imposed boundary conditions a +=\",\n \"lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS\",\n \"petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size =\",\n \"num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return\",\n \"velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(),\",\n \"eta_u_bc * delta_3 * p * q * ds # may decrease convergente\",\n \"a += (eta_u / h) * dot(p * n, q * n) *\",\n \"* dx # Stabilizing terms a += delta_1 * inner(u + grad(p), v\",\n \"terms below are based on ASGS Badia-Codina (2010), it is not a classical\",\n \"= plt.subplots(1, 1) petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"* ds F = a - L a_form = lhs(F) _A = Tensor(a_form)\",\n \"dot(v, n) * ds # Mass balance least-square a += delta_2 * div(u)\",\n \"from scipy.sparse.linalg import svds from scipy.sparse import csr_matrix from slepc4py import SLEPc import\",\n \"+= 0.5 * inner(curl(u), curl(v)) * dx # L += 0.5 * f\",\n \"# a += dot(jump(p, n), jump(q, n)) * dS # a += p\",\n \"plot of a condensed hybrid-mixed matrix for single scale problems.\\\"\\\"\\\" fig, ax =\",\n \"* h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) *\",\n \"* ds # ### # a += ( # (mu_h - q) *\",\n \"{degree} - N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh,\",\n \"Edge stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS\",\n \"delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) * h *\",\n \"\\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" V\",\n \"* (p - lambda_h) * n # HDG classical form a = (dot(u,\",\n \"delta_2 * f * div(v) * dx # Irrotational least-squares a += delta_3\",\n \"= f * q * dx # DG edge terms a += s\",\n \"Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s = Constant(-1.0) beta\",\n \"jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a += lambda_h\",\n \"if use_quads else 'DG' velocity_family = 'DQ' if use_quads else 'DG' trace_family =\",\n \"dx a += 0.5 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs,\",\n \"+= 0.5 * h * h * inner(curl(u), curl(v)) * dx # L\",\n \"the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) #\",\n \"mu_h(\\\"+\\\")) * dS # a += delta_4 * (p - lambda_h) * (q\",\n \"test functions p = TrialFunction(V) q = TestFunction(V) # Mesh entities n =\",\n \"Trial and test functions # solution = Function(W) # u, p, lambda_h =\",\n \"ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed\",\n \"bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1],\",\n \"ds # ** The terms below are based on ASGS Badia-Codina (2010), it\",\n \"in the Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0)\",\n \"* f * q * dx # Stabilizing terms a += delta_1 *\",\n \"classical form a = -dot(u, grad(q)) * dx + jump(u_hat, n) * q(\\\"+\\\")\",\n \"v + grad(q)) * dx a += delta_1 * div(u) * div(v) *\",\n \"eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds # **\",\n \"== \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' velocity_family = 'DQ' if\",\n \"ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] + f1_size[0]\",\n \"= dot(grad(p), grad(q)) * dx L = f * q * dx #\",\n \"Constant(0.5) * h * h delta_3 = Constant(0.5) * h * h #\",\n \"condition a += jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC\",\n \"only real numbers. \\\"\\\"\\\" real_part_array = array.real[abs(array.imag) < 1e-5] return real_part_array def calculate_condition_number(\",\n \"h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # Boundary\",\n \"Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz\",\n \"Nitsche # a += beta * p * q * ds # may\",\n \"+= eta_u_bc * delta_4 * dot(u, n) * dot(v, n) * ds #\",\n \"* (p - lambda_h) * n # HDG classical form a = -dot(u,\",\n \"delta_1 * lambda_h * dot(v, n) * ds # Mass balance least-square a\",\n \"h * f * div(v) * dx # a += 0.5 * div(u)\",\n \"ds # Mass balance least-square a += delta_2 * div(u) * div(v) *\",\n \"df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg, #\",\n \"# Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs,\",\n \"delta = Constant(1.0) # delta = h delta_0 = delta delta_1 = delta\",\n \"h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact = sin(2\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function\",\n \"* p - q * div(u)) * dx # DG terms a +=\",\n \"= str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V =\",\n \"# \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, #\",\n \"+ inner(grad(p), grad(q))) # * delta_1 # * dx # ) # #\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1): #\",\n \"# # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs, cmap=my_cmap) # # plot_matrix(result.assembled_form,\",\n \"curl(v)) * dx L += delta_2 * f * div(v) * dx #\",\n \"L += s * dot(grad(q), n) * p_boundaries * ds F = a\",\n \"= Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function f_expression = div(-grad(p_exact)) f =\",\n \"a += jump(u, n) * jump(v, n) * dS # a += dot(jump(p,\",\n \"is currently unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration\",\n \"mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv\",\n \"beta = beta_0 / h beta = beta_0 # Stabilization parameters delta_0 =\",\n \"FunctionSpace(mesh, trace_family, degree) W = U * V * T # Trial and\",\n \"n) * ds # # L = delta_1 * p_exact * dot(v, n)\",\n \"Badia-Codina stabilized dG method # L0 = 1 # eta_p = L0 *\",\n \"primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if\",\n \"* dS # Volumetric stabilizing terms # a += 0.5 * inner(u +\",\n \"is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family =\",\n \"h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG method\",\n \"# Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2\",\n \"p * ds - q * dot(u, n) * ds # ** The\",\n \"stabilized dG method # L0 = 1 # eta_p = L0 * h_avg\",\n \"pi * x) * sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact\",\n \"\\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement\",\n \"beta * p * q * ds # may decrease convergente rates #\",\n \"+ q * div(u)) * dx # Stabilizing terms a += 0.5 *\",\n \"p_exact * dot(v, n) * ds # Flux Least-squares as in DG a\",\n \"* p) * dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) *\",\n \"ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx = trace_index\",\n \"jump(q, n)) * dS # Volumetric stabilizing terms # a += 0.5 *\",\n \"axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] -\",\n \"csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) -\",\n \"= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows\",\n \"* dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx\",\n \"beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0 / h(\\\"+\\\")\",\n \"# Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if\",\n \"n) * v * ds # How to set this bc?? # a\",\n \"bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a condensed hybrid-mixed matrix for single scale\",\n \"hdiv_family = 'RT' pressure_family = 'DG' U = FunctionSpace(mesh, hdiv_family, degree + 1)\",\n \"h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5) * h\",\n \"terms a += 0.5 * inner(u + grad(p), grad(q) - v) * dx\",\n \"Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\")\",\n \"idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix plot =\",\n \"n) * avg(p) * dS - avg(q) * jump(u, n) * dS #\",\n \"= delta_0 * f * q * dx # Stabilizing terms a +=\",\n \"beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5) *\",\n \"T # Trial and test functions # solution = Function(W) # u, p,\",\n \"functions u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n\",\n \"grad(p), v + grad(q)) * dx a += delta_1 * div(u) * div(v)\",\n \"* inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\\\"+\\\") * jump(u_hat,\",\n \"Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 # Stabilization parameters\",\n \"velocity_family = 'DQ' if use_quads else 'DG' trace_family = \\\"HDiv Trace\\\" U =\",\n \"Least-Squares weights delta = Constant(1.0) # delta = h delta_0 = delta delta_1\",\n \"L = f * q * dx # Hybridization terms a += s\",\n \"lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a += delta_1 * lambda_h *\",\n \"L0 = 1 # eta_p = L0 * h_avg # method B in\",\n \"= (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina\",\n \"Darcy eq. first-order terms as stabilizing terms a += delta_1 * (dot(u, v)\",\n \"delta_1 = delta delta_2 = delta delta_3 = 1 / h delta_4 =\",\n \"= Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()\",\n \"Least-squares terms a = delta_1 * inner(u + grad(p), v + grad(q)) *\",\n \"solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"options solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls,\",\n \"CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) * dx\",\n \"trace_family = \\\"HDiv Trace\\\" U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family,\",\n \"Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Stabilization parameters delta_1 = Constant(1)\",\n \"trace_family, degree) W = U * V * T # Trial and test\",\n \"_A.blocks idx = trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx,\",\n \"pressure_family, degree) # Trial and test functions p = TrialFunction(V) q = TestFunction(V)\",\n \"n, grad(q)) * ds - dot(grad(p), q * n) * ds a +=\",\n \"# Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected =\",\n \"a = (dot(u, v) - div(v) * p + delta_0 * q *\",\n \"0.5 * f * div(v) * dx # ** Badia-Codina based a +=\",\n \"(q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta / h_avg) * (p(\\\"+\\\") -\",\n \"U * V * T # Trial and test functions # solution =\",\n \"\\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms,\",\n \"= L0 * L0 # method D in the Badia-Codina paper eta_u =\",\n \"nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number =\",\n \"Mnp.nnz number_of_dofs = V.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors)\",\n \"a += -0.5 * inner(u + grad(p), v + grad(q)) * dx #\",\n \"The terms below are based on ASGS Badia-Codina (2010), it is not a\",\n \"mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp =\",\n \"assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp\",\n \"q * div(u)) * dx # Stabilizing terms a += 0.5 * inner(u\",\n \"- 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs):\",\n \"# Exact solution p_exact = sin(2 * pi * x) * sin(2 *\",\n \"dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent (original) # a\",\n \"* p * ds - q * dot(u, n) * ds # **\",\n \"least-squares # a = ( # (inner(u, v) - q * div(u) -\",\n \"+= delta_3 * inner(curl(u), curl(v)) * dx # Hybridization terms a += mu_h(\\\"+\\\")\",\n \"list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(), } element_kind =\",\n \"u, p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities h =\",\n \"this is not a good way to impose BC, but this necessary _A\",\n \"# ** Badia-Codina based a += -eta_u * inner(u + grad(p), v +\",\n \"svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values\",\n \"petsc_mat = assembled_form.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp =\",\n \"check_finite=False) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend\",\n \"= assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp\",\n \"= assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize()\",\n \"Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries =\",\n \"Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter\",\n \"div(v) * dx # a += 0.5 * div(u) * div(v) * dx\",\n \"csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) -\",\n \"T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial and\",\n \"\\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v)) * dx A = assemble(a,\",\n \"split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h = TestFunctions(W) # Mesh\",\n \"h) * dot(p * n, q * n) * ds # may decrease\",\n \"* inner(curl(u), curl(v)) * dx # Edge stabilizing terms # ** Badia-Codina based\",\n \"ds # # a += delta_1 * dot(u, n) * q * ds\",\n \"dS # a += delta_4 * (p - lambda_h) * (q - mu_h)\",\n \"S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list()\",\n \"ds # ) # maybe this is not a good way to impose\",\n \"1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize()\",\n \"ax.set_yticklabels([]) return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\"\",\n \"* V # Trial and test functions u, p = TrialFunctions(W) v, q\",\n \"= FunctionSpace(mesh, hdiv_family, degree + 1) V = FunctionSpace(mesh, pressure_family, degree) W =\",\n \"V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement\",\n \"Classical mixed Darcy eq. first-order terms as stabilizing terms a += delta_1 *\",\n \"attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list())\",\n \"(q - mu_h) * n # Flux least-squares # a = ( #\",\n \"np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg\",\n \"= Constant(1.0e-18) # beta = beta_0 / h beta = beta_0 # Stabilization\",\n \"+= eta_u * inner(u + grad(p), grad(q) - v) * dx a +=\",\n \"kwargs solver = solvers_options[current_solver] # Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree\",\n \"ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5) ->\",\n \"= ( # (inner(u, v) - q * div(u) - p * div(v)\",\n \"else 'DG' trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:\",\n \"bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta =\",\n \"div(v) * p + q * div(u)) * dx # Stabilizing terms a\",\n \"Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER = Constant(1e0) delta = h *\",\n \"a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS\",\n \"quadrilateral=True) # result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}')\",\n \"mesh dependent stabilization h_avg = (h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing\",\n \"mixed Darcy eq. first-order terms as stabilizing terms a += delta_1 * (dot(u,\",\n \"space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family\",\n \"# Performing the convergence study hp_refinement_cond_number_calculation( solver, min_degree=degree, max_degree=degree + last_degree, quadrilateral=True, name=name\",\n \"1 # Least-Squares weights delta = Constant(1.0) # delta = h delta_0 =\",\n \"Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization\",\n \"* dx # Hybridization terms a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS\",\n \"degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\"\",\n \"# Flux least-squares # a = ( # (inner(u, v) - q *\",\n \"p * q * ds # may decrease convergente rates # ** The\",\n \"* dS # ** Mesh independent (original) # a += jump(u, n) *\",\n \"jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a += dot(u_hat,\",\n \"matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([])\",\n \"= FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact solution p_exact\",\n \"_A.blocks S = A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2,\",\n \"+= (eta_u / h) * dot(p * n, q * n) * ds\",\n \"plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form =\",\n \"beta_0 / h # beta = beta_0 # Numerical flux trace u_hat =\",\n \"a += delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) -\",\n \"quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric)\",\n \"jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q,\",\n \"(q - mu_h) * ds # a += delta_5 * (dot(u, n)(\\\"+\\\") -\",\n \"= FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement =\",\n \"+ mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") -\",\n \"# Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks and\",\n \"# a = ( # (inner(u, v) - q * div(u) - p\",\n \"# not considered in the original paper # a += dot(jump(p, n), jump(q,\",\n \"declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\"\",\n \"decrease convergente rates # ** The terms below are based on ASGS Badia-Codina\",\n \"sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected\",\n \"attr.ib() is_operator_symmetric = attr.ib() bcs = attr.ib(default=list()) def plot_matrix(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot\",\n \"\\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact))\",\n \"of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(),\",\n \"and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\")\",\n \"n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n))\",\n \"* div(v) * dx a += 0.5 * inner(curl(u), curl(v)) * dx A\",\n \"* dS # Weakly imposed BC a += (p_boundaries * dot(v, n) +\",\n \"+= (beta / h) * (p- p_boundaries) * q * ds # is\",\n \"imaginary part in complex number. :return: Filtered array with only real numbers. \\\"\\\"\\\"\",\n \"- p_boundaries) * ds # a += mu_h * lambda_h * ds #\",\n \"# ### # a += ( # (mu_h - q) * (lambda_h -\",\n \"curl(v)) * dx # ** Badia-Codina based a += -eta_u * inner(u +\",\n \"BC, but this necessary _A = Tensor(a) A = _A.blocks S = A[2,\",\n \"color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a\",\n \"# Trial and test functions p = TrialFunction(V) q = TestFunction(V) # Mesh\",\n \"degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number\",\n \"Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression)\",\n \"Trial and test functions u, p = TrialFunctions(W) v, q = TestFunctions(W) #\",\n \"range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values\",\n \"+= eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10,\",\n \"q * ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) *\",\n \"str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' V = FunctionSpace(mesh,\",\n \"return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool =\",\n \"plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1,\",\n \"div(u) * div(v) * dx # Weakly imposed boundary conditions a += dot(v,\",\n \"**kwargs) # Below there is the spy alternative # plot = plt.spy(Am, **kwargs)\",\n \"n)) * (dot(v, n) - dot(v_hat, n)) * ds # Weakly imposed BC\",\n \"f * q * dx # Stabilizing terms a += delta_1 * inner(u\",\n \"= solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n\",\n \"matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"p_boundaries) * q * ds # is this necessary? L += s *\",\n \"+ 1) V = FunctionSpace(mesh, pressure_family, degree) W = U * V #\",\n \"dx # ) # # These terms below are unsymmetric # a +=\",\n \"degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree)\",\n \"M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values > zero_tol]\",\n \"Classical term a = dot(grad(p), grad(q)) * dx L = f * q\",\n \"(dot(u, v) - div(v) * p + q * div(u)) * dx A\",\n \"# eta_p = L0 * L0 # method D in the Badia-Codina paper\",\n \"h beta = beta_0 # Stabilization parameters delta_0 = Constant(-1) delta_1 = Constant(-0.5)\",\n \"imposed boundary conditions a += dot(v, n) * p * ds - q\",\n \"= Constant(1) # delta_4 = Constant(1) # delta_5 = Constant(1) # LARGE_NUMBER =\",\n \"of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" %\",\n \"= h_avg / L0 # method B in the Badia-Codina paper eta_u =\",\n \"zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S = SLEPc.SVD()\",\n \"Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 / h\",\n \"A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize()\",\n \"f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"+ grad(p), v + grad(q)) * dx a += eta_p * div(u) *\",\n \"raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values >\",\n \"Function(W) # u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v,\",\n \"# a += 0.5 * div(u) * div(v) * dx # a +=\",\n \"} element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for\",\n \"* f * div(v) * dx # Hybridization terms a += lambda_h(\\\"+\\\") *\",\n \"velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree)\",\n \"Badia-Codina based a += eta_u * inner(u + grad(p), grad(q) - v) *\",\n \"- lambda_h) * n v_hat = v + beta * (q - mu_h)\",\n \"matrix for single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form)\",\n \"dot(jump(p, n), jump(q, n)) * dS # a += p * q *\",\n \"Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions\",\n \"_A = Tensor(a) A = _A.blocks S = A[2, 2] - A[2, :2]\",\n \"delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a = delta_1 *\",\n \"a = inner(grad(u), grad(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta / h_avg) *\",\n \"not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n) results_dict[\\\"Degree\\\"].append(degree)\",\n \"= assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size =\",\n \"assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1,\",\n \"** Mesh independent ** # a += jump(u, n) * jump(v, n) *\",\n \") return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration\",\n \"attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib()\",\n \"dx # L += 0.5 * f * div(v) * dx # **\",\n \"Constant(1.0) # delta = h delta_0 = delta delta_1 = delta delta_2 =\",\n \"h * h delta_2 = Constant(0.5) * h * h delta_3 = Constant(0.5)\",\n \"n) * dS # Edge stabilizing terms # ** Badia-Codina based a +=\",\n \"# Classical mixed Darcy eq. first-order terms as stabilizing terms a += delta_1\",\n \"beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a\",\n \"= Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p), v\",\n \"# L += 0.5 * h * h * f * div(v) *\",\n \"\\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh dependent stabilization h_avg = (h(\\\"+\\\")\",\n \"div(v) * p + q * div(u)) * dx # DG terms a\",\n \"solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, #\",\n \"last_degree = 1 for current_solver in solvers_options: # Setting the output file name\",\n \"== \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family =\",\n \"# a += jump(u, n) * jump(v, n) * dS # a +=\",\n \"0.5 * h * h * div(u) * div(v) * dx # a\",\n \"# eta_u = h_avg / L0 # method B in the Badia-Codina paper\",\n \"are unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS\",\n \"n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a += delta_5\",\n \"inner(u + grad(p), v + grad(q)) * dx # a += 0.5 *\",\n \"= FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V) q\",\n \"if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind) results_dict[\\\"Number of Elements\\\"].append(n * n)\",\n \"- lambda_h) * n # HDG classical form a = (dot(u, v) -\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): # Function space declaration\",\n \"Weak boundary conditions a += s * dot(p * n, grad(q)) * ds\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_sdhm( mesh, degree=1,\",\n \"= Constant(1.0) beta = beta_0 / h # Mixed classical terms a =\",\n \"terms a += s * dot(grad(q), n)(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * dS\",\n \"Hybridization parameter beta_0 = Constant(1.0) beta = beta_0 / h beta_avg = beta_0\",\n \"* A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize()\",\n \"* jump(u_hat, n=n) * q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\") * dot(u_hat,\",\n \"delta_2 * div(u) * div(v) * dx a += delta_3 * inner(curl(u), curl(v))\",\n \"plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\"\",\n \"h * div(u) * div(v) * dx # a += 0.5 * h\",\n \"independent (original) # a += jump(u, n) * jump(v, n) * dS #\",\n \"delta = h delta_0 = delta delta_1 = delta delta_2 = delta delta_3\",\n \"# Function space declaration V = FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test\",\n \"A[2, 2] - A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat =\",\n \"p + q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"# \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\": solve_poisson_sipg, } degree = 1 last_degree = 1\",\n \"# Weakly imposed BC a += dot(u_hat, n) * q * ds F\",\n \"= plt.subplots(1, 1) assembled_form = assemble(a_form, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = assembled_form.M.handle f0_size =\",\n \"div(u)) * dx L = delta_0 * f * q * dx #\",\n \"degree) # Trial and test functions u = TrialFunction(V) v = TestFunction(V) #\",\n \"* jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n),\",\n \"+ grad(q)) * dx a += eta_p * div(u) * div(v) * dx\",\n \"1 # eta_p = L0 * h_avg # method B in the Badia-Codina\",\n \"matplotlib.pyplot as plt import matplotlib from scipy.linalg import svd from scipy.sparse.linalg import svds\",\n \"dx L += delta_2 * f * div(v) * dx # Hybridization terms\",\n \"+ grad(p), grad(q) - v) * dx a += eta_p * div(u) *\",\n \"L = f * q * dx # Transmission condition a += jump(u_hat,\",\n \"= split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities\",\n \"n # Flux least-squares # a = ( # (inner(u, v) - q\",\n \"* dot(u_hat, n) * q * ds # # a += delta_1 *\",\n \"ds # # L = -delta_1 * dot(u_projected, n) * q * ds\",\n \"BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta\",\n \"* h * h * f * div(v) * dx a += 0.5\",\n \"jump(q, n)) * dS # ** Mesh independent terms # a += jump(u,\",\n \"problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A = _A.blocks idx\",\n \"p = TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)\",\n \"a += beta * p * q * ds # may decrease convergente\",\n \"* dx + lambda_h(\\\"+\\\") * jump(v, n) * dS a += -dot(u, grad(q))\",\n \"import attr from firedrake import * import numpy as np import matplotlib.pyplot as\",\n \"beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical terms a\",\n \"if backend == \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if\",\n \"= Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx,\",\n \"v * ds # How to set this bc?? # a += (beta\",\n \"delta_3 = Constant(1) # Least-squares terms a = delta_1 * inner(u + grad(p),\",\n \"str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else \\\"DG\\\" V = FunctionSpace(mesh,\",\n \"* f * div(v) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat =\",\n \"p * q * ds # may decrease convergente rates a += eta_u_bc\",\n \"* h # method B in the Badia-Codina paper # eta_p = L0\",\n \"eta_p = L0 * h # method B in the Badia-Codina paper #\",\n \"- v) * dx # a += 0.5 * h * h *\",\n \"= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs p_boundaries = Constant(0.0) bc_multiplier =\",\n \"= Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a =\",\n \"* lambda_h * dot(v, n) * ds # # L = delta_1 *\",\n \"paper # eta_u = 1 # Nitsche's penalizing term beta_0 = Constant(1.0) beta\",\n \"* inner(u + grad(p), grad(q) - v) * dx # a += 0.5\",\n \"= assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"= delta delta_2 = delta delta_3 = 1 / h delta_4 = 1\",\n \"f * div(v) * dx # Hybridization terms a += lambda_h(\\\"+\\\") * dot(v,\",\n \") return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"* (jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg)\",\n \"# How to set this bc?? # a += (beta / h) *\",\n \"** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u, n) * jump(v,\",\n \"mu_h) * ds # a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\"))\",\n \"div(v) * p + q * div(u)) * dx A = assemble(a, bcs=bcs,\",\n \"dot(v, n) * ds a += (eta_u / h) * dot(p * n,\",\n \"a += -dot(grad(p), n)(\\\"+\\\") * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS a += (beta\",\n \"degree = {degree} - N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result\",\n \"p_boundaries) * ds # ) # maybe this is not a good way\",\n \"assembled_form = attr.ib() condition_number = attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz\",\n \"eta_p * inner(curl(u), curl(v)) * dx # Weakly imposed boundary conditions a +=\",\n \"0.5, color=\\\"k\\\") return plot def plot_matrix_hybrid_multiplier(a_form, trace_index=2, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of\",\n \"ds - q * dot(u, n) * ds a += beta * p\",\n \"A[:1, :1].inv * A[:1, 1] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric\",\n \"# result = solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') #\",\n \"petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = V.dim() num_of_factors\",\n \"# beta = beta_0 / h beta = beta_0 # Stabilization parameters delta_0\",\n \"Trial and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs\",\n \"Nitsche's method a += (eta_p / h) * dot(u, n) * dot(v, n)\",\n \"a = delta_0 * inner(u + grad(p), v + grad(q)) * dx a\",\n \"entities n = FacetNormal(mesh) h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh) # Exact\",\n \"+= delta_1 * (dot(u, v) - div(v) * p) * dx a +=\",\n \"= Constant(1e1) beta = beta0 / h # Symmetry term. Choose if the\",\n \"float = 1e-5 ): backend = backend.lower() if backend == \\\"scipy\\\": size =\",\n \"S = SLEPc.SVD() S.create() S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged()\",\n \"* h * h * f * div(v) * dx # a +=\",\n \"q * dx # DG edge terms a += s * dot(jump(p, n),\",\n \"results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number = pd.DataFrame(data=results_dict) path_to_save_results\",\n \"* dot(jump(p, n), jump(q, n)) * dS # ** Mesh independent terms #\",\n \"* div(v) * dx # a += -0.5 * inner(u + grad(p), v\",\n \"rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis ticks\",\n \"the Badia-Codina paper # Mixed classical terms a = (dot(u, v) - div(v)\",\n \"unavailable.\\\") return condition_number def solve_poisson_cg(mesh, degree=1, use_quads=False): # Function space declaration V =\",\n \"= 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh,\",\n \"0.5 * inner(u + grad(p), grad(q) - v) * dx # a +=\",\n \"spy alternative # plot = plt.spy(Am, **kwargs) # Remove axis ticks and values\",\n \"* h # delta = Constant(1) # delta = h delta_0 = delta\",\n \"0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a\",\n \"= delta_1 * inner(u + grad(p), v + grad(q)) * dx a +=\",\n \"result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 /\",\n \"h * h # Mixed classical terms a = (dot(u, v) - div(v)\",\n \"# Jump stabilizing parameters based on Badia-Codina stabilized dG method L0 = 1\",\n \"div(v) * dx # Weakly imposed boundary conditions a += dot(v, n) *\",\n \"sin(2 * pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e =\",\n \"\\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, # \\\"sipg\\\":\",\n \"Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns\",\n \"* avg(p) * dS - avg(q) * jump(u, n) * dS # Edge\",\n \"eta_p * div(u) * div(v) * dx a += eta_p * inner(curl(u), curl(v))\",\n \"a += 0.5 * h * h * inner(curl(u), curl(v)) * dx #\",\n \"\\\"CG\\\", degree) # Trial and test functions u = TrialFunction(V) v = TestFunction(V)\",\n \"split(solution) p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n\",\n \"* div(v) * dx # Weakly imposed boundary conditions a += dot(v, n)\",\n \"paper # eta_p = L0 * L0 # method D in the Badia-Codina\",\n \"dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing terms a +=\",\n \"* ds # # a += delta_1 * dot(u, n) * q *\",\n \"# Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx # Hybridization\",\n \"imposed BC from hybridization # a += mu_h * (lambda_h - p_boundaries) *\",\n \"pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T\",\n \"q = TestFunctions(W) # Mesh entities h = CellDiameter(mesh) x, y = SpatialCoordinate(mesh)\",\n \"real part in a numpy array. :param array: Array with real and complex\",\n \"* dS # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric\",\n \"dS a += (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") -\",\n \"* ds # # L = -delta_1 * dot(u_projected, n) * q *\",\n \") return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function\",\n \"C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree)\",\n \"method D in the Badia-Codina paper # eta_u = h_avg / L0 #\",\n \"def solve_poisson_sdhm( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"p - q * div(u)) * dx # DG terms a += jump(v,\",\n \"singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The\",\n \"L0 * h # method B in the Badia-Codina paper # eta_p =\",\n \"ax.matshow(Am, **kwargs) # Remove axis ticks and values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot\",\n \"return plot def plot_matrix_mixed(assembled_form, **kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig,\",\n \"* pi * y) exact_solution = Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") # Forcing function\",\n \"W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult(\",\n \"# Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q) - v)\",\n \"h * h * f * div(v) * dx # a += -0.5\",\n \"Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number\",\n \"L = -delta_1 * dot(u_projected, n) * q * ds # a +=\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_mixed_RT(mesh, degree=1):\",\n \"Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition\",\n \"number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result\",\n \"Stabilization parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares\",\n \"a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n)) * dS #\",\n \"sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical terms\",\n \"space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): #\",\n \"stabilizing terms # ** Badia-Codina based a += (avg(eta_p) / h_avg) * (jump(u,\",\n \"* dx L = f * q * dx # Hybridization terms a\",\n \"- 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of\",\n \"ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[],\",\n \"num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a,\",\n \"this bc?? # a += (beta / h) * (p- p_boundaries) * q\",\n \"@attr.s class ConditionNumberResult(object): form = attr.ib() assembled_form = attr.ib() condition_number = attr.ib() sparse_operator\",\n \"degree) V = FunctionSpace(mesh, pressure_family, degree) W = U * V # Trial\",\n \"h_avg / L0 # method B in the Badia-Codina paper eta_u = 1\",\n \"SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms a\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False\",\n \"element_kind = \\\"Quad\\\" if quadrilateral else \\\"Tri\\\" pbar = tqdm(range(min_degree, max_degree)) for degree\",\n \") return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration\",\n \"+= 0.5 * f * div(v) * dx # ** Badia-Codina based a\",\n \"Badia-Codina paper # Mixed classical terms a = (dot(u, v) - div(v) *\",\n \"delta_3 = 1 / h delta_4 = 1 / h # Least-squares terms\",\n \"delta = Constant(1) # delta = h delta_0 = delta delta_1 = delta\",\n \"v) * dx # a += 0.5 * h * h * div(u)\",\n \"solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral else 1 / n results_dict[\\\"Element\\\"].append(element_kind)\",\n \"beta * (p - lambda_h) * n v_hat = v + beta *\",\n \"exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs\",\n \"dx a += delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs,\",\n \"= \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\",\",\n \"+= -dot(vel_projected, n) * v * ds # How to set this bc??\",\n \"# \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\": solve_poisson_cgh, # \\\"ldgc\\\": solve_poisson_ldgc, #\",\n \"* dS a += (beta / h_avg) * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\")\",\n \"# Volumetric stabilizing terms # a += 0.5 * h * h *\",\n \"BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0) beta\",\n \"singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min()\",\n \"results_dict[\\\"Degree\\\"].append(degree) results_dict[\\\"Symmetric\\\"].append(result.is_operator_symmetric) results_dict[\\\"nnz\\\"].append(result.nnz) results_dict[\\\"dofs\\\"].append(result.number_of_dofs) results_dict[\\\"h\\\"].append(current_cell_size) results_dict[\\\"Condition Number\\\"].append(result.condition_number) os.makedirs(\\\"./cond_number_results/results_%s\\\" % name, exist_ok=True) df_cond_number =\",\n \"A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"* n # HDG classical form a = (dot(u, v) - div(v) *\",\n \"+= jump(u_hat, n) * mu_h(\\\"+\\\") * dS # Weakly imposed BC a +=\",\n \"FunctionSpace(mesh, pressure_family, degree) # Trial and test functions p = TrialFunction(V) q =\",\n \"unsymmetric # a += delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS #\",\n \"= \\\"./cond_number_results/results_%s/cond_numbers.csv\\\" % name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = {\",\n \"\\\"\\\"\\\"Provides a plot of a matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat =\",\n \"* inner(u + grad(p), v + grad(q)) * dx a += delta_1 *\",\n \"= beta_0 # Numerical flux trace u_hat = u + beta * (p\",\n \"in the Badia-Codina paper # Mixed classical terms a = (dot(u, v) -\",\n \"f * q * dx # Hybridization terms a += s * dot(grad(q),\",\n \"Badia-Codina (2010), it is not a classical Nitsche's method a += (eta_p /\",\n \"method B in the Badia-Codina paper eta_p = 1 # eta_p = L0\",\n \"delta_5 * (dot(u, n) - dot(u_hat, n)) * (dot(v, n) - dot(v_hat, n))\",\n \"pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2),\",\n \"result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\"\",\n \"method B in the Badia-Codina paper # eta_u = 1 # Nitsche's penalizing\",\n \"= h / L0 # method B in the Badia-Codina paper # eta_u\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_vms(mesh, degree=1): # Function\",\n \"function to filter real part in a numpy array. :param array: Array with\",\n \"Function(W) # u, p, lambda_h = split(solution) p, lambda_h = TrialFunctions(W) q, mu_h\",\n \"** Badia-Codina based (better results) ** a += eta_u * avg(delta_3) * (jump(u,\",\n \"C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U * V *\",\n \"(1) s = Constant(-1) # Classical volumetric terms a = inner(grad(p), grad(q)) *\",\n \"# ** Mesh independent (original) # a += jump(u, n) * jump(v, n)\",\n \"* n) * ds a += beta * p * q * ds\",\n \"\\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, # \\\"hdg\\\": solve_poisson_hdg, # \\\"cgh\\\":\",\n \"Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\",\",\n \"= FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree)\",\n \"* (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) *\",\n \"dot(jump(p, n), jump(q, n)) * dS # Weak boundary conditions a += s\",\n \"backend = backend.lower() if backend == \\\"scipy\\\": size = A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1],\",\n \"+= delta_1 * jump(u_hat, n=n) * q(\\\"+\\\") * dS # a += delta_1(\\\"+\\\")\",\n \"parameter beta0 = Constant(1e1) beta = beta0 / h # Symmetry term. Choose\",\n \"list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(),\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads\",\n \"* A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat = Smat.M.handle\",\n \"Constant(1) # delta_1 = Constant(1) # delta_2 = Constant(1) # delta_3 = Constant(1)\",\n \"h * h * inner(curl(u), curl(v)) * dx # L += 0.5 *\",\n \"a plot of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) petsc_mat =\",\n \"+ h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized dG\",\n \"+= delta_2 * f * div(v) * dx # Hybridization terms a +=\",\n \"mu_h(\\\"+\\\") * dS # Weakly imposed BC a += lambda_h * dot(v, n)\",\n \"Stabilizing parameter # delta_0 = Constant(1) # delta_1 = Constant(1) # delta_2 =\",\n \"n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q, n)) * dS # Edge stabilizing\",\n \"(p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a += delta_4\",\n \"20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict = { \\\"Element\\\": list(), \\\"Number of\",\n \"A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier) petsc_mat\",\n \"= div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\")\",\n \"v, q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh) h =\",\n \"p * q * ds # may decrease convergente rates (Nitsche) A =\",\n \"= h delta_0 = delta delta_1 = delta delta_2 = delta delta_3 =\",\n \"(jump(u, n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg) *\",\n \"else 'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if\",\n \"dS L = f * q * dx # Transmission condition a +=\",\n \"Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0\",\n \"q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"+= (avg(eta_p) / h_avg) * (jump(u, n) * jump(v, n)) * dS a\",\n \"= Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet\",\n \"set this bc?? # a += (beta / h) * (p- p_boundaries) *\",\n \") return result def solve_poisson_cgls(mesh, degree=1): # Function space declaration pressure_family = 'CG'\",\n \"* dx a += eta_p * div(u) * div(v) * dx # Weakly\",\n \"= np.delete(Mnp, idx, axis=1) Am = np.ma.masked_values(Mnp, 0, rtol=1e-13) # Plot the matrix\",\n \"Numerical flux trace u_hat = u + beta * (p - lambda_h) *\",\n \"return plot def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed\",\n \"dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS\",\n \"a += delta_2 * div(u) * div(v) * dx # L = delta_2\",\n \"pbar.set_description(f\\\"Processing {name} - degree = {degree} - N = {n}\\\") mesh = UnitSquareMesh(n,\",\n \"dot(v, n) * ds # ** Mesh independent ** # a += jump(u,\",\n \"u, p, lambda_h = split(solution) u, p, lambda_h = TrialFunctions(W) v, q, mu_h\",\n \"< 1e-5] return real_part_array def calculate_condition_number( A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse:\",\n \"n, q * n) * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"delta_3 * inner(curl(u), curl(v)) * dx L += delta_2 * f * div(v)\",\n \"**kwargs): \\\"\\\"\\\"Provides a plot of a mixed matrix.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1)\",\n \"A.getSize() Mnp = csr_matrix(A.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors,\",\n \"TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h = CellDiameter(mesh)\",\n \"1 # eta_p = L0 * L0 # method D in the Badia-Codina\",\n \"single scale problems.\\\"\\\"\\\" fig, ax = plt.subplots(1, 1) _A = Tensor(a_form) A =\",\n \"jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n), jump(q,\",\n \"> zero_tol] condition_number = singular_values.max() / singular_values.min() elif backend == \\\"slepc\\\": S =\",\n \"a += mu_h(\\\"+\\\") * jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\")\",\n \"output file name name = f\\\"{current_solver}\\\" # Selecting the solver and its kwargs\",\n \"L0 * L0 # method D in the Badia-Codina paper eta_u = h\",\n \"= solve_poisson_lsh(mesh, degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}')\",\n \"FunctionSpace(mesh, \\\"CG\\\", degree) # Trial and test functions u = TrialFunction(V) v =\",\n \"div(u) * div(v) * dx # a += 0.5 * h * h\",\n \"else \\\"DG\\\" V = FunctionSpace(mesh, primal_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(),\",\n \"* jump(u, n) * dS # Edge stabilizing terms # ** Badia-Codina based\",\n \"* (lambda_h - p_boundaries) * mu_h * ds F = a - L\",\n \"method is SIPG (-1) or NIPG (1) s = Constant(-1) # Classical volumetric\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_cgh( mesh, degree=1, is_multiplier_continuous=False\",\n \"Forcing function f_expression = div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # BCs u_projected = sigma_e\",\n \"= svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray()\",\n \"h = CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q))\",\n \"def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs\",\n \"_A.blocks S = A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1,\",\n \"+= 0.5 * h * h * div(u) * div(v) * dx #\",\n \"solve_poisson_dls(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family =\",\n \"p, lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n =\",\n \"= csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs)\",\n \"Constant(-1) delta_1 = Constant(-0.5) * h * h delta_2 = Constant(0.5) * h\",\n \"test functions # solution = Function(W) # u, p, lambda_h = split(solution) p,\",\n \"# delta_4 = LARGE_NUMBER / h delta_5 = delta # Numerical flux trace\",\n \"values ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) return plot def filter_real_part_in_array(array: np.ndarray, imag_threshold: float = 1e-5)\",\n \"n) * ds # Flux Least-squares as in DG a = delta_0 *\",\n \"/ h # beta = beta_0 # Numerical flux trace u = -grad(p)\",\n \"# Weakly imposed BC a += lambda_h * dot(v, n) * ds a\",\n \"Mixed classical terms a = (dot(u, v) - div(v) * p + delta_0\",\n \"not considered in the original paper # a += dot(jump(p, n), jump(q, n))\",\n \"\\\"cgls\\\": solve_poisson_cgls, # \\\"dgls\\\": solve_poisson_dgls, # \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\":\",\n \"= TrialFunction(V) q = TestFunction(V) # Mesh entities n = FacetNormal(mesh) h =\",\n \"quadrilateral=True, name=name ) # N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True)\",\n \"beta * (lambda_h - p_boundaries) * mu_h * ds F = a -\",\n \"ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] + f1_size[0] -\",\n \"* dx a += delta_1 * div(u) * div(v) * dx a +=\",\n \"ds # ** Mesh independent ** # a += jump(u, n) * jump(v,\",\n \"# These terms below are unsymmetric # a += delta_1 * jump(u_hat, n=n)\",\n \"and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs\",\n \"+= -0.5 * inner(u + grad(p), v + grad(q)) * dx # a\",\n \"Setting the output file name name = f\\\"{current_solver}\\\" # Selecting the solver and\",\n \"= VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement =\",\n \"num_converged_values = S.getConverged() singular_values_list = list() if num_converged_values > 0: for i in\",\n \"* div(u) * div(v) * dx a += delta_2 * inner(curl(u), curl(v)) *\",\n \"columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] ==\",\n \"\\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\": list(),\",\n \"Mixed classical terms a = (dot(u, v) - div(v) * p - q\",\n \"/ L0 # method B in the Badia-Codina paper # eta_u = 1\",\n \"# plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) # plot_matrix_hybrid_multiplier(result.form, trace_index=2, bcs=result.bcs,\",\n \"* dS a += beta(\\\"+\\\") * (lambda_h(\\\"+\\\") - p(\\\"+\\\")) * (mu_h(\\\"+\\\") - q(\\\"+\\\"))\",\n \"div(v) + inner(grad(p), grad(q))) # * delta_1 # * dx # ) #\",\n \"L = delta_0 * f * q * dx # Stabilizing terms a\",\n \"= assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size = petsc_mat.getSize() Mnp\",\n \"(h(\\\"+\\\") + h(\\\"-\\\")) / 2.0 # Jump stabilizing parameters based on Badia-Codina stabilized\",\n \"s = Constant(-1.0) beta = Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) #\",\n \"# Least-Squares weights delta = Constant(1.0) # delta = h delta_0 = delta\",\n \"def plot_matrix_primal_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed matrix.\\\"\\\"\\\" fig,\",\n \"= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing function f_expression = div(-grad(p_exact)) f =\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4, numel_xy=(5,\",\n \"form=a, assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation(\",\n \"shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray() # Eliminate rows and columns filled with zero\",\n \"a += delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds # # a\",\n \"str = \\\"scipy\\\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend\",\n \"A[:2, 2] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size\",\n \"= attr.ib() sparse_operator = attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric =\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1):\",\n \"np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number = singular_values.max() / singular_values.min() else: raise\",\n \"df_cond_number # Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg, # \\\"cgls\\\": solve_poisson_cgls,\",\n \"f * q * dx # Transmission condition a += jump(u_hat, n) *\",\n \"boundary conditions a += dot(v, n) * p * ds - q *\",\n \"0.0, \\\"on_boundary\\\") # Variational form a = inner(grad(u), grad(v)) * dx A =\",\n \"= 1 # Least-Squares weights delta = Constant(1.0) # delta = h delta_0\",\n \"# a += 0.5 * h * h * div(u) * div(v) *\",\n \"C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W =\",\n \"# my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap) #\",\n \"original paper # a += dot(jump(p, n), jump(q, n)) * dS # Volumetric\",\n \"degree = 1 last_degree = 1 for current_solver in solvers_options: # Setting the\",\n \"beta * p * q * ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat =\",\n \"rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Below there is\",\n \"Mnp.toarray() # Eliminate rows and columns filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)]\",\n \"(-1) or NIPG (1) s = Constant(-1) # Classical volumetric terms a =\",\n \"print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition Number: {result.condition_number}')\",\n \"but this necessary _A = Tensor(a) A = _A.blocks S = A[2, 2]\",\n \"- 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result = ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp,\",\n \"# a += delta_1(\\\"+\\\") * dot(u_hat, n) * q * ds # #\",\n \"k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M = Mnp.toarray() singular_values = svd(M,\",\n \"copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # # plot_matrix_primal_hybrid_full(result.form, result.bcs, cmap=my_cmap) # # plot_matrix_mixed_hybrid_full(result.form, result.bcs, cmap=my_cmap)\",\n \"'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous:\",\n \"{ \\\"Element\\\": list(), \\\"Number of Elements\\\": list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(),\",\n \"n) * p * ds - q * dot(u, n) * ds a\",\n \"BCs bc_multiplier = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0) beta\",\n \"0, rtol=1e-13) # Plot the matrix plot = ax.matshow(Am, **kwargs) # Remove axis\",\n \"q * ds # may decrease convergente rates a += eta_u_bc * delta_4\",\n \"* (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a += delta_4 * (p -\",\n \"+ grad(q)) * dx a += delta_2 * div(u) * div(v) * dx\",\n \"a += eta_u * inner(u + grad(p), grad(q) - v) * dx a\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1): #\",\n \"+= delta_3 * inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat\",\n \"- lambda_h(\\\"+\\\")) * (q(\\\"+\\\") - mu_h(\\\"+\\\")) * dS # a += delta_4 *\",\n \"Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter beta_0 = Constant(1.0e0)\",\n \"\\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact,\",\n \"= A[1, 1] - A[1, :1] * A[:1, :1].inv * A[:1, 1] Smat\",\n \"= ConditionNumberResult( form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def\",\n \"= 1 # eta_p = L0 * L0 # method D in the\",\n \"/ h) * dot(p * n, q * n) * ds # may\",\n \"n)) * dS # a += p * q * ds A =\",\n \"jump(q, n)) * dS # ** Mesh independent (original) # a += jump(u,\",\n \"= \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = V * T\",\n \"= \\\"scipy\\\", use_sparse: bool = False, zero_tol: float = 1e-5 ): backend =\",\n \"dot(grad(p), grad(q)) * dx L = f * q * dx # Hybridization\",\n \"a += lambda_h(\\\"+\\\") * dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\")\",\n \"jump(u_hat, n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\")\",\n \"terms # a += 0.5 * h * h * div(u) * div(v)\",\n \"degree=1) # print(f'Is symmetric? {result.is_operator_symmetric}') # print(f'nnz: {result.nnz}') # print(f'DoFs: {result.number_of_dofs}') # print(f'Condition\",\n \"return result def solve_poisson_sipg(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1,\",\n \"condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_cgls(mesh, degree=1): # Function\",\n \"if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else:\",\n \"* p + q * div(u)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\")\",\n \"BC from hybridization # a += mu_h * (lambda_h - p_boundaries) * ds\",\n \"for current_solver in solvers_options: # Setting the output file name name = f\\\"{current_solver}\\\"\",\n \"filled with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0,\",\n \"p) * dx + lambda_h(\\\"+\\\") * jump(v, n) * dS a += -dot(u,\",\n \"= Function(V).interpolate(p_exact) exact_solution.rename(\\\"Exact pressure\\\", \\\"label\\\") sigma_e = Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # Forcing\",\n \"f * div(v) * dx # ** Badia-Codina based a += eta_u *\",\n \"dot(v, n) * p * ds - q * dot(u, n) * ds\",\n \"+= delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS # a += delta_1\",\n \"a += dot(jump(p, n), jump(q, n)) * dS # a += p *\",\n \"+ beta * (p - lambda_h) * n # HDG classical form a\",\n \"TrialFunctions(W) v, q = TestFunctions(W) # Mesh entities x, y = SpatialCoordinate(mesh) #\",\n \"penalizing term beta_0 = Constant(1.0) beta = beta_0 / h # Mixed classical\",\n \"= S.getValue(i) singular_values_list.append(singular_value) else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list)\",\n \"ds A = assemble(a, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-12) size =\",\n \"assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_ls(mesh, degree=1): #\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dgls(mesh, degree=1): # Function space\",\n \"DirichletBC(W[0], sigma_e, \\\"on_boundary\\\", method=\\\"geometric\\\") # Average cell size and mesh dependent stabilization h_avg\",\n \"dx a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS a +=\",\n \"number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ):\",\n \"# Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a =\",\n \"A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat = Smat.M.handle size = petsc_mat.getSize() Mnp\",\n \"decrease convergente rates # ** Classical Nitsche # a += beta * p\",\n \"U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement\",\n \"div(-grad(p_exact)) f = Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") #\",\n \"# matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) # my_cmap.set_bad(color=\\\"lightgray\\\") # #\",\n \"= attr.ib() number_of_dofs = attr.ib() nnz = attr.ib() is_operator_symmetric = attr.ib() bcs =\",\n \"* dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8)\",\n \"f * div(v) * dx # a += -0.5 * inner(u + grad(p),\",\n \"from slepc4py import SLEPc import pandas as pd from tqdm import tqdm import\",\n \"form=a, assembled_form=A, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric ) return result def solve_poisson_dls(mesh, degree=1):\",\n \"n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p, n), jump(q, n))\",\n \"bcs=bcs ) return result def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space\",\n \"* jump(v, n)) * dS a += eta_p * avg(delta_4) * dot(jump(p, n),\",\n \"min_degree=1, max_degree=4, numel_xy=(5, 10, 15, 20, 25), quadrilateral=True, name=\\\"\\\", **kwargs ): results_dict =\",\n \"inner(u + grad(p), grad(q) - v) * dx # a += 0.5 *\",\n \"eta_u_bc = h / L0 # method B in the Badia-Codina paper eta_u_bc\",\n \"LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else:\",\n \"a += 0.5 * inner(u + grad(p), grad(q) - v) * dx #\",\n \"assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() Mnp = Mnp.toarray()\",\n \"* dx a += 0.5 * div(u) * div(v) * dx a +=\",\n \"n) * jump(v, n)) * dS a += (avg(eta_u) / h_avg) * dot(jump(p,\",\n \"dS # Volumetric stabilizing terms # a += 0.5 * h * h\",\n \"- dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\") - dot(v_hat, n)(\\\"+\\\")) * dS # a\",\n \"dx L = delta_0 * f * q * dx # Stabilizing terms\",\n \"= beta_0 # Numerical flux trace u = -grad(p) u_hat = u +\",\n \":idx] * A[:idx, :idx].inv * A[:idx, idx] Smat = assemble(S, bcs=bcs) petsc_mat =\",\n \"= {degree} - N = {n}\\\") mesh = UnitSquareMesh(n, n, quadrilateral=quadrilateral) result =\",\n \"* ds # ) # maybe this is not a good way to\",\n \"delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms a\",\n \"L0 # method B in the Badia-Codina paper # Mixed classical terms a\",\n \"= Function(V).interpolate(f_expression) # Dirichlet BCs bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter\",\n \"L0 # method B in the Badia-Codina paper eta_u_bc = 1 # Least-Squares\",\n \"mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh,\",\n \"Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = V * T # Trial\",\n \"use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" pressure_family = 'DQ' if use_quads else 'DG' trace_family\",\n \"return result def solve_poisson_dvms(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"inner(u + grad(p), v + grad(q)) * dx a += delta_2 * div(u)\",\n \"solvers_options: # Setting the output file name name = f\\\"{current_solver}\\\" # Selecting the\",\n \"n)) * dS # Volumetric stabilizing terms # a += 0.5 * h\",\n \"# Function space declaration pressure_family = 'CG' velocity_family = 'CG' U = VectorFunctionSpace(mesh,\",\n \"dot(v, n) * ds # # L = delta_1 * p_exact * dot(v,\",\n \"sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bcs ) return result def hp_refinement_cond_number_calculation( solver, min_degree=1, max_degree=4,\",\n \"0.5 * h * h * f * div(v) * dx a +=\",\n \"= petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0]\",\n \"beta * (p - lambda_h) * n # HDG classical form a =\",\n \"FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family =\",\n \"* div(u)) * dx # Stabilizing terms a += 0.5 * inner(u +\",\n \"A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv * A[:idx, idx] Smat =\",\n \"terms a += jump(v, n) * avg(p) * dS - avg(q) * jump(u,\",\n \"else: raise RuntimeError(\\\"SLEPc SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values\",\n \"SVD has not converged.\\\") singular_values = np.array(singular_values_list) singular_values = singular_values[singular_values > zero_tol] condition_number\",\n \"# ** Classical Nitsche # a += beta * p * q *\",\n \"a - L a_form = lhs(F) _A = Tensor(a_form) A = _A.blocks S\",\n \"assembled_form=Smat, condition_number=condition_number, sparse_operator=Mnp, number_of_dofs=number_of_dofs, nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh,\",\n \"trace_family = \\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = V *\",\n \"curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle is_symmetric =\",\n \"p_boundaries = Constant(0.0) bc_multiplier = DirichletBC(W.sub(1), p_exact, \\\"on_boundary\\\") # Hybridization parameter s =\",\n \"on ASGS Badia-Codina (2010), it is not a classical Nitsche's method a +=\",\n \"TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0, \\\"on_boundary\\\") # Variational form a\",\n \"# # a += delta_1 * dot(u, n) * q * ds #\",\n \"# a += beta * p * q * ds # may decrease\",\n \"# a += -0.5 * inner(u + grad(p), v + grad(q)) * dx\",\n \"= Function(U, name='Exact velocity') sigma_e.project(-grad(p_exact)) # BCs bcs = DirichletBC(W.sub(2), p_exact, \\\"on_boundary\\\") #\",\n \"Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number\",\n \"# \\\"sdhm\\\": solve_poisson_sdhm, # \\\"ls\\\": solve_poisson_ls, # \\\"dls\\\": solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\":\",\n \"= Constant(32.0) h = CellDiameter(mesh) h_avg = avg(h) # Classical term a =\",\n \"= str(mesh.ufl_cell()) == \\\"quadrilateral\\\" if use_quads: hdiv_family = 'RTCF' pressure_family = 'DQ' else:\",\n \"it is not a classical Nitsche's method a += (eta_p / h) *\",\n \"if num_converged_values > 0: for i in range(num_converged_values): singular_value = S.getValue(i) singular_values_list.append(singular_value) else:\",\n \"= assembled_form.M.handle f0_size = assembled_form.M[0, 0].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat, num_of_factors) result\",\n \"u = -grad(p) u_hat = u + beta * (p - lambda_h) *\",\n \"* v * ds # How to set this bc?? # a +=\",\n \"= singular_values.max() / singular_values.min() else: raise NotImplementedError(\\\"The required method for condition number estimation\",\n \"a += -dot(vel_projected, n) * v * ds # How to set this\",\n \"name df_cond_number.to_csv(path_to_save_results) return df_cond_number # Solver options solvers_options = { # \\\"cg\\\": solve_poisson_cg,\",\n \"D in the Badia-Codina paper # eta_u = h_avg / L0 # method\",\n \"filter real part in a numpy array. :param array: Array with real and\",\n \"* inner(curl(u), curl(v)) * dx A = assemble(a, bcs=bcs, mat_type=\\\"aij\\\") petsc_mat = A.M.handle\",\n \"* dx # Stabilizing terms a += 0.5 * inner(u + grad(p), grad(q)\",\n \"+= delta_2 * inner(curl(u), curl(v)) * dx # Edge stabilizing terms # **\",\n \"convergente rates a += eta_u_bc * delta_4 * dot(u, n) * dot(v, n)\",\n \"return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of a full hybrid-mixed\",\n \"A.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz\",\n \"the Badia-Codina paper eta_u = 1 # eta_u_bc = h / L0 #\",\n \"for n in numel_xy: pbar.set_description(f\\\"Processing {name} - degree = {degree} - N =\",\n \"* dot(v, n) * ds # ** Mesh independent ** # a +=\",\n \"Badia-Codina paper eta_u_bc = 1 # Least-Squares weights delta = Constant(1.0) # delta\",\n \"this necessary? L += s * dot(grad(q), n) * p_boundaries * ds F\",\n \"trace_family = \\\"HDiv Trace\\\" V = FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement =\",\n \"): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\"\",\n \"# N = 5 # mesh = UnitSquareMesh(N, N, quadrilateral=True) # result =\",\n \"assembled_form.M[0, 0].handle.getSize() f1_size = assembled_form.M[1, 1].handle.getSize() size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size)\",\n \"T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W = U\",\n \"+= beta * (lambda_h - p_boundaries) * mu_h * ds F = a\",\n \"* div(v) * dx # L = delta_2 * f * div(v) *\",\n \"dx # Irrotational least-squares a += delta_3 * inner(curl(u), curl(v)) * dx #\",\n \"n=n) * dS a += delta_4(\\\"+\\\") * (p(\\\"+\\\") - lambda_h(\\\"+\\\")) * (q(\\\"+\\\") -\",\n \"h # Least-squares terms a = delta_0 * inner(u + grad(p), v +\",\n \"h * f * div(v) * dx a += 0.5 * div(u) *\",\n \"beta = beta0 / h # Symmetry term. Choose if the method is\",\n \"delta_4 * dot(u, n) * dot(v, n) * ds # ** Mesh independent\",\n \"DG a = delta_0 * inner(u + grad(p), v + grad(q)) * dx\",\n \"singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\" ) else: M =\",\n \"* avg(delta_4) * dot(jump(p, n), jump(q, n)) * dS a += eta_u_bc *\",\n \"* dot(v, n)(\\\"+\\\") * dS + mu_h(\\\"+\\\") * dot(u, n)(\\\"+\\\") * dS a\",\n \"n # HDG classical form a = (dot(u, v) - div(v) * p)\",\n \"n)) * dS # ** Mesh independent terms # a += jump(u, n)\",\n \"ax.tick_params(length=0) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.axhline(y=f0_size[0] - 0.5, color=\\\"k\\\") ax.axvline(x=f0_size[0] - 0.5, color=\\\"k\\\") ax.axhline(y=f0_size[0] +\",\n \"# may decrease convergente rates # ** Classical Nitsche # a += beta\",\n \"n=n) * dS a += delta_1 * lambda_h * dot(v, n) * ds\",\n \"1 # eta_u_bc = h / L0 # method B in the Badia-Codina\",\n \"* ds a += (eta_u / h) * dot(p * n, q *\",\n \"name='Exact velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed\",\n \"def solve_poisson_hdg( mesh, degree=1, is_multiplier_continuous=False ): # Function space declaration use_quads = str(mesh.ufl_cell())\",\n \"nnz=nnz, is_operator_symmetric=is_symmetric, bcs=bc_multiplier ) return result def solve_poisson_lsh( mesh, degree=1, is_multiplier_continuous=False ): #\",\n \"# a += delta_5 * (dot(u, n)(\\\"+\\\") - dot(u_hat, n)(\\\"+\\\")) * (dot(v, n)(\\\"+\\\")\",\n \"hdiv_family = 'RTCF' pressure_family = 'DQ' else: hdiv_family = 'RT' pressure_family = 'DG'\",\n \"beta_0 = Constant(1.0e0) beta = beta_0 / h # beta = beta_0 #\",\n \"a += jump(v, n) * avg(p) * dS - avg(q) * jump(u, n)\",\n \"return result def solve_poisson_mixed_RT(mesh, degree=1): # Function space declaration use_quads = str(mesh.ufl_cell()) ==\",\n \"imposed BC a += (p_boundaries * dot(v, n) + mu_h * (dot(u, n)\",\n \"with zero entries Mnp = Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0))\",\n \"grad(q) - v) * dx a += eta_p * div(u) * div(v) *\",\n \"* ds # may decrease convergente rates # ** Classical Nitsche # a\",\n \"DG terms a += jump(v, n) * avg(p) * dS - avg(q) *\",\n \"S.setOperator(A) S.setType(SLEPc.SVD.Type.LAPACK) S.setDimensions(nsv=num_of_factors) S.setTolerances(max_it=5000) S.setWhichSingularTriplets(SLEPc.SVD.Which.LARGEST) S.solve() num_converged_values = S.getConverged() singular_values_list = list() if\",\n \"* ds # a += delta_1(\\\"+\\\") * lambda_h(\\\"+\\\") * jump(v, n=n) * dS\",\n \"dot(p * n, grad(q)) * ds - dot(grad(p), q * n) * ds\",\n \"velocity') sigma_e.project(-grad(p_exact)) # Dirichlet BCs bcs = DirichletBC(W[0], sigma_e, \\\"on_boundary\\\") # Mixed classical\",\n \"conditions a += s * dot(p * n, grad(q)) * ds - dot(grad(p),\",\n \"h delta_4 = 1 / h # Least-squares terms a = delta_0 *\",\n \"FunctionSpace(mesh, pressure_family, degree) if is_multiplier_continuous: LagrangeElement = FiniteElement(\\\"Lagrange\\\", mesh.ufl_cell(), degree) C0TraceElement = LagrangeElement[\\\"facet\\\"]\",\n \"shape=size) Mnp.eliminate_zeros() nnz = Mnp.nnz number_of_dofs = Mnp.shape[0] num_of_factors = int(number_of_dofs) - 1\",\n \"assemble(S, bcs=bcs) petsc_mat = Smat.M.handle is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp =\",\n \"* dot(v, n) + mu_h * (dot(u, n) - dot(u_projected, n))) * ds\",\n \"C0TraceElement = LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: trace_family = \\\"HDiv Trace\\\" T\",\n \"UnitSquareMesh(n, n, quadrilateral=quadrilateral) result = solver(mesh, degree=degree) current_cell_size = mesh.cell_sizes.dat.data_ro.min() if not quadrilateral\",\n \"- A[2, :2] * A[:2, :2].inv * A[:2, 2] Smat = assemble(S, bcs=bc_multiplier)\",\n \"maybe this is not a good way to impose BC, but this necessary\",\n \"Tensor(a_form) A = _A.blocks S = A[2, 2] - A[2, :2] * A[:2,\",\n \"a = dot(grad(p), grad(q)) * dx L = f * q * dx\",\n \"A, num_of_factors, backend: str = \\\"scipy\\\", use_sparse: bool = False, zero_tol: float =\",\n \") else: M = Mnp.toarray() singular_values = svd(M, compute_uv=False, check_finite=False) singular_values = singular_values[singular_values\",\n \"Mnp[~(Mnp==0).all(1)] idx = np.argwhere(np.all(Mnp[..., :] == 0, axis=0)) Mnp = np.delete(Mnp, idx, axis=1)\",\n \"# Trial and test functions u = TrialFunction(V) v = TestFunction(V) # Dirichlet\",\n \"\\\"HDiv Trace\\\" T = FunctionSpace(mesh, trace_family, degree) W = V * T #\",\n \"based a += -eta_u * inner(u + grad(p), v + grad(q)) * dx\",\n \"# plot_matrix(result.assembled_form, cmap=my_cmap) # # plot_matrix_mixed(result.assembled_form, cmap=my_cmap) # plt.tight_layout() # plt.savefig(\\\"sparse_pattern.png\\\") # plt.show()\",\n \"# method D in the Badia-Codina paper # eta_u = h_avg / L0\",\n \"parameters delta_1 = Constant(1) delta_2 = Constant(1) delta_3 = Constant(1) # Least-squares terms\",\n \"'DG' U = VectorFunctionSpace(mesh, velocity_family, degree) V = FunctionSpace(mesh, pressure_family, degree) W =\",\n \"= Mnp.nnz number_of_dofs = W.dim() num_of_factors = int(number_of_dofs) - 1 condition_number = calculate_condition_number(petsc_mat,\",\n \"* q * ds # may decrease convergente rates # ** The terms\",\n \"- 0.5, color=\\\"k\\\") return plot def plot_matrix_mixed_hybrid_full(a_form, bcs=[], **kwargs): \\\"\\\"\\\"Provides a plot of\",\n \"-0.5 * inner(u + grad(p), v + grad(q)) * dx # a +=\",\n \"Edge stabilizing terms # ** Badia-Codina based (better results) ** a += eta_u\",\n \"* dx a += eta_p * div(u) * div(v) * dx a +=\",\n \"u = TrialFunction(V) v = TestFunction(V) # Dirichlet BCs bcs = DirichletBC(V, 0.0,\",\n \"* ds a += beta * p * q * ds A =\",\n \"import numpy as np import matplotlib.pyplot as plt import matplotlib from scipy.linalg import\",\n \"shape=size) Mnp.eliminate_zeros() if use_sparse: singular_values = svds( A=Mnp, k=num_of_factors, which=\\\"LM\\\", maxiter=5000, return_singular_vectors=False, solver=\\\"lobpcg\\\"\",\n \"= Function(V).interpolate(f_expression) # Edge stabilizing parameter beta0 = Constant(1e1) beta = beta0 /\",\n \"terms a += s * dot(jump(p, n), avg(grad(q))) * dS - dot(avg(grad(p)), jump(q,\",\n \"LagrangeElement[\\\"facet\\\"] T = FunctionSpace(mesh, C0TraceElement) else: T = FunctionSpace(mesh, trace_family, degree) W =\",\n \"lambda_h = TrialFunctions(W) q, mu_h = TestFunctions(W) # Mesh entities n = FacetNormal(mesh)\",\n \"is_multiplier_continuous=True ): # Function space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family =\",\n \"Badia-Codina paper eta_u = h / L0 # method B in the Badia-Codina\",\n \"idx = trace_index S = A[idx, idx] - A[idx, :idx] * A[:idx, :idx].inv\",\n \"p_boundaries) * ds # a += mu_h * lambda_h * ds # ###\",\n \"stabilizing terms a += beta(\\\"+\\\") * dot(jump(p, n), jump(q, n)) * dS #\",\n \"space declaration use_quads = str(mesh.ufl_cell()) == \\\"quadrilateral\\\" primal_family = \\\"DQ\\\" if use_quads else\",\n \"list(), \\\"Degree\\\": list(), \\\"Symmetric\\\": list(), \\\"nnz\\\": list(), \\\"dofs\\\": list(), \\\"h\\\": list(), \\\"Condition Number\\\":\",\n \"solve_poisson_dls, \\\"lsh\\\": solve_poisson_lsh, # \\\"vms\\\": solve_poisson_vms, # \\\"dvms\\\": solve_poisson_dvms, # \\\"mixed_RT\\\": solve_poisson_mixed_RT, #\",\n \"below are based on ASGS Badia-Codina (2010), it is not a classical Nitsche's\",\n \"v) - div(v) * p + q * div(u)) * dx A =\",\n \"is_symmetric = petsc_mat.isSymmetric(tol=1e-8) size = petsc_mat.getSize() Mnp = csr_matrix(petsc_mat.getValuesCSR()[::-1], shape=size) Mnp.eliminate_zeros() nnz =\",\n \"the resulting matrix # matplotlib.use('TkAgg') # import copy # my_cmap = copy.copy(plt.cm.get_cmap(\\\"winter\\\")) #\"\n]","isConversational":false}}},{"rowIdx":2199,"cells":{"ngram":{"kind":"list like","value":["Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) #","f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined:","ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm","calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L","if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\" Return","motion, box counting, Higuchi and so on. Sevcik method: fast calculation and robust","so on. Sevcik method: fast calculation and robust analysis of noise Higuchi: closer","= f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if","theoretical value than box counting The higuchi method is used here because it","- data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility","higuchi method is used here because it is easier to implement Parameters ----------","data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff","8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]:","f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f","Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In","f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f","the absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std #","= np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2]))","n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \"\"\"","Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f","---------- data array data, for DEAP dataset, It's shape may be (n_trials, n_channels,","axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L =","= np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd =","combined=True): \"\"\"Solving the feature of hoc. Hoc is a high order zero crossing","1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the","Higuchi and so on. Sevcik method: fast calculation and robust analysis of noise","std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f",": array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points)","the mean of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[...,","32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc =","/ np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f","absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]),","is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In","l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32,","In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power power = np.mean(data**2,","= np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1)","x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count =","Sevick method is used here because it is easier to implement Parameters ----------","return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved, which is","def calc_L_average(X, k): \"\"\" Return <L(k)> as the average value over k sets","return Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as the average value over","over k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m),","(40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40,","(n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is similar to","count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f =","n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1))","the mean of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd","np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0],","values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1)","axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L","combined: f = f return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series =","Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st,","hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32,","Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) #","points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape","32, 5) \"\"\" nzc = [] for i in range(k): curr_diff = np.diff(data,","/ std # the mean of the absolute values of 2nd difference mean","= x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1),","= f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc.","closer to the theoretical value than box counting The Sevick method is used","axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean of the","n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape,","((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\"","= np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc,","diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1)","of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] -","this feature can be used to judge the electrooculogram and EEG.The calculation methods","(n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064),","Higuchi: closer to the theoretical value than box counting The Sevick method is","f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined:","Solved feature, It's shape is similar to the shape of your input data.","Out[14]: (40, 32, 7) \"\"\" # Power power = np.mean(data**2, axis=-1) # Mean","fractal Brownian motion, box counting, Higuchi and so on. Sevcik method: fast calculation","k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc is a high order zero","Return ---------- f: Solved feature, It's shape is similar to the shape of","n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40,","The higuchi method is used here because it is easier to implement Parameters","= np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of Normalized","- data[..., :-2]), axis=-1) # the mean of the absolute values of Normalized","---------- data : array data, for DEAP dataset, It's shape may be (n_trials,","n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count","of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std,","-*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import","box counting, Higuchi and so on. Sevcik method: fast calculation and robust analysis","the theoretical value than box counting The Sevick method is used here because","ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f","points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy =","calc_L_average(X, k): \"\"\" Return <L(k)> as the average value over k sets of","np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def","mean of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:]","20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True):","# the mean of the absolute values of 2nd difference mean diff_2nd =","ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd","Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average =","values of Normalized 1st difference normal_diff_1st = diff_1st / std # the mean","Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std,","In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc = [] for","used to describe the shape information of EEG time series data. It seems","range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t)","data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) k :","is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32,","crossing quantity. Parameters ---------- data : array data, for DEAP dataset, It's shape","np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t)","because it is easier to implement Parameters ---------- Parameters ---------- data array data,","n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape,","of EEG time series data. It seems that this feature can be used","f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data,","def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved, which is used to","absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power,","Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1)","Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters","used to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian","1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension","D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ],","load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d,","[5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40,",":_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np","k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1,","mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of","+ np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2]))","= (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm)","easier to implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset,","axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if","Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference.","= np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def","n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1))","1) \"\"\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x /","], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f =","array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) k","normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving","axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f","the theoretical value than box counting The higuchi method is used here because","is used to describe the shape information of EEG time series data. It","np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] =","to the theoretical value than box counting The Sevick method is used here","7) \"\"\" # Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data,","np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f =","to the shape of your input data. e.g. for input.shape is (n_trials, n_channels,","Example ---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape","f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40,","calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1)))","np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] #","[8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1] x = np.arange(1,","features， include activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset,","x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data,","Out[16]: (40, 32, 3) \"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis]","---------- f: Solved feature, It's shape is similar to the shape of your","combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd","l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In","data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data,","difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP","combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array data,","f = f return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda","\"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1)","Hjorth features， include activity, mobility, complexity Parameters ---------- data array data, for DEAP","of noise Higuchi: closer to the theoretical value than box counting The higuchi","theoretical value than box counting The Sevick method is used here because it","/ np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_","= np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) /","1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power power =","= np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1)","(40, 32, 1) \"\"\" points = data.shape[-1] x = np.arange(1, points+1) x_ =","print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff /","complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True):","f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc is a","higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc is a high order","quantity. Parameters ---------- data : array data, for DEAP dataset, It's shape may","similar to the shape of your input data. e.g. for input.shape is (n_trials,","[8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1) \"\"\"","(N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) /","In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1] x =","include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd","[14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power power = np.mean(data**2, axis=-1)","method is used here because it is easier to implement Parameters ---------- Parameters","= f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which","fast calculation and robust analysis of noise Higuchi: closer to the theoretical value","return f def calc_L(X, k, m): \"\"\" Return Lm(k) as the length of","difference normal_diff_1st = diff_1st / std # the mean of the absolute values","it is easier to implement Parameters ---------- Parameters ---------- data array data, for","used here because it is easier to implement Parameters ---------- Parameters ---------- data","data. It seems that this feature can be used to judge the electrooculogram","f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which is","miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny)","difference. Parameters ---------- data array data, for DEAP dataset, It's shape may be","2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st,","# Standard Deviation std = np.std(data, axis=-1) # the mean of the absolute","= np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D,","1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data","5) \"\"\" nzc = [] for i in range(k): curr_diff = np.diff(data, n=i)","average value over k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X,","np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average","than box counting The higuchi method is used here because it is easier","to describe the shape information of EEG time series data. It seems that","axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility =","return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which is used","axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if","calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so on.","array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return","power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation","the mean of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1,","\"\"\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x)",").shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1,","electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi","np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff =","std = np.std(data, axis=-1) # the mean of the absolute values of 1st","8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power","n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d,","Higuchi: closer to the theoretical value than box counting The higuchi method is","# Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) #","32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k =","= f return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k:","Standard Deviation std = np.std(data, axis=-1) # the mean of the absolute values","np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[...,","activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's shape","nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def","[15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]:","= curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t,","np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k)","# Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f =","shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape","if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features，","the mean of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st","Deviation std = np.std(data, axis=-1) # the mean of the absolute values of","np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] -","f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path,",":-2]), axis=-1) # the mean of the absolute values of Normalized 2nd difference","2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) #","EEG time series data. It seems that this feature can be used to","(n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064),","shape of your input data. e.g. for input.shape is (n_trials, n_channels, points), the","n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape,","methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik","values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean","1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for","and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and","axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1),","combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature","diff_1st / std # the mean of the absolute values of 2nd difference","= calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[...,","varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity","f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40,","# print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff","hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[...,","curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k)","the curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) /","axis=-1) # the mean of the absolute values of Normalized 1st difference normal_diff_1st","= np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape)","/ k return Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as the average","* (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X,","input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ----------","Lm(k) as the length of the curve. \"\"\" N = X.shape[-1] n =","np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2]))","In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40,","feature, It's shape is similar to the shape of your input data. e.g.","(40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1]","\"\"\" # Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1)","= diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2))",": int, optional Order, by default 10 Return ---------- nzc: Solved feature, It's","\"\"\"Fractal dimension feature is solved, which is used to describe the shape information","((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\"","data : array data, for DEAP dataset, It's shape may be (n_trials, n_channels,","and so on. Sevcik method: fast calculation and robust analysis of noise Higuchi:","= f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\" Return Lm(k) as the","length of the curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm =","= np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp =","[7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]:","the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]:","f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc","than box counting The Sevick method is used here because it is easier","f = f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1)","Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ----------","for DEAP dataset, It's shape may be (n_trials, n_channels, points) k : int,","zero crossing quantity. Parameters ---------- data : array data, for DEAP dataset, It's","activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f","may be (n_trials, n_channels, points) k : int, optional Order, by default 10","= np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff)","np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny)","normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if","is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0)","of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std","calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]):","[6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc = [] for i","[7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined:","value than box counting The Sevick method is used here because it is","[] for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >=","m): \"\"\" Return Lm(k) as the length of the curve. \"\"\" N =","# the mean of the absolute values of Normalized 1st difference normal_diff_1st =","Parameters ---------- data : array data, for DEAP dataset, It's shape may be","np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) /","(40, 32, 5) \"\"\" nzc = [] for i in range(k): curr_diff =","L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved, which is used","the length of the curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm","It's shape is similar to the shape of your input data. e.g. for","in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t =","axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension","(points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k,","Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3)","be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is similar",":wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True): \"\"\"Statistical features，","can be used to judge the electrooculogram and EEG.The calculation methods include Sevcik,","as the length of the curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64)","axis=-1) # the mean of the absolute values of 1st differece mean diff_1st","= np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f","varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility,","value than box counting The higuchi method is used here because it is","In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape","@Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True): \"\"\"Statistical","n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity","\"\"\" Return Lm(k) as the length of the curve. \"\"\" N = X.shape[-1]","= np.std(data, axis=-1) # the mean of the absolute values of 1st differece","axis=-1)), axis=-1) # the mean of the absolute values of Normalized 1st difference","1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1] x","the feature of hoc. Hoc is a high order zero crossing quantity. Parameters","k : int, optional Order, by default 10 Return ---------- nzc: Solved feature,","the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) #","return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k),","calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L =","combined=True): \"\"\"Fractal dimension feature is solved, which is used to describe the shape","return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters","series data. It seems that this feature can be used to judge the","32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data","def calc_L(X, k, m): \"\"\" Return Lm(k) as the length of the curve.","k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved, which","# print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity =","print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2,","@Version :1.0 ''' import numpy as np def statistics(data, combined=True): \"\"\"Statistical features， include","data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1)","noise Higuchi: closer to the theoretical value than box counting The higuchi method","np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm","(40, 32, 7) \"\"\" # Power power = np.mean(data**2, axis=-1) # Mean ave","l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32,","data array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points)","/ std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f =","np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1)","Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1] x = np.arange(1, points+1) x_","value over k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k,","n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40,","d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40,","EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so","k_max, combined=True): \"\"\"Fractal dimension feature is solved, which is used to describe the","is a high order zero crossing quantity. Parameters ---------- data : array data,","Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as the average value over k","curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t","combined: f = f return ], ind[2]] = - D f = np.expand_dims(fd,","8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data =","\"\"\" nzc = [] for i in range(k): curr_diff = np.diff(data, n=i) x_t","of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) #","dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature,","(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]:","\"\"\" Return <L(k)> as the average value over k sets of Lm(k). \"\"\"","int, optional Order, by default 10 Return ---------- nzc: Solved feature, It's shape","Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik method: fast","mean of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)),","32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points","complexity Parameters ---------- data array data, for DEAP dataset, It's shape may be","np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 +","may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is","analysis of noise Higuchi: closer to the theoretical value than box counting The","hoc. Hoc is a high order zero crossing quantity. Parameters ---------- data :","import numpy as np def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std,","calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data,","f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved,","(40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k","f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters ----------","is solved, which is used to describe the shape information of EEG time","2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the","(n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In","np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return","np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f","if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the","(maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 +","Normalized 1st difference normal_diff_1st = diff_1st / std # the mean of the","def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st","normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd),","---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]:","normal_diff_1st = diff_1st / std # the mean of the absolute values of","---------- nzc: Solved feature, It's shape is similar to the shape of your","= diff_1st / std # the mean of the absolute values of 2nd","k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5)","your input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is","the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]:","= np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L =","to implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's","n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40,","hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array","(n_trials, n_channels, points) k : int, optional Order, by default 10 Return ----------","= np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t =","n_channels, points) Return ---------- f: Solved feature, It's shape is similar to the","order zero crossing quantity. Parameters ---------- data : array data, for DEAP dataset,","higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved, which is used to describe","data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ----------","n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1))","information of EEG time series data. It seems that this feature can be","combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include","---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]:","<reponame>Wlgls/pyDEAP<gh_stars>0 # -*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author","32, 7) \"\"\" # Power power = np.mean(data**2, axis=-1) # Mean ave =","n_channels, points) k : int, optional Order, by default 10 Return ---------- nzc:","method: fast calculation and robust analysis of noise Higuchi: closer to the theoretical","1st difference normal_diff_1st = diff_1st / std # the mean of the absolute","ind[1if combined: f = f return ], ind[2]] = - D f =","= np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return","Hoc is a high order zero crossing quantity. Parameters ---------- data : array","= np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True):","statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power power = np.mean(data**2, axis=-1) #","(n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return","In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1)","Brownian motion, box counting, Higuchi and so on. Sevcik method: fast calculation and","for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f:","(40, 32, 3) \"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st","of the curve. \"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1)","= np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f =","2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset,","1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1,","nzc = [] for i in range(k): curr_diff = np.diff(data, n=i) x_t =","k return Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as the average value","= np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean","\"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1,","the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting,","diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1)","mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values","= f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility,","= np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) #","= np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std","std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power,","np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy","np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] = -","l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32,","absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std # the","f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\" Return Lm(k) as","1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1)","here because it is easier to implement Parameters ---------- Parameters ---------- data array","axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape)","/ (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1","8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points =","of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average","data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape)","normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data,","diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[...,","f: Solved feature, It's shape is similar to the shape of your input","axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 *","diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def","<L(k)> as the average value over k sets of Lm(k). \"\"\" calc_L_series =","optional Order, by default 10 Return ---------- nzc: Solved feature, It's shape is",":1.0 ''' import numpy as np def statistics(data, combined=True): \"\"\"Statistical features， include Power,","N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss =","maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_,","if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature","std # the mean of the absolute values of 2nd difference mean diff_2nd","# Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st,","---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]:","Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st,","i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t","Parameters ---------- data array data, for DEAP dataset, It's shape may be (n_trials,","d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f","axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \"\"\" Return","0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f","# print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m):","to the theoretical value than box counting The higuchi method is used here","k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max,","It seems that this feature can be used to judge the electrooculogram and","difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset, It's","1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32,","the shape of your input data. e.g. for input.shape is (n_trials, n_channels, points),","ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return","on. Sevcik method: fast calculation and robust analysis of noise Higuchi: closer to","return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc is","_= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]]","combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is","np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) #","= (ss*norm) / k return Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as","Return ---------- nzc: Solved feature, It's shape is similar to the shape of","shape information of EEG time series data. It seems that this feature can","axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity","The Sevick method is used here because it is easier to implement Parameters","---------- Parameters ---------- data array data, for DEAP dataset, It's shape may be","\"\"\"Solving the feature of hoc. Hoc is a high order zero crossing quantity.","be used to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal","the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[...,","y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1)","be (n_trials, n_channels, points) k : int, optional Order, by default 10 Return","np def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st differece, Normalized","d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10)","sevcik_fd(d).shape Out[8]: (40, 32, 1) \"\"\" points = data.shape[-1] x = np.arange(1, points+1)","X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k],","for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k),","Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In","32, 3) \"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st =","It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's","2nd difference. Parameters ---------- data array data, for DEAP dataset, It's shape may","feature is solved, which is used to describe the shape information of EEG","= - D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return","[13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]:","(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]:","sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which is used to describe the","axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2),","that this feature can be used to judge the electrooculogram and EEG.The calculation","= np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) /","Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset, It's shape","/ (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k","\"\"\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference,","counting The higuchi method is used here because it is easier to implement","np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X,","which is used to describe the shape information of EEG time series data.","np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ =","(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]:","= np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity),","seems that this feature can be used to judge the electrooculogram and EEG.The","n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)),","= X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[...,","points) Return ---------- f: Solved feature, It's shape is similar to the shape","L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp","feature can be used to judge the electrooculogram and EEG.The calculation methods include","norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm =","difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean","np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean of","(data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f =","as the average value over k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda","higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series","= load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]:","mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's shape may","ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the","Return <L(k)> as the average value over k sets of Lm(k). \"\"\" calc_L_series","axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1],","as np def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st differece,","np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return","def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of hoc. Hoc is a high","numpy as np def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st","Return Lm(k) as the length of the curve. \"\"\" N = X.shape[-1] n","to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion,","1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal","/ mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2]))","describe the shape information of EEG time series data. It seems that this","mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return","np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is solved,","In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data = np.array(data) ave =","k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd =","2:] - data[..., :-2]), axis=-1) # the mean of the absolute values of","''' import numpy as np def statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean,","points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape","Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\"","by default 10 Return ---------- nzc: Solved feature, It's shape is similar to","high order zero crossing quantity. Parameters ---------- data : array data, for DEAP","for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0","nzc: Solved feature, It's shape is similar to the shape of your input","np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the absolute values","32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" #","Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1)","(40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data = np.array(data)","shape is similar to the shape of your input data. e.g. for input.shape","np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for","= (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f","d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40,","(40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \"\"\" # Power power","hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc = [] for i in","x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1)","= [] for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff","Sevcik method: fast calculation and robust analysis of noise Higuchi: closer to the","curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1)","encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0","axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth","= np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2]","(ss*norm) / k return Lm def calc_L_average(X, k): \"\"\" Return <L(k)> as the","tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined:","1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f =","diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the","input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials,","points = data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny","axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data,","of Normalized 1st difference normal_diff_1st = diff_1st / std # the mean of","Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1)","x = np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1),","np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data,","# Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1)","ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return","[4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32,","feature of hoc. Hoc is a high order zero crossing quantity. Parameters ----------","include Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik method:","# the mean of the absolute values of 1st differece mean diff_1st =","(40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc","DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved","activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) #","# -*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls","= np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature is","robust analysis of noise Higuchi: closer to the theoretical value than box counting",":2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data,","the average value over k sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m:","points) k : int, optional Order, by default 10 Return ---------- nzc: Solved","np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2","(n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064),","= np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def","is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32,","in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1)","axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined:","# print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1)",":-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff =","box counting The Sevick method is used here because it is easier to","k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc = [] for i in range(k):","the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std #",">= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count)","f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity","= 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f","Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7)","include activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's","l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f =","is used here because it is easier to implement Parameters ---------- Parameters ----------","calc_L(X, k, m): \"\"\" Return Lm(k) as the length of the curve. \"\"\"","# Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave,","# Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity)","differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute","It's shape may be (n_trials, n_channels, points) k : int, optional Order, by","difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd,","axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving","f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40,","DEAP dataset, It's shape may be (n_trials, n_channels, points) k : int, optional","differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array","1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] = - D","L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in","((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\"","m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True):","is easier to implement Parameters ---------- Parameters ---------- data array data, for DEAP","32, 1) \"\"\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x","for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example","data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels,","def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which is used to describe","print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\"","np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _=","= np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2","0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape","and robust analysis of noise Higuchi: closer to the theoretical value than box","k): \"\"\" Return <L(k)> as the average value over k sets of Lm(k).","of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1)","shape may be (n_trials, n_channels, points) k : int, optional Order, by default","@File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as","+ np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1))","m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k):","fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]]","combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\" Return Lm(k)","10 Return ---------- nzc: Solved feature, It's shape is similar to the shape","Out[6]: (40, 32, 5) \"\"\" nzc = [] for i in range(k): curr_diff","[16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data = np.array(data) ave = np.mean(data,","L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \"\"\"Fractal dimension feature","data[..., :-2]), axis=-1) # the mean of the absolute values of Normalized 2nd","box counting The higuchi method is used here because it is easier to","L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L)","of noise Higuchi: closer to the theoretical value than box counting The Sevick","1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd","np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \"\"\"Fractal","dimension feature is solved, which is used to describe the shape information of","mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity =","---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]:","f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity,","((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return","statistics(data, combined=True): \"\"\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference,","of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std","a high order zero crossing quantity. Parameters ---------- data : array data, for","n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d,","f return ).shape Out[8]: (40, 32, 1) \"\"\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data,","= L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f","- D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f","= np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1])","Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \"\"\" Return <L(k)>","np.std(data, axis=-1) # the mean of the absolute values of 1st differece mean","32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape","k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L","Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data,","points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l","e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features)","@Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def","np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std =","In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif","is similar to the shape of your input data. e.g. for input.shape is","return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f","of your input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape","Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st,","default 10 Return ---------- nzc: Solved feature, It's shape is similar to the","points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape","k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind","axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd,","fd[ind[0], ind[1if combined: f = f return ], ind[2]] = - D f","In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape","noise Higuchi: closer to the theoretical value than box counting The Sevick method","\"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1,","\"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array data, for","diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd,","implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's shape","= np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined:","f def calc_L(X, k, m): \"\"\" Return Lm(k) as the length of the","judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box","axis=-1) # the mean of the absolute values of Normalized 2nd difference normal_diff_2nd","f def sevcik_fd(data, combined=True): \"\"\"Fractal dimension feature is solved, which is used to","x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f =","calculation and robust analysis of noise Higuchi: closer to the theoretical value than","dataset, It's shape may be (n_trials, n_channels, points) k : int, optional Order,","of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of","np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0],","the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]:","= data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) #","= data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny =","In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]:","= np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the absolute","f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \"\"\"Solving the feature of","mean of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd /","m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def","Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's shape may","d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40,","L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f =","print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity","k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L,","counting, Higuchi and so on. Sevcik method: fast calculation and robust analysis of","of hoc. Hoc is a high order zero crossing quantity. Parameters ---------- data","absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the","(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]:","utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 '''","normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd),","Order, by default 10 Return ---------- nzc: Solved feature, It's shape is similar","complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined:","n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In [5]:","time series data. It seems that this feature can be used to judge","10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \"\"\" nzc = []","1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \"\"\" data = np.array(data) ave","-*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version","''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy","diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f","closer to the theoretical value than box counting The higuchi method is used","k, m): \"\"\" Return Lm(k) as the length of the curve. \"\"\" N","mean of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st /","diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True):","features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized","Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity,","np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility","f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \"\"\" Return Lm(k) as the length","x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if","Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In","\"\"\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss","counting The Sevick method is used here because it is easier to implement","np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape)","axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:]","/ activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility","np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of Normalized 1st","is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32,","values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean,","# the mean of the absolute values of Normalized 2nd difference normal_diff_2nd =","the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l =","the shape information of EEG time series data. It seems that this feature","sets of Lm(k). \"\"\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1)","ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp),","0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if","solved, which is used to describe the shape information of EEG time series","def hjorth(data, combined=True): \"\"\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data","3) \"\"\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data,","= np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff","mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10,","In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape","ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2]))","np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1)","Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard","diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of"],"string":"[\n \"Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) #\",\n \"f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined:\",\n \"ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm\",\n \"calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L\",\n \"if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\" Return\",\n \"motion, box counting, Higuchi and so on. Sevcik method: fast calculation and robust\",\n \"so on. Sevcik method: fast calculation and robust analysis of noise Higuchi: closer\",\n \"= f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if\",\n \"theoretical value than box counting The higuchi method is used here because it\",\n \"- data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility\",\n \"higuchi method is used here because it is easier to implement Parameters ----------\",\n \"data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff\",\n \"8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]:\",\n \"f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f\",\n \"Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In\",\n \"f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f\",\n \"the absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std #\",\n \"= np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2]))\",\n \"n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \\\"\\\"\\\"\",\n \"Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f\",\n \"---------- data array data, for DEAP dataset, It's shape may be (n_trials, n_channels,\",\n \"axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L =\",\n \"= np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd =\",\n \"combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is a high order zero crossing\",\n \"1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the\",\n \"Higuchi and so on. Sevcik method: fast calculation and robust analysis of noise\",\n \"std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f\",\n \": array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points)\",\n \"the mean of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[...,\",\n \"32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc =\",\n \"/ np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f\",\n \"absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]),\",\n \"is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In\",\n \"l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32,\",\n \"In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2,\",\n \"= np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1)\",\n \"x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count =\",\n \"Sevick method is used here because it is easier to implement Parameters ----------\",\n \"return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is\",\n \"def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average value over k sets\",\n \"return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average value over\",\n \"over k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m),\",\n \"(40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40,\",\n \"(n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is similar to\",\n \"count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f =\",\n \"n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1))\",\n \"the mean of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd\",\n \"np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0],\",\n \"values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1)\",\n \"axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L\",\n \"combined: f = f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series =\",\n \"Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st,\",\n \"hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32,\",\n \"Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) #\",\n \"points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape\",\n \"32, 5) \\\"\\\"\\\" nzc = [] for i in range(k): curr_diff = np.diff(data,\",\n \"/ std # the mean of the absolute values of 2nd difference mean\",\n \"= x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1),\",\n \"= f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc.\",\n \"closer to the theoretical value than box counting The Sevick method is used\",\n \"axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean of the\",\n \"n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape,\",\n \"((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\"\",\n \"= np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc,\",\n \"diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1)\",\n \"of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] -\",\n \"this feature can be used to judge the electrooculogram and EEG.The calculation methods\",\n \"(n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064),\",\n \"Higuchi: closer to the theoretical value than box counting The Sevick method is\",\n \"f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined:\",\n \"Solved feature, It's shape is similar to the shape of your input data.\",\n \"Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) # Mean\",\n \"fractal Brownian motion, box counting, Higuchi and so on. Sevcik method: fast calculation\",\n \"k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is a high order zero\",\n \"Return ---------- f: Solved feature, It's shape is similar to the shape of\",\n \"n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40,\",\n \"The higuchi method is used here because it is easier to implement Parameters\",\n \"= np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of Normalized\",\n \"- data[..., :-2]), axis=-1) # the mean of the absolute values of Normalized\",\n \"---------- data : array data, for DEAP dataset, It's shape may be (n_trials,\",\n \"n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count\",\n \"of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std,\",\n \"-*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import\",\n \"box counting, Higuchi and so on. Sevcik method: fast calculation and robust analysis\",\n \"the theoretical value than box counting The Sevick method is used here because\",\n \"ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f\",\n \"points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy =\",\n \"calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average value over k sets of\",\n \"np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def\",\n \"mean of the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:]\",\n \"20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True):\",\n \"# the mean of the absolute values of 2nd difference mean diff_2nd =\",\n \"ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd\",\n \"Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average =\",\n \"values of Normalized 1st difference normal_diff_1st = diff_1st / std # the mean\",\n \"Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std,\",\n \"In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = [] for\",\n \"used to describe the shape information of EEG time series data. It seems\",\n \"range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t)\",\n \"data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) k :\",\n \"is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32,\",\n \"crossing quantity. Parameters ---------- data : array data, for DEAP dataset, It's shape\",\n \"np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t)\",\n \"because it is easier to implement Parameters ---------- Parameters ---------- data array data,\",\n \"n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape,\",\n \"of EEG time series data. It seems that this feature can be used\",\n \"f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data,\",\n \"def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to\",\n \"absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power,\",\n \"Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1)\",\n \"Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters\",\n \"used to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian\",\n \"1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension\",\n \"D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ],\",\n \"load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d,\",\n \"[5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40,\",\n \":_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np\",\n \"k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1,\",\n \"mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of\",\n \"+ np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2]))\",\n \"= (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm)\",\n \"easier to implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset,\",\n \"axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if\",\n \"Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference.\",\n \"= np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def\",\n \"n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1))\",\n \"1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x /\",\n \"], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f =\",\n \"array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) k\",\n \"normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving\",\n \"axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f\",\n \"the theoretical value than box counting The higuchi method is used here because\",\n \"is used to describe the shape information of EEG time series data. It\",\n \"np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] =\",\n \"to the theoretical value than box counting The Sevick method is used here\",\n \"7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data,\",\n \"np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f =\",\n \"to the shape of your input data. e.g. for input.shape is (n_trials, n_channels,\",\n \"Example ---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape\",\n \"f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40,\",\n \"calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1)))\",\n \"np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] #\",\n \"[8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1,\",\n \"features， include activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset,\",\n \"x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data,\",\n \"Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis]\",\n \"---------- f: Solved feature, It's shape is similar to the shape of your\",\n \"combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd\",\n \"l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In\",\n \"data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data,\",\n \"difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP\",\n \"combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array data,\",\n \"f = f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda\",\n \"\\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1)\",\n \"Hjorth features， include activity, mobility, complexity Parameters ---------- data array data, for DEAP\",\n \"of noise Higuchi: closer to the theoretical value than box counting The higuchi\",\n \"theoretical value than box counting The Sevick method is used here because it\",\n \"/ np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_\",\n \"= np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) /\",\n \"1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power =\",\n \"= np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1)\",\n \"(40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1) x_ =\",\n \"print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff /\",\n \"complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True):\",\n \"f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is a\",\n \"higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is a high order\",\n \"quantity. Parameters ---------- data : array data, for DEAP dataset, It's shape may\",\n \"similar to the shape of your input data. e.g. for input.shape is (n_trials,\",\n \"[8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\"\",\n \"(N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) /\",\n \"In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x =\",\n \"include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd\",\n \"[14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1)\",\n \"method is used here because it is easier to implement Parameters ---------- Parameters\",\n \"= f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which\",\n \"fast calculation and robust analysis of noise Higuchi: closer to the theoretical value\",\n \"return f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the length of\",\n \"difference normal_diff_1st = diff_1st / std # the mean of the absolute values\",\n \"it is easier to implement Parameters ---------- Parameters ---------- data array data, for\",\n \"used here because it is easier to implement Parameters ---------- Parameters ---------- data\",\n \"data. It seems that this feature can be used to judge the electrooculogram\",\n \"f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is\",\n \"miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny)\",\n \"difference. Parameters ---------- data array data, for DEAP dataset, It's shape may be\",\n \"2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st,\",\n \"# Standard Deviation std = np.std(data, axis=-1) # the mean of the absolute\",\n \"= np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D,\",\n \"1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data\",\n \"5) \\\"\\\"\\\" nzc = [] for i in range(k): curr_diff = np.diff(data, n=i)\",\n \"average value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X,\",\n \"np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average\",\n \"than box counting The higuchi method is used here because it is easier\",\n \"to describe the shape information of EEG time series data. It seems that\",\n \"axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility =\",\n \"return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used\",\n \"axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if\",\n \"calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so on.\",\n \"array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return\",\n \"power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation\",\n \"the mean of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1,\",\n \"\\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x)\",\n \").shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1,\",\n \"electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi\",\n \"np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff =\",\n \"std = np.std(data, axis=-1) # the mean of the absolute values of 1st\",\n \"8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power\",\n \"n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d,\",\n \"Higuchi: closer to the theoretical value than box counting The higuchi method is\",\n \"# Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) #\",\n \"32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k =\",\n \"= f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k:\",\n \"Standard Deviation std = np.std(data, axis=-1) # the mean of the absolute values\",\n \"np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[...,\",\n \"activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's shape\",\n \"nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def\",\n \"[15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]:\",\n \"= curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t,\",\n \"np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k)\",\n \"# Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f =\",\n \"shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape\",\n \"if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features，\",\n \"the mean of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st\",\n \"Deviation std = np.std(data, axis=-1) # the mean of the absolute values of\",\n \"np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] -\",\n \"f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path,\",\n \":-2]), axis=-1) # the mean of the absolute values of Normalized 2nd difference\",\n \"2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) #\",\n \"EEG time series data. It seems that this feature can be used to\",\n \"(n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064),\",\n \"shape of your input data. e.g. for input.shape is (n_trials, n_channels, points), the\",\n \"n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape,\",\n \"methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik\",\n \"values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean\",\n \"1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for\",\n \"and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and\",\n \"axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1),\",\n \"combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature\",\n \"diff_1st / std # the mean of the absolute values of 2nd difference\",\n \"= calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[...,\",\n \"varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity\",\n \"f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40,\",\n \"# print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff\",\n \"hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[...,\",\n \"curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k)\",\n \"the curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) /\",\n \"axis=-1) # the mean of the absolute values of Normalized 1st difference normal_diff_1st\",\n \"= np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape)\",\n \"/ k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average\",\n \"* (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X,\",\n \"input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ----------\",\n \"Lm(k) as the length of the curve. \\\"\\\"\\\" N = X.shape[-1] n =\",\n \"np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2]))\",\n \"In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40,\",\n \"feature, It's shape is similar to the shape of your input data. e.g.\",\n \"(40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1]\",\n \"\\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1)\",\n \"= diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2))\",\n \": int, optional Order, by default 10 Return ---------- nzc: Solved feature, It's\",\n \"\\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe the shape information\",\n \"((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\"\",\n \"data : array data, for DEAP dataset, It's shape may be (n_trials, n_channels,\",\n \"and so on. Sevcik method: fast calculation and robust analysis of noise Higuchi:\",\n \"= f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the\",\n \"length of the curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm =\",\n \"= np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp =\",\n \"[7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]:\",\n \"the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]:\",\n \"f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc\",\n \"than box counting The Sevick method is used here because it is easier\",\n \"f = f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1)\",\n \"Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ----------\",\n \"for DEAP dataset, It's shape may be (n_trials, n_channels, points) k : int,\",\n \"zero crossing quantity. Parameters ---------- data : array data, for DEAP dataset, It's\",\n \"activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f\",\n \"may be (n_trials, n_channels, points) k : int, optional Order, by default 10\",\n \"= np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff)\",\n \"np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny)\",\n \"normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if\",\n \"is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0)\",\n \"of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std\",\n \"calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]):\",\n \"[6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = [] for i\",\n \"[7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined:\",\n \"value than box counting The Sevick method is used here because it is\",\n \"[] for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >=\",\n \"m): \\\"\\\"\\\" Return Lm(k) as the length of the curve. \\\"\\\"\\\" N =\",\n \"# the mean of the absolute values of Normalized 1st difference normal_diff_1st =\",\n \"Parameters ---------- data : array data, for DEAP dataset, It's shape may be\",\n \"np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) /\",\n \"(40, 32, 5) \\\"\\\"\\\" nzc = [] for i in range(k): curr_diff =\",\n \"L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used\",\n \"the length of the curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm\",\n \"It's shape is similar to the shape of your input data. e.g. for\",\n \"in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t =\",\n \"axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension\",\n \"(points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k,\",\n \"Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3)\",\n \"be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is similar\",\n \":wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features，\",\n \"can be used to judge the electrooculogram and EEG.The calculation methods include Sevcik,\",\n \"as the length of the curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64)\",\n \"axis=-1) # the mean of the absolute values of 1st differece mean diff_1st\",\n \"= np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f\",\n \"varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility,\",\n \"value than box counting The higuchi method is used here because it is\",\n \"In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape\",\n \"@Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical\",\n \"n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity\",\n \"\\\"\\\"\\\" Return Lm(k) as the length of the curve. \\\"\\\"\\\" N = X.shape[-1]\",\n \"= np.std(data, axis=-1) # the mean of the absolute values of 1st differece\",\n \"axis=-1)), axis=-1) # the mean of the absolute values of Normalized 1st difference\",\n \"1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x\",\n \"the feature of hoc. Hoc is a high order zero crossing quantity. Parameters\",\n \"k : int, optional Order, by default 10 Return ---------- nzc: Solved feature,\",\n \"the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) #\",\n \"return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k),\",\n \"calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L =\",\n \"combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe the shape\",\n \"return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters\",\n \"series data. It seems that this feature can be used to judge the\",\n \"32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data\",\n \"def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the length of the curve.\",\n \"k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which\",\n \"# print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity =\",\n \"print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2,\",\n \"@Version :1.0 ''' import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include\",\n \"data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1)\",\n \"noise Higuchi: closer to the theoretical value than box counting The higuchi method\",\n \"np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm\",\n \"(40, 32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) # Mean ave\",\n \"l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32,\",\n \"data array data, for DEAP dataset, It's shape may be (n_trials, n_channels, points)\",\n \"/ std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f =\",\n \"np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1)\",\n \"Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1) x_\",\n \"value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k,\",\n \"n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40,\",\n \"d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40,\",\n \"EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting, Higuchi and so\",\n \"k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe the\",\n \"is a high order zero crossing quantity. Parameters ---------- data : array data,\",\n \"Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average value over k\",\n \"curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t\",\n \"combined: f = f return ], ind[2]] = - D f = np.expand_dims(fd,\",\n \"8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data =\",\n \"\\\"\\\"\\\" nzc = [] for i in range(k): curr_diff = np.diff(data, n=i) x_t\",\n \"of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) #\",\n \"dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature,\",\n \"(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]:\",\n \"\\\"\\\"\\\" Return <L(k)> as the average value over k sets of Lm(k). \\\"\\\"\\\"\",\n \"int, optional Order, by default 10 Return ---------- nzc: Solved feature, It's shape\",\n \"Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik method: fast\",\n \"mean of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)),\",\n \"32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points\",\n \"complexity Parameters ---------- data array data, for DEAP dataset, It's shape may be\",\n \"np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 +\",\n \"may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's shape is\",\n \"analysis of noise Higuchi: closer to the theoretical value than box counting The\",\n \"hoc. Hoc is a high order zero crossing quantity. Parameters ---------- data :\",\n \"import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std,\",\n \"calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data,\",\n \"f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved,\",\n \"(40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k\",\n \"f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters ----------\",\n \"is solved, which is used to describe the shape information of EEG time\",\n \"2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the\",\n \"(n_trials, n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In\",\n \"np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return\",\n \"np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined: f\",\n \"if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the\",\n \"(maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 +\",\n \"Normalized 1st difference normal_diff_1st = diff_1st / std # the mean of the\",\n \"def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st\",\n \"normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd),\",\n \"---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]:\",\n \"normal_diff_1st = diff_1st / std # the mean of the absolute values of\",\n \"---------- nzc: Solved feature, It's shape is similar to the shape of your\",\n \"= diff_1st / std # the mean of the absolute values of 2nd\",\n \"k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5)\",\n \"your input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is\",\n \"the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]:\",\n \"= np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L =\",\n \"to implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's\",\n \"n_channels, n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40,\",\n \"hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array\",\n \"(n_trials, n_channels, points) k : int, optional Order, by default 10 Return ----------\",\n \"= np.diff(data, n=i) x_t = curr_diff >= 0 x_t = np.diff(x_t) x_t =\",\n \"n_channels, points) Return ---------- f: Solved feature, It's shape is similar to the\",\n \"order zero crossing quantity. Parameters ---------- data : array data, for DEAP dataset,\",\n \"higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe\",\n \"data, for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ----------\",\n \"n_features) Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1))\",\n \"information of EEG time series data. It seems that this feature can be\",\n \"combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include\",\n \"---------- In [13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]:\",\n \"<reponame>Wlgls/pyDEAP<gh_stars>0 # -*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author\",\n \"32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) # Mean ave =\",\n \"n_channels, points) k : int, optional Order, by default 10 Return ---------- nzc:\",\n \"method: fast calculation and robust analysis of noise Higuchi: closer to the theoretical\",\n \"1st difference normal_diff_1st = diff_1st / std # the mean of the absolute\",\n \"ind[1if combined: f = f return ], ind[2]] = - D f =\",\n \"= np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return\",\n \"Hoc is a high order zero crossing quantity. Parameters ---------- data : array\",\n \"= np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True):\",\n \"statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power = np.mean(data**2, axis=-1) #\",\n \"(n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return\",\n \"In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1)\",\n \"Brownian motion, box counting, Higuchi and so on. Sevcik method: fast calculation and\",\n \"for DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f:\",\n \"(40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st\",\n \"of the curve. \\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1)\",\n \"= np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f =\",\n \"2nd difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset,\",\n \"1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1) k = np.arange(1,\",\n \"nzc = [] for i in range(k): curr_diff = np.diff(data, n=i) x_t =\",\n \"k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the average value\",\n \"= np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean\",\n \"\\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1,\",\n \"the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box counting,\",\n \"diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1)\",\n \"mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values\",\n \"= f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility,\",\n \"= np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) #\",\n \"= np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std\",\n \"std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power,\",\n \"np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy\",\n \"np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] = -\",\n \"l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32,\",\n \"absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std # the\",\n \"f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as\",\n \"1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1)\",\n \"here because it is easier to implement Parameters ---------- Parameters ---------- data array\",\n \"axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape)\",\n \"/ (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1\",\n \"8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points =\",\n \"of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average\",\n \"data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape)\",\n \"normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data,\",\n \"diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[...,\",\n \"f: Solved feature, It's shape is similar to the shape of your input\",\n \"axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 *\",\n \"diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def\",\n \"<L(k)> as the average value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series =\",\n \"optional Order, by default 10 Return ---------- nzc: Solved feature, It's shape is\",\n \":1.0 ''' import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power,\",\n \"N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss =\",\n \"maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_,\",\n \"if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature\",\n \"std # the mean of the absolute values of 2nd difference mean diff_2nd\",\n \"# Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st,\",\n \"---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]:\",\n \"Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st,\",\n \"i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0 x_t\",\n \"Parameters ---------- data array data, for DEAP dataset, It's shape may be (n_trials,\",\n \"d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f\",\n \"axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return\",\n \"0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f\",\n \"# print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m):\",\n \"to the theoretical value than box counting The higuchi method is used here\",\n \"k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max,\",\n \"It seems that this feature can be used to judge the electrooculogram and\",\n \"difference, Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset, It's\",\n \"1)) In [8]: higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32,\",\n \"the shape of your input data. e.g. for input.shape is (n_trials, n_channels, points),\",\n \"ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return\",\n \"on. Sevcik method: fast calculation and robust analysis of noise Higuchi: closer to\",\n \"return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is\",\n \"_= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f = f return ], ind[2]]\",\n \"combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is\",\n \"np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1)) #\",\n \"= (ss*norm) / k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as\",\n \"Return ---------- nzc: Solved feature, It's shape is similar to the shape of\",\n \"shape information of EEG time series data. It seems that this feature can\",\n \"axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2] # Activity activity\",\n \"The Sevick method is used here because it is easier to implement Parameters\",\n \"---------- Parameters ---------- data array data, for DEAP dataset, It's shape may be\",\n \"\\\"\\\"\\\"Solving the feature of hoc. Hoc is a high order zero crossing quantity.\",\n \"be used to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal\",\n \"the absolute values of 2nd difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[...,\",\n \"y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1)\",\n \"be (n_trials, n_channels, points) k : int, optional Order, by default 10 Return\",\n \"np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece, Normalized\",\n \"d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10)\",\n \"sevcik_fd(d).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1)\",\n \"X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k],\",\n \"for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k),\",\n \"Example ---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In\",\n \"32, 3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st =\",\n \"It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved feature, It's\",\n \"2nd difference. Parameters ---------- data array data, for DEAP dataset, It's shape may\",\n \"feature is solved, which is used to describe the shape information of EEG\",\n \"= - D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return\",\n \"[13]: d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]:\",\n \"(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]:\",\n \"sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe the\",\n \"axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2),\",\n \"that this feature can be used to judge the electrooculogram and EEG.The calculation\",\n \"= np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) /\",\n \"Normalized 2nd difference. Parameters ---------- data array data, for DEAP dataset, It's shape\",\n \"/ (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k\",\n \"\\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference,\",\n \"counting The higuchi method is used here because it is easier to implement\",\n \"np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X,\",\n \"which is used to describe the shape information of EEG time series data.\",\n \"np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1) y_ =\",\n \"(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [15]:\",\n \"= np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity),\",\n \"seems that this feature can be used to judge the electrooculogram and EEG.The\",\n \"n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)),\",\n \"= X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[...,\",\n \"points) Return ---------- f: Solved feature, It's shape is similar to the shape\",\n \"L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp\",\n \"feature can be used to judge the electrooculogram and EEG.The calculation methods include\",\n \"norm = (N-1) / (n*k) ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm =\",\n \"difference mean diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean\",\n \"np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the mean of\",\n \"(data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f =\",\n \"as the average value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda\",\n \"higuchi_fd(dif combined: f = f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series\",\n \"= load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]:\",\n \"mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's shape may\",\n \"ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1) # the\",\n \"Return <L(k)> as the average value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series\",\n \"axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1],\",\n \"as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece,\",\n \"np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return\",\n \"def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of hoc. Hoc is a high\",\n \"numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st\",\n \"Return Lm(k) as the length of the curve. \\\"\\\"\\\" N = X.shape[-1] n\",\n \"to judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion,\",\n \"1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal\",\n \"/ mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2]))\",\n \"describe the shape information of EEG time series data. It seems that this\",\n \"mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return\",\n \"np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved,\",\n \"In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave =\",\n \"k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd =\",\n \"2:] - data[..., :-2]), axis=-1) # the mean of the absolute values of\",\n \"''' import numpy as np def statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean,\",\n \"points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape\",\n \"Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\"\",\n \"by default 10 Return ---------- nzc: Solved feature, It's shape is similar to\",\n \"high order zero crossing quantity. Parameters ---------- data : array data, for DEAP\",\n \"for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff >= 0\",\n \"nzc: Solved feature, It's shape is similar to the shape of your input\",\n \"np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the absolute values\",\n \"32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" #\",\n \"Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape Out[8]: (40, 32, 1)\",\n \"(40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data)\",\n \"shape is similar to the shape of your input data. e.g. for input.shape\",\n \"np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for\",\n \"= (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f\",\n \"d.shape, l.shape Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40,\",\n \"(40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7) \\\"\\\"\\\" # Power power\",\n \"hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = [] for i in\",\n \"x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1), axis=-1) maxy = np.expand_dims(np.max(data, axis=-1), axis=-1)\",\n \"= [] for i in range(k): curr_diff = np.diff(data, n=i) x_t = curr_diff\",\n \"Sevcik method: fast calculation and robust analysis of noise Higuchi: closer to the\",\n \"curr_diff >= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1)\",\n \"encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0\",\n \"axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth\",\n \"= np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:] - data[..., :-2]\",\n \"(ss*norm) / k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)> as the\",\n \"tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined:\",\n \"1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f =\",\n \"diff_2nd = np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the\",\n \"input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials,\",\n \"points = data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny\",\n \"axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data,\",\n \"of Normalized 1st difference normal_diff_1st = diff_1st / std # the mean of\",\n \"Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data, k), 1, 1)\",\n \"x = np.arange(1, points+1) x_ = x / np.max(x) miny = np.expand_dims(np.min(data, axis=-1),\",\n \"np.stack((activity, mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data,\",\n \"# Mean ave = np.mean(data, axis=-1) # Standard Deviation std = np.std(data, axis=-1)\",\n \"ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return\",\n \"[4]: d, l = load_deap(path, 0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32,\",\n \"feature of hoc. Hoc is a high order zero crossing quantity. Parameters ----------\",\n \"include Sevcik, fractal Brownian motion, box counting, Higuchi and so on. Sevcik method:\",\n \"# the mean of the absolute values of 1st differece mean diff_1st =\",\n \"(40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc\",\n \"DEAP dataset, It's shape may be (n_trials, n_channels, points) Return ---------- f: Solved\",\n \"activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) #\",\n \"# -*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls\",\n \"= np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature is\",\n \"robust analysis of noise Higuchi: closer to the theoretical value than box counting\",\n \":2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def statistics(data,\",\n \"the average value over k sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m:\",\n \"points) k : int, optional Order, by default 10 Return ---------- nzc: Solved\",\n \"np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2\",\n \"(n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064),\",\n \"= np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def\",\n \"is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]: ((40, 32,\",\n \"in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1)\",\n \"axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=-1) if combined:\",\n \"# print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1)\",\n \":-2] # Activity activity = np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff =\",\n \"box counting The Sevick method is used here because it is easier to\",\n \"k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = [] for i in range(k):\",\n \"the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std #\",\n \">= 0 x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count)\",\n \"f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity\",\n \"= 1 + np.log(L) / np.log(2 * (points-1)) # print(FD.shape) if combined: f\",\n \"Out[13]: ((40, 32, 8064), (40, 1)) In [14]: statistics_feature(d).shape Out[14]: (40, 32, 7)\",\n \"include activity, mobility, complexity Parameters ---------- data array data, for DEAP dataset, It's\",\n \"l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f =\",\n \"is used here because it is easier to implement Parameters ---------- Parameters ----------\",\n \"calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the length of the curve. \\\"\\\"\\\"\",\n \"# Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave,\",\n \"# Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity)\",\n \"differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute\",\n \"It's shape may be (n_trials, n_channels, points) k : int, optional Order, by\",\n \"difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd,\",\n \"axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving\",\n \"f.shape is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40,\",\n \"DEAP dataset, It's shape may be (n_trials, n_channels, points) k : int, optional\",\n \"differece, Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array\",\n \"1) fd[ind[0], ind[1if combined: f = f return ], ind[2]] = - D\",\n \"L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in\",\n \"((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\"\",\n \"m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True):\",\n \"is easier to implement Parameters ---------- Parameters ---------- data array data, for DEAP\",\n \"32, 1) \\\"\\\"\\\" points = data.shape[-1] x = np.arange(1, points+1) x_ = x\",\n \"for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example\",\n \"data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels,\",\n \"def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to describe\",\n \"print(FD.shape) if combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\"\",\n \"np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _=\",\n \"= np.expand_dims(np.max(data, axis=-1), axis=-1) y_ = (data-miny) / (maxy-miny) L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2\",\n \"0) In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape\",\n \"and robust analysis of noise Higuchi: closer to the theoretical value than box\",\n \"k): \\\"\\\"\\\" Return <L(k)> as the average value over k sets of Lm(k).\",\n \"of the absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1)\",\n \"shape may be (n_trials, n_channels, points) k : int, optional Order, by default\",\n \"@File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as\",\n \"+ np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L) / np.log(2 * (points-1))\",\n \"m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm def calc_L_average(X, k):\",\n \"fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]]\",\n \"combined: f = f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k)\",\n \"10 Return ---------- nzc: Solved feature, It's shape is similar to the shape\",\n \"Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = [] for i in range(k): curr_diff\",\n \"[16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data,\",\n \"L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def higuchi_fd(data, k_max, combined=True): \\\"\\\"\\\"Fractal dimension feature\",\n \"data[..., :-2]), axis=-1) # the mean of the absolute values of Normalized 2nd\",\n \"box counting The higuchi method is used here because it is easier to\",\n \"L = np.expand_dims(np.sum(np.sqrt(np.diff(y_, axis=-1)**2 + np.diff(x_)**2), axis=-1), axis=-1) f = 1 + np.log(L)\",\n \"of noise Higuchi: closer to the theoretical value than box counting The Sevick\",\n \"1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd\",\n \"np.stack(nzc, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal\",\n \"dimension feature is solved, which is used to describe the shape information of\",\n \"mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity =\",\n \"---------- In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]:\",\n \"f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity,\",\n \"((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return\",\n \"statistics(data, combined=True): \\\"\\\"\\\"Statistical features， include Power, Mean, Std, 1st differece, Normalized 1st difference,\",\n \"of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st / std\",\n \"a high order zero crossing quantity. Parameters ---------- data : array data, for\",\n \"n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In [5]: hoc(d,\",\n \"f return ).shape Out[8]: (40, 32, 1) \\\"\\\"\\\" calc_L_average_series = np.frompyfunc(lambda k: calc_L_average(data,\",\n \"= L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f\",\n \"- D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f\",\n \"= np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1])\",\n \"Lm = (ss*norm) / k return Lm def calc_L_average(X, k): \\\"\\\"\\\" Return <L(k)>\",\n \"np.std(data, axis=-1) # the mean of the absolute values of 1st differece mean\",\n \"32, 8064), (40, 1)) In [8]: higuchi_fd(dif combined: f = f return ).shape\",\n \"k: calc_L_average(data, k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L\",\n \"Normalized 1st difference, 2nd difference, Normalized 2nd difference. Parameters ---------- data array data,\",\n \"points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l\",\n \"e.g. for input.shape is (n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features)\",\n \"@Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy as np def\",\n \"np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard Deviation std =\",\n \"In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: higuchi_fd(dif\",\n \"is similar to the shape of your input data. e.g. for input.shape is\",\n \"return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f\",\n \"of your input data. e.g. for input.shape is (n_trials, n_channels, points), the f.shape\",\n \"Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st,\",\n \"default 10 Return ---------- nzc: Solved feature, It's shape is similar to the\",\n \"points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape\",\n \"k_max+1) L = calc_L_average_series(k) L = np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind\",\n \"axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd,\",\n \"fd[ind[0], ind[1if combined: f = f return ], ind[2]] = - D f\",\n \"In [15]: d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape\",\n \"noise Higuchi: closer to the theoretical value than box counting The Sevick method\",\n \"\\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1,\",\n \"\\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data array data, for\",\n \"diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd,\",\n \"implement Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's shape\",\n \"= np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if combined:\",\n \"f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the length of the\",\n \"judge the electrooculogram and EEG.The calculation methods include Sevcik, fractal Brownian motion, box\",\n \"axis=-1) # the mean of the absolute values of Normalized 2nd difference normal_diff_2nd\",\n \"f def sevcik_fd(data, combined=True): \\\"\\\"\\\"Fractal dimension feature is solved, which is used to\",\n \"x_t = np.diff(x_t) x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f =\",\n \"calculation and robust analysis of noise Higuchi: closer to the theoretical value than\",\n \"dataset, It's shape may be (n_trials, n_channels, points) k : int, optional Order,\",\n \"of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of\",\n \"np.stack(L, axis=-1) fd = np.zeros(data.shape[:-1]) for ind in np.argwhere(L[..., 0]): tmp = L[ind[0],\",\n \"the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]: d.shape, l.shape Out[13]:\",\n \"= data[..., 2:] - data[..., :-2] # Activity activity = np.mean((data-ave)**2, axis=-1) #\",\n \"= data.shape[-1] x = np.arange(1, points+1) x_ = x / np.max(x) miny =\",\n \"In [5]: hoc(d, k=10).shape Out[5]: (40, 32, 10) In [6]: hoc(d, k=5).shape Out[6]:\",\n \"= np.mean(np.abs(data[..., 2:] - data[..., :-2]), axis=-1) # the mean of the absolute\",\n \"f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10, combined=True): \\\"\\\"\\\"Solving the feature of\",\n \"mean of the absolute values of Normalized 2nd difference normal_diff_2nd = diff_2nd /\",\n \"m: calc_L(X, k, m), 1, 1) L_average = np.average(calc_L_series(np.arange(1, k+1))) return L_average def\",\n \"Parameters ---------- Parameters ---------- data array data, for DEAP dataset, It's shape may\",\n \"d.shape, l.shape Out[15]: ((40, 32, 8064), (40, 1)) In [16]: hjorth_features(d).shape Out[16]: (40,\",\n \"L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if combined: f =\",\n \"print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity\",\n \"k), 1, 1) k = np.arange(1, k_max+1) L = calc_L_average_series(k) L = np.stack(L,\",\n \"counting, Higuchi and so on. Sevcik method: fast calculation and robust analysis of\",\n \"of hoc. Hoc is a high order zero crossing quantity. Parameters ---------- data\",\n \"absolute values of 1st differece mean diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the\",\n \"(n_trials, n_channels, points), the f.shape is (n_trials, n_channels, n_features) Example ---------- In [13]:\",\n \"utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 '''\",\n \"normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f = np.stack((power, ave, std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd),\",\n \"Order, by default 10 Return ---------- nzc: Solved feature, It's shape is similar\",\n \"complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1) if combined:\",\n \"n_channels, n_features) Example ---------- In [4]: d, l = load_deap(path, 0) In [5]:\",\n \"time series data. It seems that this feature can be used to judge\",\n \"10) In [6]: hoc(d, k=5).shape Out[6]: (40, 32, 5) \\\"\\\"\\\" nzc = []\",\n \"1)) In [16]: hjorth_features(d).shape Out[16]: (40, 32, 3) \\\"\\\"\\\" data = np.array(data) ave\",\n \"-*- encoding: utf-8 -*- ''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version\",\n \"''' @File :_time_domain_features.py @Time :2021/04/16 20:02:55 @Author :wlgls @Version :1.0 ''' import numpy\",\n \"diff_2nd / std # Features.append(np.concatenate((Power, Mean, Std, diff_1st, normal_diff_1st, diff_2nd, normal_diff_2nd), axis=2)) f\",\n \"closer to the theoretical value than box counting The higuchi method is used\",\n \"k, m): \\\"\\\"\\\" Return Lm(k) as the length of the curve. \\\"\\\"\\\" N\",\n \"mean of the absolute values of Normalized 1st difference normal_diff_1st = diff_1st /\",\n \"diff_2nd, normal_diff_2nd), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def hjorth(data, combined=True):\",\n \"features， include Power, Mean, Std, 1st differece, Normalized 1st difference, 2nd difference, Normalized\",\n \"Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity,\",\n \"np.mean((data-ave)**2, axis=-1) # print(Activity.shape) # Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility\",\n \"f.reshape((*f.shape[:-2])) return f def calc_L(X, k, m): \\\"\\\"\\\" Return Lm(k) as the length\",\n \"x_t = np.abs(x_t) count = np.count_nonzero(x_t, axis=-1) nzc.append(count) f = np.stack(nzc, axis=-1) if\",\n \"Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In\",\n \"\\\"\\\"\\\" N = X.shape[-1] n = np.floor((N-m)/k).astype(np.int64) norm = (N-1) / (n*k) ss\",\n \"counting The Sevick method is used here because it is easier to implement\",\n \"np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape)\",\n \"axis=-1)[..., np.newaxis] diff_1st = np.diff(data, n=1, axis=-1) # print(diff_1st.shape) diff_2nd = data[..., 2:]\",\n \"/ activity) # Complexity varsdiff = np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility\",\n \"np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of Normalized 1st\",\n \"is (n_trials, n_channels, n_features) Example ---------- In [7]: d.shape, l.shape Out[7]: ((40, 32,\",\n \"values of Normalized 2nd difference normal_diff_2nd = diff_2nd / std # Features.append(np.concatenate((Power, Mean,\",\n \"# the mean of the absolute values of Normalized 2nd difference normal_diff_2nd =\",\n \"the f.shape is (n_trials, n_channels, n_features) Example ---------- In [4]: d, l =\",\n \"the shape information of EEG time series data. It seems that this feature\",\n \"sets of Lm(k). \\\"\\\"\\\" calc_L_series = np.frompyfunc(lambda m: calc_L(X, k, m), 1, 1)\",\n \"ind in np.argwhere(L[..., 0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp),\",\n \"0]): tmp = L[ind[0], ind[1], ind[2]] D, _= np.polyfit(np.log2(k), np.log2(tmp), 1) fd[ind[0], ind[1if\",\n \"solved, which is used to describe the shape information of EEG time series\",\n \"def hjorth(data, combined=True): \\\"\\\"\\\"Solving Hjorth features， include activity, mobility, complexity Parameters ---------- data\",\n \"3) \\\"\\\"\\\" data = np.array(data) ave = np.mean(data, axis=-1)[..., np.newaxis] diff_1st = np.diff(data,\",\n \"= np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) # Complexity varsdiff\",\n \"mobility, complexity), axis=-1) if combined: f = f.reshape((*f.shape[:-2])) return f def higher_order_crossing(data, k=10,\",\n \"In [7]: d.shape, l.shape Out[7]: ((40, 32, 8064), (40, 1)) In [8]: sevcik_fd(d).shape\",\n \"ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined: f = f.reshape((*f.shape[:-2]))\",\n \"np.var(diff_2nd, axis=-1) complexity = np.sqrt(varsdiff/varfdiff) / mobility f = np.stack((activity, mobility, complexity), axis=-1)\",\n \"Power power = np.mean(data**2, axis=-1) # Mean ave = np.mean(data, axis=-1) # Standard\",\n \"diff_1st = np.mean(np.abs(np.diff(data,n=1, axis=-1)), axis=-1) # the mean of the absolute values of\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":21,"numItemsPerPage":100,"numTotalItems":12866649,"offset":2100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzQzNTUxMSwianRpIjoiMmMxMTU5MmUtNTExZi00NTYyLWJiOTctNjE0YWMwNWUxNWJiIiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzQzOTExMSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.qXPWX7a_iMvYsNSiP7SRc16g2yX9OV2vJiJXB8RiF1yPRrfcaZDpbNHeLZK7I3b47cun3e_l6lY8jG-CnK0TBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"i[2] == 2: card[f'{index}']['creature_name']= nameSplit[2] card[f'{index}']['creature_modifier']= nameSplit[1] card[f'{index}']['name_modifier']= nameSplit[0] card[f'{index}']['location']= nameSplit[2] = keyword",
"card[f'{index}']['enchantment_name']=nameSplit[2] card[f'{index}']['spell_type']=nameSplit[1] card... |
[
"= videos[video_file_path] predicted_label = predictor.predict(video_file_path) print('predicted: ' + predicted_label + ' actual: '",
"label_index) in predictor.labels.items()]) video_file_path_list = np.array([file_path for file_path in videos.keys()]) np.random.shuffle(video_file_path_list) for video_file_path"... |
[
"from django.conf import settings def less_settings(request): return { 'use_dynamic_less_in_debug': getattr(settings, 'LESS_USE_DYNAMIC_IN_DEBUG', True) }"
] |
[
"non-None values will serve as pass-through arrays. Returns ------- y_transform : array-like or",
"non-negative scalar value\") if lam1 is None: y, _ = self._check_y_X(y, X) _,",
"array-like or None, shape=(n_samples, n_features), optional The exogenous array of additional covariates. Not",
": float, optional... |
[
"be added to the response payload after the user authenticates. :param user: The",
"dict containing the additional information that must be added to the response payload",
"\"\"\" The user data type can be used to inject an additional payload",
"must be implemented and should return a dict.\" ) class UserData... |
[
"circuitComponents[IVS][i].node1 != 'GND': matrixM[nodeNumbers[circuitComponents[IVS][i].node1]][numNodes+i] = 1.0 if circuitComponents[IVS][i].node2 != 'GND': matrixM[nodeNumbers[circuitComponents[IVS][i].node2]][numNodes+i] = -1.0",
"Supported engineer units are: M, k, m, u, n\\nYou can also enter values",
"s... |
[
"for dir in dirs: subdirectories.append(dirname + '/' + dir) return subdirectories \"\"\" Rocket",
"dir in dirs: subdirectories.append(dirname + '/' + dir) return subdirectories \"\"\" Rocket science",
"science \"\"\" def answer(string): a = input(string) if a == \"Y\" or a",
"'/' + dir) return subdirectories... |
[
"plt from skimage.exposure import rescale_intensity from unsharp import * # Load file and",
"<gh_stars>1-10 import numpy as np import matplotlib.pyplot as plt from skimage.exposure import rescale_intensity",
"unsharp import * # Load file and normalize to 0-1 fname = 'iguana.jpg'",
"rescale_intensity from unsh... |
[
"db_table = 'registro_establecimiento_domicilio' def __unicode__(self): if self.cp: cp = \" (CP: \" +",
"localidad = models.ForeignKey(Localidad, related_name='domicilios_establecimientos') calle = models.CharField(max_length=100) altura = models.CharField(max_length=15) referencia = models.CharField(max_length=2... |
[
"find() and string slicing (see section 6.10) to extract # the number at",
"end of the line below. # Convert the extracted value to a floating",
"(see section 6.10) to extract # the number at the end of the",
"and string slicing (see section 6.10) to extract # the number at the",
"extract # the number at th... |
[
"self._model = None logger.info(f\"NAME={name}/{self.model_name} : {self._model}\") self._layer = None self._unit = None self.change(model_changed=True,",
"= next(x for x in self._model.layers if x['name'] == name) self._unit = unit",
"else self.layer_id + ':' + str(self._unit)) def _doRun(self, running: bool=T... |
[
"them. txn.execute( \"INSERT INTO events_to_purge\" \" SELECT event_id, %s\" \" FROM events AS",
"to the database Args: events_and_contexts: list of tuples of (event, context) backfilled (bool):",
"FROM events_to_purge\") event_rows = txn.fetchall() logger.info( \"[purge] found %i events before cutoff, of",
"... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"from buildtool import in_memory_metrics from buildtool import prometheus_metrics from buildtool import stackdriver_metrics from",
"def singleton()... |
[
"input_dirs)) result = process.wait() if result != 0: raise TaskError('Android aapt tool exited",
"is the main aapt operation (see class docstring for more info). # :",
"down while collecting resources. # : '-I' packages to add to base \"include\"",
"and assets. This class gathers compiled classes (an Android... |
[
"NQ evaluation.') class ApproximatePageRank(object): \"\"\"APR main lib which is used to wrap functions",
"as a wrapper around the PPR algorithm. This class has the following functionality:",
"KIND, either express or implied. # See the License for the specific language",
"a neighbourhood around given entities... |
[
"same bed thereafter.\"\"\" perennial_plan = plan.copy() #what was planted the year before plant_last_year",
"not match. \"\"\" return plan*sun_constraint def enforce_perennial_constraint(plan,plant,bed,year,perennials): \"\"\"Forward fill plan for perennial plants.",
"year and every year thereafter if plant in... |
[
"forwards and {:.4g} backwards.'.format( metrics['time'], metrics['num_forwards'], metrics['num_backwards'])) success = metrics['success'].numpy() fail = bool(success.mean()",
"is different from the original label. Parameters ---------- labels: Tensor Original labels. Generated",
"classes): {} - Average: {:.2f}... |
[
"== \"__main__\": usage = \"usage: %prog [options] old_shape_file new_shape_file \" parser = OptionParser(usage=usage,",
"old_shp_path = args[0] new_shp_path = args[1] # check files do exist assert io_function.is_file_exist(new_shp_path)",
"= 'expand_' + main_shp_name output_path_shrink = 'shrink_' + main_shp_n... |
[
"= 0 def drive(self, gear): if self.type == \"trailer\": self.speed = gear *",
"def drive(self, gear): if self.type == \"trailer\": self.speed = gear * 77 /",
"drive(self, gear): if self.type == \"trailer\": self.speed = gear * 77 / 7",
"num_of_wheels = 4 num_of_doors = 4 if car_type == \"trailer\": num_of_wh... |
[
"as tf # Convert the model. converter = tf.lite.TFLiteConverter.from_saved_model('model.py') tflite_model = converter.convert() open(\"trash_ai.tflite\",",
"<filename>CV Model/Model - JupyterNotebook/mrcnn/tfliteconverter.py import tensorflow as tf # Convert the model. converter",
"tensorflow as tf # Convert th... |
[
"= 1 # every 2 hours schedule = Schedule(run_every_mins=RUN_EVERY_MINS) code = 'basicapp.cron' #",
"= Link.objects.all() for obj in links: print(\"Checking last hit date for: \", obj.shortenURL)",
"django_cron import CronJobBase, Schedule from .models import Link from django.utils import timezone class",
"\",... |
[
"# The image is *not* in this context: assert serialize([context.box]) == [ ('html',",
"weasyprint.tests.stacking ------------------------- :copyright: Copyright 2011-2012 <NAME> and contributors, see AUTHORS. :license: BSD, see",
"def to_lists(page): html, = page.children return serialize_stacking(StackingCont... |
[
"is your <a href=\"{}\">magic link</a> </p> <p>Thanks,</p> <p>Django Admin</p> \"\"\".format(login_link) send_mail( 'Django Magic",
"html_message ) return render(request, \"login.html\", context={\"message\":\"Please check your email for magic link.\"}) return",
"import utils from django.core.mail import send_m... |
[
"test fails. It will raise an Exception with understandable text Args: errors(list[CustomCheckError]: Errors",
"all checks are successful. Prints a success message\"\"\" self.print_success_message() def print_success_message(self): \"\"\"Formats the",
"parameter value(Any): Value of the parameter checks(list[di... |
[
"station.uic_code = '' if uic_code and len(uic_code) == 7 and uic_code[:2] == '85':",
"Unless required by applicable law or agreed to in writing, software # distributed",
"cStringIO except ImportError: import cStringIO import csv import datetime import optparse import sys",
"= {} # This is ugly as hell, I kno... |
[
"more than one file. This is a limitation, obvious in cases like Gotlands",
"elif rule_key == \"HEADER_CONTAINS\": try: pos = make_unicode(header).find(rule_val.upper()) except UnicodeDecodeError: pos = -1",
"def get_document_type(self): \"\"\" Return the first matching document type, based on this header",
"... |
[
"json_file) = sys.argv[1:4] tmpName = str(uuid.uuid4()) tmpdir = \"/tmp\" temp_input_file = f\"{tmpdir}/{tmpName}.dat\" temp_output_file",
"sif = mgm_utils.get_sif_dir(root_dir) + \"/ina_segmentation.sif\" r = subprocess.run([\"singularity\", \"run\", sif, temp_input_file, temp_output_file]) shutil.copy(temp_outp... |
[
"import etree from django import forms from django.db import models class XMLFileField(models.FileField): def",
"kwargs.pop('schema') super(XMLFileField, self).__init__(*args, **kwargs) def clean(self, *args, **kwargs): data = super(XMLFileField, self).clean(*args, **kwargs)",
"clean(self, *args, **kwargs): dat... |
[
"= {\"aspect_ratios\": anchor_aspect_ratios, \"num_scales\": anchor_num_scales, \"anchor_scale\": anchor_scale, \"grid_zero_start\": anchor_grid_zero_start} pyramid_levels = [pyramid_levels_min, pyramid_levels_min",
"ii in inputs] = }\") target_channel = inputs[-1].shape[-1] fpn_out = conv_dw_pw_block(inputs[0], ... |
[
"10, \"price\": 1.20}, } my_response_obj = ItemResponse(**obj_to_validate) assert my_response_obj.dict() == { \"links\": None,",
"response.dict() == { \"links\": None, \"data\": [ { \"id\": \"1\", \"name\": \"apple\", \"price\":",
"\"name\": \"banana\", \"quantity\": 20, \"price\": 2.34, }, ], } def test_attrib... |
[
"SCREEN_WIDTH = constants.SCREEN_WIDTH SCREEN_HEIGHT = constants.SCREEN_HEIGHT SCREEN_TITLE = constants.SCREEN_TITLE window = arcade.Window(SCREEN_WIDTH, SCREEN_HEIGHT,",
"SCREEN_TITLE = constants.SCREEN_TITLE window = arcade.Window(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE) start_view = menu_view() window.show_v... |
[
"License is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"in session.query(qos_policy).filter(qos_policy.c.shared).all(): values.append({'id': uuidutils.generate_uuid(), 'object_id': row[0], 'tenant_id... |
[
"message.ack() streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback) print(f\"Listening for messages on {subscription_path}..\\n\") # Wrap subscriber",
"pubsub_v1 project_id = \"pubsub-testing-331300\" subscription_id = \"test-sub\" # Number of seconds the subscriber",
"5.0 subscrib... |
[
"_('Set up refresh token'), 'type': 'ir.actions.act_window', 'res_model': 'res.config.settings', 'views': [(template.id, 'form')], 'target': 'new',",
"= self.env['google.service']._get_google_token_uri('drive', scope=self.env['google.drive.config'].get_google_scope()) for config in self: config.google_drive_uri =... |
[
"mode='valid'): super(MyTestDataset).__init__() dataset = MyDataset(data_path, dataset_name, cvfold=cvfold, n_way=n_way, k_shot=k_shot, n_queries=n_queries, mode='test', num_point=num_point, pc_attribs=pc_attribs,",
"set(sampled_classes)) try: sampled_valid_classes = np.random.choice(np.array(remain_classes), sel... |
[
"have either format of (opcode, var1) or (opcode, var1, var2) \"\"\" opcode: ConditionOpcode",
"CLVM conditions Conditions in CLVM have either format of (opcode, var1) or (opcode,",
"from greendoge.types.condition_opcodes import ConditionOpcode from greendoge.util.streamable import Streamable, streamable @datac... |
[
"= shade_name self._shade = shade @property def device_info(self) -> DeviceInfo: \"\"\"Return the device_info",
"DeviceInfo( identifiers={(DOMAIN, self._device_info[DEVICE_SERIAL_NUMBER])}, connections={ (dr.CONNECTION_NETWORK_MAC, self._device_info[DEVICE_MAC_ADDRESS]) }, name=self._device_info[DEVICE_NAME], sug... |
[
"to identify a given set of clients with some identification method. The request",
"select clients and identification methods to server. The server then tries to identify",
"__init__(self, name, size, item_size): rq.LengthOf.__init__(self, name, size) self.item_size = item_size def parse_value(self, length,",
... |
[
"\\ from start tasks (default is fromstart)') parser.add_argument( '--version', action='version', version='%(prog)s ' +",
"from start tasks (default is fromstart)') parser.add_argument( '--version', action='version', version='%(prog)s ' + rubra_version)",
"return parser.parse_args() parser = argparse.ArgumentPa... |
[
"def main(): generator = Generator() latitude, longitude = generator.getCoordinates() webbrowser.open(config.api_request.format(latitude, longitude)) if __name__",
"import webbrowser import config from Generator import Generator def main(): generator = Generator()",
"Generator import Generator def main(): gener... |
[
"60) execute_get(\"coap://\"+my_base+\"/fp/g\", 40) execute_del(\"coap://\"+my_base+\"/fp/g/3\", 60) execute_get(\"coap://\"+my_base+\"/fp/g/3\", 60) execute_get(\"coap://\"+my_base+\"/fp/g\", 40) def do_sequence_fp_g(my_base): # url,",
"\"href\": \"xxxxyyy3\", \"cflag\": [1,4,5], \"ga\":[44,55,33]} ] execute_pos... |
[
"= ['sip', 'PyQt4.QtGui', 'PyQt4._qt'] from PyInstaller.hooks.hookutils import qt4_plugins_binaries def hook(mod): mod.binaries.extend(qt4_plugins_binaries('phonon_backend')) return mod",
"hiddenimports = ['sip', 'PyQt4.QtGui', 'PyQt4._qt'] from PyInstaller.hooks.hookutils import qt4_plugins_binaries def hook(mod... |
[
"chain :type symbol: str :param expiration: Expiration for the chain :type expiration: Union[str,",
"optional :param end: End date represented as YYYY-MM-DD, defaults to None :type end:",
"the chain :type expiration: Union[str, datetime] :return: [description] :rtype: list \"\"\" return self._get(",
"an optio... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"QtWidgets.QPushButton(self.controlWidget) self.accumulateButton.setCheckable(True) self.accumulateButton.setObjectName(\"accumulateButton\") self.co... |
[
"Slave(object): __slots__ = [\"_conn\", \"namespace\"] def __init__(self): self._conn = None self.namespace = {}",
"class FakeSlaveService(VoidService): \"\"\"VoidService that can be used for connecting to peers that operate",
"their name by ``exposed_``, e.g. :: class FooService(Service): def exposed_add(self,... |
[
"core, util from taskmator import context class ManagerTest(TaskmatorTestBase): def testManager(self): print (\"Pending\") def",
"from testbase import TaskmatorTestBase from taskmator.task import core, util from taskmator import context",
"<filename>tests/task/manager_test.py import unittest from testbase impor... |
[
"a fan.\"\"\" node = ge_12730 assert node.device_class.specific.label == \"Multilevel Power Switch\" state =",
"\"\"\"Test GE 12730 Fan Controller v2.0 multilevel switch is discovered as a fan.\"\"\"",
"GE 12730 Fan Controller v2.0 multilevel switch is discovered as a fan.\"\"\" node",
"entities for device-sp... |
[
"None) -> Dict: pass def start_monitoring_members(self, AccountIds: List, DetectorId: str) -> Dict: pass",
"None) -> Dict: pass def create_ip_set(self, Activate: bool, DetectorId: str, Format: str, Location:",
"Enable: bool, ClientToken: str = None, FindingPublishingFrequency: str = None) -> Dict: pass",
"fro... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, softwar... |
[
"= Own fields = ('ownID', 'user', 'art', 'duplicates') class SaleSerializer(serializers.ModelSerializer): class Meta: model",
"= serializers.ReadOnlyField() class Meta: model = Art fields = ('artID', 'title', 'filename', 'rarity',",
"thumb_url = serializers.ReadOnlyField() class Meta: model = Art fields = ('art... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"EDIT.\"\"\" google_cloud_cpp_common_unit_tests = [ \"common_options_test.cc\", \"future_generic_test.cc\", \"future_generic_then_test.cc\", \"future... |
[
"import create_app class TestBase(unittest.TestCase): \"\"\"Default super class for api ver 1 tests\"\"\" #",
"api ver 1 tests\"\"\" # setup testing def setUp(self): self.app = create_app('testing') self.client",
"1 tests\"\"\" # setup testing def setUp(self): self.app = create_app('testing') self.client = self... |
[
"def get_nav(value): return value.split('/')[-2] @register.filter(name=\"encode_url\") def encode_url(value): return urllib.parse.quote(value) @register.filter def get_post_id(url): \"\"\"",
"import markdown import urllib register = template.Library() @register.filter def strip_space(value): return value.replace(... |
[
"o.od = d['od'] if 'time' in d: o.time = d['time'] if 'user_info' in",
"'to_alipay_dict'): params['od'] = self.od.to_alipay_dict() else: params['od'] = self.od if self.time: if hasattr(self.time, 'to_alipay_dict'):",
"d['time'] if 'user_info' in d: o.user_info = d['user_info'] if 'week_od' in d: o.week_od",
"... |
[
"the given redirect path', verbose_name='Catchall redirect'), ), migrations.AddField( model_name='redirect', name='subpath_match', field=models.BooleanField(default=False, help_text='If selected",
"on 2019-08-10 08:09 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [... |
[
"arguments can be accepted parser = argparse.ArgumentParser() parser.add_argument('query',metavar=\"\\\"SEARCH_KEYWORDS\\\"\", help=\"keywords to query in quotes",
"Calculate how many multiples of 100s of hits there are num_hundreds = total_hits",
"def find_total_hits(search_query): \"\"\" Function: find_total_... |
[
"check_server(payload_type='coap'): template_str = \">>> Make sure south {} plugin service is running \\n",
"import os import random import json from datetime import datetime, timezone import argparse",
"Read those objects * Send those to CoAP or HTTP south plugin server,",
"= \">>> Make sure south {} plugin ... |
[
"error mitigation filter. Produced from a tensored measurement calibration fitter and can be",
"matrix a list of the logical qubit indices (as int, states in the",
"qubits_to_clbits[qubit] = i # Apply the correction for data_idx, _ in enumerate(raw_data2): if",
"count_keys class MeasurementFilter(): \"\"\" Me... |
[
"count = connected_species.count(sp) labels.append((count, sp)) labels = sorted(labels, reverse=True) if anonymous: mapping =",
"to be unambiguous, \" \"trying to automatically detect.\") dist, to_jimage = self.structure[from_index].distance_and_image(self.structure[to_index]) if",
"= np.subtract(to_jimage, jim... |
[] |
[
"request.user instance.save( ) return redirect ('articles:list') else: form = forms.CreateArticle() return render(request, 'articles/article_create.html',",
"form.is_valid(): #save article to DB instance = form.save(commit=False) instance.author = request.user instance.save( )",
"import login_required from . im... |
[
"logging.getLogger(\"sifter\") log.error((\"No handler registered for test '%s' on line %d\" % (p[1], p.lineno(1))))",
"p_argument_tag(p): \"\"\"argument : TAG\"\"\" p[0] = sifter.grammar.Tag(p[1]) def p_stringlist_error(p): \"\"\"argument : '[' error",
"ELSE must follow IF or another ELSIF elif p[2].RULE_IDENT... |
[
"sequence #fig, ax = plt.sublots() p1 = plt.bar(ind, base, width, color='r') p2 =",
"sklearn.feature_extraction.text import CountVectorizer from sklearn.cross_validation import train_test_split from sklearn.cross_validation import cross_val_score from sklearn.cross_validation",
"X.shape # sklearn's grid search ... |
[
"import * from fast import * from spreadsheet import * from tab import",
"import * from spreadsheet import * from tab import * from utils import",
"from fast import * from spreadsheet import * from tab import * from",
"utils import * from web import * __all__ = [] __all__.extend(io.__all__) __all__.extend(fas... |
[
"rtn def build_url(self, targetid): \"\"\"Get the string representing the url for targetid. Gets",
"= { 'IPAddress': \"Host name or ip address\", 'CompanyID': \"DB id of company\",",
"not exist ' 'in local directory or config directory %s' % (fn, db_dict['directory']))",
"return true, else return false. \"\"\... |
[
"Copyright (c) Amazon.com, Inc. or its affiliates or # its licensors. # #",
"# or, if provided, by the license below or the license accompanying this",
"or the license accompanying this file. Do not # remove or modify any",
"\"AzEBusInline.tpl\", template_kwargs, '{}.generated.inline'.format(input_file_name))... |
[
"60XXXX , #_stockMarke = 1 # 00XXXX , # _stockMarke = 2 #",
"in string_lines: aline = aline.strip() if 'EM_CapitalFlowInterface' in aline: # print(aline) # print('------------------')",
"# print(_stockCode) # print(_stockMarke) # print(_stockName) # print(_market) values = [] for aline",
"目录存在 😁\") print(\"\... |
[
"app as application def setup_flask_logging(): # Log to stdout handler = logging.StreamHandler(sys.stdout) #",
"= logging.FileHandler('./application.log') handler.setLevel(logging.INFO) handler.setFormatter(logging.Formatter( '%(asctime)s [%(funcName)s] %(levelname)s: %(message)s ' )) application.logger.addHandle... |
[
"Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs) self.friendly =",
"game.base.being import Being class Enemy(Being): def __init__(self, app, scene, **kwargs): super().__init__(app, scene, **kwargs)",
"class Enemy(Being): def __init__(self, app, scene, *... |
[
"= [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField( model_name='rate', old_name='euro_rate', new_name='eur_rate', ),",
"migrations class Migration(migrations.Migration): dependencies = [ ('rates', '0001_initial'), ] operations = [ migrations.RenameField(",
"= [ migrations.RenameField( model... |
[
"using [openapi-generator](https://github.com/OpenAPITools/openapi-generator) from an OpenAPI specification of the Quetzal API. An improvement layer",
"files and their associated metadata. OpenAPI spec version: 0.5.0 Contact: <EMAIL> Generated by:",
"'Development Status :: 4 - Beta', 'Intended Audience :: Devel... |
[
"chapter in stream_data.get('contentSegments', []): start_time = float_or_none(chapter.get('start')) chapter_duration = float_or_none(chapter.get('duration')) if start_time is",
"subtitles.setdefault('en', []).append({ 'ext': ext, 'url': video_url, }) elif ext == 'png': thumbnails.append({ 'id':",
"self._sort_f... |
[
"time_window='daily_adj'): credentials = json.load(open('creds.json', 'r')) api_key = credentials['av_api_key'] print(symbol, time_window) ts = TimeSeries(key=api_key,",
"= ts.get_daily_adjusted(symbol, outputsize='full') pprint(data.head(10)) data.to_csv(f'./{symbol}_{time_window}.csv') if __name__ == \"__main__... |
[
"debug) self.path = path self._used = set() self.archive = None def playback(self): self.archive",
"result[\"__module__\"] = obj.__module__ except AttributeError: pass # Convert objects to dictionary representation based",
"class UTC(tzinfo): \"\"\"UTC\"\"\" def utcoffset(self, dt): return timedelta(0) def tzna... |
[
"<<: *nss_from_start series: - Test Series 22: <<: *series_ep_pack max_reruns: 0 test_next_series_seasons_season_pack_and_ep_gap_from_start_backfill: <<:",
"_config = \"\"\" templates: global: parsing: series: internal anchors: _nss_backfill: &nss_backfill next_series_seasons: backfill:",
"Series 10 S02', 'Test... |
[
"import mpl_toolkits.mplot3d as mpl3 color_list, color_proxy, color_proxy_on_wulff, miller_on_wulff, e_surf_on_wulff = self._get_colors( color_set, alpha,",
"normal. Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html Process: 1. get",
"= [0.0] * len(... |
[
"self.PAD_s = state[6] self.voc_size = state[7] self._s2i = state[8] self._i2s = state[9] self.frequency",
"self.get_s2i(s, -1) if i >= 0: return i else: raise Exception(f\"out of vocabulary",
"default def __getitem__(self, s): return self.s2i(s) def s2i(self, s): i = self.get_s2i(s, -1)",
"i = self._s2i.get(... |
[
"g import sqlite3 import os import json from random import randint from flask_jwt_extended",
"m.execute(sql, data) # stat min max data = [value, value, rom] sql =",
"exist\" %rom) return False def update_sensor_tmp(rom,value): m = mysql.connection.cursor() rom1 = [rom] sql=\"SELECT",
"sql = \"UPDATE sensors S... |
[
"python_on_rails.either import as_either, Failure, Success @as_either(TypeError) def add_one(x): return x + 1 @as_either()",
"add_one(x): return x + 1 @as_either() def times_five(x): return x * 5 def",
"+ 1 @as_either() def times_five(x): return x * 5 def test_success_executes_bindings(): result =",
"from pyt... |
[
"= models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model):",
"= models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stati... |
[
"input files that have spaces in their names, since bazel # cannot cope",
"_default_size, size_override = {}, data = [], per_test_extra_data = {}, default_tags = _default_tags,",
"\"//tensorflow/compiler/mlir/disc/tests:mlir_feature_test\", \"//tensorflow/core:test\", \"//tensorflow/core:test_main\", \"//tensor... |
[
"<uuid/uuid.h> int main() { uuid_t uu; char uuid_str[37]; uuid_generate(uu); uuid_unparse(uu, uuid_str); return 0;",
"= 0x00FF; uint32_t b32 = 0x0011EEFF; uint64_t b64 = 0x00112233CCDDEEFF; bswap_16(b16); bswap_32(b32); bswap_64(b64);",
"['selinux']) byteswap_source = ''' #include <byteswap.h> #include <stdint.... |
[
"weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) K.batch_set_value(weight_value_tuples) if tf_inputs is not None: tf_model(tf_inputs, training=False) # Make",
"separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) return tuple",
"allow_missing_keys=allo... |
[
"res != v: raise ValueError(f' actual: {res}\\n expected: {v}') def assert_all_eval_to(*expr_and_expected): exprs, expecteds",
"return {k: convert_struct_to_dict(v) for k, v in x._fields.items()} elif isinstance(x, list): return [convert_struct_to_dict(elt)",
"return x _dataset = None def get_dataset(): global ... |
[
"L] self.cross_entropy_losses = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.label_scores, targets=true_label_ids, name=\"labeling_loss\") self.labeling_loss = tf.reduce_sum( self.cross_entropy_losses) / tf.to_float(self.batch_size)",
"Discrete-State VAE\"\"\" def __init__(self, batch_size, num_labels, co... |
[
"exit(1) mol = pmd.load_file(sys.argv[1]) crds = mol.coordinates dist = distance.cdist(crds, crds, 'euclidean') print",
"pmd import numpy as np from scipy.spatial import distance if len(sys.argv) < 2:",
"len(sys.argv) < 2: print \"Usage: molecular_diameter.py <mymolecule.mol2>\" exit(1) mol = pmd.load_file(sys.... |
[
"= pd.read_csv(prefix + \"train.csv\", header=None) train_df.head() eval_df = pd.read_csv(prefix + \"test.csv\", header=None) eval_df.head()",
"= \"xlnet-base-cased\" train_args = { \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"use_cached_eval_features\": True, \"output_dir\": f\"outputs/{mode... |
[
"not os.path.isfile(path): print(\"Error: file \" + path + \" not found!\") exit(-1) with",
"file: for line in file.readlines(): line = line.split(delimiter) v1 = int(line[0]) v2 =",
"float(line[3]) if (self_loop and v1 == v2) or (v1 != v2): graph.add_edge(v1, v2,",
"\" + path + \" not found!\") exit(-1) with... |
[
"as _ from model_utils.managers import InheritanceManager from mayan.apps.django_gpg.exceptions import VerificationError from mayan.apps.django_gpg.models import",
"as exception: # Not signed logger.debug( 'detached signature verification error; %s', exception )",
"**kwargs): if self.signature_file.name: self.s... |
[
"argv[1] universe_config_file = \"config/galaxy.ini\" if len(argv) > 2: universe_config_file = argv[2] parser =",
"= set([]) with open(reports_config_file, \"w\") as f: # Write all properties from reports",
"replacement_property) and \\ not (replacement_property in replaced_properties): f.write(get_universe_lin... |
[
"copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE",
"-l\")) print(exe.exec_command_sync(\"cat /proc/self/cmdline | xargs -0\")) print(exe.exec_command_sync(\"echo -e 'hello \\\\n world'\")[0].count('\\n')) print(exe.exec_command_sync(\"echo -e",
"Technologies Co.,Ltd. # # ope... |
[
"tf.div(tf.pow((x-mu), 2), tf.exp(logvar)), reduction_indices=1) def sample_gaussian(mu, logvar): epsilon = tf.random_normal(tf.shape(logvar), name=\"epsilon\") std =",
"tf.div(tf.exp(recog_logvar), tf.exp(prior_logvar)), reduction_indices=1) return kld def norm_log_liklihood(x, mu, logvar): return -0.5*tf.reduce... |
[
"distributed on an # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY",
"size arg') if random.uniform(0, 1) > self.probability: return x width, height, _ =",
"values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N))",
"not isinstance(self.s_max, float): raise TypeError('... |
[
": {record.comments}') signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotation.sample, name=ANNOTATIONS_COL_NAME) full_df =",
"= pd.DataFrame(record.p_signal, columns=record.sig_name) annot_serie = pd.Series(annotation.symbol, index=annotatio... |
[
"pass def MoveNext(self): \"\"\" MoveNext(self: CharEnumerator) -> bool Increments the internal index of",
"current System.CharEnumerator object to the next character of the enumerated string. Returns: true",
"be inherited. \"\"\" def ZZZ(self): \"\"\"hardcoded/mock instance of the class\"\"\" return CharEnumer... |
[
"<filename>src/home_automation_hub/config.py from .config_store import ConfigStore config = ConfigStore() config.set_mqtt_broker(\"mqtt\", 1883) config.set_redis_config(\"redis\", 6379, 0)"
] |
[
"send(self): size = len(self.buffer) _size = pack('!Ih',size,self.status) data = _size + self.buffer sent",
"#f float float 4 #d double float 8 #s char[] string #p char[]",
"0 self.clear() self.add(\"i\",func_id) if types: self.add(types,*args) self.send() size = self.receive() if size: if",
"from website.con... |
[
"name name_face = AttrFace(\"name\") else: # If internal node, draws label with smaller",
"style[\"hz_line_color\"] = 'black' n.set_style(style) #check if sample was misclassified and add misclassified label",
"ax = fig.add_subplot(111) x = [0,1] lines = [ax.plot(x, pd.np.ones(len(x)), 'o', color =",
"\"_lege... |
[
"= CellDiameter(mesh) h_avg = avg(h) # Classical term a = dot(grad(p), grad(q)) *",
"/ singular_values.min() else: raise NotImplementedError(\"The required method for condition number estimation is currently",
"singular_values.max() / singular_values.min() elif backend == \"slepc\": S = SLEPc.SVD() S.create() S... |
[
"Mobility varfdiff = np.var(diff_1st, axis=-1) # print(varfdiff.shape) mobility = np.sqrt(varfdiff / activity) #",
"f return ], ind[2]] = - D f = np.expand_dims(fd, axis=-1) if combined:",
"ss = np.sum(np.abs(np.diff(X[..., m::k], n=1)), axis=-1) Lm = (ss*norm) / k return Lm",
"calc_L_average_series = np.from... |