Reset23/the-stack-v2-processed · Datasets at Hugging Face

65e39f05502ee1e4f16aa0274c7f28f5ee17a0e9

bf2390ac5b116177acebd424707167c5c22d081a

/include/ClientData.py

e10c43a7a89c916e059cfdba2a21e69ac7991739

[ "WTFPL" ]

permissive

bigretromike/hydrus

1cd842129da3e1851a7ad0559790a4895cad6887

6da58bc9df51c75fa65173b182166deb3259278e

refs/heads/master

2021-01-24T20:25:24.164062

2015-11-13T09:06:38

44,671,599

0

null

2015-11-13T09:06:38

2015-10-21T11:26:12

Python

UTF-8

Python

false

110,042

py

import ClientConstants as CC import ClientDefaults import ClientDownloading import ClientFiles import ClientNetworking import collections import datetime import HydrusConstants as HC import HydrusExceptions import HydrusSerialisable import HydrusTags import threading import traceback import os import random import shutil import sqlite3 import stat import sys import time import wx import yaml import HydrusData import HydrusGlobals import HydrusThreading def AddPaddingToDimensions( dimensions, padding ): ( x, y ) = dimensions return ( x + padding, y + padding ) def CatchExceptionClient( etype, value, tb ): try: job_key = HydrusThreading.JobKey() if etype == HydrusExceptions.ShutdownException: return elif etype == HydrusExceptions.DBException: ( text, caller_traceback, db_traceback ) = value job_key.SetVariable( 'popup_title', 'Database Error!' ) job_key.SetVariable( 'popup_text_1', text ) job_key.SetVariable( 'popup_caller_traceback', caller_traceback ) job_key.SetVariable( 'popup_db_traceback', db_traceback ) else: trace_list = traceback.format_tb( tb ) trace = ''.join( trace_list ) if etype == wx.PyDeadObjectError: print( 'Got a PyDeadObjectError, which can probably be ignored, but here it is anyway:' ) print( HydrusData.ToUnicode( value ) ) print( trace ) return try: job_key.SetVariable( 'popup_title', HydrusData.ToUnicode( etype.__name__ ) ) except: job_key.SetVariable( 'popup_title', HydrusData.ToUnicode( etype ) ) job_key.SetVariable( 'popup_text_1', HydrusData.ToUnicode( value ) ) job_key.SetVariable( 'popup_traceback', trace ) text = job_key.ToString() print( '' ) print( 'The following uncaught exception occured at ' + HydrusData.ConvertTimestampToPrettyTime( HydrusData.GetNow() ) + ':' ) try: print( text ) except: print( repr( text ) ) sys.stdout.flush() sys.stderr.flush() HydrusGlobals.client_controller.pub( 'message', job_key ) except: text = 'Encountered an error I could not parse:' text += os.linesep text += HydrusData.ToUnicode( ( etype, value, tb ) ) try: text += traceback.format_exc() except: pass HydrusData.ShowText( text ) time.sleep( 1 ) def ConvertServiceKeysToContentUpdatesToPrettyString( service_keys_to_content_updates ): num_files = 0 actions = set() locations = set() extra_words = '' for ( service_key, content_updates ) in service_keys_to_content_updates.items(): if len( content_updates ) > 0: name = HydrusGlobals.client_controller.GetServicesManager().GetService( service_key ).GetName() locations.add( name ) for content_update in content_updates: ( data_type, action, row ) = content_update.ToTuple() if data_type == HC.CONTENT_TYPE_MAPPINGS: extra_words = ' tags for' actions.add( HC.content_update_string_lookup[ action ] ) num_files += len( content_update.GetHashes() ) s = ', '.join( locations ) + '->' + ', '.join( actions ) + extra_words + ' ' + HydrusData.ConvertIntToPrettyString( num_files ) + ' files' return s def ConvertServiceKeysToTagsToServiceKeysToContentUpdates( hashes, service_keys_to_tags ): service_keys_to_content_updates = {} for ( service_key, tags ) in service_keys_to_tags.items(): if service_key == CC.LOCAL_TAG_SERVICE_KEY: action = HC.CONTENT_UPDATE_ADD else: action = HC.CONTENT_UPDATE_PEND content_updates = [ HydrusData.ContentUpdate( HC.CONTENT_TYPE_MAPPINGS, action, ( tag, hashes ) ) for tag in tags ] service_keys_to_content_updates[ service_key ] = content_updates return service_keys_to_content_updates def ConvertShortcutToPrettyShortcut( modifier, key ): if modifier == wx.ACCEL_NORMAL: modifier = '' elif modifier == wx.ACCEL_ALT: modifier = 'alt' elif modifier == wx.ACCEL_CTRL: modifier = 'ctrl' elif modifier == wx.ACCEL_SHIFT: modifier = 'shift' if key in range( 65, 91 ): key = chr( key + 32 ) # + 32 for converting ascii A -> a elif key in range( 97, 123 ): key = chr( key ) else: key = CC.wxk_code_string_lookup[ key ] return ( modifier, key ) def ConvertZoomToPercentage( zoom ): zoom_percent = zoom * 100 if zoom in CC.ZOOMS: pretty_zoom = '%i' % zoom_percent + '%' else: pretty_zoom = '%.2f' % zoom_percent + '%' return pretty_zoom def DeletePath( path ): if HC.options[ 'delete_to_recycle_bin' ] == True: HydrusData.RecyclePath( path ) else: HydrusData.DeletePath( path ) def GetMediasTagCount( pool, tag_service_key = CC.COMBINED_TAG_SERVICE_KEY, collapse_siblings = False ): siblings_manager = HydrusGlobals.client_controller.GetManager( 'tag_siblings' ) tags_managers = [] for media in pool: if media.IsCollection(): tags_managers.extend( media.GetSingletonsTagsManagers() ) else: tags_managers.append( media.GetTagsManager() ) current_tags_to_count = collections.Counter() deleted_tags_to_count = collections.Counter() pending_tags_to_count = collections.Counter() petitioned_tags_to_count = collections.Counter() for tags_manager in tags_managers: statuses_to_tags = tags_manager.GetStatusesToTags( tag_service_key ) if collapse_siblings: statuses_to_tags = siblings_manager.CollapseStatusesToTags( statuses_to_tags ) current_tags_to_count.update( statuses_to_tags[ HC.CURRENT ] ) deleted_tags_to_count.update( statuses_to_tags[ HC.DELETED ] ) pending_tags_to_count.update( statuses_to_tags[ HC.PENDING ] ) petitioned_tags_to_count.update( statuses_to_tags[ HC.PETITIONED ] ) return ( current_tags_to_count, deleted_tags_to_count, pending_tags_to_count, petitioned_tags_to_count ) def ShowExceptionClient( e ): job_key = HydrusThreading.JobKey() if isinstance( e, HydrusExceptions.ShutdownException ): return elif isinstance( e, HydrusExceptions.DBException ): ( text, caller_traceback, db_traceback ) = e.args job_key.SetVariable( 'popup_title', 'Database Error!' ) job_key.SetVariable( 'popup_text_1', text ) job_key.SetVariable( 'popup_caller_traceback', caller_traceback ) job_key.SetVariable( 'popup_db_traceback', db_traceback ) else: ( etype, value, tb ) = sys.exc_info() if etype is None: etype = type( e ) value = HydrusData.ToUnicode( e ) trace = ''.join( traceback.format_stack() ) else: trace = ''.join( traceback.format_exception( etype, value, tb ) ) if etype == wx.PyDeadObjectError: print( 'Got a PyDeadObjectError, which can probably be ignored, but here it is anyway:' ) print( HydrusData.ToUnicode( value ) ) print( trace ) return if hasattr( etype, '__name__' ): title = HydrusData.ToUnicode( etype.__name__ ) else: title = HydrusData.ToUnicode( etype ) job_key.SetVariable( 'popup_title', title ) job_key.SetVariable( 'popup_text_1', HydrusData.ToUnicode( value ) ) job_key.SetVariable( 'popup_traceback', trace ) text = job_key.ToString() print( '' ) print( 'The following exception occured at ' + HydrusData.ConvertTimestampToPrettyTime( HydrusData.GetNow() ) + ':' ) try: print( text ) except: print( repr( text ) ) sys.stdout.flush() sys.stderr.flush() HydrusGlobals.client_controller.pub( 'message', job_key ) time.sleep( 1 ) def ShowTextClient( text ): job_key = HydrusThreading.JobKey() job_key.SetVariable( 'popup_text_1', HydrusData.ToUnicode( text ) ) text = job_key.ToString() try: print( text ) except: print( repr( text ) ) HydrusGlobals.client_controller.pub( 'message', job_key ) class Booru( HydrusData.HydrusYAMLBase ): yaml_tag = u'!Booru' def __init__( self, name, search_url, search_separator, advance_by_page_num, thumb_classname, image_id, image_data, tag_classnames_to_namespaces ): self._name = name self._search_url = search_url self._search_separator = search_separator self._advance_by_page_num = advance_by_page_num self._thumb_classname = thumb_classname self._image_id = image_id self._image_data = image_data self._tag_classnames_to_namespaces = tag_classnames_to_namespaces def GetData( self ): return ( self._search_url, self._search_separator, self._advance_by_page_num, self._thumb_classname, self._image_id, self._image_data, self._tag_classnames_to_namespaces ) def GetGalleryParsingInfo( self ): return ( self._search_url, self._advance_by_page_num, self._search_separator, self._thumb_classname ) def GetName( self ): return self._name def GetNamespaces( self ): return self._tag_classnames_to_namespaces.values() sqlite3.register_adapter( Booru, yaml.safe_dump ) class ClientOptions( HydrusSerialisable.SerialisableBase ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_CLIENT_OPTIONS SERIALISABLE_VERSION = 1 def __init__( self ): HydrusSerialisable.SerialisableBase.__init__( self ) self._dictionary = HydrusSerialisable.SerialisableDictionary() self._lock = threading.Lock() self._InitialiseDefaults() def _GetSerialisableInfo( self ): with self._lock: serialisable_info = self._dictionary.GetSerialisableTuple() return serialisable_info def _InitialiseDefaults( self ): self._dictionary[ 'default_import_tag_options' ] = HydrusSerialisable.SerialisableDictionary() def _InitialiseFromSerialisableInfo( self, serialisable_info ): self._dictionary = HydrusSerialisable.CreateFromSerialisableTuple( serialisable_info ) def ClearDefaultImportTagOptions( self ): with self._lock: self._dictionary[ 'default_import_tag_options' ] = HydrusSerialisable.SerialisableDictionary() def GetDefaultImportTagOptions( self, gallery_identifier = None ): with self._lock: default_import_tag_options = self._dictionary[ 'default_import_tag_options' ] if gallery_identifier is None: return default_import_tag_options else: if gallery_identifier in default_import_tag_options: import_tag_options = default_import_tag_options[ gallery_identifier ] else: default_booru_gallery_identifier = ClientDownloading.GalleryIdentifier( HC.SITE_TYPE_BOORU ) default_hentai_foundry_gallery_identifier = ClientDownloading.GalleryIdentifier( HC.SITE_TYPE_HENTAI_FOUNDRY ) default_pixiv_gallery_identifier = ClientDownloading.GalleryIdentifier( HC.SITE_TYPE_PIXIV ) default_gallery_identifier = ClientDownloading.GalleryIdentifier( HC.SITE_TYPE_DEFAULT ) guidance_import_tag_options = None site_type = gallery_identifier.GetSiteType() if site_type == HC.SITE_TYPE_BOORU and default_booru_gallery_identifier in default_import_tag_options: guidance_import_tag_options = default_import_tag_options[ default_booru_gallery_identifier ] elif site_type in ( HC.SITE_TYPE_HENTAI_FOUNDRY_ARTIST, HC.SITE_TYPE_HENTAI_FOUNDRY_TAGS ) and default_hentai_foundry_gallery_identifier in default_import_tag_options: guidance_import_tag_options = default_import_tag_options[ default_hentai_foundry_gallery_identifier ] elif site_type in ( HC.SITE_TYPE_PIXIV_ARTIST_ID, HC.SITE_TYPE_PIXIV_TAG ) and default_pixiv_gallery_identifier in default_import_tag_options: guidance_import_tag_options = default_import_tag_options[ default_pixiv_gallery_identifier ] elif default_gallery_identifier in default_import_tag_options: guidance_import_tag_options = default_import_tag_options[ default_gallery_identifier ] service_keys_to_namespaces = {} service_keys_to_explicit_tags = {} if guidance_import_tag_options is not None: ( namespaces, search_value ) = ClientDefaults.GetDefaultNamespacesAndSearchValue( gallery_identifier ) guidance_service_keys_to_namespaces = guidance_import_tag_options.GetServiceKeysToNamespaces() for ( service_key, guidance_namespaces ) in guidance_service_keys_to_namespaces.items(): if 'all namespaces' in guidance_namespaces: service_keys_to_namespaces[ service_key ] = namespaces else: service_keys_to_namespaces[ service_key ] = [ namespace for namespace in namespaces if namespace in guidance_namespaces ] service_keys_to_explicit_tags = guidance_import_tag_options.GetServiceKeysToExplicitTags() import_tag_options = ImportTagOptions( service_keys_to_namespaces = service_keys_to_namespaces, service_keys_to_explicit_tags = service_keys_to_explicit_tags ) return import_tag_options def SetDefaultImportTagOptions( self, gallery_identifier, import_tag_options ): with self._lock: self._dictionary[ 'default_import_tag_options' ][ gallery_identifier ] = import_tag_options HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_CLIENT_OPTIONS ] = ClientOptions class Credentials( HydrusData.HydrusYAMLBase ): yaml_tag = u'!Credentials' def __init__( self, host, port, access_key = None ): HydrusData.HydrusYAMLBase.__init__( self ) if host == 'localhost': host = '127.0.0.1' self._host = host self._port = port self._access_key = access_key def __eq__( self, other ): return self.__hash__() == other.__hash__() def __hash__( self ): return ( self._host, self._port, self._access_key ).__hash__() def __ne__( self, other ): return self.__hash__() != other.__hash__() def __repr__( self ): return 'Credentials: ' + HydrusData.ToUnicode( ( self._host, self._port, self._access_key.encode( 'hex' ) ) ) def GetAccessKey( self ): return self._access_key def GetAddress( self ): return ( self._host, self._port ) def GetConnectionString( self ): connection_string = '' if self.HasAccessKey(): connection_string += self._access_key.encode( 'hex' ) + '@' connection_string += self._host + ':' + str( self._port ) return connection_string def HasAccessKey( self ): return self._access_key is not None and self._access_key is not '' def SetAccessKey( self, access_key ): self._access_key = access_key class FileQueryResult( object ): def __init__( self, media_results ): self._hashes_to_media_results = { media_result.GetHash() : media_result for media_result in media_results } self._hashes_ordered = [ media_result.GetHash() for media_result in media_results ] self._hashes = set( self._hashes_ordered ) HydrusGlobals.client_controller.sub( self, 'ProcessContentUpdates', 'content_updates_data' ) HydrusGlobals.client_controller.sub( self, 'ProcessServiceUpdates', 'service_updates_data' ) def __iter__( self ): for hash in self._hashes_ordered: yield self._hashes_to_media_results[ hash ] def __len__( self ): return len( self._hashes_ordered ) def _Remove( self, hashes ): for hash in hashes: if hash in self._hashes_to_media_results: del self._hashes_to_media_results[ hash ] self._hashes_ordered.remove( hash ) self._hashes.difference_update( hashes ) def AddMediaResults( self, media_results ): for media_result in media_results: hash = media_result.GetHash() if hash in self._hashes: continue # this is actually important, as sometimes we don't want the media result overwritten self._hashes_to_media_results[ hash ] = media_result self._hashes_ordered.append( hash ) self._hashes.add( hash ) def GetHashes( self ): return self._hashes def GetMediaResult( self, hash ): return self._hashes_to_media_results[ hash ] def GetMediaResults( self ): return [ self._hashes_to_media_results[ hash ] for hash in self._hashes_ordered ] def ProcessContentUpdates( self, service_keys_to_content_updates ): for ( service_key, content_updates ) in service_keys_to_content_updates.items(): for content_update in content_updates: hashes = content_update.GetHashes() if len( hashes ) > 0: for hash in self._hashes.intersection( hashes ): media_result = self._hashes_to_media_results[ hash ] media_result.ProcessContentUpdate( service_key, content_update ) def ProcessServiceUpdates( self, service_keys_to_service_updates ): for ( service_key, service_updates ) in service_keys_to_service_updates.items(): for service_update in service_updates: ( action, row ) = service_update.ToTuple() if action == HC.SERVICE_UPDATE_DELETE_PENDING: for media_result in self._hashes_to_media_results.values(): media_result.DeletePending( service_key ) elif action == HC.SERVICE_UPDATE_RESET: for media_result in self._hashes_to_media_results.values(): media_result.ResetService( service_key ) class FileSearchContext( HydrusSerialisable.SerialisableBase ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_FILE_SEARCH_CONTEXT SERIALISABLE_VERSION = 1 def __init__( self, file_service_key = CC.COMBINED_FILE_SERVICE_KEY, tag_service_key = CC.COMBINED_TAG_SERVICE_KEY, include_current_tags = True, include_pending_tags = True, predicates = None ): if predicates is None: predicates = [] self._file_service_key = file_service_key self._tag_service_key = tag_service_key self._include_current_tags = include_current_tags self._include_pending_tags = include_pending_tags self._predicates = predicates self._search_complete = False self._InitialiseTemporaryVariables() def _GetSerialisableInfo( self ): serialisable_predicates = [ predicate.GetSerialisableTuple() for predicate in self._predicates ] return ( self._file_service_key.encode( 'hex' ), self._tag_service_key.encode( 'hex' ), self._include_current_tags, self._include_pending_tags, serialisable_predicates, self._search_complete ) def _InitialiseFromSerialisableInfo( self, serialisable_info ): ( file_service_key, tag_service_key, self._include_current_tags, self._include_pending_tags, serialisable_predicates, self._search_complete ) = serialisable_info self._file_service_key = file_service_key.decode( 'hex' ) self._tag_service_key = tag_service_key.decode( 'hex' ) self._predicates = [ HydrusSerialisable.CreateFromSerialisableTuple( pred_tuple ) for pred_tuple in serialisable_predicates ] self._InitialiseTemporaryVariables() def _InitialiseTemporaryVariables( self ): system_predicates = [ predicate for predicate in self._predicates if predicate.GetType() in HC.SYSTEM_PREDICATES ] self._system_predicates = FileSystemPredicates( system_predicates ) tag_predicates = [ predicate for predicate in self._predicates if predicate.GetType() == HC.PREDICATE_TYPE_TAG ] self._tags_to_include = [] self._tags_to_exclude = [] for predicate in tag_predicates: tag = predicate.GetValue() if predicate.GetInclusive(): self._tags_to_include.append( tag ) else: self._tags_to_exclude.append( tag ) namespace_predicates = [ predicate for predicate in self._predicates if predicate.GetType() == HC.PREDICATE_TYPE_NAMESPACE ] self._namespaces_to_include = [] self._namespaces_to_exclude = [] for predicate in namespace_predicates: namespace = predicate.GetValue() if predicate.GetInclusive(): self._namespaces_to_include.append( namespace ) else: self._namespaces_to_exclude.append( namespace ) wildcard_predicates = [ predicate for predicate in self._predicates if predicate.GetType() == HC.PREDICATE_TYPE_WILDCARD ] self._wildcards_to_include = [] self._wildcards_to_exclude = [] for predicate in wildcard_predicates: wildcard = predicate.GetValue() if predicate.GetInclusive(): self._wildcards_to_include.append( wildcard ) else: self._wildcards_to_exclude.append( wildcard ) def GetFileServiceKey( self ): return self._file_service_key def GetNamespacesToExclude( self ): return self._namespaces_to_exclude def GetNamespacesToInclude( self ): return self._namespaces_to_include def GetPredicates( self ): return self._predicates def GetSystemPredicates( self ): return self._system_predicates def GetTagServiceKey( self ): return self._tag_service_key def GetTagsToExclude( self ): return self._tags_to_exclude def GetTagsToInclude( self ): return self._tags_to_include def GetWildcardsToExclude( self ): return self._wildcards_to_exclude def GetWildcardsToInclude( self ): return self._wildcards_to_include def IncludeCurrentTags( self ): return self._include_current_tags def IncludePendingTags( self ): return self._include_pending_tags def IsComplete( self ): return self._search_complete def SetComplete( self ): self._search_complete = True def SetPredicates( self, predicates ): self._predicates = predicates self._InitialiseTemporaryVariables() HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_FILE_SEARCH_CONTEXT ] = FileSearchContext class FileSystemPredicates( object ): def __init__( self, system_predicates ): self._inbox = False self._archive = False self._local = False self._not_local = False self._common_info = {} self._limit = None self._similar_to = None self._file_services_to_include_current = [] self._file_services_to_include_pending = [] self._file_services_to_exclude_current = [] self._file_services_to_exclude_pending = [] self._ratings_predicates = [] for predicate in system_predicates: predicate_type = predicate.GetType() value = predicate.GetValue() if predicate_type == HC.PREDICATE_TYPE_SYSTEM_INBOX: self._inbox = True if predicate_type == HC.PREDICATE_TYPE_SYSTEM_ARCHIVE: self._archive = True if predicate_type == HC.PREDICATE_TYPE_SYSTEM_LOCAL: self._local = True if predicate_type == HC.PREDICATE_TYPE_SYSTEM_NOT_LOCAL: self._not_local = True if predicate_type == HC.PREDICATE_TYPE_SYSTEM_HASH: hash = value self._common_info[ 'hash' ] = hash if predicate_type == HC.PREDICATE_TYPE_SYSTEM_AGE: ( operator, years, months, days, hours ) = value age = ( ( ( ( ( ( ( years * 12 ) + months ) * 30 ) + days ) * 24 ) + hours ) * 3600 ) now = HydrusData.GetNow() # this is backwards because we are talking about age, not timestamp if operator == '<': self._common_info[ 'min_timestamp' ] = now - age elif operator == '>': self._common_info[ 'max_timestamp' ] = now - age elif operator == u'\u2248': self._common_info[ 'min_timestamp' ] = now - int( age * 1.15 ) self._common_info[ 'max_timestamp' ] = now - int( age * 0.85 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_MIME: mimes = value if type( mimes ) == int: mimes = ( mimes, ) self._common_info[ 'mimes' ] = mimes if predicate_type == HC.PREDICATE_TYPE_SYSTEM_DURATION: ( operator, duration ) = value if operator == '<': self._common_info[ 'max_duration' ] = duration elif operator == '>': self._common_info[ 'min_duration' ] = duration elif operator == '=': self._common_info[ 'duration' ] = duration elif operator == u'\u2248': if duration == 0: self._common_info[ 'duration' ] = 0 else: self._common_info[ 'min_duration' ] = int( duration * 0.85 ) self._common_info[ 'max_duration' ] = int( duration * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_RATING: ( operator, value, service_key ) = value self._ratings_predicates.append( ( operator, value, service_key ) ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_RATIO: ( operator, ratio_width, ratio_height ) = value if operator == '=': self._common_info[ 'ratio' ] = ( ratio_width, ratio_height ) elif operator == u'\u2248': self._common_info[ 'min_ratio' ] = ( ratio_width * 0.85, ratio_height ) self._common_info[ 'max_ratio' ] = ( ratio_width * 1.15, ratio_height ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIZE: ( operator, size, unit ) = value size = size * unit if operator == '<': self._common_info[ 'max_size' ] = size elif operator == '>': self._common_info[ 'min_size' ] = size elif operator == '=': self._common_info[ 'size' ] = size elif operator == u'\u2248': self._common_info[ 'min_size' ] = int( size * 0.85 ) self._common_info[ 'max_size' ] = int( size * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_TAGS: ( operator, num_tags ) = value if operator == '<': self._common_info[ 'max_num_tags' ] = num_tags elif operator == '=': self._common_info[ 'num_tags' ] = num_tags elif operator == '>': self._common_info[ 'min_num_tags' ] = num_tags if predicate_type == HC.PREDICATE_TYPE_SYSTEM_WIDTH: ( operator, width ) = value if operator == '<': self._common_info[ 'max_width' ] = width elif operator == '>': self._common_info[ 'min_width' ] = width elif operator == '=': self._common_info[ 'width' ] = width elif operator == u'\u2248': if width == 0: self._common_info[ 'width' ] = 0 else: self._common_info[ 'min_width' ] = int( width * 0.85 ) self._common_info[ 'max_width' ] = int( width * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_PIXELS: ( operator, num_pixels, unit ) = value num_pixels = num_pixels * unit if operator == '<': self._common_info[ 'max_num_pixels' ] = num_pixels elif operator == '>': self._common_info[ 'min_num_pixels' ] = num_pixels elif operator == '=': self._common_info[ 'num_pixels' ] = num_pixels elif operator == u'\u2248': self._common_info[ 'min_num_pixels' ] = int( num_pixels * 0.85 ) self._common_info[ 'max_num_pixels' ] = int( num_pixels * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_HEIGHT: ( operator, height ) = value if operator == '<': self._common_info[ 'max_height' ] = height elif operator == '>': self._common_info[ 'min_height' ] = height elif operator == '=': self._common_info[ 'height' ] = height elif operator == u'\u2248': if height == 0: self._common_info[ 'height' ] = 0 else: self._common_info[ 'min_height' ] = int( height * 0.85 ) self._common_info[ 'max_height' ] = int( height * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_WORDS: ( operator, num_words ) = value if operator == '<': self._common_info[ 'max_num_words' ] = num_words elif operator == '>': self._common_info[ 'min_num_words' ] = num_words elif operator == '=': self._common_info[ 'num_words' ] = num_words elif operator == u'\u2248': if num_words == 0: self._common_info[ 'num_words' ] = 0 else: self._common_info[ 'min_num_words' ] = int( num_words * 0.85 ) self._common_info[ 'max_num_words' ] = int( num_words * 1.15 ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_LIMIT: limit = value self._limit = limit if predicate_type == HC.PREDICATE_TYPE_SYSTEM_FILE_SERVICE: ( operator, current_or_pending, service_key ) = value if operator == True: if current_or_pending == HC.CURRENT: self._file_services_to_include_current.append( service_key ) else: self._file_services_to_include_pending.append( service_key ) else: if current_or_pending == HC.CURRENT: self._file_services_to_exclude_current.append( service_key ) else: self._file_services_to_exclude_pending.append( service_key ) if predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIMILAR_TO: ( hash, max_hamming ) = value self._similar_to = ( hash, max_hamming ) def GetFileServiceInfo( self ): return ( self._file_services_to_include_current, self._file_services_to_include_pending, self._file_services_to_exclude_current, self._file_services_to_exclude_pending ) def GetSimpleInfo( self ): return self._common_info def GetLimit( self ): return self._limit def GetRatingsPredicates( self ): return self._ratings_predicates def GetSimilarTo( self ): return self._similar_to def HasSimilarTo( self ): return self._similar_to is not None def MustBeArchive( self ): return self._archive def MustBeInbox( self ): return self._inbox def MustBeLocal( self ): return self._local def MustNotBeLocal( self ): return self._not_local class Imageboard( HydrusData.HydrusYAMLBase ): yaml_tag = u'!Imageboard' def __init__( self, name, post_url, flood_time, form_fields, restrictions ): self._name = name self._post_url = post_url self._flood_time = flood_time self._form_fields = form_fields self._restrictions = restrictions def IsOkToPost( self, media_result ): ( hash, inbox, size, mime, timestamp, width, height, duration, num_frames, num_words, tags_manager, locations_manager, local_ratings, remote_ratings ) = media_result.ToTuple() if CC.RESTRICTION_MIN_RESOLUTION in self._restrictions: ( min_width, min_height ) = self._restrictions[ CC.RESTRICTION_MIN_RESOLUTION ] if width < min_width or height < min_height: return False if CC.RESTRICTION_MAX_RESOLUTION in self._restrictions: ( max_width, max_height ) = self._restrictions[ CC.RESTRICTION_MAX_RESOLUTION ] if width > max_width or height > max_height: return False if CC.RESTRICTION_MAX_FILE_SIZE in self._restrictions and size > self._restrictions[ CC.RESTRICTION_MAX_FILE_SIZE ]: return False if CC.RESTRICTION_ALLOWED_MIMES in self._restrictions and mime not in self._restrictions[ CC.RESTRICTION_ALLOWED_MIMES ]: return False return True def GetBoardInfo( self ): return ( self._post_url, self._flood_time, self._form_fields, self._restrictions ) def GetName( self ): return self._name sqlite3.register_adapter( Imageboard, yaml.safe_dump ) class ImportFileOptions( HydrusSerialisable.SerialisableBase ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_IMPORT_FILE_OPTIONS SERIALISABLE_VERSION = 1 def __init__( self, automatic_archive = None, exclude_deleted = None, min_size = None, min_resolution = None ): HydrusSerialisable.SerialisableBase.__init__( self ) self._automatic_archive = automatic_archive self._exclude_deleted = exclude_deleted self._min_size = min_size self._min_resolution = min_resolution def _GetSerialisableInfo( self ): return ( self._automatic_archive, self._exclude_deleted, self._min_size, self._min_resolution ) def _InitialiseFromSerialisableInfo( self, serialisable_info ): ( self._automatic_archive, self._exclude_deleted, self._min_size, self._min_resolution ) = serialisable_info def FileIsValid( self, size, resolution = None ): if self._min_size is not None and size < self._min_size: return False if resolution is not None and self._min_resolution is not None: ( x, y ) = resolution ( min_x, min_y ) = self._min_resolution if x < min_x or y < min_y: return False return True def GetAutomaticArchive( self ): return self._automatic_archive def GetExcludeDeleted( self ): return self._exclude_deleted def ToTuple( self ): return ( self._automatic_archive, self._exclude_deleted, self._min_size, self._min_resolution ) HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_IMPORT_FILE_OPTIONS ] = ImportFileOptions class ImportTagOptions( HydrusSerialisable.SerialisableBase ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_IMPORT_TAG_OPTIONS SERIALISABLE_VERSION = 2 def __init__( self, service_keys_to_namespaces = None, service_keys_to_explicit_tags = None ): HydrusSerialisable.SerialisableBase.__init__( self ) if service_keys_to_namespaces is None: service_keys_to_namespaces = {} if service_keys_to_explicit_tags is None: service_keys_to_explicit_tags = {} self._service_keys_to_namespaces = service_keys_to_namespaces self._service_keys_to_explicit_tags = service_keys_to_explicit_tags def _GetSerialisableInfo( self ): safe_service_keys_to_namespaces = { service_key.encode( 'hex' ) : list( namespaces ) for ( service_key, namespaces ) in self._service_keys_to_namespaces.items() } safe_service_keys_to_explicit_tags = { service_key.encode( 'hex' ) : list( tags ) for ( service_key, tags ) in self._service_keys_to_explicit_tags.items() } return ( safe_service_keys_to_namespaces, safe_service_keys_to_explicit_tags ) def _InitialiseFromSerialisableInfo( self, serialisable_info ): ( safe_service_keys_to_namespaces, safe_service_keys_to_explicit_tags ) = serialisable_info self._service_keys_to_namespaces = { service_key.decode( 'hex' ) : set( namespaces ) for ( service_key, namespaces ) in safe_service_keys_to_namespaces.items() } self._service_keys_to_explicit_tags = { service_key.decode( 'hex' ) : set( tags ) for ( service_key, tags ) in safe_service_keys_to_explicit_tags.items() } def _UpdateSerialisableInfo( self, version, old_serialisable_info ): if version == 1: safe_service_keys_to_namespaces = old_serialisable_info safe_service_keys_to_explicit_tags = {} new_serialisable_info = ( safe_service_keys_to_namespaces, safe_service_keys_to_explicit_tags ) return ( 2, new_serialisable_info ) def GetServiceKeysToExplicitTags( self ): return dict( self._service_keys_to_explicit_tags ) def GetServiceKeysToNamespaces( self ): return dict( self._service_keys_to_namespaces ) def GetServiceKeysToContentUpdates( self, hash, tags ): tags = [ tag for tag in tags if tag is not None ] service_keys_to_tags = collections.defaultdict( set ) siblings_manager = HydrusGlobals.client_controller.GetManager( 'tag_siblings' ) parents_manager = HydrusGlobals.client_controller.GetManager( 'tag_parents' ) for ( service_key, namespaces ) in self._service_keys_to_namespaces.items(): tags_to_add_here = [] if len( namespaces ) > 0: for namespace in namespaces: if namespace == '': tags_to_add_here.extend( [ tag for tag in tags if not ':' in tag ] ) else: tags_to_add_here.extend( [ tag for tag in tags if tag.startswith( namespace + ':' ) ] ) tags_to_add_here = HydrusTags.CleanTags( tags_to_add_here ) if len( tags_to_add_here ) > 0: tags_to_add_here = siblings_manager.CollapseTags( tags_to_add_here ) tags_to_add_here = parents_manager.ExpandTags( service_key, tags_to_add_here ) service_keys_to_tags[ service_key ].update( tags_to_add_here ) for ( service_key, explicit_tags ) in self._service_keys_to_explicit_tags.items(): tags_to_add_here = HydrusTags.CleanTags( explicit_tags ) if len( tags_to_add_here ) > 0: tags_to_add_here = siblings_manager.CollapseTags( tags_to_add_here ) tags_to_add_here = parents_manager.ExpandTags( service_key, tags_to_add_here ) service_keys_to_tags[ service_key ].update( tags_to_add_here ) service_keys_to_content_updates = ConvertServiceKeysToTagsToServiceKeysToContentUpdates( { hash }, service_keys_to_tags ) return service_keys_to_content_updates def ShouldFetchTags( self ): i_am_interested_in_namespaces = len( self._service_keys_to_namespaces ) > 0 return i_am_interested_in_namespaces HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_IMPORT_TAG_OPTIONS ] = ImportTagOptions class Predicate( HydrusSerialisable.SerialisableBase ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_PREDICATE SERIALISABLE_VERSION = 1 def __init__( self, predicate_type = None, value = None, inclusive = True, counts = None ): if counts is None: counts = {} if type( value ) == list: value = tuple( value ) self._predicate_type = predicate_type self._value = value self._inclusive = inclusive self._counts = {} self._counts[ HC.CURRENT ] = 0 self._counts[ HC.PENDING ] = 0 for ( current_or_pending, count ) in counts.items(): self.AddToCount( current_or_pending, count ) def __eq__( self, other ): return self.__hash__() == other.__hash__() def __hash__( self ): return ( self._predicate_type, self._value ).__hash__() def __ne__( self, other ): return self.__hash__() != other.__hash__() def __repr__( self ): return 'Predicate: ' + HydrusData.ToUnicode( ( self._predicate_type, self._value, self._counts ) ) def _GetSerialisableInfo( self ): if self._predicate_type in ( HC.PREDICATE_TYPE_SYSTEM_RATING, HC.PREDICATE_TYPE_SYSTEM_FILE_SERVICE ): ( operator, value, service_key ) = self._value serialisable_value = ( operator, value, service_key.encode( 'hex' ) ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIMILAR_TO: ( hash, max_hamming ) = self._value serialisable_value = ( hash.encode( 'hex' ), max_hamming ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_HASH: hash = self._value serialisable_value = hash.encode( 'hex' ) else: serialisable_value = self._value return ( self._predicate_type, serialisable_value, self._inclusive ) def _InitialiseFromSerialisableInfo( self, serialisable_info ): ( self._predicate_type, serialisable_value, self._inclusive ) = serialisable_info if self._predicate_type in ( HC.PREDICATE_TYPE_SYSTEM_RATING, HC.PREDICATE_TYPE_SYSTEM_FILE_SERVICE ): ( operator, value, service_key ) = serialisable_value self._value = ( operator, value, service_key.decode( 'hex' ) ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIMILAR_TO: ( serialisable_hash, max_hamming ) = serialisable_value self._value = ( serialisable_hash.decode( 'hex' ), max_hamming ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_HASH: self._value = serialisable_value.decode( 'hex' ) else: self._value = serialisable_value if type( self._value ) == list: self._value = tuple( self._value ) def AddToCount( self, current_or_pending, count ): self._counts[ current_or_pending ] += count def GetCopy( self ): return Predicate( self._predicate_type, self._value, self._inclusive, self._counts ) def GetCountlessCopy( self ): return Predicate( self._predicate_type, self._value, self._inclusive ) def GetCount( self, current_or_pending = None ): if current_or_pending is None: return sum( self._counts.values() ) else: return self._counts[ current_or_pending ] def GetInclusive( self ): # patch from an upgrade mess-up ~v144 if not hasattr( self, '_inclusive' ): if self._predicate_type not in HC.SYSTEM_PREDICATES: ( operator, value ) = self._value self._value = value self._inclusive = operator == '+' else: self._inclusive = True return self._inclusive def GetInfo( self ): return ( self._predicate_type, self._value, self._inclusive ) def GetType( self ): return self._predicate_type def GetUnicode( self, with_count = True ): count_text = u'' if with_count: if self._counts[ HC.CURRENT ] > 0: count_text += u' (' + HydrusData.ConvertIntToPrettyString( self._counts[ HC.CURRENT ] ) + u')' if self._counts[ HC.PENDING ] > 0: count_text += u' (+' + HydrusData.ConvertIntToPrettyString( self._counts[ HC.PENDING ] ) + u')' if self._predicate_type in HC.SYSTEM_PREDICATES: if self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_EVERYTHING: base = u'system:everything' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_INBOX: base = u'system:inbox' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_ARCHIVE: base = u'system:archive' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_UNTAGGED: base = u'system:untagged' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_LOCAL: base = u'system:local' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_NOT_LOCAL: base = u'system:not local' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_DIMENSIONS: base = u'system:dimensions' elif self._predicate_type in ( HC.PREDICATE_TYPE_SYSTEM_NUM_TAGS, HC.PREDICATE_TYPE_SYSTEM_WIDTH, HC.PREDICATE_TYPE_SYSTEM_HEIGHT, HC.PREDICATE_TYPE_SYSTEM_DURATION, HC.PREDICATE_TYPE_SYSTEM_NUM_WORDS ): if self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_TAGS: base = u'system:number of tags' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_WIDTH: base = u'system:width' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_HEIGHT: base = u'system:height' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_DURATION: base = u'system:duration' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_WORDS: base = u'system:number of words' if self._value is not None: ( operator, value ) = self._value base += u' ' + operator + u' ' + HydrusData.ConvertIntToPrettyString( value ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_RATIO: base = u'system:ratio' if self._value is not None: ( operator, ratio_width, ratio_height ) = self._value base += u' ' + operator + u' ' + str( ratio_width ) + u':' + str( ratio_height ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIZE: base = u'system:size' if self._value is not None: ( operator, size, unit ) = self._value base += u' ' + operator + u' ' + str( size ) + HydrusData.ConvertIntToUnit( unit ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_LIMIT: base = u'system:limit' if self._value is not None: value = self._value base += u' is ' + HydrusData.ConvertIntToPrettyString( value ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_AGE: base = u'system:age' if self._value is not None: ( operator, years, months, days, hours ) = self._value base += u' ' + operator + u' ' + str( years ) + u'y' + str( months ) + u'm' + str( days ) + u'd' + str( hours ) + u'h' elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_NUM_PIXELS: base = u'system:num_pixels' if self._value is not None: ( operator, num_pixels, unit ) = self._value base += u' ' + operator + u' ' + str( num_pixels ) + ' ' + HydrusData.ConvertIntToPixels( unit ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_HASH: base = u'system:hash' if self._value is not None: hash = self._value base += u' is ' + hash.encode( 'hex' ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_MIME: base = u'system:mime' if self._value is not None: mimes = self._value if set( mimes ) == set( HC.SEARCHABLE_MIMES ): mime_text = 'anything' elif set( mimes ) == set( HC.SEARCHABLE_MIMES ).intersection( set( HC.APPLICATIONS ) ): mime_text = 'application' elif set( mimes ) == set( HC.SEARCHABLE_MIMES ).intersection( set( HC.AUDIO ) ): mime_text = 'audio' elif set( mimes ) == set( HC.SEARCHABLE_MIMES ).intersection( set( HC.IMAGES ) ): mime_text = 'image' elif set( mimes ) == set( HC.SEARCHABLE_MIMES ).intersection( set( HC.VIDEO ) ): mime_text = 'video' else: mime_text = ', '.join( [ HC.mime_string_lookup[ mime ] for mime in mimes ] ) base += u' is ' + mime_text elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_RATING: base = u'system:rating' if self._value is not None: ( operator, value, service_key ) = self._value service = HydrusGlobals.client_controller.GetServicesManager().GetService( service_key ) base += u' for ' + service.GetName() + u' ' + operator + u' ' + HydrusData.ToUnicode( value ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_SIMILAR_TO: base = u'system:similar to' if self._value is not None: ( hash, max_hamming ) = self._value base += u' ' + hash.encode( 'hex' ) + u' using max hamming of ' + str( max_hamming ) elif self._predicate_type == HC.PREDICATE_TYPE_SYSTEM_FILE_SERVICE: base = u'system:' if self._value is None: base += 'file service' else: ( operator, current_or_pending, service_key ) = self._value if operator == True: base += u'is' else: base += u'is not' if current_or_pending == HC.PENDING: base += u' pending to ' else: base += u' currently in ' service = HydrusGlobals.client_controller.GetServicesManager().GetService( service_key ) base += service.GetName() base += count_text elif self._predicate_type == HC.PREDICATE_TYPE_TAG: tag = self._value if not self._inclusive: base = u'-' else: base = u'' base += tag base += count_text siblings_manager = HydrusGlobals.client_controller.GetManager( 'tag_siblings' ) sibling = siblings_manager.GetSibling( tag ) if sibling is not None: base += u' (will display as ' + sibling + ')' elif self._predicate_type == HC.PREDICATE_TYPE_PARENT: base = ' ' tag = self._value base += tag base += count_text elif self._predicate_type == HC.PREDICATE_TYPE_NAMESPACE: namespace = self._value if not self._inclusive: base = u'-' else: base = u'' base += namespace + u':*anything*' elif self._predicate_type == HC.PREDICATE_TYPE_WILDCARD: wildcard = self._value if not self._inclusive: base = u'-' else: base = u'' base += wildcard return base def GetValue( self ): return self._value def SetInclusive( self, inclusive ): self._inclusive = inclusive HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_PREDICATE ] = Predicate class Service( HydrusData.HydrusYAMLBase ): yaml_tag = u'!Service' def __init__( self, service_key, service_type, name, info ): HydrusData.HydrusYAMLBase.__init__( self ) self._service_key = service_key self._service_type = service_type self._name = name self._info = info self._lock = threading.Lock() HydrusGlobals.client_controller.sub( self, 'ProcessServiceUpdates', 'service_updates_data' ) def __hash__( self ): return self._service_key.__hash__() def _RecordHydrusBandwidth( self, method, command, data_used ): if ( self._service_type, method, command ) in HC.BANDWIDTH_CONSUMING_REQUESTS: HydrusGlobals.client_controller.pub( 'service_updates_delayed', { self._service_key : [ HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_REQUEST_MADE, data_used ) ] } ) def _ReportSyncProcessingError( self, path, error_text ): text = 'While synchronising ' + self._name + ', the expected update file ' + path + ', ' + error_text + '.' text += os.linesep * 2 text += 'The service has been indefinitely paused.' text += os.linesep * 2 text += 'This is a serious error. Unless you know what went wrong, you should contact the developer.' HydrusData.ShowText( text ) service_updates = [ HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_PAUSE ) ] service_keys_to_service_updates = { self._service_key : service_updates } self.ProcessServiceUpdates( service_keys_to_service_updates ) HydrusGlobals.client_controller.Write( 'service_updates', service_keys_to_service_updates ) def CanDownload( self ): return self._info[ 'account' ].HasPermission( HC.GET_DATA ) and not self.HasRecentError() def CanDownloadUpdate( self ): update_due = HydrusData.TimeHasPassed( self._info[ 'next_download_timestamp' ] + HC.UPDATE_DURATION + 1800 ) return self.CanDownload() and update_due and not self.IsPaused() def CanProcessUpdate( self ): update_is_downloaded = self._info[ 'next_download_timestamp' ] > self._info[ 'next_processing_timestamp' ] it_is_time = HydrusData.TimeHasPassed( self._info[ 'next_processing_timestamp' ] + HC.UPDATE_DURATION + HC.options[ 'processing_phase' ] ) return update_is_downloaded and it_is_time and not self.IsPaused() def CanUpload( self ): return self._info[ 'account' ].HasPermission( HC.POST_DATA ) and not self.HasRecentError() def GetCredentials( self ): host = self._info[ 'host' ] port = self._info[ 'port' ] if 'access_key' in self._info: access_key = self._info[ 'access_key' ] else: access_key = None credentials = Credentials( host, port, access_key ) return credentials def GetInfo( self, key = None ): if key is None: return self._info else: return self._info[ key ] def GetServiceKey( self ): return self._service_key def GetName( self ): return self._name def GetRecentErrorPending( self ): if 'account' in self._info and self._info[ 'account' ].HasPermission( HC.GENERAL_ADMIN ): return HydrusData.ConvertTimestampToPrettyPending( self._info[ 'last_error' ] + 600 ) else: return HydrusData.ConvertTimestampToPrettyPending( self._info[ 'last_error' ] + 3600 * 4 ) def GetServiceType( self ): return self._service_type def GetTimestamps( self ): return ( self._info[ 'first_timestamp' ], self._info[ 'next_download_timestamp' ], self._info[ 'next_processing_timestamp' ] ) def GetUpdateStatus( self ): account = self._info[ 'account' ] now = HydrusData.GetNow() first_timestamp = self._info[ 'first_timestamp' ] next_download_timestamp = self._info[ 'next_download_timestamp' ] next_processing_timestamp = self._info[ 'next_processing_timestamp' ] if first_timestamp is None: num_updates = 0 num_updates_downloaded = 0 num_updates_processed = 0 else: num_updates = ( now - first_timestamp ) / HC.UPDATE_DURATION num_updates_downloaded = ( next_download_timestamp - first_timestamp ) / HC.UPDATE_DURATION num_updates_processed = max( 0, ( next_processing_timestamp - first_timestamp ) / HC.UPDATE_DURATION ) downloaded_text = 'downloaded ' + HydrusData.ConvertValueRangeToPrettyString( num_updates_downloaded, num_updates ) processed_text = 'processed ' + HydrusData.ConvertValueRangeToPrettyString( num_updates_processed, num_updates ) if self.IsPaused() or not self._info[ 'account' ].HasPermission( HC.GET_DATA ): status = 'updates on hold' else: if self.CanDownloadUpdate(): status = 'downloaded up to ' + HydrusData.ConvertTimestampToPrettySync( self._info[ 'next_download_timestamp' ] ) elif self.CanProcessUpdate(): status = 'processed up to ' + HydrusData.ConvertTimestampToPrettySync( self._info[ 'next_processing_timestamp' ] ) elif self.HasRecentError(): status = 'due to a previous error, update is delayed - next check ' + self.GetRecentErrorPending() else: if HydrusData.TimeHasPassed( self._info[ 'next_download_timestamp' ] + HC.UPDATE_DURATION ): status = 'next update will be downloaded soon' else: status = 'fully synchronised - next update ' + HydrusData.ConvertTimestampToPrettyPending( self._info[ 'next_download_timestamp' ] + HC.UPDATE_DURATION + 1800 ) return downloaded_text + ' - ' + processed_text + ' - ' + status def HasRecentError( self ): if 'account' in self._info and self._info[ 'account' ].HasPermission( HC.GENERAL_ADMIN ): return self._info[ 'last_error' ] + 900 > HydrusData.GetNow() else: return self._info[ 'last_error' ] + 3600 * 4 > HydrusData.GetNow() def IsInitialised( self ): if self._service_type == HC.SERVER_ADMIN: return 'access_key' in self._info else: return True def IsPaused( self ): return self._info[ 'paused' ] def ProcessServiceUpdates( self, service_keys_to_service_updates ): for ( service_key, service_updates ) in service_keys_to_service_updates.items(): for service_update in service_updates: if service_key == self._service_key: ( action, row ) = service_update.ToTuple() if action == HC.SERVICE_UPDATE_ERROR: self._info[ 'last_error' ] = HydrusData.GetNow() elif action == HC.SERVICE_UPDATE_RESET: self._info[ 'last_error' ] = 0 if 'next_processing_timestamp' in self._info: self._info[ 'next_processing_timestamp' ] = 0 elif action == HC.SERVICE_UPDATE_ACCOUNT: account = row self._info[ 'account' ] = account self._info[ 'last_error' ] = 0 elif action == HC.SERVICE_UPDATE_REQUEST_MADE: num_bytes = row if self._service_type == HC.LOCAL_BOORU: self._info[ 'used_monthly_data' ] += num_bytes self._info[ 'used_monthly_requests' ] += 1 else: self._info[ 'account' ].RequestMade( num_bytes ) elif action == HC.SERVICE_UPDATE_NEXT_DOWNLOAD_TIMESTAMP: next_download_timestamp = row if next_download_timestamp > self._info[ 'next_download_timestamp' ]: if self._info[ 'first_timestamp' ] is None: self._info[ 'first_timestamp' ] = next_download_timestamp self._info[ 'next_download_timestamp' ] = next_download_timestamp elif action == HC.SERVICE_UPDATE_NEXT_PROCESSING_TIMESTAMP: next_processing_timestamp = row if next_processing_timestamp > self._info[ 'next_processing_timestamp' ]: self._info[ 'next_processing_timestamp' ] = next_processing_timestamp elif action == HC.SERVICE_UPDATE_PAUSE: self._info[ 'paused' ] = True def Request( self, method, command, request_args = None, request_headers = None, report_hooks = None, temp_path = None, return_cookies = False ): if request_args is None: request_args = {} if request_headers is None: request_headers = {} if report_hooks is None: report_hooks = [] try: credentials = self.GetCredentials() if command in ( 'access_key', 'init', '' ): pass elif command in ( 'session_key', 'access_key_verification' ): ClientNetworking.AddHydrusCredentialsToHeaders( credentials, request_headers ) else: ClientNetworking.AddHydrusSessionKeyToHeaders( self._service_key, request_headers ) path = '/' + command if method == HC.GET: query = ClientNetworking.ConvertHydrusGETArgsToQuery( request_args ) body = '' elif method == HC.POST: query = '' if command == 'file': content_type = HC.APPLICATION_OCTET_STREAM body = request_args[ 'file' ] del request_args[ 'file' ] else: if isinstance( request_args, HydrusSerialisable.SerialisableDictionary ): content_type = HC.APPLICATION_JSON body = request_args.DumpToNetworkString() else: content_type = HC.APPLICATION_YAML body = yaml.safe_dump( request_args ) request_headers[ 'Content-Type' ] = HC.mime_string_lookup[ content_type ] if query != '': path_and_query = path + '?' + query else: path_and_query = path ( host, port ) = credentials.GetAddress() url = 'http://' + host + ':' + str( port ) + path_and_query ( response, size_of_response, response_headers, cookies ) = HydrusGlobals.client_controller.DoHTTP( method, url, request_headers, body, report_hooks = report_hooks, temp_path = temp_path, return_everything = True ) ClientNetworking.CheckHydrusVersion( self._service_key, self._service_type, response_headers ) if method == HC.GET: data_used = size_of_response elif method == HC.POST: data_used = len( body ) self._RecordHydrusBandwidth( method, command, data_used ) if return_cookies: return ( response, cookies ) else: return response except Exception as e: if not isinstance( e, HydrusExceptions.ServerBusyException ): if isinstance( e, HydrusExceptions.SessionException ): session_manager = HydrusGlobals.client_controller.GetManager( 'hydrus_sessions' ) session_manager.DeleteSessionKey( self._service_key ) HydrusGlobals.client_controller.Write( 'service_updates', { self._service_key : [ HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_ERROR, HydrusData.ToUnicode( e ) ) ] } ) if isinstance( e, HydrusExceptions.PermissionException ): if 'account' in self._info: account_key = self._info[ 'account' ].GetAccountKey() unknown_account = HydrusData.GetUnknownAccount( account_key ) else: unknown_account = HydrusData.GetUnknownAccount() HydrusGlobals.client_controller.Write( 'service_updates', { self._service_key : [ HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_ACCOUNT, unknown_account ) ] } ) raise def SetCredentials( self, credentials ): ( host, port ) = credentials.GetAddress() self._info[ 'host' ] = host self._info[ 'port' ] = port if credentials.HasAccessKey(): self._info[ 'access_key' ] = credentials.GetAccessKey() def Sync( self, only_when_idle = False, stop_time = None ): job_key = HydrusThreading.JobKey( pausable = False, cancellable = True ) ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: return if self._info[ 'paused' ]: return if not self.CanDownloadUpdate() and not self.CanProcessUpdate(): return num_updates_downloaded = 0 num_updates_processed = 0 total_content_weight_processed = 0 try: options = HydrusGlobals.client_controller.GetOptions() HydrusGlobals.client_controller.pub( 'splash_set_title_text', self._name ) job_key.SetVariable( 'popup_title', 'repository synchronisation - ' + self._name ) HydrusGlobals.client_controller.pub( 'message', job_key ) try: while self.CanDownloadUpdate(): if only_when_idle: if not HydrusGlobals.client_controller.CurrentlyIdle(): break else: if stop_time is not None and HydrusData.TimeHasPassed( stop_time ): break if options[ 'pause_repo_sync' ]: break ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: break if self._info[ 'first_timestamp' ] is None: gauge_range = None gauge_value = 0 update_index_string = 'initial update: ' else: gauge_range = ( ( HydrusData.GetNow() - self._info[ 'first_timestamp' ] ) / HC.UPDATE_DURATION ) + 1 gauge_value = ( ( self._info[ 'next_download_timestamp' ] - self._info[ 'first_timestamp' ] ) / HC.UPDATE_DURATION ) + 1 update_index_string = 'update ' + HydrusData.ConvertValueRangeToPrettyString( gauge_value, gauge_range ) + ': ' subupdate_index_string = 'service update: ' HydrusGlobals.client_controller.pub( 'splash_set_title_text', self._name + ' - ' + update_index_string + subupdate_index_string ) HydrusGlobals.client_controller.pub( 'splash_set_status_text', 'downloading' ) job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'downloading and parsing' ) job_key.SetVariable( 'popup_gauge_1', ( gauge_value, gauge_range ) ) service_update_package = self.Request( HC.GET, 'service_update_package', { 'begin' : self._info[ 'next_download_timestamp' ] } ) begin = service_update_package.GetBegin() subindex_count = service_update_package.GetSubindexCount() for subindex in range( subindex_count ): path = ClientFiles.GetExpectedContentUpdatePackagePath( self._service_key, begin, subindex ) if os.path.exists( path ): info = os.lstat( path ) size = info[6] if size == 0: os.remove( path ) if not os.path.exists( path ): subupdate_index_string = 'content update ' + HydrusData.ConvertValueRangeToPrettyString( subindex + 1, subindex_count ) + ': ' HydrusGlobals.client_controller.pub( 'splash_set_title_text', self._name + ' - ' + update_index_string + subupdate_index_string ) job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'downloading and parsing' ) content_update_package = self.Request( HC.GET, 'content_update_package', { 'begin' : begin, 'subindex' : subindex } ) obj_string = content_update_package.DumpToString() job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'saving to disk' ) with open( path, 'wb' ) as f: f.write( obj_string ) job_key.SetVariable( 'popup_text_1', update_index_string + 'committing' ) path = ClientFiles.GetExpectedServiceUpdatePackagePath( self._service_key, begin ) obj_string = service_update_package.DumpToString() with open( path, 'wb' ) as f: f.write( obj_string ) service_updates = [ HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_NEXT_DOWNLOAD_TIMESTAMP, service_update_package.GetNextBegin() ) ] service_keys_to_service_updates = { self._service_key : service_updates } self.ProcessServiceUpdates( service_keys_to_service_updates ) HydrusGlobals.client_controller.Write( 'service_updates', service_keys_to_service_updates ) HydrusGlobals.client_controller.WaitUntilPubSubsEmpty() num_updates_downloaded += 1 except Exception as e: if 'Could not connect' in str( e ): job_key.SetVariable( 'popup_text_1', 'Could not connect to service, will continue with processing.' ) time.sleep( 5 ) else: raise e while self.CanProcessUpdate(): if only_when_idle: if not HydrusGlobals.client_controller.CurrentlyIdle(): break else: if stop_time is not None and HydrusData.TimeHasPassed( stop_time ): break if options[ 'pause_repo_sync' ]: break ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: break gauge_range = ( ( HydrusData.GetNow() - self._info[ 'first_timestamp' ] ) / HC.UPDATE_DURATION ) + 1 gauge_value = ( ( self._info[ 'next_processing_timestamp' ] - self._info[ 'first_timestamp' ] ) / HC.UPDATE_DURATION ) + 1 update_index_string = 'update ' + HydrusData.ConvertValueRangeToPrettyString( gauge_value, gauge_range ) + ': ' subupdate_index_string = 'service update: ' HydrusGlobals.client_controller.pub( 'splash_set_title_text', self._name + ' - ' + update_index_string + subupdate_index_string ) HydrusGlobals.client_controller.pub( 'splash_set_status_text', 'processing' ) job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'loading from disk' ) job_key.SetVariable( 'popup_gauge_1', ( gauge_value, gauge_range ) ) path = ClientFiles.GetExpectedServiceUpdatePackagePath( self._service_key, self._info[ 'next_processing_timestamp' ] ) if not os.path.exists( path ): self._ReportSyncProcessingError( path, 'was missing' ) return with open( path, 'rb' ) as f: obj_string = f.read() try: service_update_package = HydrusSerialisable.CreateFromString( obj_string ) except: self._ReportSyncProcessingError( path, 'did not parse' ) return subindex_count = service_update_package.GetSubindexCount() processing_went_ok = True for subindex in range( subindex_count ): if only_when_idle: if not HydrusGlobals.client_controller.CurrentlyIdle(): break else: if stop_time is not None and HydrusData.TimeHasPassed( stop_time ): break if options[ 'pause_repo_sync' ]: break ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: break subupdate_index_string = 'content update ' + HydrusData.ConvertValueRangeToPrettyString( subindex + 1, subindex_count ) + ': ' path = ClientFiles.GetExpectedContentUpdatePackagePath( self._service_key, self._info[ 'next_processing_timestamp' ], subindex ) if not os.path.exists( path ): self._ReportSyncProcessingError( path, 'was missing' ) return job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'loading from disk' ) with open( path, 'rb' ) as f: obj_string = f.read() job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'parsing' ) try: content_update_package = HydrusSerialisable.CreateFromString( obj_string ) except: self._ReportSyncProcessingError( path, 'did not parse' ) return HydrusGlobals.client_controller.pub( 'splash_set_title_text', self._name + ' - ' + update_index_string + subupdate_index_string ) job_key.SetVariable( 'popup_text_1', update_index_string + subupdate_index_string + 'processing' ) ( did_it_all, c_u_p_weight_processed ) = HydrusGlobals.client_controller.WriteSynchronous( 'content_update_package', self._service_key, content_update_package, job_key, only_when_idle ) total_content_weight_processed += c_u_p_weight_processed if not did_it_all: processing_went_ok = False break HydrusGlobals.client_controller.WaitUntilPubSubsEmpty() if options[ 'pause_repo_sync' ]: break ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: break if processing_went_ok: job_key.SetVariable( 'popup_text_2', 'committing service updates' ) service_updates = [ service_update for service_update in service_update_package.IterateServiceUpdates() ] service_updates.append( HydrusData.ServiceUpdate( HC.SERVICE_UPDATE_NEXT_PROCESSING_TIMESTAMP, service_update_package.GetNextBegin() ) ) service_keys_to_service_updates = { self._service_key : service_updates } self.ProcessServiceUpdates( service_keys_to_service_updates ) HydrusGlobals.client_controller.Write( 'service_updates', service_keys_to_service_updates ) HydrusGlobals.client_controller.pub( 'notify_new_pending' ) HydrusGlobals.client_controller.WaitUntilPubSubsEmpty() job_key.SetVariable( 'popup_gauge_2', ( 0, 1 ) ) job_key.SetVariable( 'popup_text_2', '' ) num_updates_processed += 1 time.sleep( 0.1 ) job_key.DeleteVariable( 'popup_gauge_1' ) job_key.DeleteVariable( 'popup_text_2' ) job_key.DeleteVariable( 'popup_gauge_2' ) if self._service_type == HC.FILE_REPOSITORY and self.CanDownload(): HydrusGlobals.client_controller.pub( 'splash_set_status_text', 'reviewing thumbnails' ) job_key.SetVariable( 'popup_text_1', 'reviewing existing thumbnails' ) thumbnail_hashes_i_have = ClientFiles.GetAllThumbnailHashes() job_key.SetVariable( 'popup_text_1', 'reviewing service thumbnails' ) thumbnail_hashes_i_should_have = HydrusGlobals.client_controller.Read( 'thumbnail_hashes_i_should_have', self._service_key ) thumbnail_hashes_i_need = thumbnail_hashes_i_should_have.difference( thumbnail_hashes_i_have ) if len( thumbnail_hashes_i_need ) > 0: def SaveThumbnails( batch_of_thumbnails ): job_key.SetVariable( 'popup_text_1', 'saving thumbnails to database' ) HydrusGlobals.client_controller.WriteSynchronous( 'thumbnails', batch_of_thumbnails ) HydrusGlobals.client_controller.pub( 'add_thumbnail_count', self._service_key, len( batch_of_thumbnails ) ) thumbnails = [] for ( i, hash ) in enumerate( thumbnail_hashes_i_need ): if options[ 'pause_repo_sync' ]: break ( i_paused, should_quit ) = job_key.WaitIfNeeded() if should_quit: break job_key.SetVariable( 'popup_text_1', 'downloading thumbnail ' + HydrusData.ConvertValueRangeToPrettyString( i, len( thumbnail_hashes_i_need ) ) ) job_key.SetVariable( 'popup_gauge_1', ( i, len( thumbnail_hashes_i_need ) ) ) request_args = { 'hash' : hash.encode( 'hex' ) } thumbnail = self.Request( HC.GET, 'thumbnail', request_args = request_args ) thumbnails.append( ( hash, thumbnail ) ) if i % 50 == 0: SaveThumbnails( thumbnails ) thumbnails = [] HydrusGlobals.client_controller.WaitUntilPubSubsEmpty() if len( thumbnails ) > 0: SaveThumbnails( thumbnails ) job_key.DeleteVariable( 'popup_gauge_1' ) HydrusGlobals.client_controller.pub( 'splash_set_status_text', '' ) job_key.SetVariable( 'popup_title', 'repository synchronisation - ' + self._name + ' - finished' ) updates_text = HydrusData.ConvertIntToPrettyString( num_updates_downloaded ) + ' updates downloaded, ' + HydrusData.ConvertIntToPrettyString( num_updates_processed ) + ' updates processed' if self._service_type == HC.TAG_REPOSITORY: content_text = HydrusData.ConvertIntToPrettyString( total_content_weight_processed ) + ' mappings added' elif self._service_type == HC.FILE_REPOSITORY: content_text = HydrusData.ConvertIntToPrettyString( total_content_weight_processed ) + ' files added' job_key.SetVariable( 'popup_text_1', updates_text + ', and ' + content_text ) print( job_key.ToString() ) time.sleep( 3 ) job_key.Delete() except Exception as e: job_key.Cancel() print( traceback.format_exc() ) HydrusData.ShowText( 'Failed to update ' + self._name + ':' ) HydrusData.ShowException( e ) time.sleep( 3 ) def ToTuple( self ): return ( self._service_key, self._service_type, self._name, self._info ) class ServicesManager( object ): def __init__( self ): self._lock = threading.Lock() self._keys_to_services = {} self._services_sorted = [] self.RefreshServices() HydrusGlobals.client_controller.sub( self, 'RefreshServices', 'notify_new_services_data' ) def GetService( self, service_key ): with self._lock: try: return self._keys_to_services[ service_key ] except KeyError: raise HydrusExceptions.NotFoundException( 'That service was not found!' ) def GetServices( self, types = HC.ALL_SERVICES ): with self._lock: return [ service for service in self._services_sorted if service.GetServiceType() in types ] def RefreshServices( self ): with self._lock: services = HydrusGlobals.client_controller.Read( 'services' ) self._keys_to_services = { service.GetServiceKey() : service for service in services } compare_function = lambda a, b: cmp( a.GetName(), b.GetName() ) self._services_sorted = list( services ) self._services_sorted.sort( cmp = compare_function ) class Shortcuts( HydrusSerialisable.SerialisableBaseNamed ): SERIALISABLE_TYPE = HydrusSerialisable.SERIALISABLE_TYPE_SHORTCUTS SERIALISABLE_VERSION = 1 def __init__( self, name ): HydrusSerialisable.SerialisableBaseNamed.__init__( self, name ) self._mouse_actions = {} self._keyboard_actions = {} def _ConvertActionToSerialisableAction( self, action ): ( service_key, data ) = action if service_key is None: return [ service_key, data ] else: serialisable_service_key = service_key.encode( 'hex' ) return [ serialisable_service_key, data ] def _ConvertSerialisableActionToAction( self, serialisable_action ): ( serialisable_service_key, data ) = serialisable_action if serialisable_service_key is None: return ( serialisable_service_key, data ) # important to return tuple, as serialisable_action is likely a list else: service_key = serialisable_service_key.decode( 'hex' ) return ( service_key, data ) def _GetSerialisableInfo( self ): serialisable_mouse_actions = [] for ( ( modifier, mouse_button ), action ) in self._mouse_actions.items(): serialisable_action = self._ConvertActionToSerialisableAction( action ) serialisable_mouse_actions.append( ( modifier, mouse_button, serialisable_action ) ) serialisable_keyboard_actions = [] for ( ( modifier, key ), action ) in self._keyboard_actions.items(): serialisable_action = self._ConvertActionToSerialisableAction( action ) serialisable_keyboard_actions.append( ( modifier, key, serialisable_action ) ) return ( serialisable_mouse_actions, serialisable_keyboard_actions ) def _InitialiseFromSerialisableInfo( self, serialisable_info ): ( serialisable_mouse_actions, serialisable_keyboard_actions ) = serialisable_info self._mouse_actions = {} for ( modifier, mouse_button, serialisable_action ) in serialisable_mouse_actions: action = self._ConvertSerialisableActionToAction( serialisable_action ) self._mouse_actions[ ( modifier, mouse_button ) ] = action self._keyboard_actions = {} for ( modifier, key, serialisable_action ) in serialisable_keyboard_actions: action = self._ConvertSerialisableActionToAction( serialisable_action ) self._keyboard_actions[ ( modifier, key ) ] = action def ClearActions( self ): self._mouse_actions = {} self._keyboard_actions = {} def DeleteKeyboardAction( self, modifier, key ): if ( modifier, key ) in self._keyboard_actions: del self._keyboard_actions[ ( modifier, key ) ] def DeleteMouseAction( self, modifier, mouse_button ): if ( modifier, mouse_button ) in self._mouse_actions: del self._mouse_actions[ ( modifier, mouse_button ) ] def GetKeyboardAction( self, modifier, key ): if ( modifier, key ) in self._keyboard_actions: return self._keyboard_actions[ ( modifier, key ) ] else: return None def GetMouseAction( self, modifier, mouse_button ): if ( modifier, mouse_button ) in self._mouse_actions: return self._mouse_actions[ ( modifier, mouse_button ) ] else: return None def IterateKeyboardShortcuts( self ): for ( ( modifier, key ), action ) in self._keyboard_actions.items(): yield ( ( modifier, key ), action ) def IterateMouseShortcuts( self ): for ( ( modifier, mouse_button ), action ) in self._mouse_actions.items(): yield ( ( modifier, mouse_button ), action ) def SetKeyboardAction( self, modifier, key, action ): self._keyboard_actions[ ( modifier, key ) ] = action def SetMouseAction( self, modifier, mouse_button, action ): self._mouse_actions[ ( modifier, mouse_button ) ] = action HydrusSerialisable.SERIALISABLE_TYPES_TO_OBJECT_TYPES[ HydrusSerialisable.SERIALISABLE_TYPE_SHORTCUTS ] = Shortcuts class UndoManager( object ): def __init__( self ): self._commands = [] self._inverted_commands = [] self._current_index = 0 self._lock = threading.Lock() HydrusGlobals.client_controller.sub( self, 'Undo', 'undo' ) HydrusGlobals.client_controller.sub( self, 'Redo', 'redo' ) def _FilterServiceKeysToContentUpdates( self, service_keys_to_content_updates ): filtered_service_keys_to_content_updates = {} for ( service_key, content_updates ) in service_keys_to_content_updates.items(): filtered_content_updates = [] for content_update in content_updates: ( data_type, action, row ) = content_update.ToTuple() if data_type == HC.CONTENT_TYPE_FILES: if action in ( HC.CONTENT_UPDATE_ADD, HC.CONTENT_UPDATE_DELETE, HC.CONTENT_UPDATE_UNDELETE, HC.CONTENT_UPDATE_RESCIND_PETITION ): continue elif data_type == HC.CONTENT_TYPE_MAPPINGS: if action in ( HC.CONTENT_UPDATE_RESCIND_PETITION, HC.CONTENT_UPDATE_ADVANCED ): continue else: continue filtered_content_update = HydrusData.ContentUpdate( data_type, action, row ) filtered_content_updates.append( filtered_content_update ) if len( filtered_content_updates ) > 0: filtered_service_keys_to_content_updates[ service_key ] = filtered_content_updates return filtered_service_keys_to_content_updates def _InvertServiceKeysToContentUpdates( self, service_keys_to_content_updates ): inverted_service_keys_to_content_updates = {} for ( service_key, content_updates ) in service_keys_to_content_updates.items(): inverted_content_updates = [] for content_update in content_updates: ( data_type, action, row ) = content_update.ToTuple() inverted_row = row if data_type == HC.CONTENT_TYPE_FILES: if action == HC.CONTENT_UPDATE_ARCHIVE: inverted_action = HC.CONTENT_UPDATE_INBOX elif action == HC.CONTENT_UPDATE_INBOX: inverted_action = HC.CONTENT_UPDATE_ARCHIVE elif action == HC.CONTENT_UPDATE_PEND: inverted_action = HC.CONTENT_UPDATE_RESCIND_PEND elif action == HC.CONTENT_UPDATE_RESCIND_PEND: inverted_action = HC.CONTENT_UPDATE_PEND elif action == HC.CONTENT_UPDATE_PETITION: inverted_action = HC.CONTENT_UPDATE_RESCIND_PETITION ( hashes, reason ) = row inverted_row = hashes elif data_type == HC.CONTENT_TYPE_MAPPINGS: if action == HC.CONTENT_UPDATE_ADD: inverted_action = HC.CONTENT_UPDATE_DELETE elif action == HC.CONTENT_UPDATE_DELETE: inverted_action = HC.CONTENT_UPDATE_ADD elif action == HC.CONTENT_UPDATE_PEND: inverted_action = HC.CONTENT_UPDATE_RESCIND_PEND elif action == HC.CONTENT_UPDATE_RESCIND_PEND: inverted_action = HC.CONTENT_UPDATE_PEND elif action == HC.CONTENT_UPDATE_PETITION: inverted_action = HC.CONTENT_UPDATE_RESCIND_PETITION ( tag, hashes, reason ) = row inverted_row = ( tag, hashes ) inverted_content_update = HydrusData.ContentUpdate( data_type, inverted_action, inverted_row ) inverted_content_updates.append( inverted_content_update ) inverted_service_keys_to_content_updates[ service_key ] = inverted_content_updates return inverted_service_keys_to_content_updates def AddCommand( self, action, *args, **kwargs ): with self._lock: inverted_action = action inverted_args = args inverted_kwargs = kwargs if action == 'content_updates': ( service_keys_to_content_updates, ) = args service_keys_to_content_updates = self._FilterServiceKeysToContentUpdates( service_keys_to_content_updates ) if len( service_keys_to_content_updates ) == 0: return inverted_service_keys_to_content_updates = self._InvertServiceKeysToContentUpdates( service_keys_to_content_updates ) if len( inverted_service_keys_to_content_updates ) == 0: return inverted_args = ( inverted_service_keys_to_content_updates, ) else: return self._commands = self._commands[ : self._current_index ] self._inverted_commands = self._inverted_commands[ : self._current_index ] self._commands.append( ( action, args, kwargs ) ) self._inverted_commands.append( ( inverted_action, inverted_args, inverted_kwargs ) ) self._current_index += 1 HydrusGlobals.client_controller.pub( 'notify_new_undo' ) def GetUndoRedoStrings( self ): with self._lock: ( undo_string, redo_string ) = ( None, None ) if self._current_index > 0: undo_index = self._current_index - 1 ( action, args, kwargs ) = self._commands[ undo_index ] if action == 'content_updates': ( service_keys_to_content_updates, ) = args undo_string = 'undo ' + ConvertServiceKeysToContentUpdatesToPrettyString( service_keys_to_content_updates ) if len( self._commands ) > 0 and self._current_index < len( self._commands ): redo_index = self._current_index ( action, args, kwargs ) = self._commands[ redo_index ] if action == 'content_updates': ( service_keys_to_content_updates, ) = args redo_string = 'redo ' + ConvertServiceKeysToContentUpdatesToPrettyString( service_keys_to_content_updates ) return ( undo_string, redo_string ) def Undo( self ): action = None with self._lock: if self._current_index > 0: self._current_index -= 1 ( action, args, kwargs ) = self._inverted_commands[ self._current_index ] if action is not None: HydrusGlobals.client_controller.WriteSynchronous( action, *args, **kwargs ) HydrusGlobals.client_controller.pub( 'notify_new_undo' ) def Redo( self ): action = None with self._lock: if len( self._commands ) > 0 and self._current_index < len( self._commands ): ( action, args, kwargs ) = self._commands[ self._current_index ] self._current_index += 1 if action is not None: HydrusGlobals.client_controller.WriteSynchronous( action, *args, **kwargs ) HydrusGlobals.client_controller.pub( 'notify_new_undo' ) def GetShortcutFromEvent( event ): modifier = wx.ACCEL_NORMAL if event.AltDown(): modifier = wx.ACCEL_ALT elif event.CmdDown(): modifier = wx.ACCEL_CTRL elif event.ShiftDown(): modifier = wx.ACCEL_SHIFT key = event.KeyCode return ( modifier, key ) class ClientServiceIdentifier( HydrusData.HydrusYAMLBase ): yaml_tag = u'!ClientServiceIdentifier' def __init__( self, service_key, service_type, name ): HydrusData.HydrusYAMLBase.__init__( self ) self._service_key = service_key self._type = service_type self._name = name def __eq__( self, other ): return self.__hash__() == other.__hash__() def __hash__( self ): return self._service_key.__hash__() def __ne__( self, other ): return self.__hash__() != other.__hash__() def __repr__( self ): return 'Client Service Identifier: ' + HydrusData.ToUnicode( ( self._name, HC.service_string_lookup[ self._type ] ) ) def GetInfo( self ): return ( self._service_key, self._type, self._name ) def GetName( self ): return self._name def GetServiceKey( self ): return self._service_key def GetServiceType( self ): return self._type

[ "[email protected]" ]

[email protected]

8303d30c8032f5f4d4810c52bb87dece4b05a65d

08a1d871f4be9ea61497751845a5ed9abe2a1012

/farbox_bucket/utils/cli_color.py

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permissive

itfanr/FarBox

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refs/heads/master

2023-04-19T07:23:28.824231

2021-05-07T02:29:06

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#coding: utf8 from __future__ import absolute_import, print_function #CSI="\x1B[" #RED = CSI+"31;40m" #GREEN = CSI+'32;40m' #RESET =CSI+"m" FLAGS = dict( RESET = "\x1B[0m", BOLD = "\x1B[1m", DIM = "\x1B[2m", UNDER = "\x1B[4m", REVERSE = "\x1B[7m", HIDE = "\x1B[8m", CLEARSCREEN = "\x1B[2J", CLEARLINE = "\x1B[2K", BLACK = "\x1B[30m", RED = "\x1B[31m", GREEN = "\x1B[32m", YELLOW = "\x1B[33m", BLUE = "\x1B[34m", MAGENTA = "\x1B[35m", CYAN = "\x1B[36m", WHITE = "\x1B[37m", BBLACK = "\x1B[40m", BRED = "\x1B[41m", BGREEN = "\x1B[42m", BYELLOW = "\x1B[43m", BBLUE = "\x1B[44m", BMAGENTA = "\x1B[45m", BCYAN = "\x1B[46m", BWHITE = "\x1B[47m", NEWLINE = "\r\n\x1B[0m", ) def print_with_color(strings, color='red', end='\r\n'): color = FLAGS.get(color.upper()) if color: print(color + strings + FLAGS['RESET'], end=end) else: print(strings) def print_colorful_parts(string_parts, end=''): for strings, color in string_parts: print_with_color(strings, color, end) print(FLAGS['NEWLINE'], end='') if __name__ == '__main__': print_with_color('hello', 'green', end=' ') print_with_color('hello', 'blue') print_colorful_parts( [('hello', 'magenta'), ('world', 'yellow'), ('hello', 'red'), ('world', 'cyan')], end=' ' )

[ "[email protected]" ]

[email protected]

701af4a6dea98585d23863e6340949745d1980e6

73758dde83d1a1823c103e1a4ba71e7c95168f71

/nsd1912/py02/day02/myfunc.py

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[]

no_license

tonggh220/md_5_nsd_notes

07ffdee7c23963a7a461f2a2340143b0e97bd9e1

a58a021ad4c7fbdf7df327424dc518f4044c5116

refs/heads/master

2023-07-02T01:34:38.798929

2021-05-12T08:48:40

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def func1(x): if x == 1: return 1 return x * func1(x- 1) # 5 * func1(4) # 5 * 4 * func1(3) # 5 * 4 * 3 * func1(2) # 5 * 4 * 3 * 2 * func1(1) # 5 * 4 * 3 * 2 * 1 if __name__ == '__main__': print(func1(5))

[ "[email protected]" ]

[email protected]

2c3ddf59ef5bbc9b91706cc3b505f3e28ba85471

1ade02a8e0c6d7e442c9d9041f15518d22da3923

/w8/mock_phase2/run/core/controllers/generic.py

4478b8a5012a51fa7a71dcc21a18532f1804d5fc

[]

no_license

fodisi/ByteAcademy-Bootcamp

7980b80636a36db6da3e0fc0e529fbc6b8e097e0

d53e3f4864f6cba1b85e806c29b01c48e3c2e81d

refs/heads/master

2020-03-19T12:55:31.489638

2018-07-25T16:19:19

136,550,128

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285

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#!/usr/bin/env python3 from flask import Blueprint, render_template controller = Blueprint('generic', __name__) @controller.route('/<product_name>') def home(product_name): # obj = model.get_product(product_name) return render_template('index.html', json_obj=obj.to_json)

[ "[email protected]" ]

[email protected]

8da80ee62fb2a9c9ee57874efa1a8a69dc421479

694d57c3e512ce916269411b51adef23532420cd

/leetcode/23merge_k_sorted_lists.py

76d8e6ea698631516ea4cfdd1e87899d4de8cc45

[]

no_license

clovery410/mycode

5541c3a99962d7949832a0859f18819f118edfba

e12025e754547d18d5bb50a9dbe5e725fd03fd9c

refs/heads/master

2021-05-16T02:46:47.996748

2017-05-10T23:43:50

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1,368

py

import heapq class ListNode(object): def __init__(self, x): self.val = x self.next = None class Solution(object): def mergeKLists(self, lists): new_head = None heap = [] for node in lists: if node: heapq.heappush(heap, (node.val, node)) if heap: new_head = heapq.heappop(heap)[1] if new_head.next: heapq.heappush(heap, (new_head.next.val, new_head.next)) pre_node = new_head while heap: curr_node = heapq.heappop(heap)[1] if curr_node.next: heapq.heappush(heap, (curr_node.next.val, curr_node.next)) pre_node.next = curr_node pre_node = curr_node return new_head if __name__ == '__main__': node1 = ListNode(1) node2 = ListNode(2) node3 = ListNode(3) node4 = ListNode(4) node5 = ListNode(5) node6 = ListNode(6) node1.next = node3 node3.next = node5 node2.next = node4 node4.next = node6 sol = Solution() new_head = sol.mergeKLists([node1, node2]) print new_head.val print new_head.next.val print new_head.next.next.val print new_head.next.next.next.val print new_head.next.next.next.next.val print new_head.next.next.next.next.next.val print sol.mergeKLists([[]])

[ "[email protected]" ]

[email protected]

515c154e112ed44885fb11d8cfbd74d1c10c102d

1b070c5fabfe7e804eac4c7d706f6ccdf6b29ed0

/partners/migrations/0005_auto_20200620_0044.py

bfe6abd78feb964304d45149cd068cdcdae946a8

[ "MIT" ]

permissive

cZachJohnson/My-Business

ef80dae6458c2fb7a08465d29e32f9405e52e43d

792bb13a5b296260e5de7e03fba6445a13922851

refs/heads/master

2023-08-25T06:51:39.330270

2021-10-25T01:20:34

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# Generated by Django 2.2.12 on 2020-06-20 00:44 import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("partners", "0004_auto_20200609_0514"), ] operations = [ migrations.AddField( model_name="partner", name="created_at", field=models.DateTimeField( auto_now_add=True, default=datetime.datetime(1970, 1, 1, 0, 0) ), preserve_default=False, ), migrations.AddField( model_name="partner", name="updated_at", field=models.DateTimeField(auto_now=True), ), ]

[ "[email protected]" ]

[email protected]

6347debad11c0a6b40a7f94a3ab778d780943f36

24a88b7dd4d81763fd4212a42c4a73f4c35f8ffc

/apiREST/api/serializers.py

f92010912774eedc0733526c21deca79cd4e444b

[]

no_license

junkluis/leccionMVC

d001c122318dde065ffd9a88aaaad0b7b4533a05

c311e69f2ae6d102651f9f7e6fc1f9750fc9e4bc

refs/heads/master

2021-01-15T19:27:48.875724

2017-08-14T14:35:29

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from rest_framework import serializers from .models import * class ticketSerializer(serializers.ModelSerializer): class Meta: model = ticket fields = ('fechaEmision', 'Precio', 'Adquiriente', 'Puesto', 'Origen', 'Destino')

[ "[email protected]" ]

[email protected]

adf34bdb17df662959d03197aa497d4f9a4eccc1

9bbf429d2c2e2f20345d613a719cf01e8f9a0bff

/project/settings.py

6c1e92b7cc86c64ef10a85cf6336b520a2f2d545

[]

no_license

sandglasscao/ENU

f78f8a8dfaf3263587885b0622ab6d3182012375

e3c26fd57f8ef582da576e1cc28b7eb42562c706

refs/heads/master

2021-01-23T05:19:03.175439

2017-04-14T09:24:22

86,297,754

0

null

UTF-8

Python

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7,673

py

""" Django settings for project project. Generated by 'django-admin startproject' using Django 1.8.2. For more information on this file, see https://docs.djangoproject.com/en/1.8/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.8/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os import socket import datetime BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ON_PAAS = 'OPENSHIFT_REPO_DIR' in os.environ if ON_PAAS: SECRET_KEY = os.environ['OPENSHIFT_SECRET_TOKEN'] else: # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = ')_7av^!cy(wfx=k#3*7x+(=j^fzv+ot^1@sh9s9t=8$bu@r(z$' # SECURITY WARNING: don't run with debug turned on in production! # adjust to turn off when on Openshift, but allow an environment variable to override on PAAS DEBUG = not ON_PAAS DEBUG = DEBUG or os.getenv("debug", "false").lower() == "true" if ON_PAAS and DEBUG: print("*** Warning - Debug mode is on ***") ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.sites', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'crispy_forms', 'userprofile', 'metadata', 'utility', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'django.middleware.security.SecurityMiddleware', 'django.middleware.locale.LocaleMiddleware', ) ROOT_URLCONF = 'project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', 'django.template.context_processors.static', 'django.template.context_processors.i18n', ], }, }, ] WSGI_APPLICATION = 'project.wsgi.application' if ON_PAAS: # determine if we are on MySQL or POSTGRESQL if "OPENSHIFT_POSTGRESQL_DB_USERNAME" in os.environ: DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ['OPENSHIFT_APP_NAME'], 'USER': os.environ['OPENSHIFT_POSTGRESQL_DB_USERNAME'], 'PASSWORD': os.environ['OPENSHIFT_POSTGRESQL_DB_PASSWORD'], 'HOST': os.environ['OPENSHIFT_POSTGRESQL_DB_HOST'], 'PORT': os.environ['OPENSHIFT_POSTGRESQL_DB_PORT'], } } elif "OPENSHIFT_MYSQL_DB_USERNAME" in os.environ: DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': os.environ['OPENSHIFT_APP_NAME'], 'USER': os.environ['OPENSHIFT_MYSQL_DB_USERNAME'], 'PASSWORD': os.environ['OPENSHIFT_MYSQL_DB_PASSWORD'], 'HOST': os.environ['OPENSHIFT_MYSQL_DB_HOST'], 'PORT': os.environ['OPENSHIFT_MYSQL_DB_PORT'], } } else: ''' # stock django, local development. DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': os.environ['OPENSHIFT_APP_NAME'], 'USER': os.environ['OPENSHIFT_MYSQL_DB_USERNAME'], 'PASSWORD': os.environ['OPENSHIFT_MYSQL_DB_PASSWORD'], 'HOST': os.environ['OPENSHIFT_MYSQL_DB_HOST'], 'PORT': os.environ['OPENSHIFT_MYSQL_DB_PORT'], } } ''' DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': os.environ['APP_NAME2'], 'USER': os.environ['MYSQL_DB_USERNAME'], 'PASSWORD': os.environ['MYSQL_DB_PASSWORD'], 'HOST': os.environ['MYSQL_DB_HOST'], 'PORT': os.environ['MYSQL_DB_PORT'], } } # Internationalization # https://docs.djangoproject.com/en/1.8/topics/i18n/ # LANGUAGE_CODE = 'en-us' LANGUAGE_CODE = 'zh-hans' # TIME_ZONE = 'America/Sao_Paulo' TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.6/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'wsgi', 'static') # STATIC_ROOT is just for production env to collect all static resources STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', ) STATICFILES_DIRS = ( # Put strings here, like "/home/html/static" or "C:/www/django/static". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. os.path.join(BASE_DIR, "static"), ) if ON_PAAS: MEDIA_ROOT = os.path.join(os.environ.get('OPENSHIFT_DATA_DIR'), 'media') MEDIA_URL = '/static/media/' else: #MEDIA_ROOT = os.path.join(os.path.dirname(BASE_DIR), 'media') MEDIA_ROOT = os.path.join(BASE_DIR, 'static', 'media') MEDIA_URL = STATIC_URL + 'media/' CRISPY_TEMPLATE_PACK = 'bootstrap3' SITE_ID = 1 LOGIN_REDIRECT_URL = '/' AUTH_PROFILE_MODULE = 'userprofile.Profile' LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'formatters': { 'verbose': { 'format': "[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s", 'datefmt': "%d/%b/%Y %H:%M:%S" }, 'simple': { 'format': '%(levelname)s %(message)s' }, }, 'handlers': { # 'file': { # 'level': 'INFO', # 'class': 'logging.FileHandler', # 'filename': 'bluepage.log', # 'formatter': 'verbose' # }, 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', 'formatter': 'verbose', }, }, 'loggers': { 'django': { 'handlers': ['console'], 'propagate': True, 'level': 'ERROR', }, 'case': { 'handlers': ['console'], 'level': 'DEBUG', }, 'page': { 'handlers': ['console'], 'level': 'DEBUG', }, } } REST_FRAMEWORK = { # Use Django's standard `django.contrib.auth` permissions, # or allow read-only access for unauthenticated users. 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework_jwt.authentication.JSONWebTokenAuthentication', 'rest_framework.authentication.TokenAuthentication', ), 'DEFAULT_PERMISSION_CLASSES': [ 'rest_framework.permissions.IsAuthenticated', ] } JWT_AUTH = { 'JWT_EXPIRATION_DELTA': datetime.timedelta(seconds=30000), }

[ "root@localhost" ]

root@localhost

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/pyc_decrypted/latest/dropbox/client/watchdog.py

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[]

no_license

mickeystone/DropBoxLibrarySRC

ed132bbffda7f47df172056845e5f8f6c07fb5de

2e4a151caa88b48653f31a22cb207fff851b75f8

refs/heads/master

2021-05-27T05:02:30.255399

2013-08-27T13:16:55

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#Embedded file name: dropbox/client/watchdog.py import hashlib import os import socket import struct import threading import time from Crypto.Random import random from trace_report import make_report from dropbox.build_common import get_build_number from dropbox.callbacks import Handler from dropbox.fileutils import safe_remove from dropbox.globals import dropbox_globals from dropbox.native_event import AutoResetEvent from dropbox.native_queue import Queue from dropbox.platform import platform from dropbox.sqlite3_helpers import sqlite3_get_memory_statistics from dropbox.threadutils import StoppableThread from dropbox.trace import TRACE, unhandled_exc_handler, add_exception_handler, report_bad_assumption import arch from dropbox.client.high_trace import sample_process, send_ds_store, send_finder_crashes, send_trace_log _ONE_WEEK = 604800 class Watchdog2(object): UNIT_REPORT_INTERVAL = 900 HIGH_CPU_USAGE = 0.3 def __init__(self, status_controller, conn, handler = None): self.status_controller = status_controller self.sync_engine = None self.conn = conn add_exception_handler(self.handle_exception) self.clear() if handler: handler.add_handler(self.update_counts) def clear(self): self.last_times = (time.time(), 0, 0) self.hung_since = None self.high_cpu_minutes = 0 self.over_quota = False self.report_interval = self.UNIT_REPORT_INTERVAL self.last_cpu_minutes = 0 self.last_dbfseventsd_broken = False self.last_counts = (0, 0, 0, 0) self.last_report = 0 self.last_exception_hash = None self.last_upload_exception_hash = None self.last_reconstruct_exception_hash = None self.last_hash_exception_hash = None self.exc_lock = threading.Lock() def handle_exception(self, *exc_info, **kw): with self.exc_lock: self.last_exception_hash = make_report(exc_info)[1] thr_name = threading.currentThread().getName() if thr_name == 'UPLOAD': self.last_upload_exception_hash = self.last_exception_hash elif thr_name == 'RECONSTRUCT': self.last_reconstruct_exception_hash = self.last_exception_hash elif thr_name == 'HASH': self.last_hash_exception_hash = self.last_exception_hash def set_sync_engine(self, sync_engine): self.sync_engine = sync_engine self.sync_engine.add_list_callback(self._handle_list) def _handle_list(self, ret): try: self.over_quota = ret['in_use'] > ret['quota'] except KeyError: pass def update_counts(self): if not self.sync_engine: return if not self.sync_engine.running: self.clear() return ct = time.time() elapsed = ct - self.last_times[0] if elapsed >= 60: sys_time = ct, utime, stime = self.sync_engine.get_system_time_tuple() cpu_usage = (stime + utime - (self.last_times[1] + self.last_times[2])) / elapsed if cpu_usage > self.HIGH_CPU_USAGE: self.high_cpu_minutes += 1 TRACE('!! Warning: high CPU usage (%d mins total; %.1f%% used)' % (self.high_cpu_minutes, cpu_usage * 100)) self.last_times = sys_time if ct - self.last_report >= self.report_interval: queue_stats = self.sync_engine.get_queue_stats() counts = (queue_stats['hash']['total_count'], queue_stats['upload']['total_count'], queue_stats['reconstruct']['total_count'], queue_stats['conflicted']['total_count']) is_hung = self.last_counts == counts and sum(counts) cpu_minutes = self.high_cpu_minutes - self.last_cpu_minutes dbfseventsd_broken = dropbox_globals.get('dbfseventsd_is_broken', False) try: if is_hung or self.last_dbfseventsd_broken != dbfseventsd_broken and dbfseventsd_broken or cpu_minutes >= 5: TRACE('!! Sync engine appears stalled: %r (%s high cpu mins) dbfseventsd_broken:%r' % (counts, cpu_minutes, dbfseventsd_broken)) if self.hung_since is None: self.hung_since = time.time() send_trace_log() if cpu_minutes >= 5: sample_process() current_upload_speed = self.status_controller.upload_status.get_transfer_speed() or 0 current_download_speed = self.status_controller.download_status.get_transfer_speed() or 0 upload_bytecount, upload_hashcount = self.sync_engine.upload_queue_size() download_bytecount, download_hashcount = self.sync_engine.download_queue_size() unreconstructable_count = 0 self.conn.report_hang2(dict(is_hung=is_hung, dbfseventsd_broken=dbfseventsd_broken, cpu_minutes=cpu_minutes, cpu_minutesp=float(cpu_minutes) / self.report_interval, hung_for=time.time() - self.hung_since, queue_stats=queue_stats, conflicted_count=queue_stats['conflicted']['total_count'], over_quota=self.over_quota, current_upload_speed=current_upload_speed, current_download_speed=current_download_speed, low_disk_space=self.status_controller.is_true('low_disk_space'), upload_bytecount=upload_bytecount, download_bytecount=download_bytecount, upload_hashcount=upload_hashcount, download_hashcount=download_hashcount, unreconstructable_count=unreconstructable_count, exception_hashes=(self.last_exception_hash, self.last_hash_exception_hash, self.last_upload_exception_hash, self.last_reconstruct_exception_hash))) elif self.hung_since: TRACE('!! Sync engine no longer stalled: %r' % (counts,)) self.conn.report_hang2(None) self.hung_since = None except Exception as e: if self.conn.is_transient_error(e): TRACE('!! Failed to communicate with server: %r%r', type(e), e.args) else: unhandled_exc_handler() self.report_interval += self.UNIT_REPORT_INTERVAL else: self.last_counts = counts self.last_dbfseventsd_broken = dbfseventsd_broken self.last_cpu_minutes = self.high_cpu_minutes self.last_report = ct self.report_interval = self.UNIT_REPORT_INTERVAL class StatReporter(object): DEFAULT_INTERVAL = 900 def __init__(self, csr, buildno, status_controller, conn, config, handler = None): self.csr = csr self.config = config self.old_stats = {} self.last_report = 0 self.buildno = buildno self.interval = self.DEFAULT_INTERVAL self.status_controller = status_controller self.conn = conn self.sync_engine = None self.failure_backoff = None try: with self.config as config: try: last_stats_build = config['stats_build'] except KeyError: self.next_report_id = config.get('stats_next_report_id', 0) self.next_report_time = config.get('stats_next_report_time') self.dont_send_until_upgrade = config.get('stats_dont_send_until_upgrade') else: if last_stats_build != get_build_number(): self.next_report_id = 0 self.next_report_time = None self.dont_send_until_upgrade = False else: self.next_report_id = config.get('stats_next_report_id', 0) self.next_report_time = config.get('stats_next_report_time') self.dont_send_until_upgrade = config.get('stats_dont_send_until_upgrade') except Exception: unhandled_exc_handler() self.next_report_id = 0 self.next_report_time = None self.dont_send_until_upgrade = False if self.next_report_time: TRACE('Client stats wants us to hit the server again at %s (%s seconds) (local: %s)' % (self.next_report_time, self.next_report_time - time.time(), time.ctime(self.next_report_time))) if handler: handler.add_handler(self.run) def set_reporting_interval(self, interval): self.interval = interval def set_sync_engine(self, sync_engine): self.sync_engine = sync_engine def report_queryable_stats(self): us = self.status_controller.upload_status.get_transfer_speed() if us is not None: self.csr.report_stat('upload_speed', str(us)) ds = self.status_controller.download_status.get_transfer_speed() if ds is not None: self.csr.report_stat('download_speed', str(ds)) if self.sync_engine: queue_stats = self.sync_engine.get_queue_stats() self.csr.report_stat('pending_cache_count', str(queue_stats['upload']['total_count'])) self.csr.report_stat('to_hash_cache_count', str(queue_stats['hash']['total_count'])) self.csr.report_stat('updated_cache_count', str(queue_stats['reconstruct']['total_count'])) self.csr.report_stat('conflicted_cache_count', str(queue_stats['conflicted']['total_count'])) self.csr.report_stat('quota_pending_cache_count', str(queue_stats['upload']['failure_counts'].get('quota', 0))) self.csr.report_stat('invalid_path_pending_cache_count', str(queue_stats['upload']['failure_counts'].get('invalid_path', 0))) self.csr.report_stat('directory_not_empty_updated_cache_count', str(queue_stats['reconstruct']['failure_counts'].get('directory_not_empty', 0))) self.csr.report_stat('low_disk_space_updated_cache_count', str(queue_stats['reconstruct']['failure_counts'].get('low_disk_space', 0))) self.csr.report_stat('permission_denied_updated_cache_count', str(queue_stats['reconstruct']['failure_counts'].get('permission_denied', 0))) n = int(time.time()) self.csr.report_stat('gui_backoff_factor', str(struct.unpack('<Q', hashlib.md5('Open %s Folder Launch %d Website Apple CarbonEventManager Windows %d' % (self.buildno, n, self.conn.host_int)).digest()[:struct.calcsize('<Q')])[0]), n) rss = arch.startup.get_rss() if rss is not None and rss != -1: self.csr.report_stat('rss', str(rss)) try: mem_used, highwater = sqlite3_get_memory_statistics() except Exception: unhandled_exc_handler() else: self.csr.report_stat('sqlite3_memory_used', str(mem_used)) self.csr.report_stat('sqlite3_memory_highwater', str(highwater)) try: if self.sync_engine and self.sync_engine.p2p_state and self.sync_engine.p2p_state.pool: peer_dict = self.sync_engine.p2p_state.pool.getPeerAndConnCount() self.csr.report_stat('peer_count', str(peer_dict['peer_count'])) self.csr.report_stat('peer_connections', str(peer_dict['total_connections'])) except Exception: unhandled_exc_handler() def run(self): if self.dont_send_until_upgrade: return if self.failure_backoff: if time.time() < self.failure_backoff[1]: return if self.next_report_time is not None and time.time() < self.next_report_time: report_bad_assumption('Client-stats backoff was less than our next report time, %s < %s' % (self.next_report_time, self.failure_backoff[1])) elif self.next_report_time is not None and time.time() < self.next_report_time: return self.report_queryable_stats() self.csr.lock_updates() try: total_stats = self.csr.total_stats() total_events = self.csr.total_events() report_id = self.next_report_id divisor = 1 done = False while not done: to_pull_from_stats = total_stats / divisor to_pull_from_events = total_events / divisor for i in xrange(divisor): stat_batch = self.csr.iterate_n_stats(to_pull_from_stats) event_batch = self.csr.iterate_n_events(to_pull_from_events) stats_to_send = [ (stat, arg, ts) for stat, (arg, ts) in stat_batch ] try: event_ids_to_clear, events_to_send = zip(*event_batch) except ValueError: event_ids_to_clear, events_to_send = (), () if not (stats_to_send or events_to_send): done = True break try: ret = self.conn.report_stats(time.time(), stats_to_send, events_to_send, self.buildno, report_id) except Exception as e: unhandled_exc_handler() if isinstance(e, socket.error) and e[0] == 32: break if self.failure_backoff: self.failure_backoff = (self.failure_backoff[0] * 2, min(random.uniform(0, self.failure_backoff[0] * 4), _ONE_WEEK) + time.time()) else: self.failure_backoff = (1, random.uniform(0, 2) + time.time()) TRACE('!! Client Stats failed, backing off until %s for (%s seconds) (local: %s)' % (self.failure_backoff[1], self.failure_backoff[1] - time.time(), time.ctime(self.failure_backoff[1]))) done = True else: self.failure_backoff = None try: self.dont_send_until_upgrade = ret['dont_send_until_upgrade'] except KeyError: stats_backoff = float(ret['backoff']) next_report_id = long(ret['report_id']) self.next_report_time = stats_backoff + time.time() self.next_report_id = next_report_id else: self.next_report_time = None self.next_report_id = 0 try: with self.config as config: config['stats_dont_send_until_upgrade'] = self.dont_send_until_upgrade config['stats_next_report_time'] = self.next_report_time config['stats_next_report_id'] = self.next_report_id config['stats_build'] = get_build_number() except Exception: unhandled_exc_handler() TRACE('Client stats wants us to hit the server again at %s (%s seconds) (local: %s)', self.next_report_time, stats_backoff, time.ctime(self.next_report_time)) self.csr.clean_reported_stats_and_event_ids(stats_to_send, event_ids_to_clear) divisor *= 2 finally: self.csr.unlock_updates() def maybe_send_explorer_crash(): if arch.util.unreported_explorer_crash(): send_trace_log() class WatchdogThread(StoppableThread): def __init__(self, app, *n, **kw): kw['name'] = 'WATCHDOG' super(WatchdogThread, self).__init__(*n, **kw) self.app = app self.bangp = AutoResetEvent() self.one_time_q = Queue(100) self.handler = Handler(handle_exc=unhandled_exc_handler) self.add_handler(self._one_time_handler) def set_wakeup_event(self, *n, **kw): self.bangp.set(*n, **kw) def add_handler(self, *n, **kw): self.handler.add_handler(*n, **kw) def remove_handler(self, *n, **kw): self.handler.remove_handler(*n, **kw) def add_one_time_handler(self, cb): try: self.one_time_q.put(cb) except Exception: unhandled_exc_handler() def _one_time_handler(self, *n, **kw): for fn in self.one_time_q.get_all_and_clear(): try: fn(*n, **kw) except Exception: unhandled_exc_handler() def run(self): TRACE('Watchdog thread starting.') if platform == 'mac': def maybe_send_finder_crashes(): sarch = self.app.sync_engine.arch if sarch.fschange and sarch.fschange.potential_finder_restart(): send_finder_crashes() self.add_handler(maybe_send_finder_crashes) self.add_handler(send_ds_store) if platform == 'win': self.add_handler(maybe_send_explorer_crash) send_finder_crashes() self.remove_installer_logs() while not self.stopped(): self.handler.run_handlers() self.bangp.wait(60) TRACE('Stopping...') def remove_installer_logs(self): if platform != 'win': return try: installer_log_dir = os.path.join(self.app.appdata_path, 'installer', 'l') if not os.path.exists(installer_log_dir): return for x in os.listdir(installer_log_dir): full_path = os.path.join(installer_log_dir, x) safe_remove(full_path) except Exception: unhandled_exc_handler()

[ "[email protected]" ]

[email protected]

3231911515adfd0365eae0b7ab08f656f1a18ce5

134c429df7d5c4d067d9761cb1435992b048adaf

/notes/0431/0431.py

11bc6ce8611f6db618e1efae727ca798d3c25e41

[]

no_license

PaulGuo5/Leetcode-notes

65c6ebb61201d6f16386062e4627291afdf2342d

431b763bf3019bac7c08619d7ffef37e638940e8

refs/heads/master

2021-06-23T09:02:58.143862

2021-02-26T01:35:15

177,007,645

1

0

null

UTF-8

Python

false

1,261

py

""" # Definition for a Node. class Node: def __init__(self, val=None, children=None): self.val = val self.children = children """ """ # Definition for a binary tree node. class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None """ class Codec: # Encodes an n-ary tree to a binary tree. def encode(self, root: 'Node') -> TreeNode: if not root: return None new = TreeNode(root.val) if not root.children: return new new.left = self.encode(root.children[0]) # node's children node = new.left for child in root.children[1:]: # node's sibling node.right = self.encode(child) node = node.right return new # Decodes your binary tree to an n-ary tree. def decode(self, data: TreeNode) -> 'Node': if not data: return None new = Node(data.val, []) node = data.left while node: new.children.append(self.decode(node)) node = node.right return new # Your Codec object will be instantiated and called as such: # codec = Codec() # codec.decode(codec.encode(root))

[ "[email protected]" ]

[email protected]

6ca88a0dbb97f37b2015940e3978efcb9c8a9f0b

bfc25f1ad7bfe061b57cfab82aba9d0af1453491

/data/external/repositories_2to3/132160/kaggle-ndsb-master/configurations/[email protected]

15acaee9e4b2e5862cb947b90c641219ff778759

[ "MIT" ]

permissive

Keesiu/meta-kaggle

77d134620ebce530d183467202cf45639d9c6ff2

87de739aba2399fd31072ee81b391f9b7a63f540

refs/heads/master

2020-03-28T00:23:10.584151

2018-12-20T19:09:50

147,406,338

0

1

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import numpy as np import theano import theano.tensor as T import lasagne as nn import data import load import nn_plankton import dihedral import tmp_dnn import tta pre_init_path = "CONVROLL4_MODEL_FILE" validation_split_path = "splits/bagging_split_27.pkl" patch_size = (95, 95) augmentation_params = { 'zoom_range': (1 / 1.6, 1.6), 'rotation_range': (0, 360), 'shear_range': (-20, 20), 'translation_range': (-10, 10), 'do_flip': True, 'allow_stretch': 1.3, } batch_size = 128 // 4 chunk_size = 32768 // 4 num_chunks_train = 580 momentum = 0.9 learning_rate_schedule = { 0: 0.003, 420: 0.0003, 540: 0.00003, } validate_every = 20 save_every = 20 def estimate_scale(img): return np.maximum(img.shape[0], img.shape[1]) / 85.0 # augmentation_transforms_test = [] # for flip in [True, False]: # for zoom in [1/1.3, 1/1.2, 1/1.1, 1.0, 1.1, 1.2, 1.3]: # for rot in np.linspace(0.0, 360.0, 5, endpoint=False): # tf = data.build_augmentation_transform(zoom=(zoom, zoom), rotation=rot, flip=flip) # augmentation_transforms_test.append(tf) augmentation_transforms_test = tta.build_quasirandom_transforms(70, **{ 'zoom_range': (1 / 1.4, 1.4), 'rotation_range': (0, 360), 'shear_range': (-10, 10), 'translation_range': (-8, 8), 'do_flip': True, 'allow_stretch': 1.2, }) data_loader = load.ZmuvRescaledDataLoader(estimate_scale=estimate_scale, num_chunks_train=num_chunks_train, patch_size=patch_size, chunk_size=chunk_size, augmentation_params=augmentation_params, augmentation_transforms_test=augmentation_transforms_test, validation_split_path=validation_split_path) # Conv2DLayer = nn.layers.cuda_convnet.Conv2DCCLayer # MaxPool2DLayer = nn.layers.cuda_convnet.MaxPool2DCCLayer Conv2DLayer = tmp_dnn.Conv2DDNNLayer MaxPool2DLayer = tmp_dnn.MaxPool2DDNNLayer def build_model(): l0 = nn.layers.InputLayer((batch_size, 1, patch_size[0], patch_size[1])) l0c = dihedral.CyclicSliceLayer(l0) l1a = Conv2DLayer(l0c, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l1b = Conv2DLayer(l1a, num_filters=16, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2)) l1r = dihedral.CyclicConvRollLayer(l1) l2a = Conv2DLayer(l1r, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l2b = Conv2DLayer(l2a, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2)) l2r = dihedral.CyclicConvRollLayer(l2) l3a = Conv2DLayer(l2r, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l3b = Conv2DLayer(l3a, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l3c = Conv2DLayer(l3b, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2)) l3r = dihedral.CyclicConvRollLayer(l3) l4a = Conv2DLayer(l3r, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l4b = Conv2DLayer(l4a, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l4c = Conv2DLayer(l4b, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2)) l4r = dihedral.CyclicConvRollLayer(l4) l5a = Conv2DLayer(l4r, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l5b = Conv2DLayer(l5a, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l5c = Conv2DLayer(l5b, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True) l5 = MaxPool2DLayer(l5c, ds=(3, 3), strides=(2, 2)) l5r = dihedral.CyclicConvRollLayer(l5) l5f = nn.layers.flatten(l5r) l6 = nn.layers.DenseLayer(nn.layers.dropout(l5f, p=0.5), num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l6r = dihedral.CyclicRollLayer(l6) l7 = nn.layers.DenseLayer(nn.layers.dropout(l6r, p=0.5), num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l7m = dihedral.CyclicPoolLayer(l7, pool_function=nn_plankton.rms) l8 = nn.layers.DenseLayer(nn.layers.dropout(l7m, p=0.5), num_units=data.num_classes, nonlinearity=T.nnet.softmax, W=nn_plankton.Orthogonal(1.0)) l_resume = l2 l_exclude = l2 return [l0], l8, l_resume, l_exclude

[ "[email protected]" ]

[email protected]

6ceb9d1a80663a73976e941ebaa5c6143e75a5ce

c7e9ec5ce6627f6f68bab1b86a27a4516595154d

/consentrecords/migrations/0089_auto_20180123_2226.py

738533784e45a55c62c2542c2229561d9a774b5b

[]

no_license

michaelcrubenstein/consentrecords

7b79e82c9ad4b5efcfbb44a50ff1d4cadf7180e2

992fe78c68d1d5c083f9e2cc0e3e9aa24363b93d

refs/heads/master

2021-01-23T19:28:13.807809

2018-07-03T16:10:34

41,223,029

1

null

2018-07-03T16:10:35

2015-08-22T20:21:26

JavaScript

UTF-8

Python

false

700

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2018-01-23 22:26 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('consentrecords', '0088_auto_20171227_2107'), ] operations = [ migrations.RemoveField( model_name='experience', name='timeframe', ), migrations.RemoveField( model_name='experiencehistory', name='timeframe', ), migrations.AlterField( model_name='experience', name='era', field=models.IntegerField(db_index=True, null=True), ), ]

[ "[email protected]" ]

[email protected]

0fe411a821c9c8d033e284c43fba39a10774bbb9

4a1273f72e7d8a07a3fa67ac9f2709b64ec6bc18

/retiresmartz/advice_responses.py

73223c61a7c14be764d3ae4c699eb9fc19d26e8d

[]

no_license

WealthCity/django-project

6668b92806d8c61ef9e20bd42daec99993cd25b2

fa31fa82505c3d0fbc54bd8436cfc0e49c896f3e

refs/heads/dev

2021-01-19T14:10:52.115301

2017-04-12T11:23:32

88,132,284

0

1

null

2017-04-13T06:26:30

2017-04-13T06:26:29

null

UTF-8

Python

false

12,283

py

# -*- coding: utf-8 -*- from main import constants from datetime import datetime import logging logger = logging.getLogger('api.v1.retiresmartz.advice_responses') # Retiresmartz Advice feed Logic Calls # On Track / Off Track def get_on_track(advice): return 'If you would like to change any of your retirement details \ you can do so by clicking on the items on this or the previous screen.' def get_off_track(advice): return 'I recommend changing some of your retirement details \ by clicking on this or the previous screen. This will help you \ to plan to be on track to a better retirement.' # Change Retirement Age def get_decrease_retirement_age_to_62(advice): # TODO: Need to insert social security benefit in data # By increasing your retirement age to 70 your social \ # security benefit would be estimated to be <estimated SS benefit \ # multiplied by inflator> return "I see you have decreased your retirement age \ to 62. This will reduce your monthly benefit by 25% compared \ to if you retired at 66 giving you an estimated social security \ benefit of ${:,.2f} per month instead of ${:,.2f} if you chose to retire \ at 66. Social security benefits increase by up to 132% the longer \ you work.".format(advice.plan.spendable_income - (advice.plan.spendable_income * .25), advice.plan.spendable_income) def get_decrease_retirement_age_to_63(advice): # TODO: Need to insert social security benefit in data # By increasing your retirement age to 70 \ # your social security benefit would be estimated to be \ # <estimated SS benefit multiplied by inflator> return "I see you have decreased your retirement age to 63. \ This will reduce your monthly benefit by 20% compared to if \ you retired at 66 giving you an estimated social security \ benefit of ${:,.2f} per month instead of ${:,.2f} if you chose to \ retire at 66. Social security benefits increase by up to 132% \ the longer you work.".format(advice.plan.spendable_income - (advice.plan.spendable_income * .2), advice.plan.spendable_income) def get_decrease_retirement_age_to_64(advice): # TODO: Need to insert social security benefit in data\ # By increasing your retirement age to 70 \ # your social security benefit would be estimated to be \ # <estimated SS benefit multiplied by inflator> return "I see you have decreased your retirement age to 64. \ This will reduce your monthly benefit by 13% compared to \ if you retired at 66 giving you an estimated social security \ benefit of ${:,.2f} per month instead of ${:,.2f} if you chose to \ retire at 66. Social security benefits increase by up to 132% \ the longer you work.".format(advice.plan.spendable_income - (advice.plan.spendable_income * .13), advice.plan.spendable_income) def get_decrease_retirement_age_to_65(advice): # TODO: Need to insert social security benefit in data # By increasing your retirement age to 70 \ # your social security benefit would be estimated # to be <estimated SS benefit multiplied by inflator> return "I see you have decreased your retirement age to 65. \ This will reduce your monthly benefit by 7% compared to if \ you retired at 66 giving you an estimated social security \ benefit of ${:,.2f} per month instead of ${:,.2f} if you chose to \ retire at 66. Social security benefits increase by up to 132% \ the longer you work.".format(advice.plan.spendable_income - (advice.plan.spendable_income * .07), advice.plan.spendable_income) def get_increase_retirement_age_to_67(advice): # TODO: Need to insert social security benefit in data return "I see you have increased your retirement age to 67. \ This will increase your monthly benefit by 8% of ${:,.2f} per \ month instead of ${:,.2f} if you chose to retire at 66. Increasing \ your retirement age will adjust the amount of social security \ benefits that you are able to obtain. Social security benefits \ increase by up to 132% the longer you work.".format(advice.plan.spendable_income + (advice.plan.spendable_income * .08), advice.plan.spendable_income) def get_increase_retirement_age_to_68(advice): # TODO: Need to insert social security benefit in data return "I see you have increased your retirement age to 68. \ This will increase your monthly benefit by 16% of ${:,.2f} per \ month instead of ${:,.2f} if you chose to retire at 66. Increasing \ your retirement age will adjust the amount of social security \ benefits that you are able to obtain. Social security benefits \ increase by up to 132% the longer you work.".format(advice.plan.spendable_income + (advice.plan.spendable_income * .16), advice.plan.spendable_income) def get_increase_retirement_age_to_69(advice): # TODO: Need to insert social security benefit in data return "I see you have increased your retirement age to 69. \ This will increase your monthly benefit by 24% of ${:,.2f} per \ month instead of ${:,.2f} if you chose to retire at 66. Increasing \ your retirement age will adjust the amount of social security \ benefits that you are able to obtain. Social security benefits \ increase by up to 132% the longer you work.".format(advice.plan.spendable_income + (advice.plan.spendable_income * .24), advice.plan.spendable_income) def get_increase_retirement_age_to_70(advice): # TODO: Need to insert social security benefit in data return "I see you have increased your retirement age to 70. \ This will increase your monthly benefit by 32% of ${:,.2f} per \ month instead of ${:,.2f} if you chose to retire at 66. Increasing \ your retirement age will adjust the amount of social security \ benefits that you are able to obtain. Social security benefits \ increase by up to 132% the longer you work.".format(advice.plan.spendable_income + (advice.plan.spendable_income * .32), advice.plan.spendable_income) # Life Expectancy def get_manually_adjusted_age(advice): return 'Your retirement age has been updated. \ Let us know if anything else changes in your wellbeing \ profile by clicking on the life expectancy bubble.' def get_smoking_yes(advice): return 'You could improve your life expectancy score \ by quitting smoking. Do you intend to quit smoking in \ the near future? Yes/No' def get_quitting_smoking(advice): # {formula = (Current Age – 18)/42]*7.7 or 7.3 years} formula = ((advice.plan.client.age - 18) / 42) * 7.7 return "We will add this to your life expectancy. \ Based on your age we will add {} years to your life expectancy.".format(formula) def get_smoking_no(advice): return "By not smoking you're already increasing your \ life expectancy by 7 years." def get_exercise_only(advice): return "Thanks for telling us about your exercise. Exercise \ does impact your life expectancy. Regular exercise for at \ least 20 minutes each day 5 times a week increases your \ life expectancy by up to 3.2 years." def get_weight_and_height_only(advice): return "Thanks for telling us your weight and height. \ These combined details help us better understand your life expectancy." def get_combination_of_more_than_one_entry_but_not_all(advice): return "Thanks for providing more wellbeing information. This helps us \ better understand your life expectancy. By completing all of the \ details we can get an even more accurate understanding." def get_all_wellbeing_entries(advice): return "Thanks for providing your wellbeing information. This gives us \ the big picture and an accurate understanding of your life expectancy." # Risk Slider def get_protective_move(advice): # TODO: Need to add risk and amounts """ This might reduce the returns from your portfolio or increase \ the amount you need to contribute from your paycheck each month \ from <previous amount> to <new amount> """ risk = round(advice.plan.recommended_risk, 2) if risk == 1.0: risk = 100 elif risk == 0.9: risk = 90 elif risk == 0.8: risk = 80 elif risk == 0.7: risk = 70 elif risk == 0.6: risk = 60 elif risk == 0.5: risk == 50 elif risk == 0.4: risk == 40 elif risk == 0.3: risk = 30 elif risk == 0.2: risk = 20 elif risk == 0.1: risk = 10 else: risk = str(risk)[2:] return "I can see you have adjusted your risk profile to be more \ protective. We base your risk profile on the risk questionnaire \ you completed and recommended {}. By adjusting the slider you \ change the asset allocation in your retirement goal.".format(str(risk)) def get_dynamic_move(advice): # TODO: Need to add risk and amounts """ This might increase the returns from your portfolio and decrease the amount \ you need to contribute from your paycheck each month from <previous amount> \ to <new amount> """ risk = round(advice.plan.recommended_risk, 2) if risk == 1.0: risk = 100 elif risk == 0.9: risk = 90 elif risk == 0.8: risk = 80 elif risk == 0.7: risk = 70 elif risk == 0.6: risk = 60 elif risk == 0.5: risk == 50 elif risk == 0.4: risk == 40 elif risk == 0.3: risk = 30 elif risk == 0.2: risk = 20 elif risk == 0.1: risk = 10 else: risk = str(risk)[2:] return "I can see you have adjusted your risk profile to be more dynamic. \ We base your risk profile on the risk questionnaire you completed and \ recommended {}. By adjusting the slider you change the asset allocation \ in your retirement goal.\nYou will be taking more risk.".format(str(risk)) # Contributions / Spending def get_increase_spending_decrease_contribution(advice, contrib, income): # TODO: Need to add $X and $Y calculations, max_contributions needs to come in # if advice.plan.client.account.account_type == constants.ACCOUNT_TYPE_401K or \ # advice.plan.client.account.account_type == constants.ACCOUNT_TYPE_ROTH401K: # # [If 401K we need to remember here that for a person under 50 the # # maximum contribution amount is $18,000 per annum and $24,000 # # if they are over 50] # if advice.plan.client.age < 50: # max_contribution = 18000 # else: # max_contribution = 24000 rv = "Hey big spender! I can see you want to spend a bit more. \ If you decreased your spending by ${:,.2f} a week, you could increase your\ retirement income by ${:,.2f} a week.".format(contrib, income) if advice.plan.client.employment_status == constants.EMPLOYMENT_STATUS_FULL_TIME or \ advice.plan.client.employment_status == constants.EMPLOYMENT_STATUS_PART_TIME: rv += " Your employer will match these contributions making it easier to reach your goal." return rv def get_increase_contribution_decrease_spending(advice, contrib, income): return "Well done, by increasing your retirement contributions to ${:,.2f} \ a month, you have increased your retirement income by ${:,.2f} a week.".format(contrib, income) def get_increase_spending_decrease_contribution_again(advice, contrib, income): # TODO: Need to add $X and $Y calculations return "Are you sure you need to increase your spending again and reduce your \ retirement contributions? Just think, if your contributions stayed \ at ${:,.2f} a month you would be ${:,.2f} a week better off in retirement.".format(contrib, income) def get_off_track_item_adjusted_to_on_track(advice): years = advice.plan.retirement_age - advice.plan.client.age return "Well done, by adjusting your details your retirement goal is now on track.\n\ We want to make sure our advice keeps you on track so that when you retire \ in {} there are no nasty surprises. If you would like to change or see the \ impact of any of your choices you can make changes to your details on \ this dashboard.".format(datetime.now().date().year + years) def get_on_track_item_adjusted_to_off_track(advice): return "Uh oh, you are now off track to achieve your retirement goal. \ We are here to give you’re the advice to ensure you get on track. \ You may want to reconsider the changes you have made to your details \ to get your retirement goal back on track."

[ "[email protected]" ]

[email protected]

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/inferencia/task/pose_estimation/pose_estimation_2d/visualization/pose_estimation_2d_visualizer_factory.py

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from .pose_estimation_2d_visualizer_name import PoseEstimation2DVisualizerName from ..label.pose_estimation_2d_label_factory import PoseEstimation2DLabelFactory class PoseEstimation2DVisualizerFactory(): def create(visualizer_name="PoseVisualizer", label_name="COCOKeyPointLabel"): if visualizer_name == PoseEstimation2DVisualizerName.pose_visualizer.value: from .visualization.pose_vilualizer import PoseVilualizer pose_label = PoseEstimation2DLabelFactory.create( label_name=label_name) pose_visualizer = PoseVilualizer(body_edges=pose_label.body_edges) return pose_visualizer else: msg = "{} is not implemented".format( visualizer_name) raise NotImplementedError(msg)

[ "[email protected]" ]

[email protected]

f9fade661402627c5a7c12936bed53fbd8d25454

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/mishamovie/pipeline_steps/combine_frames.py

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2023-06-08T21:28:30.605756

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""" This script trains a simple model Cats and Dogs dataset and saves it in the SavedModel format """ import argparse import subprocess def parse_args(): parser = argparse.ArgumentParser(description='Split video into frames') parser.add_argument('--input_dir', help='Input dir', required=True) parser.add_argument('--output_dir', help='Output dir', required=True) parser.add_argument('--output_filename', help='Output file name', required=True) parser.add_argument('--input_name_template', default='frame_%5d.png', help='Input name template', required=False) parser.add_argument('--fps', default=30, type=int, help='Frames per second', required=False) args = parser.parse_args() return args def main(): # TODO: fmpeg -i face_close_aged_long.mp4 -filter "minterpolate='fps=120'" zzzz3.mp4 # https://superuser.com/a/1185430 # https://github.com/dthpham/butterflow args = parse_args() print(args) input_filename = f'{args.input_dir}/{args.input_name_template}' output_filename = f'{args.output_dir}/{args.output_filename}' cmd = [ 'ffmpeg', '-y', '-framerate', str(args.fps), '-i', input_filename, '-c:v', 'libx264', '-vf', f'fps={args.fps},pad=ceil(iw/2)*2:ceil(ih/2)*2', '-pix_fmt', 'yuv420p', output_filename ] subprocess.run(cmd, check=True) if __name__ == '__main__': main()

[ "[email protected]" ]

[email protected]

9112a8d3de06671acf5e9fded056dc27c0c8b4a3

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/DjangoBlog/admin_site.py

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2022-12-27T05:06:27.578712

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#!/usr/bin/env python # encoding: utf-8 """ @version: ?? @author: liangliangyy @license: MIT Licence @contact: [email protected] @site: https://www.lylinux.net/ @software: PyCharm @file: admin_site.py @time: 2018/1/7 上午2:21 """ from django.contrib.admin import AdminSite from DjangoBlog.utils import get_current_site from django.contrib.sites.admin import SiteAdmin from django.contrib.admin.models import LogEntry from django.contrib.sites.models import Site from DjangoBlog.logentryadmin import LogEntryAdmin from blog.admin import * from accounts.admin import * from oauth.admin import * from servermanager.admin import * from comments.admin import * from owntracks.admin import * class DjangoBlogAdminSite(AdminSite): site_header = 'DjangoBlog administration' site_title = 'DjangoBlog site admin' def __init__(self, name='admin'): super().__init__(name) def has_permission(self, request): return request.user.is_superuser # def get_urls(self): # urls = super().get_urls() # from django.urls import path # from blog.views import refresh_memcache # # my_urls = [ # path('refresh/', self.admin_view(refresh_memcache), name="refresh"), # ] # return urls + my_urls admin_site = DjangoBlogAdminSite(name='admin') admin_site.register(Article, ArticlelAdmin) admin_site.register(Category, CategoryAdmin) admin_site.register(Tag, TagAdmin) admin_site.register(Links, LinksAdmin) admin_site.register(SideBar, SideBarAdmin) admin_site.register(BlogSettings, BlogSettingsAdmin) admin_site.register(commands, CommandsAdmin) admin_site.register(EmailSendLog, EmailSendLogAdmin) admin_site.register(BlogUser, BlogUserAdmin) admin_site.register(Comment, CommentAdmin) admin_site.register(OAuthUser, OAuthUserAdmin) admin_site.register(OAuthConfig, OAuthConfigAdmin) admin_site.register(OwnTrackLog, OwnTrackLogsAdmin) admin_site.register(Site, SiteAdmin) admin_site.register(LogEntry, LogEntryAdmin)

[ "[email protected]" ]

[email protected]

3ecb1f5f3ec4c2e5ce2a71a98e9fe46cea17d818

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/venv/lib/python3.8/site-packages/cryptography/hazmat/primitives/keywrap.py

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baruwaa12/Projects

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/home/runner/.cache/pip/pool/2f/84/6a/ccb5454b00837ef6351bc65055c9fc4ab3ccb02f3c73ed14ffceb09910

[ "[email protected]" ]

[email protected]

d6ae857b950288a40f300ca27e242e60df1df9a0

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/python/src/yatzy/YatzyTest.py

0b2bf06d8f872a60d16e9e5cdc496e24a2794c6a

[]

no_license

mebusw/Test-Driven-Development

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refs/heads/master

2021-01-22T06:32:07.448971

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""" The game of yatzy is a simple dice game. Each player rolls five six-sided dice. The player places the roll in a category, such as ones, twos, fives, pair, two pairs etc (see below). If the roll is compatible with the category, the player gets a score for the roll according to the rules. If the roll is not compatible with the category, the player scores zero for the roll. For example, if a player rolls 5,6,5,5,2 and scores the dice in the fives category they would score 15 (three fives). Your task is to score a GIVEN roll in a GIVEN category. You do NOT have to program the random dice rolling. You do NOT have to program re-rolls (as in the real game). You do NOT play by letting the computer choose the highest scoring category for a given roll. Yatzy Categories and Scoring Rules ================================== Chance: The player scores the sum of all dice, no matter what they read. For example, 1,1,3,3,6 placed on "chance" scores 14 (1+1+3+3+6) 4,5,5,6,1 placed on "chance" scores 21 (4+5+5+6+1) Yatzy: If all dice have the same number, the player scores 50 points. For example, 1,1,1,1,1 placed on "yatzy" scores 50 5,5,5,5,5 placed on "yatzy" scores 50 1,1,1,2,1 placed on "yatzy" scores 0 Ones, Twos, Threes, Fours, Fives, Sixes: The player scores the sum of the dice that reads one, two, three, four, five or six, respectively. For example, 1,1,2,4,4 placed on "fours" scores 8 (4+4) 2,3,2,5,1 placed on "twos" scores 4 (2+2) 3,3,3,4,5 placed on "ones" scores 0 Pair: If exactly two dice have the same value then the player scores the sum of the two highest matching dice. For example, when placed on "pair" 3,3,3,4,4 scores 8 (4+4) 1,1,6,2,6 scores 12 (6+6) 3,3,3,4,1 scores 0 3,3,3,3,1 scores 0 Two pairs: If exactly two dice have the same value and exactly two dice have a different value then the player scores the sum of these four dice. For example, when placed on "two pairs" 1,1,2,3,3 scores 8 (1+1+3+3) 1,1,2,3,4 scores 0 1,1,2,2,2 scores 0 Three of a kind: If there are exactly three dice with the same number then the player scores the sum of these dice. For example, when placed on "three of a kind" 3,3,3,4,5 scores 9 (3+3+3) 3,3,4,5,6 scores 0 3,3,3,3,1 scores 0 Four of a kind: If there are exactly four dice with the same number then the player scores the sum of these dice. For example, when placed on "four of a kind" 2,2,2,2,5 scores 8 (2+2+2+2) 2,2,2,5,5 scores 0 2,2,2,2,2 scores 0 Small straight: When placed on "small straight", if the dice read 1,2,3,4,5, the player scores 15 (the sum of all the dice). Large straight: When placed on "large straight", if the dice read 2,3,4,5,6, the player scores 20 (the sum of all the dice). Full house: If the dice are two of a kind and three of a different kind then the player scores the sum of all five dice. For example, when placed on "full house" 1,1,2,2,2 scores 8 (1+1+2+2+2) 2,2,3,3,4 scores 0 4,4,4,4,4 scores 0 """ import unittest from itertools import groupby class Game(object): @staticmethod def chance(*dice): return sum(dice) @staticmethod def yatzy(*dice): return 50 if all(d == dice[0] for d in dice) else 0 @staticmethod def _single(n): return lambda *dice: n * dice.count(n) def __init__(self): self.sixes = Game._single(6) self.fives = Game._single(5) self.fours = Game._single(4) self.threes = Game._single(3) self.twos = Game._single(2) self.ones = Game._single(1) def pair(self, *dice): for b in self._buckets(dice): if b[1] == 2: return b[0] * 2 return 0 def _buckets(self, dice): g = groupby(sorted(dice, reverse=True), lambda x: x) buckets = [(m, len(list(n))) for m, n in g] buckets = sorted(buckets, key=lambda x: x[1], reverse=True) return buckets def three_kind(self, *dice): b = self._buckets(dice) if b[0][1] == 3: return b[0][0] * 3 return 0 def four_kind(self, *dice): b = self._buckets(dice) if b[0][1] == 4: return b[0][0] * 4 return 0 def two_pairs(self, *dice): b = self._buckets(dice) if b[0][1] == 2 and b[1][1] == 2: return b[0][0] * 2 + b[1][0] * 2 return 0 def straight(self, *dice): if all(z[0] == z[1] + 1 for z in zip(dice[1:], dice[:-1])): return sum(dice) return 0 def full_house(self, *dice): b = self._buckets(dice) if b[0][1] == 3 and b[1][1] == 2: return sum(dice) return 0 class YatzyTest(unittest.TestCase): def setUp(self): pass def test_chance(self): self.assertEquals(1 + 1 + 3 + 3 + 6, Game.chance(1, 1, 3, 3, 6)) self.assertEquals(4 + 5 + 5 + 6 + 1, Game.chance(4, 5, 5, 6, 1)) def test_yatzy(self): self.assertEquals(50, Game.yatzy(1, 1, 1, 1, 1)) self.assertEquals(50, Game.yatzy(5, 5, 5, 5, 5)) self.assertEquals(0, Game.yatzy(1, 1, 1, 2, 1)) def test_ones(self): self.assertEquals(4 + 4, Game().fours(1, 1, 2, 4, 4)) self.assertEquals(2 + 2, Game().twos(2, 3, 2, 5, 1)) self.assertEquals(0, Game().ones(3, 3, 3, 4, 5)) def test_pair(self): self.assertEquals(4 + 4, Game().pair(3, 3, 3, 4, 4)) self.assertEquals(6 + 6, Game().pair(1, 1, 6, 2, 6)) self.assertEquals(0, Game().pair(3, 3, 3, 4, 1)) self.assertEquals(0, Game().pair(3, 3, 3, 3, 1)) def test_two_pairs(self): self.assertEquals(1 + 1 + 3 + 3, Game().two_pairs(1, 1, 2, 3, 3)) self.assertEquals(0, Game().two_pairs(1, 1, 2, 3, 4)) self.assertEquals(0, Game().two_pairs(1, 1, 2, 2, 2)) def test_three_of_a_kind(self): self.assertEquals(3 + 3 + 3, Game().three_kind(3, 3, 3, 4, 5)) self.assertEquals(0, Game().three_kind(3, 3, 4, 5, 6)) self.assertEquals(0, Game().three_kind(3, 3, 3, 3, 1)) def test_four_of_a_kind(self): self.assertEquals(2 + 2 + 2 + 2, Game().four_kind(2, 2, 2, 2, 5)) self.assertEquals(0, Game().four_kind(2, 2, 2, 5, 5)) self.assertEquals(0, Game().four_kind(2, 2, 2, 2, 2)) def test_straight(self): self.assertEquals(1 + 2 + 3 + 4 + 5, Game().straight(1, 2, 3, 4, 5)) self.assertEquals(2 + 3 + 4 + 5 + 6, Game().straight(2, 3, 4, 5, 6)) def test_full_house(self): self.assertEquals(1 + 1 + 2 + 2 + 2, Game().full_house(1, 1, 2, 2, 2)) if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName'] unittest.main()

[ "[email protected]" ]

[email protected]

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/Problems/2400_2499/2475_Number_of_Unequal_Triplets_in_Array/Project_Python3/Number_of_Unequal_Triplets_in_Array.py

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# coding: utf-8 import collections import os import sys import time from typing import List, Dict, Tuple class Solution: def unequalTriplets(self, nums: List[int]) -> int: # 35ms - 71ms trips = pairs = 0 cnts = collections.Counter() for i, num in enumerate(nums): trips += pairs - cnts[num] * (i - cnts[num]) pairs += i - cnts[num] cnts[num] += 1 return trips def main(): argv = sys.argv argc = len(argv) if argc < 2: print("Usage: python {0} <testdata.txt>".format(argv[0])) exit(0) if not os.path.exists(argv[1]): print("{0} not found...".format(argv[1])) exit(0) testDataFile = open(argv[1], "r") lines = testDataFile.readlines() for temp in lines: temp = temp.strip() if temp == "": continue print("args = {0}".format(temp)) loop_main(temp) # print("Hit Return to continue...") # input() def loop_main(temp): flds = temp.replace("[","").replace("]","").replace(", ",",").rstrip() nums = [int(n) for n in flds.split(",")] print("nums = {0}".format(nums)) sl = Solution() time0 = time.time() result = sl.unequalTriplets(nums) time1 = time.time() print("result = {0:d}".format(result)) print("Execute time ... : {0:f}[s]\n".format(time1 - time0)) if __name__ == "__main__": main()

[ "[email protected]" ]

[email protected]

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"""Classes for reading and writing Valve's VPK format, version 1.""" from typing import IO, Dict, Final, Iterable, Iterator, List, Optional, Tuple, Type, Union from enum import Enum from types import TracebackType import operator import os import struct import attrs from srctools.binformat import EMPTY_CHECKSUM, checksum, struct_read __all__ = [ 'VPK_SIG', 'DIR_ARCH_INDEX', 'OpenModes', 'script_write', 'get_arch_filename', 'FileInfo', 'VPK', ] VPK_SIG: Final = 0x55aa1234 #: The first byte of VPK files. DIR_ARCH_INDEX: Final = 0x7fff #: The file index used for the ``_dir`` file. FileName = Union[str, Tuple[str, str], Tuple[str, str, str]] class OpenModes(Enum): """Modes for opening VPK files.""" READ = 'r' WRITE = 'w' APPEND = 'a' @property def writable(self) -> bool: """Check if this mode allows modifying the VPK.""" return self.value in 'wa' def iter_nullstr(file: IO[bytes]) -> Iterator[str]: """Read a null-terminated ASCII string from the file. This continuously yields strings, with empty strings indicting the end of a section. """ chars = bytearray() while True: char = file.read(1) if char == b'\x00': string = chars.decode('ascii', 'surrogateescape') chars.clear() if string == ' ': # Blank strings are saved as ' ' yield '' elif string == '': return # Actual blanks end the array. else: yield string elif char == b'': raise Exception(f'Reached EOF without null-terminator in {bytes(chars)!r}!') else: chars.extend(char) def _write_nullstring(file: IO[bytes], string: str) -> None: """Write a null-terminated ASCII string back to the file.""" if string: file.write(string.encode('ascii', 'surrogateescape') + b'\x00') else: # Empty strings are written as a space. file.write(b' \x00') def get_arch_filename(prefix: str = 'pak01', index: Optional[int] = None) -> str: """Generate the name for a VPK file. Prefix is the name of the file, usually 'pak01'. index is the index of the data file, or None for the directory. """ if index is None: return prefix + '_dir.vpk' else: return f'{prefix}_{index:>03}.vpk' def _get_file_parts(value: FileName, relative_to: str = '') -> Tuple[str, str, str]: """Get folder, name, ext parts from a string/tuple. Possible arguments: 'fold/ers/name.ext' ('fold/ers', 'name.ext') ('fold/ers', 'name', 'ext') """ path: str filename: str ext: str if isinstance(value, str): path, filename = os.path.split(value) ext = '' elif len(value) == 2: path, filename = value # type: ignore # len() can't narrow. ext = '' else: path, filename, ext = value # type: ignore # len() can't narrow. if not ext and '.' in filename: filename, ext = filename.rsplit('.', 1) if relative_to: path = os.path.relpath(path, relative_to) # Strip '/' off the end, and './' from the beginning. path = os.path.normpath(path).replace('\\', '/').rstrip('/') # Special case - empty path gets returned as '.'... if path == '.': path = '' return path, filename, ext def _join_file_parts(path: str, filename: str, ext: str) -> str: """Join together path components to the full path. Any of the segments can be blank, to skip them. """ return f"{path}{'/' if path else ''}{filename}{'.' if ext else ''}{ext}" def _check_is_ascii(value: str) -> bool: """VPK filenames must be ascii, it doesn't store or care about encoding. Allow the surrogateescape bytes also, so roundtripping existing VPKs is allowed. """ for char in value: # Do straightforward string comparison, only call ord for the surrogate-escape bytes # which can't be direct constants. if char >= '\x80' and not (0xDC80 <= ord(char) <= 0xDCFF): return False return True @attrs.define(eq=False) class FileInfo: """Represents a file stored inside a VPK. Do not call the constructor, it is only meant for VPK's use. """ vpk: 'VPK' dir: str = attrs.field(on_setattr=attrs.setters.frozen) _filename: str = attrs.field(on_setattr=attrs.setters.frozen) ext: str = attrs.field(on_setattr=attrs.setters.frozen) crc: int arch_index: Optional[int] # pack_01_000.vpk file to use, or None for _dir. offset: int # Offset into the archive file, including directory data if in _dir arch_len: int # Number of bytes in archive files start_data: bytes # The bytes saved into the directory header @property def filename(self) -> str: """The full filename for this file.""" return _join_file_parts(self.dir, self._filename, self.ext) name = filename def __repr__(self) -> str: return f'<VPK File: "{_join_file_parts(self.dir, self._filename, self.ext)}">' @property def size(self) -> int: """The total size of this file.""" return self.arch_len + len(self.start_data) def read(self) -> bytes: """Return the contents for this file.""" if self.arch_len: if self.arch_index is None: return self.start_data + self.vpk.footer_data[self.offset: self.offset + self.arch_len] else: arch_file = get_arch_filename(self.vpk.file_prefix, self.arch_index) with open(os.path.join(self.vpk.folder, arch_file), 'rb') as data: data.seek(self.offset) return self.start_data + data.read(self.arch_len) else: return self.start_data def verify(self) -> bool: """Check this file matches the checksum.""" chk = checksum(self.start_data) if self.arch_len: if self.arch_index is None: chk = checksum( self.vpk.footer_data[self.offset: self.offset + self.arch_len], chk ) else: arch_file = get_arch_filename(self.vpk.file_prefix, self.arch_index) with open(os.path.join(self.vpk.folder, arch_file), 'rb') as data: data.seek(self.offset) chk = checksum( data.read(self.arch_len), chk, ) return chk == self.crc def write(self, data: bytes, arch_index: Optional[int] = None) -> None: """Replace this file with the given byte data. arch_index is the pak_01_000 file to put data into (or None for _dir). If this file already exists in the VPK, the old data is not removed. For this reason VPK writes should be done once per file if possible. """ if not self.vpk.mode.writable: raise ValueError(f"VPK mode {self.vpk.mode.name} does not allow writing!") # Split the file based on a certain limit. new_checksum = checksum(data) if new_checksum == self.crc: return # Same data, don't do anything. self.crc = new_checksum self.start_data = data[:self.vpk.dir_limit] arch_data = data[self.vpk.dir_limit:] self.arch_len = len(arch_data) if self.arch_len: self.arch_index = arch_index arch_file = get_arch_filename(self.vpk.file_prefix, arch_index) with open(os.path.join(self.vpk.folder, arch_file), 'ab') as file: self.offset = file.seek(0, os.SEEK_END) file.write(arch_data) else: # Only stored in the main index self.arch_index = None self.offset = 0 class VPK: """Represents a VPK file set in a directory.""" folder: str """The directory the VPK is located in, used to find the numeric files.""" file_prefix: str """The VPK filename, without ``_dir.vpk``.""" # fileinfo[extension][directory][filename] _fileinfo: Dict[str, Dict[str, Dict[str, FileInfo]]] mode: OpenModes """How the file was opened. - Read mode, the file will not be modified and it must already exist. - Write mode will create the directory if needed. - Append mode will also create the directory, but not wipe the file. """ dir_limit: Optional[int] """ The maximum amount of data for files saved to the dir file. - :external:py:data:`None`: No limit. - ``0``: Save all to a data file. """ footer_data: bytes """ The block of data after the header, which contains the file data for files stored in the ``_dir`` file, not numeric files. """ version: int """The VPK version, 1 or 2.""" def __init__( self, dir_file: Union[str, 'os.PathLike[str]'], *, mode: Union[OpenModes, str] = 'r', dir_data_limit: Optional[int] = 1024, version: int = 1, ) -> None: """Create a VPK file. :param dir_file: The path to the directory file. This must end in ``_dir.vpk``. :param mode: The (r)ead, (w)rite or (a)ppend mode. :param dir_data_limit: The maximum amount of data to save in the dir file. :param version: The desired version if the file is not read. """ if version not in (1, 2): raise ValueError(f"Invalid version ({version}) - must be 1 or 2!") self.folder = self.file_prefix = '' # Calls the property which sets the above correctly and checks the type. self.path = dir_file # fileinfo[extension][directory][filename] self._fileinfo = {} self.mode = OpenModes(mode) self.dir_limit = dir_data_limit self.footer_data = b'' self.version = version self.load_dirfile() def _check_writable(self) -> None: """Verify that this is writable.""" if not self.mode.writable: raise ValueError(f"VPK mode {self.mode.name} does not allow writing!") @property def path(self) -> Union[str, 'os.PathLike[str]']: # TODO: Incorrect, Mypy doesn't have 2-type properties. """The filename of the directory VPK file. This can be assigned to set :py:attr:`folder` and :py:attr:`file_prefix`. """ return os.path.join(self.folder, self.file_prefix + '_dir.vpk') @path.setter def path(self, path: Union[str, 'os.PathLike[str]']) -> None: """Set the location and folder from the directory VPK file.""" folder, filename = os.path.split(path) if not filename.endswith('_dir.vpk'): raise Exception('Must create with a _dir VPK file!') self.folder = folder self.file_prefix = filename[:-8] def load_dirfile(self) -> None: """Read in the directory file to get all filenames. This erases all changes in the file.""" if self.mode is OpenModes.WRITE: # Erase the directory file, we ignore current contents. open(self.path, 'wb').close() self.version = 1 return try: dirfile = open(self.path, 'rb') except FileNotFoundError: if self.mode is OpenModes.APPEND: # No directory file - generate a blank file. open(self.path, 'wb').close() self.version = 1 return else: raise # In read mode, don't overwrite and error when reading. with dirfile: vpk_sig, version, tree_length = struct_read('<III', dirfile) if vpk_sig != VPK_SIG: raise ValueError('Bad VPK directory signature!') if version not in (1, 2): raise ValueError(f"Bad VPK version {self.version}!") self.version = version if version >= 2: ( data_size, ext_md5_size, dir_md5_size, sig_size, ) = struct_read('<4I', dirfile) header_len = dirfile.tell() + tree_length self._fileinfo.clear() entry = struct.Struct('<IHHIIH') # Read directory contents # These are in a tree of extension, directory, file. '' terminates a part. for ext in iter_nullstr(dirfile): try: ext_dict = self._fileinfo[ext] except KeyError: ext_dict = self._fileinfo[ext] = {} for directory in iter_nullstr(dirfile): try: dir_dict = ext_dict[directory] except KeyError: dir_dict = ext_dict[directory] = {} for file in iter_nullstr(dirfile): crc, index_len, arch_ind, offset, arch_len, end = entry.unpack(dirfile.read(entry.size)) if arch_ind == DIR_ARCH_INDEX: arch_ind = None if arch_len == 0: offset = 0 if end != 0xffff: raise Exception( f'"{_join_file_parts(directory, file, ext)}" has bad terminator! ' f'{(crc, index_len, arch_ind, offset, arch_len, end)}' ) dir_dict[file] = FileInfo( self, directory, file, ext, crc, arch_ind, offset, arch_len, dirfile.read(index_len), ) # 1 for the ending b'' section if dirfile.tell() + 1 == header_len: dirfile.read(1) # Skip null byte. break self.footer_data = dirfile.read() def write_dirfile(self) -> None: """Write the directory file with the changes. This must be performed after writing to the VPK.""" self._check_writable() if self.version > 1: raise NotImplementedError("Can't write V2 VPKs!") # We don't know how big the directory section is, so we first write the directory, # then come back and overwrite the length value. with open(self.path, 'wb') as file: file.write(struct.pack('<III', VPK_SIG, self.version, 0)) header_len = file.tell() key_getter = operator.itemgetter(0) # Write in sorted order - not required, but this ensures multiple # saves are deterministic. for ext, folders in sorted(self._fileinfo.items(), key=key_getter): if not folders: continue _write_nullstring(file, ext) for folder, files in sorted(folders.items(), key=key_getter): if not files: continue _write_nullstring(file, folder) for filename, info in sorted(files.items(), key=key_getter): _write_nullstring(file, filename) if info.arch_index is None: arch_ind = DIR_ARCH_INDEX else: arch_ind = info.arch_index file.write(struct.pack( '<IHHIIH', info.crc, len(info.start_data), arch_ind, info.offset, info.arch_len, 0xffff, )) file.write(info.start_data) # Each block is terminated by an empty null-terminated # string -> one null byte. file.write(b'\x00') file.write(b'\x00') file.write(b'\x00') # Calculate the length of the header.. dir_len = file.tell() - header_len file.write(self.footer_data) # Write the directory size now we know it. file.seek(struct.calcsize('<II')) # Skip signature and version file.write(struct.pack('<I', dir_len)) def __enter__(self) -> 'VPK': return self def __exit__( self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], exc_trace: Optional[TracebackType], ) -> None: """When exiting a context sucessfully, the index will be saved.""" if exc_type is None and self.mode.writable: self.write_dirfile() def __getitem__(self, item: FileName) -> FileInfo: """Get the FileInfo object for a file. Possible arguments: vpk['folders/name.ext'] vpk['folders', 'name.ext'] vpk['folders', 'name', 'ext'] """ path, filename, ext = _get_file_parts(item) try: return self._fileinfo[ext][path][filename] except KeyError: raise KeyError( f'No file "{_join_file_parts(path, filename, ext)}"!' ) from None def __delitem__(self, item: FileName) -> None: """Delete a file. Possible arguments: del vpk['folders/name.ext'] del vpk['folders', 'name.ext'] del vpk['folders', 'name', 'ext'] """ self._check_writable() path, filename, ext = _get_file_parts(item) try: folders = self._fileinfo[ext] files = folders[path] files.pop(filename) except KeyError: raise KeyError( f'No file "{_join_file_parts(path, filename, ext)}"!' ) from None if not files: # Clear this folder. folders.pop(path) if not folders: # Clear extension too. self._fileinfo.pop(ext) def __iter__(self) -> Iterator[FileInfo]: """Yield all FileInfo objects.""" for folders in self._fileinfo.values(): for files in folders.values(): yield from files.values() def filenames(self, ext: str = '', folder: str = '') -> Iterator[str]: """Yield filenames from this VPK. If an extension or folder is specified, only files with this extension or in this folder are returned. """ all_folders: Iterable[Dict[str, Dict[str, FileInfo]]] if ext: all_folders = [self._fileinfo.get(ext, {})] else: all_folders = self._fileinfo.values() for folders in all_folders: for subfolder, files in folders.items(): if not subfolder.startswith(folder): continue for info in files.values(): yield info.filename def fileinfos(self, ext: str = '', folder: str = '') -> Iterator[FileInfo]: """Yield file info objects from this VPK. If an extension or folder is specified, only files with this extension or in this folder are returned. """ all_folders: Iterable[Dict[str, Dict[str, FileInfo]]] if ext: all_folders = [self._fileinfo.get(ext, {})] else: all_folders = self._fileinfo.values() for folders in all_folders: for subfolder, files in folders.items(): if not subfolder.startswith(folder): continue yield from files.values() def __len__(self) -> int: """Returns the number of files we have.""" count = 0 for folders in self._fileinfo.values(): for files in folders.values(): count += len(files) return count def __contains__(self, item: FileName) -> bool: """Check if the specified filename is present in the VPK.""" path, filename, ext = _get_file_parts(item) try: return filename in self._fileinfo[ext][path] except KeyError: return False def extract_all(self, dest_dir: str) -> None: """Extract the contents of this VPK to a directory.""" for folders in self._fileinfo.values(): for folder, files in folders.items(): os.makedirs(os.path.join(dest_dir, folder), exist_ok=True) for info in files.values(): with open(os.path.join(dest_dir, info.filename), 'wb') as f: f.write(info.read()) def new_file(self, filename: FileName, root: str = '') -> FileInfo: """Create the given file, making it empty by default. If root is set, files are treated as relative to there, otherwise the filename must be relative. FileExistsError will be raised if the file is already present. """ self._check_writable() path, name, ext = _get_file_parts(filename, root) if not _check_is_ascii(path) or not _check_is_ascii(name) or not _check_is_ascii(ext): raise ValueError(f'VPK filename {filename!r} must be ASCII format!') try: ext_infos = self._fileinfo[ext] except KeyError: ext_infos = self._fileinfo[ext] = {} try: dir_infos = ext_infos[path] except KeyError: dir_infos = ext_infos[path] = {} if name in dir_infos: raise FileExistsError( f'Filename already exists! ({_join_file_parts(path, name, ext)!r})' ) dir_infos[name] = info = FileInfo( self, path, name, ext, EMPTY_CHECKSUM, None, 0, 0, b'', ) return info def add_file( self, filename: FileName, data: bytes, root: str = '', arch_index: Optional[int] = 0, ) -> None: """Add the given data to the VPK. If root is set, files are treated as relative to there, otherwise the filename must be relative. arch_index is the pak01_xxx file to copy this to, if the length is larger than self.dir_limit. If None it's written to the _dir file. FileExistsError will be raised if the file is already present. """ self.new_file(filename, root).write(data, arch_index) def add_folder(self, folder: str, prefix: str = '') -> None: """Write all files in a folder to the VPK. If prefix is set, the folders will be written to that subfolder. """ self._check_writable() if prefix: prefix = prefix.replace('\\', '/') for subfolder, _, filenames, in os.walk(folder): # Prefix + subfolder relative to the folder. # normpath removes '.' and similar values from the beginning vpk_path = os.path.normpath( os.path.join( prefix, os.path.relpath(subfolder, folder) ) ) for filename in filenames: with open(os.path.join(subfolder, filename), 'rb') as f: self.add_file((vpk_path, filename), f.read()) def verify_all(self) -> bool: """Check all files have a correct checksum.""" return all(file.verify() for file in self) def script_write(args: List[str]) -> None: """Create a VPK archive.""" if len(args) not in (1, 2): raise ValueError("Usage: make_vpk.py [max_arch_mb] <folder>") folder = args[-1] vpk_name_base = folder.rstrip('\\/_dir') if len(args) > 1: arch_len = int(args[0]) * 1024 * 1024 else: arch_len = 100 * 1024 * 1024 current_arch = 1 vpk_folder, vpk_name = os.path.split(vpk_name_base) for filename in os.listdir(vpk_folder): if filename.startswith(vpk_name + '_'): print(f'removing existing "{filename}"') os.remove(os.path.join(vpk_folder, filename)) with VPK(vpk_name_base + '_dir.vpk', mode='w') as vpk: arch_filename = get_arch_filename(vpk_name_base, current_arch) for subfolder, _, filenames, in os.walk(folder): # normpath removes '.' and similar values from the beginning vpk_path = os.path.normpath(os.path.relpath(subfolder, folder)) print(vpk_path + '/') for filename in filenames: print('\t' + filename) with open(os.path.join(subfolder, filename), 'rb') as f: vpk.add_file( (vpk_path, filename), f.read(), arch_index=current_arch, ) if os.path.exists(arch_filename) and os.stat(arch_filename).st_size > arch_len: current_arch += 1 arch_filename = get_arch_filename(vpk_name_base, current_arch) # This function requires accumulating a character at a time, parsing the VPK # is very slow without a speedup. _Py_iter_nullstr = _Cy_iter_nullstr = iter_nullstr try: from srctools._tokenizer import _VPK_IterNullstr as _Cy_iter_nullstr # type: ignore # noqa iter_nullstr = _Cy_iter_nullstr except ImportError: pass if __name__ == '__main__': import sys script_write(sys.argv[1:])

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[email protected]

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/sdk/automanage/azure-mgmt-automanage/azure/mgmt/automanage/models/_models.py

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2021-06-17T22:52:28

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2019-08-11T21:16:01

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from azure.core.exceptions import HttpResponseError import msrest.serialization class Resource(msrest.serialization.Model): """Resource. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__( self, **kwargs ): super(Resource, self).__init__(**kwargs) self.id = None self.name = None self.type = None class TrackedResource(Resource): """The resource model definition for a ARM tracked top level resource. Variables are only populated by the server, and will be ignored when sending a request. All required parameters must be populated in order to send to Azure. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str :param tags: A set of tags. Resource tags. :type tags: dict[str, str] :param location: Required. The geo-location where the resource lives. :type location: str """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, 'location': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '{str}'}, 'location': {'key': 'location', 'type': 'str'}, } def __init__( self, **kwargs ): super(TrackedResource, self).__init__(**kwargs) self.tags = kwargs.get('tags', None) self.location = kwargs['location'] class Account(TrackedResource): """Definition of the Automanage account. Variables are only populated by the server, and will be ignored when sending a request. All required parameters must be populated in order to send to Azure. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str :param tags: A set of tags. Resource tags. :type tags: dict[str, str] :param location: Required. The geo-location where the resource lives. :type location: str :param identity: The identity of the Automanage account. :type identity: ~automanage_client.models.AccountIdentity """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, 'location': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '{str}'}, 'location': {'key': 'location', 'type': 'str'}, 'identity': {'key': 'identity', 'type': 'AccountIdentity'}, } def __init__( self, **kwargs ): super(Account, self).__init__(**kwargs) self.identity = kwargs.get('identity', None) class AccountIdentity(msrest.serialization.Model): """Identity for the Automanage account. Variables are only populated by the server, and will be ignored when sending a request. :ivar principal_id: The principal id of Automanage account identity. :vartype principal_id: str :ivar tenant_id: The tenant id associated with the Automanage account. :vartype tenant_id: str :param type: The type of identity used for the Automanage account. Currently, the only supported type is 'SystemAssigned', which implicitly creates an identity. Possible values include: "SystemAssigned", "None". :type type: str or ~automanage_client.models.ResourceIdentityType """ _validation = { 'principal_id': {'readonly': True}, 'tenant_id': {'readonly': True}, } _attribute_map = { 'principal_id': {'key': 'principalId', 'type': 'str'}, 'tenant_id': {'key': 'tenantId', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__( self, **kwargs ): super(AccountIdentity, self).__init__(**kwargs) self.principal_id = None self.tenant_id = None self.type = kwargs.get('type', None) class AccountList(msrest.serialization.Model): """The response of the list Account operation. :param value: Result of the list Account operation. :type value: list[~automanage_client.models.Account] """ _attribute_map = { 'value': {'key': 'value', 'type': '[Account]'}, } def __init__( self, **kwargs ): super(AccountList, self).__init__(**kwargs) self.value = kwargs.get('value', None) class UpdateResource(msrest.serialization.Model): """Represents an update resource. :param tags: A set of tags. The tags of the resource. :type tags: dict[str, str] """ _attribute_map = { 'tags': {'key': 'tags', 'type': '{str}'}, } def __init__( self, **kwargs ): super(UpdateResource, self).__init__(**kwargs) self.tags = kwargs.get('tags', None) class AccountUpdate(UpdateResource): """Definition of the Automanage account. :param tags: A set of tags. The tags of the resource. :type tags: dict[str, str] :param identity: The identity of the Automanage account. :type identity: ~automanage_client.models.AccountIdentity """ _attribute_map = { 'tags': {'key': 'tags', 'type': '{str}'}, 'identity': {'key': 'identity', 'type': 'AccountIdentity'}, } def __init__( self, **kwargs ): super(AccountUpdate, self).__init__(**kwargs) self.identity = kwargs.get('identity', None) class ConfigurationProfileAssignment(Resource): """Configuration profile assignment is an association between a VM and automanage profile configuration. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str :param properties: Properties of the configuration profile assignment. :type properties: ~automanage_client.models.ConfigurationProfileAssignmentProperties """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ConfigurationProfileAssignmentProperties'}, } def __init__( self, **kwargs ): super(ConfigurationProfileAssignment, self).__init__(**kwargs) self.properties = kwargs.get('properties', None) class ConfigurationProfileAssignmentCompliance(msrest.serialization.Model): """The compliance status for the configuration profile assignment. Variables are only populated by the server, and will be ignored when sending a request. :ivar update_status: The state of compliance, which only appears in the response. Possible values include: "Succeeded", "Failed", "Created". :vartype update_status: str or ~automanage_client.models.UpdateStatus """ _validation = { 'update_status': {'readonly': True}, } _attribute_map = { 'update_status': {'key': 'updateStatus', 'type': 'str'}, } def __init__( self, **kwargs ): super(ConfigurationProfileAssignmentCompliance, self).__init__(**kwargs) self.update_status = None class ConfigurationProfileAssignmentList(msrest.serialization.Model): """The response of the list configuration profile assignment operation. :param value: Result of the list configuration profile assignment operation. :type value: list[~automanage_client.models.ConfigurationProfileAssignment] """ _attribute_map = { 'value': {'key': 'value', 'type': '[ConfigurationProfileAssignment]'}, } def __init__( self, **kwargs ): super(ConfigurationProfileAssignmentList, self).__init__(**kwargs) self.value = kwargs.get('value', None) class ConfigurationProfileAssignmentProperties(msrest.serialization.Model): """Automanage configuration profile assignment properties. Variables are only populated by the server, and will be ignored when sending a request. :param configuration_profile: A value indicating configuration profile. Possible values include: "Azure virtual machine best practices – Dev/Test", "Azure virtual machine best practices – Production". :type configuration_profile: str or ~automanage_client.models.ConfigurationProfile :param target_id: The target VM resource URI. :type target_id: str :param account_id: The Automanage account ARM Resource URI. :type account_id: str :param configuration_profile_preference_id: The configuration profile custom preferences ARM resource URI. :type configuration_profile_preference_id: str :ivar provisioning_status: The state of onboarding, which only appears in the response. Possible values include: "Succeeded", "Failed", "Created". :vartype provisioning_status: str or ~automanage_client.models.ProvisioningStatus :param compliance: The configuration setting for the configuration profile. :type compliance: ~automanage_client.models.ConfigurationProfileAssignmentCompliance """ _validation = { 'provisioning_status': {'readonly': True}, } _attribute_map = { 'configuration_profile': {'key': 'configurationProfile', 'type': 'str'}, 'target_id': {'key': 'targetId', 'type': 'str'}, 'account_id': {'key': 'accountId', 'type': 'str'}, 'configuration_profile_preference_id': {'key': 'configurationProfilePreferenceId', 'type': 'str'}, 'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'}, 'compliance': {'key': 'compliance', 'type': 'ConfigurationProfileAssignmentCompliance'}, } def __init__( self, **kwargs ): super(ConfigurationProfileAssignmentProperties, self).__init__(**kwargs) self.configuration_profile = kwargs.get('configuration_profile', None) self.target_id = kwargs.get('target_id', None) self.account_id = kwargs.get('account_id', None) self.configuration_profile_preference_id = kwargs.get('configuration_profile_preference_id', None) self.provisioning_status = None self.compliance = kwargs.get('compliance', None) class ConfigurationProfilePreference(TrackedResource): """Definition of the configuration profile preference. Variables are only populated by the server, and will be ignored when sending a request. All required parameters must be populated in order to send to Azure. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str :param tags: A set of tags. Resource tags. :type tags: dict[str, str] :param location: Required. The geo-location where the resource lives. :type location: str :param properties: Properties of the configuration profile preference. :type properties: ~automanage_client.models.ConfigurationProfilePreferenceProperties """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, 'location': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '{str}'}, 'location': {'key': 'location', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'ConfigurationProfilePreferenceProperties'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreference, self).__init__(**kwargs) self.properties = kwargs.get('properties', None) class ConfigurationProfilePreferenceAntiMalware(msrest.serialization.Model): """Automanage configuration profile Antimalware preferences. :param enable_real_time_protection: Enables or disables Real Time Protection. Possible values include: "True", "False". :type enable_real_time_protection: str or ~automanage_client.models.EnableRealTimeProtection :param exclusions: Extensions, Paths and Processes that must be excluded from scan. :type exclusions: object :param run_scheduled_scan: Enables or disables a periodic scan for antimalware. Possible values include: "True", "False". :type run_scheduled_scan: str or ~automanage_client.models.RunScheduledScan :param scan_type: Type of scheduled scan. Possible values include: "Quick", "Full". :type scan_type: str or ~automanage_client.models.ScanType :param scan_day: Schedule scan settings day. :type scan_day: str :param scan_time_in_minutes: Schedule scan settings time. :type scan_time_in_minutes: str """ _attribute_map = { 'enable_real_time_protection': {'key': 'enableRealTimeProtection', 'type': 'str'}, 'exclusions': {'key': 'exclusions', 'type': 'object'}, 'run_scheduled_scan': {'key': 'runScheduledScan', 'type': 'str'}, 'scan_type': {'key': 'scanType', 'type': 'str'}, 'scan_day': {'key': 'scanDay', 'type': 'str'}, 'scan_time_in_minutes': {'key': 'scanTimeInMinutes', 'type': 'str'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreferenceAntiMalware, self).__init__(**kwargs) self.enable_real_time_protection = kwargs.get('enable_real_time_protection', None) self.exclusions = kwargs.get('exclusions', None) self.run_scheduled_scan = kwargs.get('run_scheduled_scan', None) self.scan_type = kwargs.get('scan_type', None) self.scan_day = kwargs.get('scan_day', None) self.scan_time_in_minutes = kwargs.get('scan_time_in_minutes', None) class ConfigurationProfilePreferenceList(msrest.serialization.Model): """The response of the list ConfigurationProfilePreference operation. :param value: Result of the list ConfigurationProfilePreference operation. :type value: list[~automanage_client.models.ConfigurationProfilePreference] """ _attribute_map = { 'value': {'key': 'value', 'type': '[ConfigurationProfilePreference]'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreferenceList, self).__init__(**kwargs) self.value = kwargs.get('value', None) class ConfigurationProfilePreferenceProperties(msrest.serialization.Model): """Automanage configuration profile preference properties. :param vm_backup: The custom preferences for Azure VM Backup. :type vm_backup: ~automanage_client.models.ConfigurationProfilePreferenceVmBackup :param anti_malware: The custom preferences for Azure Antimalware. :type anti_malware: ~automanage_client.models.ConfigurationProfilePreferenceAntiMalware """ _attribute_map = { 'vm_backup': {'key': 'vmBackup', 'type': 'ConfigurationProfilePreferenceVmBackup'}, 'anti_malware': {'key': 'antiMalware', 'type': 'ConfigurationProfilePreferenceAntiMalware'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreferenceProperties, self).__init__(**kwargs) self.vm_backup = kwargs.get('vm_backup', None) self.anti_malware = kwargs.get('anti_malware', None) class ConfigurationProfilePreferenceUpdate(UpdateResource): """Definition of the configuration profile preference. :param tags: A set of tags. The tags of the resource. :type tags: dict[str, str] :param properties: Properties of the configuration profile preference. :type properties: ~automanage_client.models.ConfigurationProfilePreferenceProperties """ _attribute_map = { 'tags': {'key': 'tags', 'type': '{str}'}, 'properties': {'key': 'properties', 'type': 'ConfigurationProfilePreferenceProperties'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreferenceUpdate, self).__init__(**kwargs) self.properties = kwargs.get('properties', None) class ConfigurationProfilePreferenceVmBackup(msrest.serialization.Model): """Automanage configuration profile VM Backup preferences. :param time_zone: TimeZone optional input as string. For example: Pacific Standard Time. :type time_zone: str :param instant_rp_retention_range_in_days: Instant RP retention policy range in days. :type instant_rp_retention_range_in_days: int :param retention_policy: Retention policy with the details on backup copy retention ranges. :type retention_policy: str :param schedule_policy: Backup schedule specified as part of backup policy. :type schedule_policy: str """ _attribute_map = { 'time_zone': {'key': 'timeZone', 'type': 'str'}, 'instant_rp_retention_range_in_days': {'key': 'instantRpRetentionRangeInDays', 'type': 'int'}, 'retention_policy': {'key': 'retentionPolicy', 'type': 'str'}, 'schedule_policy': {'key': 'schedulePolicy', 'type': 'str'}, } def __init__( self, **kwargs ): super(ConfigurationProfilePreferenceVmBackup, self).__init__(**kwargs) self.time_zone = kwargs.get('time_zone', None) self.instant_rp_retention_range_in_days = kwargs.get('instant_rp_retention_range_in_days', None) self.retention_policy = kwargs.get('retention_policy', None) self.schedule_policy = kwargs.get('schedule_policy', None) class ErrorAdditionalInfo(msrest.serialization.Model): """The resource management error additional info. Variables are only populated by the server, and will be ignored when sending a request. :ivar type: The additional info type. :vartype type: str :ivar info: The additional info. :vartype info: object """ _validation = { 'type': {'readonly': True}, 'info': {'readonly': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'info': {'key': 'info', 'type': 'object'}, } def __init__( self, **kwargs ): super(ErrorAdditionalInfo, self).__init__(**kwargs) self.type = None self.info = None class ErrorResponse(msrest.serialization.Model): """The resource management error response. :param error: The error object. :type error: ~automanage_client.models.ErrorResponseError """ _attribute_map = { 'error': {'key': 'error', 'type': 'ErrorResponseError'}, } def __init__( self, **kwargs ): super(ErrorResponse, self).__init__(**kwargs) self.error = kwargs.get('error', None) class ErrorResponseError(msrest.serialization.Model): """The error object. Variables are only populated by the server, and will be ignored when sending a request. :ivar code: The error code. :vartype code: str :ivar message: The error message. :vartype message: str :ivar target: The error target. :vartype target: str :ivar details: The error details. :vartype details: list[~automanage_client.models.ErrorResponse] :ivar additional_info: The error additional info. :vartype additional_info: list[~automanage_client.models.ErrorAdditionalInfo] """ _validation = { 'code': {'readonly': True}, 'message': {'readonly': True}, 'target': {'readonly': True}, 'details': {'readonly': True}, 'additional_info': {'readonly': True}, } _attribute_map = { 'code': {'key': 'code', 'type': 'str'}, 'message': {'key': 'message', 'type': 'str'}, 'target': {'key': 'target', 'type': 'str'}, 'details': {'key': 'details', 'type': '[ErrorResponse]'}, 'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'}, } def __init__( self, **kwargs ): super(ErrorResponseError, self).__init__(**kwargs) self.code = None self.message = None self.target = None self.details = None self.additional_info = None class Operation(msrest.serialization.Model): """Automanage REST API operation. :param name: Operation name: For ex. providers/Microsoft.Automanage/configurationProfileAssignments/write or read. :type name: str :param is_data_action: Indicates whether the operation is a data action. :type is_data_action: str :param display: Provider, Resource, Operation and description values. :type display: ~automanage_client.models.OperationDisplay :param status_code: Service provider: Microsoft.Automanage. :type status_code: str """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'is_data_action': {'key': 'isDataAction', 'type': 'str'}, 'display': {'key': 'display', 'type': 'OperationDisplay'}, 'status_code': {'key': 'properties.statusCode', 'type': 'str'}, } def __init__( self, **kwargs ): super(Operation, self).__init__(**kwargs) self.name = kwargs.get('name', None) self.is_data_action = kwargs.get('is_data_action', None) self.display = kwargs.get('display', None) self.status_code = kwargs.get('status_code', None) class OperationDisplay(msrest.serialization.Model): """Provider, Resource, Operation and description values. :param provider: Service provider: Microsoft.Automanage. :type provider: str :param resource: Resource on which the operation is performed: For ex. :type resource: str :param operation: Operation type: Read, write, delete, etc. :type operation: str :param description: Description about operation. :type description: str """ _attribute_map = { 'provider': {'key': 'provider', 'type': 'str'}, 'resource': {'key': 'resource', 'type': 'str'}, 'operation': {'key': 'operation', 'type': 'str'}, 'description': {'key': 'description', 'type': 'str'}, } def __init__( self, **kwargs ): super(OperationDisplay, self).__init__(**kwargs) self.provider = kwargs.get('provider', None) self.resource = kwargs.get('resource', None) self.operation = kwargs.get('operation', None) self.description = kwargs.get('description', None) class OperationList(msrest.serialization.Model): """The response model for the list of Automanage operations. :param value: List of Automanage operations supported by the Automanage resource provider. :type value: list[~automanage_client.models.Operation] """ _attribute_map = { 'value': {'key': 'value', 'type': '[Operation]'}, } def __init__( self, **kwargs ): super(OperationList, self).__init__(**kwargs) self.value = kwargs.get('value', None) class ProxyResource(Resource): """The resource model definition for a ARM proxy resource. It will have everything other than required location and tags. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: Fully qualified resource Id for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}. :vartype id: str :ivar name: The name of the resource. :vartype name: str :ivar type: The type of the resource. Ex- Microsoft.Compute/virtualMachines or Microsoft.Storage/storageAccounts. :vartype type: str """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__( self, **kwargs ): super(ProxyResource, self).__init__(**kwargs)

[ "[email protected]" ]

[email protected]

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2022-08-28T10:24:47.849638

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#!/usr/bin/env python # -*- coding: utf-8 -*- # # The MIT License (MIT) # # Copyright (c) 2017 cytopia <[email protected]> """ vHost creator for Apache 2.2, Apache 2.4 and Nginx. """ ############################################################ # Imports ############################################################ from __future__ import print_function import os import sys import time import re import getopt import itertools import yaml ############################################################ # Globals ############################################################ # Default paths CONFIG_PATH = '/etc/vhost-gen/conf.yml' TEMPLATE_DIR = '/etc/vhost-gen/templates' # stdout/stderr log paths STDOUT_ACCESS = '/tmp/www-access.log' STDERR_ERROR = '/tmp/www-error.log' # Default configuration DEFAULT_CONFIG = { 'server': 'nginx', 'conf_dir': '/etc/nginx/conf.d', 'custom': '', 'vhost': { 'port': '80', 'ssl_port': '443', 'name': { 'prefix': '', 'suffix': '' }, 'docroot': { 'suffix': '' }, 'index': [ 'index.php', 'index.html', 'index.htm' ], 'ssl': { 'path_crt': '', 'path_key': '', 'honor_cipher_order': 'on', 'ciphers': 'HIGH:!aNULL:!MD5', 'protocols': 'TLSv1 TLSv1.1 TLSv1.2' }, 'log': { 'access': { 'prefix': '', 'stdout': False }, 'error': { 'prefix': '', 'stderr': False }, 'dir': { 'create': False, 'path': '/var/log/nginx' } }, 'php_fpm': { 'enable': False, 'address': '', 'port': 9000, 'timeout': 180 }, 'alias': [], 'deny': [], 'server_status': { 'enable': False, 'alias': '/server-status' } } } # Available templates TEMPLATES = { 'apache22': 'apache22.yml', 'apache24': 'apache24.yml', 'nginx': 'nginx.yml' } ############################################################ # System Functions ############################################################ def print_help(): """Show program help.""" print(""" Usage: vhost_gen.py -p|r <str> -n <str> [-l <str> -c <str> -t <str> -o <str> -d -s -v] vhost_gen.py --help vhost_gen.py --version vhost_gen.py will dynamically generate vhost configuration files for Nginx, Apache 2.2 or Apache 2.4 depending on what you have set in /etc/vhot-gen/conf.yml Required arguments: -p|r <str> You need to choose one of the mutually exclusive arguments. -p: Path to document root/ -r: http(s)://Host:Port for reverse proxy. Depening on the choice, it will either generate a document serving vhost or a reverse proxy vhost. Note, when using -p, this can also have a suffix directory to be set in conf.yml -l <str> Location path when using reverse proxy. Note, this is not required for normal document root server (-p) -n <str> Name of vhost Note, this can also have a prefix and/or suffix to be set in conf.yml Optional arguments: -m <str> Vhost generation mode. Possible values are: -m plain: Only generate http version (default) -m ssl: Only generate https version -m both: Generate http and https version -m redir: Generate https version and make http redirect to https -c <str> Path to global configuration file. If not set, the default location is /etc/vhost-gen/conf.yml If no config is found, a default is used with all features turned off. -t <str> Path to global vhost template directory. If not set, the default location is /etc/vhost-gen/templates/ If vhost template files are not found in this directory, the program will abort. -o <str> Path to local vhost template directory. This is used as a secondary template directory and definitions found here will be merged with the ones found in the global template directory. Note, definitions in local vhost teplate directory take precedence over the ones found in the global template directory. -d Make this vhost the default virtual host. Note, this will also change the server_name directive of nginx to '_' as well as discarding any prefix or suffix specified for the name. Apache does not have any specialities, the first vhost takes precedence. -s If specified, the generated vhost will be saved in the location found in conf.yml. If not specified, vhost will be printed to stdout. -v Be verbose. Misc arguments: --help Show this help. --version Show version. """) def print_version(): """Show program version.""" print('vhost_gen v0.5 (2018-05-02)') print('cytopia <[email protected]>') print('https://github.com/devilbox/vhost-gen') print('The MIT License (MIT)') ############################################################ # Wrapper Functions ############################################################ def str_replace(string, replacer): """Generic string replace.""" # Replace all 'keys' with 'values' for key, val in replacer.items(): string = string.replace(key, val) return string def str_indent(text, amount, char=' '): """Indent every newline inside str by specified value.""" padding = amount * char return ''.join(padding+line for line in text.splitlines(True)) def to_str(string): """Dummy string retriever.""" if string is None: return '' return str(string) def load_yaml(path): """Wrapper to load yaml file safely.""" try: with open(path, 'r') as stream: try: data = yaml.safe_load(stream) if data is None: data = dict() return (True, data, '') except yaml.YAMLError as err: return (False, dict(), str(err)) except IOError: return (False, dict(), 'File does not exist: '+path) def merge_yaml(yaml1, yaml2): """Merge two yaml strings. The secondary takes precedence.""" return dict(itertools.chain(yaml1.items(), yaml2.items())) def symlink(src, dst, force=False): """ Wrapper function to create a symlink with the addition of being able to overwrite an already existing file. """ if os.path.isdir(dst): return (False, '[ERR] destination is a directory: '+dst) if force and os.path.exists(dst): try: os.remove(dst) except OSError as err: return (False, '[ERR] Cannot delete: '+dst+': '+str(err)) try: os.symlink(src, dst) except OSError as err: return (False, '[ERR] Cannot create link: '+str(err)) return (True, None) ############################################################ # Argument Functions ############################################################ def parse_args(argv): """Parse command line arguments.""" # Config location, can be overwritten with -c l_config_path = CONFIG_PATH l_template_dir = TEMPLATE_DIR o_template_dir = None save = None path = None name = None proxy = None mode = None location = None default = False verbose = False # Define command line options try: opts, argv = getopt.getopt(argv, 'vm:c:p:r:l:n:t:o:ds', ['version', 'help']) except getopt.GetoptError as err: print('[ERR]', str(err), file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(2) # Get command line options for opt, arg in opts: if opt == '--version': print_version() sys.exit() elif opt == '--help': print_help() sys.exit() # Verbose elif opt == '-v': verbose = True # Config file overwrite elif opt == '-c': l_config_path = arg # Vhost document root path elif opt == '-p': path = arg # Vhost reverse proxy (ADDR:PORT) elif opt == '-r': proxy = arg # Mode overwrite elif opt == '-m': mode = arg # Location for reverse proxy elif opt == '-l': location = arg # Vhost name elif opt == '-n': name = arg # Global template dir elif opt == '-t': l_template_dir = arg # Local template dir elif opt == '-o': o_template_dir = arg # Save? elif opt == '-d': default = True elif opt == '-s': save = True return ( l_config_path, l_template_dir, o_template_dir, path, proxy, mode, location, name, default, save, verbose ) def validate_args_req(name, docroot, proxy, mode, location): """Validate required arguments.""" # Validate required command line options are set if docroot is None and proxy is None: print('[ERR] -p or -r is required', file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) if docroot is not None and proxy is not None: print('[ERR] -p and -r are mutually exclusive', file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) # Check proxy string if proxy is not None: if location is None: print('[ERR] When specifying -r, -l is also required.', file=sys.stderr) sys.exit(1) # Regex: HOSTNAME/IP:PORT regex = re.compile('(^http(s)?://[-_.a-zA-Z0-9]+:[0-9]+$)', re.IGNORECASE) if not regex.match(proxy): print('[ERR] Invalid proxy argument string: \'%s\', should be: %s or %s.' % (proxy, 'http(s)://HOST:PORT', 'http(s)://IP:PORT'), file=sys.stderr) sys.exit(1) port = int(re.sub('^.*:', '', proxy)) if port < 1 or port > 65535: print('[ERR] Invalid reverse proxy port range: \'%d\', should between 1 and 65535' % (port), file=sys.stderr) sys.exit(1) # Check mode string if mode is not None: if mode not in ('plain', 'ssl', 'both', 'redir'): print('[ERR] Invalid -m mode string: \'%s\', should be: %s, %s, %s or %s' % (mode, 'plain', 'ssl', 'both', 'redir'), file=sys.stderr) sys.exit(1) # Check normal server settings if docroot is not None: if location is not None: print('[WARN] -l is ignored when using normal vhost (-p)', file=sys.stderr) if name is None: print('[ERR] -n is required', file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) regex = re.compile('(^[-_.a-zA-Z0-9]+$)', re.IGNORECASE) if not regex.match(name): print('[ERR] Invalid name:', name, file=sys.stderr) sys.exit(1) def validate_args_opt(config_path, tpl_dir): """Validate optional arguments.""" if not os.path.isfile(config_path): print('[WARN] Config file not found:', config_path, file=sys.stderr) if not os.path.isdir(tpl_dir): print('[ERR] Template path does not exist:', tpl_dir, file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) # Validate global templates tpl_file = os.path.join(tpl_dir, TEMPLATES['apache22']) if not os.path.isfile(tpl_file): print('[ERR] Apache 2.2 template file does not exist:', tpl_file, file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) tpl_file = os.path.join(tpl_dir, TEMPLATES['apache24']) if not os.path.isfile(tpl_file): print('[ERR] Apache 2.4 template file does not exist:', tpl_file, file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) tpl_file = os.path.join(tpl_dir, TEMPLATES['nginx']) if not os.path.isfile(tpl_file): print('[ERR] Nginx template file does not exist:', tpl_file, file=sys.stderr) print('Type --help for help', file=sys.stderr) sys.exit(1) ############################################################ # Config File Functions ############################################################ def validate_config(config): """Validate some important keys in config dict.""" # Validate server type valid_hosts = list(TEMPLATES.keys()) if config['server'] not in valid_hosts: print('[ERR] httpd.server must be \'apache22\', \'apache24\' or \'nginx\'', file=sys.stderr) print('[ERR] Your configuration is:', config['server'], file=sys.stderr) sys.exit(1) # # Validate if log dir can be created # log_dir = config['vhost']['log']['dir']['path'] # if config['vhost']['log']['dir']['create']: # if not os.path.isdir(log_dir): # if not os.access(os.path.dirname(log_dir), os.W_OK): # print('[ERR] log directory does not exist and cannot be created:', log_dir, # file=sys.stderr) # sys.exit(1) ############################################################ # Get vHost Skeleton placeholders ############################################################ def vhost_get_port(config, ssl): """Get listen port.""" if ssl: if config['server'] == 'nginx': return to_str(config['vhost']['ssl_port']) + ' ssl' return to_str(config['vhost']['ssl_port']) return to_str(config['vhost']['port']) def vhost_get_default_server(config, default): """ Get vhost default directive which makes it the default vhost. :param dict config: Configuration dictionary :param bool default: Default vhost """ if default: if config['server'] == 'nginx': # The leading space is required here for the template to # separate it from the port directive left to it. return ' default_server' if config['server'] in ('apache22', 'apache24'): return '_default_' else: if config['server'] in ('apache22', 'apache24'): return '*' return '' def vhost_get_server_name(config, server_name, default): """Get server name.""" # Nginx uses: "server_name _;" as the default if default and config['server'] == 'nginx': return '_' # Apache does not have any specialities. The first one takes precedence. # The name will be the same as with every other vhost. prefix = to_str(config['vhost']['name']['prefix']) suffix = to_str(config['vhost']['name']['suffix']) return prefix + server_name + suffix def vhost_get_access_log(config, server_name): """Get access log directive.""" if config['vhost']['log']['access']['stdout']: return STDOUT_ACCESS prefix = to_str(config['vhost']['log']['access']['prefix']) name = prefix + server_name + '-access.log' path = os.path.join(config['vhost']['log']['dir']['path'], name) return path def vhost_get_error_log(config, server_name): """Get error log directive.""" if config['vhost']['log']['error']['stderr']: return STDERR_ERROR prefix = to_str(config['vhost']['log']['error']['prefix']) name = prefix + server_name + '-error.log' path = os.path.join(config['vhost']['log']['dir']['path'], name) return path ############################################################ # Get vHost Type (normal or reverse proxy ############################################################ def vhost_get_vhost_docroot(config, template, docroot, proxy): """Get document root directive.""" if proxy is not None: return '' return str_replace(template['vhost_type']['docroot'], { '__DOCUMENT_ROOT__': vhost_get_docroot_path(config, docroot, proxy), '__INDEX__': vhost_get_index(config) }) def vhost_get_vhost_rproxy(template, proxy, location): """Get reverse proxy definition.""" if proxy is not None: return str_replace(template['vhost_type']['rproxy'], { '__LOCATION__': location, '__PROXY_PROTO__': re.sub('://.*$', '', proxy), '__PROXY_ADDR__': re.search('^.*://(.+):[0-9]+', proxy).group(1), '__PROXY_PORT__': re.sub('^.*:', '', proxy) }) return '' ############################################################ # Get vHost Features ############################################################ def vhost_get_vhost_ssl(config, template, server_name): """Get ssl definition.""" return str_replace(template['features']['ssl'], { '__SSL_PATH_CRT__': to_str(vhost_get_ssl_crt_path(config, server_name)), '__SSL_PATH_KEY__': to_str(vhost_get_ssl_key_path(config, server_name)), '__SSL_PROTOCOLS__': to_str(config['vhost']['ssl']['protocols']), '__SSL_HONOR_CIPHER_ORDER__': to_str(config['vhost']['ssl']['honor_cipher_order']), '__SSL_CIPHERS__': to_str(config['vhost']['ssl']['ciphers']) }) def vhost_get_vhost_redir(config, template, server_name): """Get redirect to ssl definition.""" return str_replace(template['features']['redirect'], { '__VHOST_NAME__': server_name, '__SSL_PORT__': to_str(config['vhost']['ssl_port']) }) def vhost_get_ssl_crt_path(config, server_name): """Get ssl crt path""" prefix = to_str(config['vhost']['name']['prefix']) suffix = to_str(config['vhost']['name']['suffix']) name = prefix + server_name + suffix + '.crt' path = to_str(config['vhost']['ssl']['dir_crt']) return os.path.join(path, name) def vhost_get_ssl_key_path(config, server_name): """Get ssl key path""" prefix = to_str(config['vhost']['name']['prefix']) suffix = to_str(config['vhost']['name']['suffix']) name = prefix + server_name + suffix + '.key' path = to_str(config['vhost']['ssl']['dir_crt']) return os.path.join(path, name) def vhost_get_docroot_path(config, docroot, proxy): """Get path of document root.""" if proxy is not None: return '' suffix = to_str(config['vhost']['docroot']['suffix']) path = os.path.join(docroot, suffix) return path def vhost_get_index(config): """Get index.""" if 'index' in config['vhost'] and config['vhost']['index']: elem = config['vhost']['index'] else: elem = DEFAULT_CONFIG['vhost']['index'] return ' '.join(elem) def vhost_get_php_fpm(config, template, docroot, proxy): """Get PHP FPM directive. If using reverse proxy, PHP-FPM will be disabled.""" if proxy is not None: return '' # Get PHP-FPM php_fpm = '' if config['vhost']['php_fpm']['enable']: php_fpm = str_replace(template['features']['php_fpm'], { '__PHP_ADDR__': to_str(config['vhost']['php_fpm']['address']), '__PHP_PORT__': to_str(config['vhost']['php_fpm']['port']), '__PHP_TIMEOUT__': to_str(config['vhost']['php_fpm']['timeout']), '__DOCUMENT_ROOT__': vhost_get_docroot_path(config, docroot, proxy) }) return php_fpm def vhost_get_aliases(config, template): """Get virtual host alias directives.""" aliases = [] for item in config['vhost']['alias']: # Add optional xdomain request if enabled xdomain_request = '' if 'xdomain_request' in item: if item['xdomain_request']['enable']: xdomain_request = str_replace(template['features']['xdomain_request'], { '__REGEX__': to_str(item['xdomain_request']['origin']) }) # Replace everything aliases.append(str_replace(template['features']['alias'], { '__ALIAS__': to_str(item['alias']), '__PATH__': to_str(item['path']), '__XDOMAIN_REQ__': str_indent(xdomain_request, 4).rstrip() })) # Join by OS independent newlines return os.linesep.join(aliases) def vhost_get_denies(config, template): """Get virtual host deny alias directives.""" denies = [] for item in config['vhost']['deny']: denies.append(str_replace(template['features']['deny'], { '__REGEX__': to_str(item['alias']) })) # Join by OS independent newlines return os.linesep.join(denies) def vhost_get_server_status(config, template): """Get virtual host server status directivs.""" status = '' if config['vhost']['server_status']['enable']: status = template['features']['server_status'] return str_replace(status, { '__REGEX__': to_str(config['vhost']['server_status']['alias']) }) def vhost_get_custom_section(config): """Get virtual host custom directives.""" return to_str(config['custom']) ############################################################ # vHost create ############################################################ def get_vhost_plain(config, tpl, docroot, proxy, location, server_name, default): """Get plain vhost""" return str_replace(tpl['vhost'], { '__PORT__': vhost_get_port(config, False), '__DEFAULT_VHOST__': vhost_get_default_server(config, default), '__DOCUMENT_ROOT__': vhost_get_docroot_path(config, docroot, proxy), '__VHOST_NAME__': vhost_get_server_name(config, server_name, default), '__VHOST_DOCROOT__': str_indent(vhost_get_vhost_docroot(config, tpl, docroot, proxy), 4), '__VHOST_RPROXY__': str_indent(vhost_get_vhost_rproxy(tpl, proxy, location), 4), '__REDIRECT__': '', '__SSL__': '', '__INDEX__': vhost_get_index(config), '__ACCESS_LOG__': vhost_get_access_log(config, server_name), '__ERROR_LOG__': vhost_get_error_log(config, server_name), '__PHP_FPM__': str_indent(vhost_get_php_fpm(config, tpl, docroot, proxy), 4), '__ALIASES__': str_indent(vhost_get_aliases(config, tpl), 4), '__DENIES__': str_indent(vhost_get_denies(config, tpl), 4), '__SERVER_STATUS__': str_indent(vhost_get_server_status(config, tpl), 4), '__CUSTOM__': str_indent(vhost_get_custom_section(config), 4) }) def get_vhost_ssl(config, tpl, docroot, proxy, location, server_name, default): """Get ssl vhost""" return str_replace(tpl['vhost'], { '__PORT__': vhost_get_port(config, True), '__DEFAULT_VHOST__': vhost_get_default_server(config, default), '__DOCUMENT_ROOT__': vhost_get_docroot_path(config, docroot, proxy), '__VHOST_NAME__': vhost_get_server_name(config, server_name, default), '__VHOST_DOCROOT__': str_indent(vhost_get_vhost_docroot(config, tpl, docroot, proxy), 4), '__VHOST_RPROXY__': str_indent(vhost_get_vhost_rproxy(tpl, proxy, location), 4), '__REDIRECT__': '', '__SSL__': str_indent(vhost_get_vhost_ssl(config, tpl, server_name), 4), '__INDEX__': vhost_get_index(config), '__ACCESS_LOG__': vhost_get_access_log(config, server_name + '_ssl'), '__ERROR_LOG__': vhost_get_error_log(config, server_name + '_ssl'), '__PHP_FPM__': str_indent(vhost_get_php_fpm(config, tpl, docroot, proxy), 4), '__ALIASES__': str_indent(vhost_get_aliases(config, tpl), 4), '__DENIES__': str_indent(vhost_get_denies(config, tpl), 4), '__SERVER_STATUS__': str_indent(vhost_get_server_status(config, tpl), 4), '__CUSTOM__': str_indent(vhost_get_custom_section(config), 4) }) def get_vhost_redir(config, tpl, docroot, proxy, server_name, default): """Get redirect to ssl vhost""" return str_replace(tpl['vhost'], { '__PORT__': vhost_get_port(config, False), '__DEFAULT_VHOST__': vhost_get_default_server(config, default), '__DOCUMENT_ROOT__': vhost_get_docroot_path(config, docroot, proxy), '__VHOST_NAME__': vhost_get_server_name(config, server_name, default), '__VHOST_DOCROOT__': '', '__VHOST_RPROXY__': '', '__REDIRECT__': str_indent(vhost_get_vhost_redir(config, tpl, server_name), 4), '__SSL__': '', '__INDEX__': '', '__ACCESS_LOG__': vhost_get_access_log(config, server_name), '__ERROR_LOG__': vhost_get_error_log(config, server_name), '__PHP_FPM__': '', '__ALIASES__': '', '__DENIES__': '', '__SERVER_STATUS__': '', '__CUSTOM__': '' }) def get_vhost(config, tpl, docroot, proxy, mode, location, server_name, default): """Create the vhost.""" if mode == 'ssl': return get_vhost_ssl(config, tpl, docroot, proxy, location, server_name, default) if mode == 'both': return ( get_vhost_ssl(config, tpl, docroot, proxy, location, server_name, default) + get_vhost_plain(config, tpl, docroot, proxy, location, server_name, default) ) if mode == 'redir': return ( get_vhost_ssl(config, tpl, docroot, proxy, location, server_name, default) + get_vhost_redir(config, tpl, docroot, proxy, server_name, default) ) return get_vhost_plain(config, tpl, docroot, proxy, location, server_name, default) ############################################################ # Load configs and templates ############################################################ def load_config(config_path): """Load config and merge with defaults in case not found or something is missing.""" # Load configuration file if os.path.isfile(config_path): succ, config, err = load_yaml(config_path) if not succ: return (False, dict(), err) else: print('[WARN] config file not found', config_path, file=sys.stderr) config = dict() # Merge config settings with program defaults (config takes precedence over defaults) config = merge_yaml(DEFAULT_CONFIG, config) return (True, config, '') def load_template(template_dir, o_template_dir, server): """Load global and optional template file and merge them.""" # Load global template file succ, template, err = load_yaml(os.path.join(template_dir, TEMPLATES[server])) if not succ: return (False, dict(), '[ERR] Error loading template' + err) # Load optional template file (if specified file and merge it) if o_template_dir is not None: succ, template2, err = load_yaml(os.path.join(o_template_dir, TEMPLATES[server])) template = merge_yaml(template, template2) return (True, template, '') ############################################################ # Post actions ############################################################ def apply_log_settings(config): """ This function will apply various settings for the log defines, including creating the directory itself as well as handling log file output (access and error) to stderr/stdout. """ # Symlink stdout to access logfile if config['vhost']['log']['access']['stdout']: succ, err = symlink('/dev/stdout', STDOUT_ACCESS, force=True) if not succ: return (False, err) # Symlink stderr to error logfile if config['vhost']['log']['error']['stderr']: succ, err = symlink('/dev/stderr', STDERR_ERROR, force=True) if not succ: return (False, err) # Create log dir if config['vhost']['log']['dir']['create']: if not os.path.isdir(config['vhost']['log']['dir']['path']): try: os.makedirs(config['vhost']['log']['dir']['path']) except OSError as err: return (False, '[ERR] Cannot create directory: '+str(err)) return (True, None) ############################################################ # Main Function ############################################################ def main(argv): """Main entrypoint.""" # Get command line arguments (config_path, tpl_dir, o_tpl_dir, docroot, proxy, mode, location, name, default, save, verbose) = parse_args(argv) # Validate command line arguments This will abort the program on error # This will abort the program on error validate_args_req(name, docroot, proxy, mode, location) validate_args_opt(config_path, tpl_dir) # Load config succ, config, err = load_config(config_path) if not succ: print('[ERR] Error loading config', err, file=sys.stderr) sys.exit(1) # Load template succ, template, err = load_template(tpl_dir, o_tpl_dir, config['server']) if not succ: print('[ERR] Error loading template', err, file=sys.stderr) sys.exit(1) # Validate configuration file # This will abort the program on error validate_config(config) # Retrieve fully build vhost vhost = get_vhost(config, template, docroot, proxy, mode, location, name, default) if verbose: print('vhostgen: [%s] Adding: %s' % (time.strftime("%Y-%m-%d %H:%M:%S"), to_str(config['vhost']['name']['prefix']) + name + to_str(config['vhost']['name']['suffix']))) if save: if not os.path.isdir(config['conf_dir']): print('[ERR] output conf_dir does not exist:', config['conf_dir'], file=sys.stderr) sys.exit(1) if not os.access(config['conf_dir'], os.W_OK): print('[ERR] directory does not have write permissions', config['conf_dir'], file=sys.stderr) sys.exit(1) vhost_path = os.path.join(config['conf_dir'], name+'.conf') with open(vhost_path, 'w') as outfile: outfile.write(vhost) # Apply settings for logging (symlinks, mkdir) only in save mode succ, err = apply_log_settings(config) if not succ: print(err, file=sys.stderr) sys.exit(1) else: print(vhost) ############################################################ # Main Entry Point ############################################################ if __name__ == '__main__': main(sys.argv[1:])

[ "[email protected]" ]

[email protected]

24a7e0e511a2d8d8023e5a267a26f01231db6504

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/sympy/parsing/autolev/test-examples/pydy-example-repo/chaos_pendulum.py

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[]

no_license

cosmosZhou/sagemath

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import sympy.physics.mechanics as me import sympy as sm import math as m import numpy as np g, lb, w, h = sm.symbols('g lb w h', real=True) theta, phi, omega, alpha = me.dynamicsymbols('theta phi omega alpha') thetad, phid, omegad, alphad = me.dynamicsymbols('theta phi omega alpha', 1) thetad2, phid2 = me.dynamicsymbols('theta phi', 2) frame_n = me.ReferenceFrame('n') body_a_cm = me.Point('a_cm') body_a_cm.set_vel(frame_n, 0) body_a_f = me.ReferenceFrame('a_f') body_a = me.RigidBody('a', body_a_cm, body_a_f, sm.symbols('m'), (me.outer(body_a_f.x,body_a_f.x),body_a_cm)) body_b_cm = me.Point('b_cm') body_b_cm.set_vel(frame_n, 0) body_b_f = me.ReferenceFrame('b_f') body_b = me.RigidBody('b', body_b_cm, body_b_f, sm.symbols('m'), (me.outer(body_b_f.x,body_b_f.x),body_b_cm)) body_a_f.orient(frame_n, 'Axis', [theta, frame_n.y]) body_b_f.orient(body_a_f, 'Axis', [phi, body_a_f.z]) point_o = me.Point('o') la = (lb-h/2)/2 body_a_cm.set_pos(point_o, la*body_a_f.z) body_b_cm.set_pos(point_o, lb*body_a_f.z) body_a_f.set_ang_vel(frame_n, omega*frame_n.y) body_b_f.set_ang_vel(body_a_f, alpha*body_a_f.z) point_o.set_vel(frame_n, 0) body_a_cm.v2pt_theory(point_o,frame_n,body_a_f) body_b_cm.v2pt_theory(point_o,frame_n,body_a_f) ma = sm.symbols('ma') body_a.mass = ma mb = sm.symbols('mb') body_b.mass = mb iaxx = 1/12*ma*(2*la)**2 iayy = iaxx iazz = 0 ibxx = 1/12*mb*h**2 ibyy = 1/12*mb*(w**2+h**2) ibzz = 1/12*mb*w**2 body_a.inertia = (me.inertia(body_a_f, iaxx, iayy, iazz, 0, 0, 0), body_a_cm) body_b.inertia = (me.inertia(body_b_f, ibxx, ibyy, ibzz, 0, 0, 0), body_b_cm) force_a = body_a.mass*(g*frame_n.z) force_b = body_b.mass*(g*frame_n.z) kd_eqs = [thetad - omega, phid - alpha] forceList = [(body_a.masscenter,body_a.mass*(g*frame_n.z)), (body_b.masscenter,body_b.mass*(g*frame_n.z))] kane = me.KanesMethod(frame_n, q_ind=[theta,phi], u_ind=[omega, alpha], kd_eqs = kd_eqs) fr, frstar = kane.kanes_equations([body_a, body_b], forceList) zero = fr+frstar from pydy.system import System sys = System(kane, constants = {g:9.81, lb:0.2, w:0.2, h:0.1, ma:0.01, mb:0.1}, specifieds={}, initial_conditions={theta:np.deg2rad(90), phi:np.deg2rad(0.5), omega:0, alpha:0}, times = np.linspace(0.0, 10, 10/0.02)) y=sys.integrate()

[ "[email protected]" ]

[email protected]

e8903d4f4699625df2cda18ac35f4222d2fd4b28

59f8bf58860d021216449b135f486f9b7145f6be

/application/utils/helper.py

b0f149aafc09f1948981c1ab3c93845eb97d3ddc

[]

no_license

JFluo2011/fisher

f5b58e2e151022b2173490b504dda7c704862b19

d899e2a3dc296cf9d1c2802067d995dde1c3d32e

refs/heads/master

2022-12-10T12:42:36.376854

2018-12-27T07:57:00

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Python

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126

py

def keyword_is_isbn(keyword): tmp = keyword.strip().replace('-', '') return (len(tmp) in [10, 13]) and tmp.isdigit()

[ "[email protected]" ]

[email protected]

58c58c2aa06311710749227f4e0405344d093517

663d429e1f552ef958d37cfe4a0707354b544a9a

/rimi_linux_mysql/tcp_ip_socket/my_async/asyncsocket_chat/client1.py

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[]

no_license

nie000/mylinuxlearn

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refs/heads/main

2023-06-20T07:46:11.842538

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import socket ss_address = ("0.0.0.0",19528) ss = socket.socket(socket.AF_INET,socket.SOCK_STREAM) print('123') ss.connect(ss_address) print('456') while True: data = input('请输入消息:') if not data: break try: ss.send(data.encode('utf8')) print(ss.recv(1024).decode('utf8')) except BrokenPipeError: print('连接已经关闭') break ss.close()

[ "[email protected]" ]

[email protected]

c7fee91cb539c4e1e261ffa6c3de60b555291813

05bf7de1337bc938578accba64416863cec1ed63

/docs/historical/MSD/MSDTutorial/Post_Processing/common/post_processing_class.py

493bc56ae42de7f8b5544648a723bab83e78ce4c

[ "MIT", "LicenseRef-scancode-unknown-license-reference", "LicenseRef-scancode-other-permissive" ]

permissive

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21

8

NOASSERTION

2023-01-14T09:31:14

2016-10-05T16:29:54

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import os import json import sys import re import numpy as np from py_modelica.mat_file_functions.mat_file_to_dict import MatFile2Dict # Rescue if the limit-checking should get stuck in an infinite while-loop. # Which should be impossible to start with, but if I am wrong... MAX_ITERATIONS = 100000 class PostProcess: filter = [] # list of all variables/parameter to load from mat-file # (does not need to include 'time' - loaded by default) time = None result = None def __init__(self, mat_file='', filter=None): """ Loads in mat-file, extracts given variables in filter (time always included) and converts lists of values into numpy arrays. These are stored in result as: {{name1: array([values1])}, ..., {nameN: array([valuesN])}} """ mat_converter = MatFile2Dict(mat_file, filter, False) result_lists = mat_converter.get_results() # convert lists into numpy arrays self.result = {} for item in result_lists.iteritems(): self.result.update({item[0]: np.array(item[1])}) self.time = self.result['time'] def data_array(self, name): """ Get time-series in numpy array format. name - name of variable e.g. data_array('time') returns with the time. """ try: A = self.result[name] return A except KeyError: print 'ERROR: cannot find [' + name + '] in Modelica results' return [] def print_data(self, name): """ Prints the time-series. name - name of variable e.g. data_array('time') returns with the time. """ data = self.data_array(name) print 'name of data: ' print name print 'here is the data: (with index)' print '[', for i in xrange(data.size - 1): print str(i) + ':', str(data[i]) + ',', print str(i + 1) + ':', str(data[i + 1]) + ']' return data def time_array(self): """ Get time-series of time in numpy array format. """ return self.time def print_time(self): """ Prints and returns with time-series of time. """ time = self.time print 'here are time intervals:', time return time def short_array(self, name, start=0, end=-1): """ Get a truncated, from n1 to n2 array for variable name name - name of variable start - start index of interval end - end index of interval N.B index goes from 0 to len(array)-1 """ return self.result[name][start:end] def plot(self, name): """ Returns a tuple, suitable for plotting, of the variable's time-series together with time. name - name of variable """ return self.data_array(name), self.time def get_data_by_time(self, name, time_val): """ Get data based on time value. name - name of variable to consider time_val - time point where to extract the value Returns the data and the index of the data """ i = 0 time = self.time while time[i] < time_val and i in xrange(time.size - 1): i += 1 data_arr = self.data_array(name) if time[i - 1] != time_val: cur = data_arr[i - 1] next = data_arr[i] data = time[i - 1] / ((time[i - 1] + time[i]) / 2) * (next - cur) + cur else: data = data_arr[i - 1] return data, i def get_data_by_index(self, name, index): return self.data_array(name)[index] def get_index_from_time(self, time_val): """ Get index based on time value. time_val - time point where to extract the value Returns index nearest to time_val """ i = 0 time = self.time while time[i] < time_val and i in xrange(time.size-1): i += 1 return i def get_time(self, name, value, atol=1e-4, rtol=1e-4, start_index=0, end_index=-1): """ Gets the first time point where the variable satisfies either atol or rtol, if no such point exists - returns with -1. name - name of variable atol - absolute tolerance rtol - relative tolerance """ index = -1 # N.B. this is only one of many ways to do this denominator = 1 if value > rtol: denominator = value data = self.data_array(name)[start_index:end_index] cnt = 0 for x in data: abs_diff = abs(x - value) rel_diff = abs_diff / denominator if abs_diff < atol or rel_diff < rtol: index = cnt break else: cnt += 1 if index >= 0: return self.time[start_index + index] return -1 def last_value(self, name): """ Get last value of variable name - name of variable """ return self.data_array(name)[-1] def global_max(self, name): """ Get maximum value of variable name - name of variable """ return self.data_array(name).max() def global_max_time(self, name): """ Get time where max occurs name - name of variable returns the time at where the max is """ index = self.data_array(name).argmax() time_at_max = self.time[index] return time_at_max def global_min(self, name): """ Get minimum value of variable name - name of variable """ return self.data_array(name).min() def global_min_time(self, name): """ Get time where min occurs name - name of variable returns the time at where the min is """ index = self.data_array(name).argmin() time_at_min = self.time[index] return time_at_min def global_abs_max(self, name): """ Get the maximum absolute value of variable name - name of variable """ return np.absolute(self.data_array(name)).max() def std_dev(self, name): """ Returns the standard deviation of variable name - name of variable """ stddev = self.data_array(name).std() return stddev def variance(self, name): """ Returns the variance of variable name - name of variable """ variance = self.data_array(name).var() return variance def sum_value(self, name): """ Returns the sum of the time-series for the variable name - name of variable """ result = self.data_array(name).sum() return result def mean(self, name): """ Returns the mean of the time-series for the variable name - name of variable """ result = np.mean(self.data_array(name), dtype=np.float64) return result def integrate(self, name): """ Returns the area under the curve of the time-series for the variable name - name of variable """ time = self.time data = self.data_array(name) sum = 0 next = data[0] next_t = time[0] for i in xrange(data.size): cur = next next = data[i] cur_t = next_t next_t = time[i] height = (next + cur) / 2 interval = next_t - cur_t sum += height * interval return sum def minima(self, name): """ Returns the minima of time-series of variable name - name of variable """ data = self.data_array(name) min = [] prev = 0 cur = 0 next = data[0] for i in xrange(data.size): if cur < prev and cur <= next: min.append(cur) prev = cur cur = next next = data[++i] minimum = np.array(min) return minimum def maxima(self, name): """ Returns the maxima of time-series of variable name - name of variable """ data = self.data_array(name) max = [] prev = 0 cur = 0 next = data[0] for i in xrange(data.size): if cur >= prev and cur > next: max.append(cur) prev = cur cur = next next = data[++i] maximum = np.array(max) return maximum def pos_neg(self, name, tol=0.00000015): """ Returns time of the roots from positive to negative of time-series of variable name - name of variable tol - tolerance """ data = self.data_array(name) time_arr = self.time time = [] next = -1 for i in xrange(data.size): cur = next next = data[i] if cur > 0 + tol and next <= 0 + tol: if cur != 0: cur_t = time_arr[i - 1] next_t = time_arr[i] time.append((cur / (cur + next) / 2) * (next_t - cur_t) + cur_t) else: time.append(time_arr[i - 1]) timing = np.array(time) return timing def neg_pos(self, name, tol=0.00000015): """ Returns time of the roots from negative to positive of time-series of variable name - name of variable tol - tolerance """ time = [] data = self.data_array(name) time_arr = self.time next = 1 for i in xrange(data.size): cur = next next = data[i] if cur <= 0 + tol and next > 0 + tol: if cur != 0: cur_t = time_arr[i - 1] next_t = time_arr[i] time.append(cur / ((cur + next) / 2) * (next_t - cur_t) + cur_t) else: time.append(time_arr[i - 1]) timing = np.array(time) return timing def to_zero(self, name, value_index): """ # time from a number to zero # (use index from print_data() function) # parameters: data array, time array, index of value # returns the time of the zero """ data = self.data_array(name) time_arr = self.time i = value_index + 1 cur = data[value_index] next = data[i] tolerance = 0.00000015 if data[value_index] >= 0: while next >= 0 + tolerance and i in xrange(data.size - 1): i += 1 cur = next next = data[i] if next >= 0 + tolerance: return -1 else: while next <= 0 + tolerance and i in xrange(data.size - 1): i += 1 cur = next next = data[i] if next <= 0 + tolerance: return -1 if cur != 0: cur_t = time_arr[i - 1] next_t = time_arr[i] time = cur / ((cur + next) / 2) * (next_t - cur_t) + cur_t else: time = time_arr[i - 1] return time def from_zero(self, name, value_index): """ # time from a number to zero # (use index from print_data() function) # parameters: data array, time array, index of value # returns the time of the zero """ data = self.data_array(name) time_arr = self.time i = value_index - 1 cur = data[value_index] next = data[i] tolerance = 0.00000015 if data[value_index - 1] >= 0: while next >= 0 + tolerance and i in xrange(data.size): i -= 1 cur = next next = data[i] if next >= 0 + tolerance: return -1 else: while next <= 0 + tolerance and i in xrange(data.size): i -= 1 cur = next next = data[i] if next <= 0 + tolerance: return -1 if cur != 0: cur_t = time_arr[i + 1] next_t = time_arr[i] time = cur / ((cur + next) / 2) * (next_t - cur_t) + cur_t else: time = time_arr[i + 1] return time def zeros(self, name): """ Find zeros of time-series for variable name - name of variable returns the time of the zero """ data_array = self.data_array(name) time = self.time data = [[], []] data[0].append(self.pos_neg(data_array, time)) data[1].append(self.neg_pos(data_array, time)) data_arr = np.array(data) return data_arr def compare(self, name1, name2): """ Compare the time-series of two variables name1 - name of variable 1 name2 - name of variable 2 returns true if the results are identical """ data1 = self.data_array(name1) data2 = self.data_array(name2) for i in xrange(data1.size): if data1[i] != data2[i]: return False return True def time_total(self, val1, val2): # finding the difference between 2 times time = abs(val2 - val1) return time def delta_t(self, start_index, end_index): """ Returns the length of the time-interval between to indices """ t1 = self.time[start_index] t2 = self.time[end_index] dt = t2 - t1 return dt def get_local_max(self, name, start_index, end_index): """ Returns the value of the maximum between two indices N.B. including both points :param name: :param start_index: :param end_index: """ if end_index == -1: maximum = self.data_array(name)[start_index:].max() else: maximum = self.data_array(name)[start_index:end_index + 1].max() return maximum def get_local_min(self, name, start_index, end_index): """ Returns the value of the minimum between two indices N.B. including both points """ if end_index == -1: minimum = self.data_array(name)[start_index:].min() else: minimum = self.data_array(name)[start_index:end_index + 1].min() return minimum def find_first_max_violation(self, name, value, start_index=0): """ Starting from start_index it looks for the first index where the time-series has a value greater than value. If it never occurs, it returns -1 """ time_series = self.data_array(name)[start_index:] n = len(time_series) for i in range(n): if time_series[i] > value: return i + start_index return -1 def find_first_min_violation(self, name, value, start_index=0): """ Starting from start_index it looks for the first index where the time-series has a value less than value. If it never occurs, it returns -1 """ time_series = self.data_array(name)[start_index:] n = len(time_series) for i in range(n): if time_series[i] < value: return i + start_index return -1 def check_max_limit(self, name, value): actual_value = '' limit_exceeded = False start_index = 0 global_max = -np.Inf cnt = 0 print 'check_max_limit' while start_index > -1: index = self.find_first_max_violation(name, value, start_index) if index > -1: end_index = self.find_first_min_violation(name, value, index) d_t = self.delta_t(index, end_index) print 'Found violation at t={0} lasting : {1}'.format(self.time[index], d_t) if d_t > 0.5: limit_exceeded = True local_max = self.get_local_max(name, index, end_index) print 'Local maximum : {0}'.format(local_max) if local_max > global_max: global_max = local_max start_index = end_index else: break cnt += 1 if cnt == MAX_ITERATIONS: print 'Limit checking for variable {0} aborted after {1} iterations' \ .format(name, MAX_ITERATIONS) sys.exit(1) if limit_exceeded: actual_value = global_max return limit_exceeded, actual_value def check_min_limit(self, name, value): actual_value = '' limit_exceeded = False start_index = 0 global_min = np.Inf cnt = 0 print 'check_min_limit' while start_index > -1: index = self.find_first_min_violation(name, value, start_index) if index > -1: end_index = self.find_first_max_violation(name, value, index) d_t = self.delta_t(index, end_index) print 'Found violation at t={0} lasting : {1} s'.format(self.time[index], d_t) if d_t > 0.5: limit_exceeded = True local_min = self.get_local_min(name, index, end_index) print 'Local minimum : {0}'.format(local_min) if local_min < global_min: global_min = local_min start_index = end_index else: break cnt += 1 if cnt == MAX_ITERATIONS: print 'Limit checking for variable {0} aborted after {1} iterations' \ .format(name, MAX_ITERATIONS) sys.exit(1) if limit_exceeded: actual_value = global_min return limit_exceeded, actual_value #find_zero(self, array1, array2): def find_zero(self, array1, array2, time_arr): tolerance = 0.000000015 tol = 0.0001 for i in xrange(array1.size): if array1[i] <= (array2[i] + tol): if array1[i] >= (array2[i] - tol): if array1[i] <= (0 + tolerance): if array1[i] >= (0 - tolerance): return time_arr[i]; return -1 def update_metrics_in_report_json(metrics, report_file='summary.testresults.json'): """ Metrics should be of the form :param metrics: :param report_file: {'name_of_metric' : {value: (int) or (float), unit: ""}, ...} """ if not os.path.exists(report_file): raise IOError('Report file does not exits : {0}'.format(report_file)) # read current summary report, which contains the metrics with open(report_file, 'r') as file_in: result_json = json.load(file_in) assert isinstance(result_json, dict) if 'Metrics' in result_json: for metric in result_json['Metrics']: if 'Name' in metric and 'Value' in metric: if metric['Name'] in metrics.keys(): new_value = metrics[metric['Name']]['value'] new_unit = metrics[metric['Name']]['unit'] if new_unit is not None: metric['Unit'] = new_unit if new_value is not None: metric['Value'] = str(new_value) else: pass else: print 'Metric item : {0} does not have right format'.format(metric) pass # update json file with the new values with open(report_file, 'wb') as file_out: json.dump(result_json, file_out, indent=4) else: print 'Report file {0} does not have any Metrics defined..' pass def read_limits(): """ Reads in limits and modifies the ModelicaUri to the correct one. Returns: - the updated limit_dict - the filter as a list """ with open('limits.json', 'r') as f_in: limit_dict = json.load(f_in) # use set to avoid checking for duplicates filter = set() for limit_item in limit_dict['LimitChecks']: # drop first part of VariableFullPath update the limit_item # once the limit.json is generated correctly these two lines can be dropped modelica_uri = '.'.join(limit_item['VariableFullPath'].split('.')[1:]) modelica_model_rel_uri = limit_item['VariableName'] split_full_path = limit_item['LimitFullPath'].split('/') modelica_model = split_full_path[-2] cyphy_relative_uri = '{0}.{1}'.format(modelica_model, modelica_model_rel_uri) modelica_uri = modelica_uri.replace(modelica_model_rel_uri, cyphy_relative_uri) limit_item['VariableFullPath'] = modelica_uri limit_item['ComponentInstanceName'] = split_full_path[-3] # filter out this variable in the .mat-file filter.add(modelica_uri) # Code specific for FANG-I, with no defined VariableName from GME #limit_var_name = limit_item['VariableName'] limit_var_name = re.sub('\.u(.*)$', '', modelica_uri) limit_var_name_split = limit_var_name.split('.') limit_var_name = limit_var_name_split[len(limit_var_name_split)-3] + '=>' + \ limit_var_name_split[len(limit_var_name_split)-1] limit_item['LimitName'] = limit_var_name filter = list(filter) print "Variables for limit-checking : {0}".format(filter) return limit_dict, filter def check_limits_and_add_to_report_json(pp, limit_dict): """ Check the limits and write out dictionary to summary.report.json """ assert isinstance(pp, PostProcess) for limit_item in limit_dict['LimitChecks']: modelica_uri = limit_item['VariableFullPath'] limit_value = limit_item['Value'] limit_type = limit_item['Type'] print "--== {0} ==--".format(modelica_uri) print "Type of Limit : {0}".format(limit_type) print "Limit : {0} ".format(limit_value) if limit_type == 'min': limit_exceeded, actual_value = pp.check_min_limit(modelica_uri, limit_value) limit_item['LimitExceeded'] = limit_exceeded limit_item['ActualValue'] = str(actual_value) elif limit_type == 'max': limit_exceeded, actual_value = pp.check_max_limit(modelica_uri, limit_value) limit_item['LimitExceeded'] = limit_exceeded limit_item['ActualValue'] = str(actual_value) else: limit_exceeded_max, actual_max_value = pp.check_max_limit(modelica_uri, limit_value) limit_exceeded_min, actual_min_value = pp.check_min_limit(modelica_uri, -limit_value) # determine the actual value depending on which limits were exceeded if limit_exceeded_max and limit_exceeded_min: if actual_max_value > abs(actual_min_value): actual_value = str(actual_max_value) else: actual_value = str(abs(actual_min_value)) elif limit_exceeded_max: actual_value = str(actual_max_value) elif limit_exceeded_min: actual_value = str(abs(actual_min_value)) else: actual_value = '' limit_item['LimitExceeded'] = limit_exceeded_max or limit_exceeded_min limit_item['ActualValue'] = actual_value limit_item['Value'] = str(limit_value) print "Violation : {0}".format(limit_item["LimitExceeded"]) with open('summary.testresults.json', 'r') as f_in: sum_rep_json = json.load(f_in) sum_rep_json['LimitChecks'] = limit_dict['LimitChecks'] with open('summary.testresults.json', 'wb') as f_out: json.dump(sum_rep_json, f_out, indent=4) print "Limits updated"

[ "[email protected]" ]

[email protected]

01cabaa83df787a973353fd6f3c195237768f961

c4a2c5d2ee3bb946333bec267c337858c2eaa87c

/bhiveapi/hivenoderpc.py

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permissive

TheCrazyGM/bhive

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from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import bytes, int, str import re import sys from .graphenerpc import GrapheneRPC from . import exceptions import logging log = logging.getLogger(__name__) class HiveNodeRPC(GrapheneRPC): """ This class allows to call API methods exposed by the witness node via websockets / rpc-json. :param str urls: Either a single Websocket/Http URL, or a list of URLs :param str user: Username for Authentication :param str password: Password for Authentication :param int num_retries: Try x times to num_retries to a node on disconnect, -1 for indefinitely :param int num_retries_call: Repeat num_retries_call times a rpc call on node error (default is 5) :param int timeout: Timeout setting for https nodes (default is 60) :param bool use_condenser: Use the old condenser_api rpc protocol on nodes with version 0.19.4 or higher. The settings has no effect on nodes with version of 0.19.3 or lower. """ def __init__(self, *args, **kwargs): """ Init HiveNodeRPC :param str urls: Either a single Websocket/Http URL, or a list of URLs :param str user: Username for Authentication :param str password: Password for Authentication :param int num_retries: Try x times to num_retries to a node on disconnect, -1 for indefinitely :param int num_retries_call: Repeat num_retries_call times a rpc call on node error (default is 5) :param int timeout: Timeout setting for https nodes (default is 60) """ super(HiveNodeRPC, self).__init__(*args, **kwargs) self.next_node_on_empty_reply = False def set_next_node_on_empty_reply(self, next_node_on_empty_reply=True): """Switch to next node on empty reply for the next rpc call""" self.next_node_on_empty_reply = next_node_on_empty_reply def rpcexec(self, payload): """ Execute a call by sending the payload. It makes use of the GrapheneRPC library. In here, we mostly deal with Hive specific error handling :param json payload: Payload data :raises ValueError: if the server does not respond in proper JSON format :raises RPCError: if the server returns an error """ if self.url is None: raise exceptions.RPCConnection("RPC is not connected!") doRetry = True maxRetryCountReached = False while doRetry and not maxRetryCountReached: doRetry = False try: # Forward call to GrapheneWebsocketRPC and catch+evaluate errors reply = super(HiveNodeRPC, self).rpcexec(payload) if self.next_node_on_empty_reply and not bool(reply) and self.nodes.working_nodes_count > 1: self._retry_on_next_node("Empty Reply") doRetry = True self.next_node_on_empty_reply = True else: self.next_node_on_empty_reply = False return reply except exceptions.RPCErrorDoRetry as e: msg = exceptions.decodeRPCErrorMsg(e).strip() try: self.nodes.sleep_and_check_retries(str(msg), call_retry=True) doRetry = True except exceptions.CallRetriesReached: if self.nodes.working_nodes_count > 1: self._retry_on_next_node(msg) doRetry = True else: self.next_node_on_empty_reply = False raise exceptions.CallRetriesReached except exceptions.RPCError as e: try: doRetry = self._check_error_message(e, self.error_cnt_call) except exceptions.CallRetriesReached: msg = exceptions.decodeRPCErrorMsg(e).strip() if self.nodes.working_nodes_count > 1: self._retry_on_next_node(msg) doRetry = True else: self.next_node_on_empty_reply = False raise exceptions.CallRetriesReached except Exception as e: self.next_node_on_empty_reply = False raise e maxRetryCountReached = self.nodes.num_retries_call_reached self.next_node_on_empty_reply = False def _retry_on_next_node(self, error_msg): self.nodes.increase_error_cnt() self.nodes.sleep_and_check_retries(error_msg, sleep=False, call_retry=False) self.next() def _check_error_message(self, e, cnt): """Check error message and decide what to do""" doRetry = False msg = exceptions.decodeRPCErrorMsg(e).strip() if re.search("missing required active authority", msg): raise exceptions.MissingRequiredActiveAuthority elif re.search("missing required active authority", msg): raise exceptions.MissingRequiredActiveAuthority elif re.match("^no method with name.*", msg): raise exceptions.NoMethodWithName(msg) elif re.search("Could not find method", msg): raise exceptions.NoMethodWithName(msg) elif re.search("Could not find API", msg): if self._check_api_name(msg): if self.nodes.working_nodes_count > 1 and self.nodes.num_retries > -1: self.nodes.disable_node() self._switch_to_next_node(msg, "ApiNotSupported") doRetry = True else: raise exceptions.ApiNotSupported(msg) else: raise exceptions.NoApiWithName(msg) elif re.search("follow_api_plugin not enabled", msg): if self.nodes.working_nodes_count > 1 and self.nodes.num_retries > -1: self._switch_to_next_node(str(e)) doRetry = True else: raise exceptions.FollowApiNotEnabled(msg) elif re.search("irrelevant signature included", msg): raise exceptions.UnnecessarySignatureDetected(msg) elif re.search("WinError", msg): raise exceptions.RPCError(msg) elif re.search("Unable to acquire database lock", msg): self.nodes.sleep_and_check_retries(str(msg), call_retry=True) doRetry = True elif re.search("Request Timeout", msg): self.nodes.sleep_and_check_retries(str(msg), call_retry=True) doRetry = True elif re.search("Bad or missing upstream response", msg): self.nodes.sleep_and_check_retries(str(msg), call_retry=True) doRetry = True elif re.search("Internal Error", msg) or re.search("Unknown exception", msg): self.nodes.sleep_and_check_retries(str(msg), call_retry=True) doRetry = True elif re.search("!check_max_block_age", str(e)): self._switch_to_next_node(str(e)) doRetry = True elif re.search("Can only vote once every 3 seconds", msg): raise exceptions.VotedBeforeWaitTimeReached(msg) elif re.search("out_of_rangeEEEE: unknown key", msg) or re.search("unknown key:unknown key", msg): raise exceptions.UnkownKey(msg) elif re.search("Assert Exception:v.is_object(): Input data have to treated as object", msg): raise exceptions.UnhandledRPCError("Use Operation(op, appbase=True) to prevent error: " + msg) elif re.search("Client returned invalid format. Expected JSON!", msg): if self.nodes.working_nodes_count > 1 and self.nodes.num_retries > -1: self.nodes.disable_node() self._switch_to_next_node(msg) doRetry = True else: raise exceptions.UnhandledRPCError(msg) elif msg: raise exceptions.UnhandledRPCError(msg) else: raise e return doRetry def _switch_to_next_node(self, msg, error_type="UnhandledRPCError"): if self.nodes.working_nodes_count == 1: if error_type == "UnhandledRPCError": raise exceptions.UnhandledRPCError(msg) elif error_type == "ApiNotSupported": raise exceptions.ApiNotSupported(msg) self.nodes.increase_error_cnt() self.nodes.sleep_and_check_retries(str(msg), sleep=False) self.next() def _check_api_name(self, msg): error_start = "Could not find API" known_apis = ['account_history_api', 'tags_api', 'database_api', 'market_history_api', 'block_api', 'account_by_key_api', 'chain_api', 'follow_api', 'condenser_api', 'debug_node_api', 'witness_api', 'test_api', 'network_broadcast_api'] for api in known_apis: if re.search(error_start + " " + api, msg): return True if msg[-18:] == error_start: return True return False def get_account(self, name, **kwargs): """ Get full account details from account name :param str name: Account name """ if isinstance(name, str): return self.get_accounts([name], **kwargs)

[ "[email protected]" ]

[email protected]

b9fb87d81b8ea6206160a6408edaca8fa28184b1

917974ea96ab36b7fa648dd57762b08e0650f459

/MySQL/实例/MysqlOperate.py

4fc8797c3170f610a8f5362ad0be20118f0f1282

[]

no_license

zhoulongqa/pythonCode

0d4957c65d2202f7551ba9ab96c06dd86e7b52d5

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refs/heads/master

2021-09-08T22:23:47.892837

2018-03-12T12:20:10

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# encoding=utf-8 import MySQLdb import random def getDatabaseConnection(): conn = MySQLdb.connect( host='localhost', port=3306, user='root', passwd='123123', charset='utf8') cur = conn.cursor() return conn, cur def closeDatabase(conn, cur): cur.close() conn.close() def createDatabase(data_base_name): conn,cur = getDatabaseConnection() result = cur.execute( 'create database if not exists %s default charset utf8 collate utf8_general_ci;' % data_base_name) print result closeDatabase(conn, cur) def create_table(database_name, table_sql): conn,cur = getDatabaseConnection() conn.select_db(database_name) result = cur.execute(table_sql) return result closeDatabase(conn, cur) def insert_data(database_name, data_sql): conn,cur = getDatabaseConnection() conn.select_db(database_name) result = cur.execute(data_sql) print result closeDatabase(conn, cur) #createDatabase('wangzeliangDB') table_sql='''CREATE TABLE user( 'id' int(11) default null,'name' VARCHAR(255) DEFAULT NULL,'passwd' VARCHAR(255) DEFAULT NULL,'birthday' DATA DEFAULT NULL)ENGINE=Innodb DEFAULT CHARSET=utf8;''' data_sql = "insert into user values(1,'Tom','123','1990-01-01')" create_table('wangzeliangDB',table_sql) insert_data("wangzeliangDB", data_sql)

[ "[email protected]" ]

[email protected]

d15edac876db06faf9c9c07283a6d10c33c1f8f7

6d82c2f984855f0d430ebeb9d5d65adae8a6ed94

/cdent/parser/pir/grammar.py

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ingydotnet/cdent-py

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refs/heads/master

2023-05-29T19:11:25.698386

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""" C'Dent Pir parser grammar module. """ from cdent.grammar import * class Grammar(): def __init__(self): self.__dict__.update( { 'BlankLine': Re({'_': '[\\ \\t]*\\r?\\n'}), 'Class': All({'_': [Rule({'_': 'ClassStart'}), Rule({'_': 'ClassBody'}), Rule({'_': 'ClassEnd'})]}), 'ClassBody': All({'_': [Indent({}), Rule({'x': '*', '_': 'Comment'}), Rule({'_': 'Method'}), Any({'x': '*', '_': [Rule({'_': 'Method'}), Rule({'_': 'Comment'})]})]}), 'ClassEnd': Re({'_': ''}), 'ClassStart': Re({'_': '.namespace[\\ \\t]+\\["(\\w+)"\\]\\r?\\n'}), 'Comment': Any({'_': [Rule({'_': 'LineComment'}), Rule({'_': 'BlankLine'})]}), 'DocComment': All({'_': [Rule({'_': 'DocCommentBegin'}), All({'x': '*', '_': [Rule({'!': True, '_': 'DocCommentEnd'}), Rule({'_': 'DocCommentLine'})]}), Rule({'_': 'DocCommentEnd'})]}), 'DocCommentBegin': Re({'_': '#{3}\\r?\\n'}), 'DocCommentEnd': Re({'_': '#{3}\\r?\\n'}), 'DocCommentLine': Re({'_': '#[\\ \\t]?(.*\\r?\\n)'}), 'Id': Re({'_': '\\w+'}), 'IncludeCDent': Re({'_': 'use CDent;'}), 'Line': Re({'_': '.*\\r?\\n'}), 'LineComment': Re({'_': '#(.*\\r?\\n)'}), 'Method': All({'_': [Rule({'_': 'MethodStart'}), Rule({'_': 'MethodBody'}), Rule({'_': 'MethodEnd'})]}), 'MethodBody': All({'_': [Indent({}), Rule({'_': 'Statement'}), Any({'x': '*', '_': [Rule({'_': 'Statement'}), Rule({'_': 'Comment'})]}), Undent({})]}), 'MethodEnd': Re({'_': '.end\\r?\\n'}), 'MethodStart': Re({'_': '.sub[\\ \\t]+(\\w+)[\\ \\t]+:method\\r?\\n'}), 'Module': All({'_': [Rule({'_': 'ModuleStart'}), Rule({'x': '?', '_': 'DocComment'}), Rule({'x': '*', '_': 'Comment'}), Rule({'x': '?', '_': 'IncludeCDent'}), Rule({'x': '*', '_': 'Comment'}), Rule({'_': 'Class'}), Any({'x': '*', '_': [Rule({'_': 'Class'}), Rule({'_': 'Comment'})]}), Rule({'_': 'ModuleEnd'}), Rule({'x': '*', '_': 'Comment'})]}), 'ModuleEnd': Re({'_': ''}), 'ModuleStart': Re({'_': ''}), 'PrintLn': Re({'_': 'say[\\ \\t]+(.+)\\r?\\n'}), 'Statement': Any({'_': [Rule({'_': 'PrintLn'}), Rule({'_': 'Comment'})]}), 'line_comment_start': Re({'_': '#'})} )

[ "[email protected]" ]

[email protected]

327f9765f5dd9fd7ec5ddb1747f3de2bffe48a72

5acc20092ee93935594a7e0522924245a43e5531

/support_vector_machines/plot_oneclass_svm.py

b3dd6215ed3a9101ee39baa85ae115e8380814cf

[]

no_license

shengchaohua/sklearn-examples

aae2332c4382a57a70c1887777c125e6dc4579d6

1dac6a9b5e703185a8da1df7c724022fbd56a9e4

refs/heads/master

2020-05-05T01:19:20.037746

2019-10-18T08:55:01

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import numpy as np import matplotlib.pyplot as plt import matplotlib.font_manager from sklearn import svm xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500)) # Generate train data X = 0.3 * np.random.randn(100, 2) X_train = np.r_[X + 2, X - 2] # Generate some regular novel observations X = 0.3 * np.random.randn(20, 2) X_test = np.r_[X + 2, X - 2] # Generate some abnormal novel observations X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2)) # Fit the model clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1) clf.fit(X_train) y_pred_train = clf.predict(X_train) y_pred_test = clf.predict(X_test) y_pred_outliers = clf.predict(X_outliers) n_error_train = y_pred_train[y_pred_train == -1].size n_error_test = y_pred_test[y_pred_test == -1].size n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size # Plot the line, the points, and the nearest vectors to the plane Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.title("Novelty Detection") plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu) a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred') plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred') s = 40 b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k') b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s, edgecolors='k') c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s, edgecolors='k') plt.axis('tight') plt.xlim((-5, 5)) plt.ylim((-5, 5)) plt.legend([a.collections[0], b1, b2, c], ["learned frontier", "training observations", "new regular observations", "new abnormal observations"], loc="upper left", prop=matplotlib.font_manager.FontProperties(size=11)) plt.xlabel( "error train: %d/200 ; errors novel regular: %d/40 ; " "errors novel abnormal: %d/40" % (n_error_train, n_error_test, n_error_outliers)) plt.show()

[ "[email protected]" ]

[email protected]

3fa18fbb6c6c984c8016aa0330fccb80274eeeb2

e4414bd8152e52855db7ab9065ae12b7329143e0

/python/src/two_more_random.py

b87bee878dbdfeb7ad6ff81d257bf7e780ba71dd

[]

no_license

catalinc/programmingpraxis-solutions

39cb847877ec46d2fb85740791c24889ab5654a8

c0b13906aa76ffac705bf108db138fb9a38bc16a

refs/heads/master

2021-03-27T16:46:47.781839

2017-09-09T15:17:38

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Python

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# A solution for http://programmingpraxis.com/2012/08/21/two-more-random-exercises/ import math def rand_middle_square(seed): n = seed seed_len = int(round(math.log(seed, 10))) while True: yield n n = (n * n) / (10 ** (seed_len / 2)) % (10 ** seed_len) def randu(seed): n = seed while True: yield n n = (65539 * n) % 2147483648 def random(count, seed, rand_fn): nums = [] random_gen = rand_fn(seed) for _ in xrange(count): nums.append(random_gen.next()) return nums print(random(5, 675248, rand_middle_square)) print(random(5, 7, randu))

[ "[email protected]" ]

[email protected]

1a9658d9fae0218278448f9af37f2b5c5e6f3593

b9696a277966d85548ebf23c77d24554dd98b1c1

/LasAndClf-dev/get_data_packages/collectdata2bc.py

9ff8239bd1826c85a88010e5fb370669aed10557

[]

no_license

hsulab/multiVASP

f1d277b015f97532588f4db21ce14bae68dafed9

e05bf8c03ff1653ad2621fdd61b8a706138dc37b

refs/heads/master

2020-03-07T09:12:32.199150

2019-10-22T14:30:18

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#!/usr/local/bin/python3 # -*- coding: utf-8 -*- import os import re import time import numpy as np import pandas as pd """ """ class GeoData(): # Geometry Features Data def __init__(self, name): self.name = name # Path Settings __dirpath = os.path.join(os.path.expanduser('~'), 'Desktop/CH4_DS') def set_path(self, path): """Set data path.""" self.__dirpath = path def get_path(self): """Get data path.""" self.__dirpath # Get Csv Name def __csvname(self): """Get data csvfile.""" for file_name in os.listdir(self.__dirpath): if re.match(self.name, file_name): csv_name = file_name return csv_name # Get df col=[name, feas] def df(self, numbers=-1): """Get dataframe.""" csv = os.path.join(self.__dirpath, self.__csvname()) df = pd.read_csv(csv, index_col=0) fea_numbers = len(df.columns) - 3 #print(self.name+' has '+str(fea_numbers)+' features.') if numbers == -1: numbers = fea_numbers feas = [] for i in range(3, numbers+3): feas.append(df.columns[i]) return df.loc[:,tuple(['name']+feas)] class EneData(GeoData): 'Energy Data' def allE(self): df = self.df() mtype = [] # mechanism type mE = [] for i in range(df.shape[0]): if df.loc[i, 'E_ts'] == 'np.nan' and df.loc[i, 'E_tsra'] == 'np.nan': mtype.append('np.nan') mE.append('np.nan') elif df.loc[i, 'E_ts'] == 'np.nan' and df.loc[i, 'E_tsra'] != 'np.nan': mtype.append('tsra') mE.append(df.loc[i, 'E_tsra']) elif df.loc[i, 'E_ts'] != 'np.nan' and df.loc[i, 'E_tsra'] == 'np.nan': mtype.append('ts') mE.append(df.loc[i, 'E_ts']) elif df.loc[i, 'E_ts'] > df.loc[i, 'E_tsra']: mtype.append('tsra') mE.append(df.loc[i, 'E_tsra']) else: mtype.append('ts') mE.append(df.loc[i, 'E_ts']) df.loc[:, 'mtype'] = mtype df.loc[:, 'mE'] = mE return df def get_data(): """ Description: Get Geo DataFrame. descriptors: distance 45, angles 360, dihedrals 630. """ print('Load Data...') suf = GeoData('suf').df() hab3 = GeoData('Hab3').df() ch3ab = GeoData('CH3ab').df() # delta_df = pd.DataFrame() delta_df.loc[:, 'name'] = suf.loc[:, 'name'] cols = suf.columns[1:] for col in cols: t = col.strip('suf') delta_df.loc[:, t+'hab3'] = hab3.loc[:, t+'Hab3'] - suf.loc[:, t+'suf'] for col in cols: t = col.strip('suf') delta_df.loc[:, t+'ch3ab'] = ch3ab.loc[:, t+'CH3ab'] - suf.loc[:, t+'suf'] 'Merge geofeas' print('This set has ', delta_df.shape[0], 'samples.') print('This set has ', delta_df.shape[1]-1, 'features.') 'Get numbers of geofeas' print('Merge Data...') E_feas = ['name', 'mtype', 'E_ts', 'E_tsra', 'mE', 'E_Hab3', 'E_CH3ab'] fE = EneData('fE').allE().loc[:, E_feas] # reaction Energy e_numbers = fE.shape[1] di = pd.merge(fE, delta_df, on='name') new_di = di.loc[di.loc[:,'mtype']!='np.nan', :] # !!! new_di = new_di.loc[di.loc[:,'name']!='pureMoO2', :] # CH3ab wrong new_di = new_di.loc[di.loc[:,'name']!='pureMnO2', :] # CH3ab wrong new_di = new_di.loc[di.loc[:,'name']!='dopCrO2_Ru', :] # CH3ab wrong print('Energy and Geometry set has ', new_di.shape[0], 'samples.') print('Energy and Geometry set has ', new_di.shape[1]-5, 'features.') # Save data -> ./CH4_DataSet.csv merged_data_csv = './CH4_neo.csv' print('Save data -> %s' %merged_data_csv) new_di.to_csv(merged_data_csv) return new_di # [name, mtype, mE, geo ... feas] if __name__ == '__main__': 'geoFeatures Total 1035' get_data()

[ "[email protected]" ]

[email protected]

6b93ecdbb92e6d5706872b8722d49a411dcbc403

fdd6c6a1b8e6e7e8cd267de97a1b435777342e1b

/tests/test_altdphi.py

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TaiSakuma/altdphi

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refs/heads/master

2021-03-16T08:45:10.249447

2019-05-14T16:44:40

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# Tai Sakuma <[email protected]> import numpy as np import pytest from altdphi import AltDphi from .testing import assert_altdphi_equal from .expected import * ##__________________________________________________________________|| @pytest.fixture( params=[ (event_nojet, altdphi_nojet, altdphi_met_nojet), (event_monojet, altdphi_monojet, altdphi_met_monojet), (event_two_jets, altdphi_two_jets, altdphi_met_two_jets), (event_three_jets, altdphi_three_jets, altdphi_met_three_jets), (event_four_jets, altdphi_four_jets, altdphi_met_four_jets), (event_twelve_jets, altdphi_twelve_jets, altdphi_met_twelve_jets), ], ids=('nojet', 'monojet', 'two_jets', 'three_jets', 'four_jets', 'twelve_jets') ) def event_altdphi(request): return request.param def test_altdphi(event_altdphi): event = event_altdphi[0] expected_altdphi = event_altdphi[1] pt = event['jet_pt'] phi = event['jet_phi'] actual_altdphi = AltDphi(pt=pt, phi=phi) assert_altdphi_equal(expected_altdphi, actual_altdphi) def test_altdphi_met(event_altdphi): event = event_altdphi[0] expected_altdphi = event_altdphi[2] pt = event['jet_pt'] phi = event['jet_phi'] met = event['met'] met_phi = event['met_phi'] actual_altdphi = AltDphi(pt=pt, phi=phi, mht=met, mht_phi=met_phi) assert_altdphi_equal(expected_altdphi, actual_altdphi) ##__________________________________________________________________|| def test_altdphi_monojet_is_minus_mht(): event = event_monojet pt = event['jet_pt'] phi = event['jet_phi'] altdphi = AltDphi(pt=pt, phi=phi) assert pt[0] == altdphi.mht assert [1] == altdphi.f assert [-1] == altdphi.cos_dphi def test_altdphi_monojet_is_not_minus_mht(): event = event_monojet pt = event['jet_pt'] phi = event['jet_phi'] mht = event['met'] mht_phi = event['met_phi'] altdphi = AltDphi(pt=pt, phi=phi, mht=mht, mht_phi=mht_phi) assert pt[0] != altdphi.mht assert [1] != altdphi.f assert [-1] != altdphi.cos_dphi ##__________________________________________________________________||

[ "[email protected]" ]

[email protected]

728158a4d9026a97e17a89c008935c78bba93cc3

2f6817fc8f6ddb48f5f88c913d8e40b672fc3dbf

/MLP/lec13-4[Kmeans].py

84ab79f09472a0230ce9c1721fc34ce47e22cf64

[]

no_license

cutz-j/TodayILearned

320b5774de68a0f4f68fda28a6a8b980097d6ada

429b24e063283a0d752ccdfbff455abd30ba3859

refs/heads/master

2020-03-23T17:34:51.389065

2018-11-24T08:49:41

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import pandas as pd from sklearn import datasets from sklearn.cluster import KMeans import matplotlib.pyplot as plt from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler iris = datasets.load_iris() labels = pd.DataFrame(iris.target) labels.columns = ['labels'] data = pd.DataFrame(iris.data) data.columns = ['Sepal_length', 'Sepal_width', 'Petal_length', 'Petal_width'] data = pd.concat([data, labels], axis=1) feature = data[['Sepal_length', 'Sepal_width']] model = KMeans(n_clusters=5, algorithm='auto') scaler = StandardScaler() pipeline = make_pipeline(scaler, model) pipeline.fit(feature) predict = pd.DataFrame(pipeline.predict(feature)) ks = range(1,10) inertias = [] for k in ks: model = KMeans(n_cluster=k) model.fit(feature) inertias.append(model.inertia_) predict.columns = ['predict'] r = pd.concat([feature, predict], axis=1) plt.scatter(r['Sepal_length'], r['Sepal_width'], c=r['predict'], alpha=0.5) centers = pd.DataFrame(model.cluster_centers_, columns=['Sepal_length', 'Sepal_width']) #center_x = centers['Sepal_length'] #center_y = centers['Sepal_width'] #plt.scatter(center_x, center_y, s=50, marker='D', c='r') #plt.show()

[ "[email protected]" ]

[email protected]

c3e43afbae66f6aa4658cc2e059a94f5e45187c6

b5d738624d7016f7e10796485624c567099374ab

/starthinker/util/dcm/schema/Activity_Metrics.py

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[ "Apache-2.0", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

dvandra/starthinker

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refs/heads/master

2020-06-14T05:19:08.348496

2019-07-02T17:54:06

194,915,001

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Apache-2.0

2019-07-02T18:25:23

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UTF-8

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########################################################################### # # Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ########################################################################### Activity_Metrics_Schema = [ { "name":"Click_Through_Conversions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Click_Through_Revenue", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"View_Through_Conversions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"View_Through_Revenue", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Click_Through_Conversions_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Click_Through_Revenue_Cross_Environment", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Click_Through_Conversion_Events_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Total_Conversions_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Total_Revenue_Cross_Environment", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Total_Conversion_Events_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"View_Through_Conversions_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"View_Through_Revenue_Cross_Environment", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"View_Through_Conversion_Events_Cross_Environment", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Dynamic_Element_Click_Through_Conversions", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Dynamic_Element_Total_Conversions", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Dynamic_Element_View_Through_Conversions", "type":"INTEGER", "mode":"NULLABLE" }, { "name":"Natural_Search_Actions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Natural_Search_Revenue", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Natural_Search_Transactions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Action_Conversion_Percentage", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Paid_Search_Actions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Average_Cost_Per_Action", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Average_Cost_Per_Transaction", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Average_Dcm_Transaction_Amount", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Paid_Search_Revenue", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Spend_Per_Transaction_Revenue", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Transaction_Conversion_Percentage", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Floodlight_Paid_Search_Transaction_Revenue_Per_Spend", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Paid_Search_Transactions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Total_Conversions", "type":"FLOAT", "mode":"NULLABLE" }, { "name":"Total_Revenue", "type":"FLOAT", "mode":"NULLABLE" } ]

[ "[email protected]" ]

[email protected]

8147ee54973388356c60c895c778940a1eee9e84

8d014c5513a0eeca086010b018b67336f8d042e0

/wicam_vlc.py

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[]

no_license

rkuo2000/cv2

26ce0a06b4040eabb82319ec44cab5c3639b9495

16e64e7092d6654ea470e469d6b15f308ecd1788

refs/heads/master

2022-10-12T00:11:35.964818

2022-09-30T06:50:35

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null

2022-09-29T11:01:48

2017-10-30T12:38:58

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# Install VLC Player on PC # Add Environtment System Variables: VLC_PLUGIN_PATH = C:\Program Files\VideoLAN\VLC\plugins # pip install python-vlc # WiFi connected to WiCam module (streaming video) import cv2 import vlc #player=vlc.MediaPlayer('rtsp://192.168.100.1/cam1/h264') player=vlc.MediaPlayer('rtsp://192.168.100.1/cam1/mpeg4') while 1: frame = player.play() cv2.imshow('VIDEO',frame) cv2.waitKey(1) cv2.destroyAllWindows()

[ "[email protected]" ]

[email protected]

2d50a33f7a6f96a094b2b5a8c3082d850f8c3b9a

dea8cfa596d52d5db0e28ac43504e7212b43081b

/python/AtCoder Beginner Contest 123/Five Dishes .py

5b3b87bc101c5841242a539782cdaf0a6b8925b9

[]

no_license

Yuta123456/AtCoder

9871a44f12a8fca87b0e2863a999b716128de1ac

ca04422699719563e311f7d973459ba1dc238c2c

refs/heads/master

2023-01-04T22:33:54.120454

2020-11-04T05:20:37

286,409,112

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py

def ceil(x): k = x % 10 if k == 0: return x else: return x + (10 - k) d = [] for i in range(5): d.append(int(input())) d_min = [] min = 124 sum = 0 index = -1 for i in range(5): d_min.append((d[i]) % 10) for i in range(5): if d_min[i] != 0: if min > d_min[i]: min = d_min[i] index = i if index == -1: index = 0 for i in range(5): if i != index: sum = sum + ceil(d[i]) sum += d[index] print(sum)

[ "[email protected]" ]

[email protected]

40b35aefa6aa53d7c9e97137d474309dfdb68a8e

0d0cf0165ca108e8d94056c2bae5ad07fe9f9377

/15_Feature_Engineering_for_Machine_Learning_in_Python/2_Dealing_with_Messy_Data/howSparseIsMyData.py

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[]

no_license

MACHEIKH/Datacamp_Machine_Learning_For_Everyone

550ec4038ebdb69993e16fe22d5136f00101b692

9fe8947f490da221430e6dccce6e2165a42470f3

refs/heads/main

2023-01-22T06:26:15.996504

2020-11-24T11:21:53

null

0

null

UTF-8

Python

false

679

py

# How sparse is my data? # Most data sets contain missing values, often represented as NaN (Not a Number). If you are working with Pandas you can easily check how many missing values exist in each column. # Let's find out how many of the developers taking the survey chose to enter their age (found in the Age column of so_survey_df) and their gender (Gender column of so_survey_df). # Instructions 1/2 # 50 XP # Subset the DataFrame to only include the 'Age' and 'Gender' columns. # Print the number of non-missing values in both columns. # Subset the DataFrame sub_df = so_survey_df[['Age', 'Gender']] # Print the number of non-missing values print(sub_df.notnull().sum())

[ "[email protected]" ]

[email protected]

42d6cbdf04e5bbb6398833f05d3653f58dbacfa6

c5291e50a3c72c885922378573a0ad423fcedf05

/analysis/analysis/urls.py

0a94cc17c066e5075d59765c816f00b7aac7ce4e

[]

no_license

raghurammanyam/django-projects

bcc3ed6285882af437a2995514cef33760fb063e

dd20ae354f7f111a0176a1cc047c099bd23e9f05

refs/heads/master

2022-12-12T19:22:31.698114

2018-12-09T09:41:45

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null

2022-11-22T03:01:07

2018-06-15T05:08:15

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Python

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929

py

"""analysis URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.conf.urls import url,include #from django.urls import path, include from django.http import HttpResponse from django.conf import settings from data import urls urlpatterns = [ url('admin/',(admin.site.urls)), url(r'^',include('data.urls')), ]

[ "[email protected]" ]

[email protected]

2093ce0cb85111f3f214151ed4bcb78b1d2e34fc

ff4fe07752b61aa6404f85a8b4752e21e8a5bac8

/challenge-209/eric-cheung/python/ch-2.py

624ac7029fd2589ae8c5e87fe90970b576910183

[]

no_license

choroba/perlweeklychallenge-club

7c7127b3380664ca829158f2b6161c2f0153dfd9

2b2c6ec6ece04737ba9a572109d5e7072fdaa14a

refs/heads/master

2023-08-10T08:11:40.142292

2023-08-06T20:44:13

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0

1

null

2019-06-01T20:56:32

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UTF-8

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## arrAccount = [["A", "[email protected]", "[email protected]"], ["B", "[email protected]"], ["A", "[email protected]", "[email protected]"]] ## Example 1 arrAccount = [["A", "[email protected]", "[email protected]"], ["B", "[email protected]"], ["A", "[email protected]"], ["B", "[email protected]", "[email protected]"]] ## Example 2 arrUser = [arrAccount[0][0]] arrEmail = [arrAccount[0][1:]] arrFinal = [] for nIndx in range(1, len(arrAccount)): if arrAccount[nIndx][0] not in arrUser: arrUser.append(arrAccount[nIndx][0]) arrEmail.append(arrAccount[nIndx][1:]) else: nFindIndx = arrUser.index(arrAccount[nIndx][0]) if len(list(set(arrEmail[nFindIndx]) & set(arrAccount[nIndx][1:]))) == 0: arrUser.append(arrAccount[nIndx][0]) arrEmail.append(arrAccount[nIndx][1:]) else: arrEmail[nFindIndx] = sorted(list(set(arrEmail[nFindIndx] + arrAccount[nIndx][1:]))) ## print (arrUser) ## print (arrEmail) for nIndx in range(0, len(arrUser)): arrFinal.append([arrUser[nIndx], str(arrEmail[nIndx][:])[1:-1]]) print (arrFinal)

[ "[email protected]" ]

[email protected]

19cdad09fcea597b9049e86da3c0e55f919ffb8e

14f30311d0c93053d159118f5b978f26352fa201

/NAIP/loop_FeatureCollection.py

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permissive

kylebarron/earthengine-py-notebooks

590a343c5011f3e9a346c2379016e262aa13801f

4e9e3bae244fb66284f77c264f19aa297fb0fea1

refs/heads/master

2022-07-31T07:33:21.109880

2020-05-18T23:42:26

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MIT

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2020-04-21T18:50:57

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py

# %% """ <table class="ee-notebook-buttons" align="left"> <td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/NAIP/loop_FeatureCollection.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td> <td><a target="_blank" href="https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/NAIP/loop_FeatureCollection.ipynb"><img width=26px src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png" />Notebook Viewer</a></td> <td><a target="_blank" href="https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/NAIP/loop_FeatureCollection.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a></td> </table> """ # %% """ ## Install Earth Engine API and geemap Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`. The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet. **Important note**: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). """ # %% # Installs geemap package import subprocess try: import geemap except ImportError: print('geemap package not installed. Installing ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap']) # Checks whether this notebook is running on Google Colab try: import google.colab import geemap.eefolium as emap except: import geemap as emap # Authenticates and initializes Earth Engine import ee try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() # %% """ ## Create an interactive map The default basemap is `Google Satellite`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py#L13) can be added using the `Map.add_basemap()` function. """ # %% Map = emap.Map(center=[40,-100], zoom=4) Map.add_basemap('ROADMAP') # Add Google Map Map # %% """ ## Add Earth Engine Python script """ # %% # Add Earth Engine dataset year = 2015 collection = ee.ImageCollection('USDA/NAIP/DOQQ') startTime = ee.Date(str(year) + '-01-01') endTime = ee.Date(str(year) + '-12-31') # year = startTime.get('year').getInfo() # print(year) fromFT = ee.FeatureCollection('ft:1CLldB-ULPyULBT2mxoRNv7enckVF0gCQoD2oH7XP') # count = fromFT.size().getInfo() # print(count) polys = fromFT.geometry() centroid = polys.centroid() lng, lat = centroid.getInfo()['coordinates'] # print("lng = {}, lat = {}".format(lng, lat)) values = fromFT.reduceColumns(ee.Reducer.toList(2), ['system:index', 'name']).getInfo()['list'] # print(values) Map.setCenter(lng, lat, 10) def subsetNAIP(img_col, startTime, endTime, fc): img = img_col.filterDate(startTime, endTime).filterBounds(fc).mosaic().clip(fc) return img def calNDWI(image): """A function to compute NDWI.""" ndwi = image.normalizedDifference(['G', 'N']) ndwiViz = {'min': 0, 'max': 1, 'palette': ['00FFFF', '0000FF']} ndwiMasked = ndwi.updateMask(ndwi.gte(0.2)) ndwi_bin = ndwiMasked.gt(0) patch_size = ndwi_bin.connectedPixelCount(500, True) large_patches = patch_size.eq(500) large_patches = large_patches.updateMask(large_patches) opened = large_patches.focal_min(1).focal_max(1) return opened def rasterToVector(img, fc): vec = img.reduceToVectors(geometry=fc, eightConnected=True, maxPixels=59568116121, crs=img.projection(), scale=1) return vec def exportToDrive(vec, filename): taskParams = { 'driveFolder': 'image', 'fileFormat': 'KML' } task = ee.batch.Export.table(vec, filename, taskParams) task.start() vis = {'bands': ['N', 'R', 'G']} for (id, name) in values: watershed = fromFT.filter(ee.Filter.eq('system:index', str(id))) filename = "Y" + str(year) + "_" + str(id) + "_" + str(name).replace(" ", "_") print(filename) image = subsetNAIP(collection, startTime, endTime, watershed) ndwi = calNDWI(image) vector = rasterToVector(ndwi, watershed) exportToDrive(vector, filename) # Map.addLayer(image, vis) # Map.addLayer(vector) # for i in range(2, 2 + count): # watershed = fromFT.filter(ee.Filter.eq('system:index', str(i))) # re = fc.filterBounds(watershed) # task = ee.batch.Export.table(re, 'watershed-' + str(i), taskParams) # task.start() # # # # lng_lat = ee.Geometry.Point(lng, lat) # naip = collection.filterBounds(polys) # naip_2015 = naip.filterDate('2015-01-01', '2015-12-31') # ppr = naip_2015.mosaic() # # count = naip_2015.size().getInfo() # print("Count: ", count) # # # print(naip_2015.size().getInfo()) # vis = {'bands': ['N', 'R', 'G']} # Map.setCenter(lng, lat, 12) # Map.addLayer(ppr,vis) # # Map.addLayer(polys) # # def NDWI(image): # """A function to compute NDWI.""" # ndwi = image.normalizedDifference(['G', 'N']) # ndwiViz = {'min': 0, 'max': 1, 'palette': ['00FFFF', '0000FF']} # ndwiMasked = ndwi.updateMask(ndwi.gte(0.05)) # ndwi_bin = ndwiMasked.gt(0) # patch_size = ndwi_bin.connectedPixelCount(500, True) # large_patches = patch_size.eq(500) # large_patches = large_patches.updateMask(large_patches) # opened = large_patches.focal_min(1).focal_max(1) # return opened # # ndwi_collection = naip_2015.map(NDWI) # # Map.addLayer(ndwi_collection) # # print(ndwi_collection.getInfo()) # # # downConfig = {'scale': 10, "maxPixels": 1.0E13, 'driveFolder': 'image'} # scale means resolution. # # img_lst = ndwi_collection.toList(100) # # # # taskParams = { # # 'driveFolder': 'image', # # 'driveFileNamePrefix': 'ndwi', # # 'fileFormat': 'KML' # # } # # # # for i in range(0, count): # # image = ee.Image(img_lst.get(i)) # # name = image.get('system:index').getInfo() # # print(name) # # # task = ee.batch.Export.image(image, "ndwi2-" + name, downConfig) # # # task.start() # # mosaic = ndwi_collection.mosaic().clip(polys) # fc = mosaic.reduceToVectors(eightConnected=True, maxPixels=59568116121, crs=mosaic.projection(), scale=1) # # Map.addLayer(fc) # taskParams = { # 'driveFolder': 'image', # 'driveFileNamePrefix': 'water', # 'fileFormat': 'KML' # } # # count = fromFT.size().getInfo() # Map.setCenter(lng, lat, 10) # # for i in range(2, 2 + count): # watershed = fromFT.filter(ee.Filter.eq('system:index', str(i))) # re = fc.filterBounds(watershed) # # task = ee.batch.Export.table(re, 'watershed-' + str(i), taskParams) # # task.start() # # Map.addLayer(fc) # # # # lpc = fromFT.filter(ee.Filter.eq('name', 'Little Pipestem Creek')) # %% """ ## Display Earth Engine data layers """ # %% Map.addLayerControl() # This line is not needed for ipyleaflet-based Map. Map

[ "[email protected]" ]

[email protected]

5e3155e560a1c4c9932aad5f2150648b1da46f76

fd7d7e1410874d18823bbe3c0f3c521cb54e079c

/news/migrations/0008_auto_20191002_1652.py

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permissive

alex-muliande/tribune

9c1728311e42b16cf90e3b6e94b64a2b1ed3c8be

86316dd4b20a76320b4b20b86266f89aac02a326

refs/heads/master

2023-08-03T13:20:47.852749

2019-10-03T09:25:46

212,532,363

1

0

MIT

2021-09-08T01:19:17

2019-10-03T08:37:00

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py

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2019-10-02 13:52 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('news', '0007_article_article_image'), ] operations = [ migrations.AlterField( model_name='article', name='article_image', field=models.ImageField(upload_to='articles/'), ), ]

[ "[email protected]" ]

[email protected]

39e6bf4ecf39d328147ec7c400a110efd5dcabae

292b43454189f1cb5e67fc0a6f77b9b76cc5c156

/dvaapp/views.py

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

permissive

junjiez/DeepVideoAnalytics

b9f4e00d800c972ae71233699f341d6f733c2d61

03e708a12e1941cedb1449fc7bbc02b3c0fca1d2

refs/heads/master

2021-06-25T21:18:49.767732

2017-09-12T19:16:22

null

0

null

UTF-8

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53,986

py

from django.shortcuts import render, redirect from django.conf import settings from django.http import JsonResponse import requests import glob import json from django.views.generic import ListView, DetailView from django.utils import timezone from .forms import UploadFileForm, YTVideoForm, AnnotationForm from .models import Video, Frame, DVAPQL, QueryResults, TEvent, IndexEntries, Region, VDNServer, \ LOPQCodes, Tube, Detector, Segment, FrameLabel, SegmentLabel, \ VideoLabel, RegionLabel, TubeLabel, Label, ManagementAction, StoredDVAPQL, Analyzer,\ Indexer, Retriever, SystemState, QueryRegion from dva.celery import app from dvaapp.operations import queuing import serializers from rest_framework import viewsets, mixins from django.contrib.auth.models import User from rest_framework.permissions import IsAuthenticatedOrReadOnly, IsAuthenticated from django.db.models import Count import math from django.db.models import Max from shared import handle_uploaded_file, create_annotation, handle_video_url, pull_vdn_list, \ import_vdn_dataset_url, create_detector_dataset, import_vdn_detector_url, refresh_task_status, \ delete_video_object from operations.processing import DVAPQLProcess from django.contrib.auth.decorators import user_passes_test,login_required from django.utils.decorators import method_decorator from django.contrib.auth.mixins import UserPassesTestMixin from django_celery_results.models import TaskResult from rest_framework.authtoken.models import Token import logging import defaults try: from django.contrib.postgres.search import SearchVector except ImportError: SearchVector = None logging.warning("Could not load Postgres full text search") from examples import EXAMPLES class LoginRequiredMixin(object): @method_decorator(login_required) def dispatch(self, *args, **kwargs): return super(LoginRequiredMixin, self).dispatch(*args, **kwargs) def user_check(user): return user.is_authenticated or settings.AUTH_DISABLED def force_user_check(user): return user.is_authenticated class UserViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = User.objects.all() serializer_class = serializers.UserSerializer class SystemStateViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = SystemState.objects.all() serializer_class = serializers.SystemStateSerializer class VideoViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Video.objects.all() serializer_class = serializers.VideoSerializer class AnalyzerViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Analyzer.objects.all() serializer_class = serializers.AnalyzerSerializer class IndexerViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Indexer.objects.all() serializer_class = serializers.IndexerSerializer class RetrieverViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Retriever.objects.all() serializer_class = serializers.RetrieverSerializer class DetectorViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Detector.objects.all() serializer_class = serializers.DetectorSerializer class FrameViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Frame.objects.all() serializer_class = serializers.FrameSerializer filter_fields = ('frame_index', 'subdir', 'name', 'video') class FrameLabelViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = FrameLabel.objects.all() serializer_class = serializers.FrameLabelSerializer filter_fields = ('frame_index','segment_index', 'video') class RegionLabelViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = RegionLabel.objects.all() serializer_class = serializers.RegionLabelSerializer class VideoLabelViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = VideoLabel.objects.all() serializer_class = serializers.VideoLabelSerializer class SegmentLabelViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = SegmentLabel.objects.all() serializer_class = serializers.SegmentLabelSerializer class TubeLabelViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = TubeLabel.objects.all() serializer_class = serializers.TubeLabelSerializer class SegmentViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Segment.objects.all() serializer_class = serializers.SegmentSerializer filter_fields = ('segment_index','video') class QueryRegionViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = QueryRegion.objects.all() serializer_class = serializers.QueryRegionSerializer class RegionViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Region.objects.all() serializer_class = serializers.RegionSerializer filter_fields = ('video',) class DVAPQLViewSet(viewsets.ModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = DVAPQL.objects.all() serializer_class = serializers.DVAPQLSerializer def perform_create(self, serializer): instance = serializer.save(user=self.request.user) p = DVAPQLProcess(instance) spec = json.loads(self.request.POST.get('script')) p.create_from_json(spec, self.request.user) p.launch() def perform_update(self, serializer): """ Immutable Not allowed :param serializer: :return: """ raise NotImplementedError def perform_destroy(self, instance): """ :param instance: :return: """ raise ValueError, "Not allowed to delete" class QueryResultsViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = QueryResults.objects.all() serializer_class = serializers.QueryResultsSerializer filter_fields = ('frame', 'video') class TEventViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = TEvent.objects.all() serializer_class = serializers.TEventSerializer filter_fields = ('video', 'operation') class IndexEntriesViewSet(viewsets.ReadOnlyModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = IndexEntries.objects.all() serializer_class = serializers.IndexEntriesSerializer filter_fields = ('video', 'algorithm', 'detection_name') class LabelViewSet(viewsets.ModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Label.objects.all() serializer_class = serializers.LabelSerializer class VDNServerViewSet(viewsets.ModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = VDNServer.objects.all() serializer_class = serializers.VDNServerSerializer class TubeViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = Tube.objects.all() serializer_class = serializers.TubeSerializer class LOPQCodesViewSet(viewsets.ModelViewSet): permission_classes = (IsAuthenticatedOrReadOnly,) if settings.AUTH_DISABLED else (IsAuthenticated,) queryset = LOPQCodes.objects.all() serializer_class = serializers.LOPQCodesSerializer class VideoList(UserPassesTestMixin, ListView): model = Video paginate_by = 100 def get_context_data(self, **kwargs): context = super(VideoList, self).get_context_data(**kwargs) context['exports'] = TEvent.objects.all().filter(operation='perform_export') return context def test_func(self): return user_check(self.request.user) class TEventDetail(UserPassesTestMixin, DetailView): model = TEvent def get_context_data(self, **kwargs): context = super(TEventDetail, self).get_context_data(**kwargs) try: tr = TaskResult.objects.get(task_id=context['object'].task_id) except TaskResult.DoesNotExist: context['celery_task'] = None pass else: context['celery_task'] = tr return context def test_func(self): return user_check(self.request.user) class TEventList(UserPassesTestMixin, ListView): model = TEvent paginate_by = 500 def get_queryset(self): kwargs = {} if self.kwargs.get('pk',None): kwargs['video_id']=self.kwargs['pk'] elif self.kwargs.get('process_pk',None): kwargs['parent_process_id']=self.kwargs['process_pk'] if self.kwargs.get('status',None): if self.kwargs['status'] == 'running': kwargs['duration__lt'] = 0 kwargs['started'] = True kwargs['completed'] = False kwargs['errored'] = False elif self.kwargs['status'] == 'successful': kwargs['completed'] = True elif self.kwargs['status'] == 'pending': kwargs['duration__lt'] = 0 kwargs['started'] = False kwargs['errored'] = False elif self.kwargs['status'] == 'failed': kwargs['errored'] = True new_context = TEvent.objects.filter(**kwargs).order_by('-created') return new_context def get_context_data(self, **kwargs): refresh_task_status() context = super(TEventList, self).get_context_data(**kwargs) context['header'] = "" if self.kwargs.get('pk',None): context['video'] = Video.objects.get(pk=self.kwargs['pk']) context['header'] = "video/dataset : {}".format(context['video'].name) if self.kwargs.get('process_pk',None): process_pk = self.kwargs.get('process_pk',None) context['header'] = "process : {}".format(process_pk) if self.kwargs.get('status',None): context['header'] += " with status {}".format(self.kwargs['status']) return context def test_func(self): return user_check(self.request.user) class VideoDetail(UserPassesTestMixin, DetailView): model = Video def get_context_data(self, **kwargs): context = super(VideoDetail, self).get_context_data(**kwargs) max_frame_index = Frame.objects.all().filter(video=self.object).aggregate(Max('frame_index'))[ 'frame_index__max'] context['exports'] = TEvent.objects.all().filter(operation='perform_export', video=self.object) context['annotation_count'] = Region.objects.all().filter(video=self.object, region_type=Region.ANNOTATION).count() context['exportable_annotation_count'] = 0 context['url'] = '{}{}/video/{}.mp4'.format(settings.MEDIA_URL, self.object.pk, self.object.pk) label_list = [] show_all = self.request.GET.get('show_all_labels', False) context['label_list'] = label_list delta = 10000 if context['object'].dataset: delta = 500 if max_frame_index <= delta: context['frame_list'] = Frame.objects.all().filter(video=self.object).order_by('frame_index') context['detection_list'] = Region.objects.all().filter(video=self.object, region_type=Region.DETECTION) context['annotation_list'] = Region.objects.all().filter(video=self.object, region_type=Region.ANNOTATION) context['offset'] = 0 context['limit'] = max_frame_index else: if self.request.GET.get('frame_index_offset', None) is None: offset = 0 else: offset = int(self.request.GET.get('frame_index_offset')) limit = offset + delta context['offset'] = offset context['limit'] = limit context['frame_list'] = Frame.objects.all().filter(video=self.object, frame_index__gte=offset, frame_index__lte=limit).order_by('frame_index') context['detection_list'] = Region.objects.all().filter(video=self.object, frame_index__gte=offset, frame_index__lte=limit, region_type=Region.DETECTION) context['annotation_list'] = Region.objects.all().filter(video=self.object, frame_index__gte=offset, frame_index__lte=limit, region_type=Region.ANNOTATION) context['frame_index_offsets'] = [(k * delta, (k * delta) + delta) for k in range(int(math.ceil(max_frame_index / float(delta))))] context['frame_first'] = context['frame_list'].first() context['frame_last'] = context['frame_list'].last() context['pending_tasks'] = TEvent.objects.all().filter(video=self.object,started=False, errored=False).count() context['running_tasks'] = TEvent.objects.all().filter(video=self.object,started=True, completed=False, errored=False).count() context['successful_tasks'] = TEvent.objects.all().filter(video=self.object,completed=True).count() context['errored_tasks'] = TEvent.objects.all().filter(video=self.object,errored=True).count() if context['limit'] > max_frame_index: context['limit'] = max_frame_index context['max_frame_index'] = max_frame_index return context def test_func(self): return user_check(self.request.user) # class RetrieverDetails(UserPassesTestMixin, DetailView): # model = Clusters # # def get_context_data(self, **kwargs): # context = super(ClustersDetails, self).get_context_data(**kwargs) # context['coarse'] = [] # context['index_entries'] = [IndexEntries.objects.get(pk=k) for k in self.object.included_index_entries_pk] # for k in ClusterCodes.objects.filter(clusters_id=self.object.pk).values('coarse_text').annotate( # count=Count('coarse_text')): # context['coarse'].append({'coarse_text': k['coarse_text'].replace(' ', '_'), # 'count': k['count'], # 'first': ClusterCodes.objects.all().filter(clusters_id=self.object.pk, # coarse_text=k['coarse_text']).first(), # 'last': ClusterCodes.objects.all().filter(clusters_id=self.object.pk, # coarse_text=k['coarse_text']).last() # }) # # return context # # def test_func(self): # return user_check(self.request.user) class DetectionDetail(UserPassesTestMixin, DetailView): model = Detector def get_context_data(self, **kwargs): context = super(DetectionDetail, self).get_context_data(**kwargs) classdist = context['object'].class_distribution.strip() context['class_distribution'] = json.loads(classdist) if classdist else {} context['phase_1_log'] = [] context['phase_2_log'] = [] for k in context['object'].phase_1_log.split('\n')[1:]: if k.strip(): epoch,train_loss,val_loss = k.strip().split(',') context['phase_1_log'].append((epoch,round(float(train_loss),2),round(float(val_loss),2))) for k in context['object'].phase_2_log.split('\n')[1:]: if k.strip(): epoch,train_loss,val_loss = k.strip().split(',') context['phase_2_log'].append((epoch,round(float(train_loss),2),round(float(val_loss),2))) return context def test_func(self): return user_check(self.request.user) class FrameList(UserPassesTestMixin, ListView): model = Frame def test_func(self): return user_check(self.request.user) class FrameDetail(UserPassesTestMixin, DetailView): model = Frame def get_context_data(self, **kwargs): context = super(FrameDetail, self).get_context_data(**kwargs) context['detection_list'] = Region.objects.all().filter(frame=self.object, region_type=Region.DETECTION) context['annotation_list'] = Region.objects.all().filter(frame=self.object, region_type=Region.ANNOTATION) context['video'] = self.object.video context['url'] = '{}{}/frames/{}.jpg'.format(settings.MEDIA_URL, self.object.video.pk, self.object.frame_index) context['previous_frame'] = Frame.objects.filter(video=self.object.video, frame_index__lt=self.object.frame_index).order_by( '-frame_index')[0:1] context['next_frame'] = Frame.objects.filter(video=self.object.video, frame_index__gt=self.object.frame_index).order_by('frame_index')[ 0:1] return context def test_func(self): return user_check(self.request.user) class SegmentDetail(UserPassesTestMixin, DetailView): model = Segment def get_context_data(self, **kwargs): context = super(SegmentDetail, self).get_context_data(**kwargs) context['video'] = self.object.video context['frame_list'] = Frame.objects.all().filter(video=self.object.video,segment_index=self.object.segment_index).order_by('frame_index') context['region_list'] = Region.objects.all().filter(video=self.object.video,segment_index=self.object.segment_index).order_by('frame_index') context['url'] = '{}{}/segments/{}.mp4'.format(settings.MEDIA_URL, self.object.video.pk, self.object.segment_index) context['previous_segment_index'] = self.object.segment_index - 1 if self.object.segment_index else None context['next_segment_index'] = self.object.segment_index + 1 if (self.object.segment_index+1) < self.object.video.segments else None return context def test_func(self): return user_check(self.request.user) class VisualSearchList(UserPassesTestMixin, ListView): model = DVAPQL template_name = "dvaapp/query_list.html" def test_func(self): return user_check(self.request.user) def get_queryset(self): new_context = DVAPQL.objects.filter(process_type=DVAPQL.QUERY).order_by('-created') return new_context class VisualSearchDetail(UserPassesTestMixin, DetailView): model = DVAPQL template_name = "dvaapp/query_detail.html" def get_context_data(self, **kwargs): context = super(VisualSearchDetail, self).get_context_data(**kwargs) qp = DVAPQLProcess(process=context['object'],media_dir=settings.MEDIA_ROOT) qp.collect() context['results'] = qp.context['results'].items() context['regions'] = qp.context['regions'] script = context['object'].script script[u'image_data_b64'] = "<excluded>" context['plan'] = script context['pending_tasks'] = TEvent.objects.all().filter(parent_process=self.object,started=False, errored=False).count() context['running_tasks'] = TEvent.objects.all().filter(parent_process=self.object,started=True, completed=False, errored=False).count() context['successful_tasks'] = TEvent.objects.all().filter(parent_process=self.object,completed=True).count() context['errored_tasks'] = TEvent.objects.all().filter(parent_process=self.object,errored=True).count() context['url'] = '{}queries/{}.png'.format(settings.MEDIA_URL, self.object.pk, self.object.pk) return context def test_func(self): return user_check(self.request.user) class ProcessList(UserPassesTestMixin, ListView): model = DVAPQL template_name = "dvaapp/process_list.html" paginate_by = 50 def get_context_data(self, **kwargs): context = super(ProcessList, self).get_context_data(**kwargs) return context def test_func(self): return user_check(self.request.user) def get_queryset(self): new_context = DVAPQL.objects.filter().order_by('-created') return new_context class ProcessDetail(UserPassesTestMixin, DetailView): model = DVAPQL template_name = "dvaapp/process_detail.html" def get_context_data(self, **kwargs): context = super(ProcessDetail, self).get_context_data(**kwargs) context['json'] = json.dumps(context['object'].script,indent=4) context['pending_tasks'] = TEvent.objects.all().filter(parent_process=self.object,started=False, errored=False).count() context['running_tasks'] = TEvent.objects.all().filter(parent_process=self.object,started=True, completed=False, errored=False).count() context['successful_tasks'] = TEvent.objects.all().filter(parent_process=self.object,completed=True).count() context['errored_tasks'] = TEvent.objects.all().filter(parent_process=self.object,errored=True).count() return context def test_func(self): return user_check(self.request.user) class StoredProcessList(UserPassesTestMixin, ListView): model = StoredDVAPQL template_name = "dvaapp/stored_process_list.html" paginate_by = 20 ordering = "-created" def get_context_data(self, **kwargs): context = super(StoredProcessList, self).get_context_data(**kwargs) context['examples'] = json.dumps(EXAMPLES, indent=None) return context def test_func(self): return user_check(self.request.user) class StoredProcessDetail(UserPassesTestMixin, DetailView): model = StoredDVAPQL template_name = "dvaapp/stored_process_detail.html" def get_context_data(self, **kwargs): context = super(StoredProcessDetail, self).get_context_data(**kwargs) context['json'] = json.dumps(context['object'].script,indent=4) return context def test_func(self): return user_check(self.request.user) @user_passes_test(user_check) def search(request): if request.method == 'POST': qp = DVAPQLProcess() qp.create_from_request(request) qp.launch() qp.wait() qp.collect() return JsonResponse(data={'task_id': "", 'primary_key': qp.process.pk, 'results': qp.context['results'], 'regions': qp.context['regions'], 'url': '{}queries/{}.png'.format(settings.MEDIA_URL, qp.process.pk) }) def home(request): if request.user.is_authenticated: return redirect("app") else: return render(request, 'home.html', {}) def paper(request): return render(request, 'paper.html', {}) @user_passes_test(user_check) def index(request, query_pk=None, frame_pk=None, detection_pk=None): if request.method == 'POST': form = UploadFileForm(request.POST, request.FILES) user = request.user if request.user.is_authenticated else None if form.is_valid(): handle_uploaded_file(request.FILES['file'], form.cleaned_data['name'], user=user,rate=form.cleaned_data['nth'], rescale=form.cleaned_data['rescale'] if 'rescale' in form.cleaned_data else 0) return redirect('video_list') else: raise ValueError else: form = UploadFileForm() context = {'form': form} context['detectors'] = Detector.objects.all() context['indexer_retrievers'] = [] for i in Indexer.objects.all(): for r in Retriever.objects.all(): if 'indexer_shasum' in r.source_filters and r.source_filters['indexer_shasum'] == i.shasum and r.last_built: context['indexer_retrievers'].append(('{} > {} retriever {} (pk:{})'.format(i.name, r.get_algorithm_display(),r.name,r.pk), '{}_{}'.format(i.pk,r.pk))) if query_pk: previous_query = DVAPQL.objects.get(pk=query_pk) context['initial_url'] = '{}queries/{}.png'.format(settings.MEDIA_URL, query_pk) elif frame_pk: frame = Frame.objects.get(pk=frame_pk) context['initial_url'] = '{}{}/frames/{}.jpg'.format(settings.MEDIA_URL, frame.video.pk, frame.frame_index) elif detection_pk: detection = Region.objects.get(pk=detection_pk) context['initial_url'] = '{}{}/regions/{}.jpg'.format(settings.MEDIA_URL, detection.video.pk, detection.pk) context['frame_count'] = Frame.objects.count() context['query_count'] = DVAPQL.objects.filter(process_type=DVAPQL.QUERY).count() context['process_count'] = DVAPQL.objects.filter(process_type=DVAPQL.PROCESS).count() context['index_entries_count'] = IndexEntries.objects.count() context['external_servers_count'] = VDNServer.objects.count() context['task_events_count'] = TEvent.objects.count() context['pending_tasks'] = TEvent.objects.all().filter(started=False, errored=False).count() context['running_tasks'] = TEvent.objects.all().filter(started=True, completed=False, errored=False).count() context['successful_tasks'] = TEvent.objects.all().filter(started=True, completed=True).count() context['errored_tasks'] = TEvent.objects.all().filter(errored=True).count() context['video_count'] = Video.objects.count() context['index_entries'] = IndexEntries.objects.all() context['region_count'] = Region.objects.all().count() context['tube_count'] = Tube.objects.all().count() context["videos"] = Video.objects.all().filter() context['detector_count'] = Detector.objects.all().count() context['rate'] = defaults.DEFAULT_RATE return render(request, 'dashboard.html', context) @user_passes_test(user_check) def assign_video_labels(request): if request.method == 'POST': video = Video.objects.get(pk=request.POST.get('video_pk')) spec = [] for k in request.POST.get('labels').split(','): if k.strip(): spec.append({ 'MODEL':'VideoLabel', 'spec':{'video_id':video.pk,'label_id':Label.objects.get_or_create(name=k,set="UI")[0].id} }) p = DVAPQLProcess() p.create_from_json({ 'process_type':DVAPQL.PROCESS, 'create':spec, },user=request.user if request.user.is_authenticated else None) p.launch() return redirect('video_detail', pk=video.pk) else: raise NotImplementedError @user_passes_test(user_check) def annotate(request, frame_pk): context = {'frame': None, 'detection': None, 'existing': []} frame = None frame = Frame.objects.get(pk=frame_pk) context['frame'] = frame context['initial_url'] = '{}{}/frames/{}.jpg'.format(settings.MEDIA_URL, frame.video.pk, frame.frame_index) context['previous_frame'] = Frame.objects.filter(video=frame.video, frame_index__lt=frame.frame_index).order_by( '-frame_index')[0:1] context['next_frame'] = Frame.objects.filter(video=frame.video, frame_index__gt=frame.frame_index).order_by( 'frame_index')[0:1] context['detections'] = Region.objects.filter(frame=frame, region_type=Region.DETECTION) for d in Region.objects.filter(frame=frame): temp = { 'x': d.x, 'y': d.y, 'h': d.h, 'w': d.w, 'pk': d.pk, 'box_type': "detection" if d.region_type == d.DETECTION else 'annotation', 'label': d.object_name, 'full_frame': d.full_frame, 'detection_pk': None } context['existing'].append(temp) context['existing'] = json.dumps(context['existing']) if request.method == 'POST': form = AnnotationForm(request.POST) if form.is_valid(): applied_tags = form.cleaned_data['tags'].split(',') if form.cleaned_data['tags'] else [] create_annotation(form, form.cleaned_data['object_name'], applied_tags, frame) return JsonResponse({'status': True}) else: raise ValueError, form.errors return render(request, 'annotate.html', context) @user_passes_test(user_check) def annotate_entire_frame(request, frame_pk): frame = Frame.objects.get(pk=frame_pk) annotation = None if request.method == 'POST': if request.POST.get('text').strip() \ or request.POST.get('metadata').strip() \ or request.POST.get('object_name', None): annotation = Region() annotation.region_type = Region.ANNOTATION annotation.x = 0 annotation.y = 0 annotation.h = 0 annotation.w = 0 annotation.full_frame = True annotation.text = request.POST.get('text') annotation.metadata = request.POST.get('metadata') annotation.object_name = request.POST.get('object_name', 'frame_metadata') annotation.frame = frame annotation.video = frame.video annotation.save() for label_name in request.POST.get('tags').split(','): if label_name.strip(): if annotation: dl = RegionLabel() dl.video = frame.video dl.frame = frame dl.label = Label.objects.get_or_create(name=label_name,set="UI")[0] dl.region = annotation dl.save() else: dl = FrameLabel() dl.video = frame.video dl.frame = frame dl.label = Label.objects.get_or_create(name=label_name,set="UI")[0] dl.save() return redirect("frame_detail", pk=frame.pk) @user_passes_test(user_check) def yt(request): if request.method == 'POST': form = YTVideoForm(request.POST, request.FILES) user = request.user if request.user.is_authenticated else None if form.is_valid(): rate = form.cleaned_data['nth'] rescale = form.cleaned_data['rescale'] if 'rescale' in form.cleaned_data else 0 video = handle_video_url(form.cleaned_data['name'], form.cleaned_data['url'], user=user) process_spec = { 'process_type': DVAPQL.PROCESS, 'tasks': [{'video_id': video.pk, 'operation': 'perform_import', 'arguments': {'source': "URL", 'next_tasks':[{'video_id': video.pk, 'operation': 'perform_video_segmentation', 'arguments': { 'next_tasks': [ {'operation': 'perform_video_decode', 'arguments': { 'rate': rate, 'rescale': rescale, 'segments_batch_size': defaults.DEFAULT_SEGMENTS_BATCH_SIZE, 'next_tasks': defaults.DEFAULT_PROCESSING_PLAN_VIDEO } } ]}, },] } },] } p = DVAPQLProcess() p.create_from_json(process_spec, user) p.launch() else: raise ValueError else: raise NotImplementedError return redirect('video_list') @user_passes_test(user_check) def segment_by_index(request,video_pk,segment_index): segment = Segment.objects.get(video_id=video_pk,segment_index=segment_index) return redirect('segment_detail',pk=segment.pk) @user_passes_test(user_check) def export_video(request): if request.method == 'POST': pk = request.POST.get('video_id') video = Video.objects.get(pk=pk) export_method = request.POST.get('export_method') if video: if export_method == 's3': key = request.POST.get('key') bucket = request.POST.get('bucket') region = request.POST.get('region','us-east-1') process_spec = {'process_type':DVAPQL.PROCESS, 'tasks':[ { 'video_id':video.pk, 'operation':'perform_export', 'arguments': {'key':key,'bucket':bucket,'region':region,'destination':'S3'} }, ]} else: process_spec = {'process_type':DVAPQL.PROCESS, 'tasks':[ { 'video_id':video.pk, 'operation':'perform_export', 'arguments':{'destination':'FILE'} }, ] } p = DVAPQLProcess() p.create_from_json(process_spec) p.launch() return redirect('video_list') else: raise NotImplementedError @user_passes_test(user_check) def coarse_code_detail(request, pk, coarse_code): coarse_code = coarse_code.replace('_', ' ') context = { 'code': coarse_code, 'objects': LOPQCodes.objects.all().filter(coarse_text=coarse_code, clusters_id=pk) } return render(request, 'coarse_code_details.html', context) @user_passes_test(user_check) def status(request): context = {} context['logs'] = [] for fname in glob.glob('logs/*.log'): context['logs'].append((fname,file(fname).read())) return render(request, 'status.html', context) @user_passes_test(user_check) def management(request): timeout = 1.0 context = { 'timeout':timeout, 'actions':ManagementAction.objects.all(), 'state':SystemState.objects.all().order_by('-created')[:100] } if request.method == 'POST': op = request.POST.get("op","") host_name = request.POST.get("host_name","").strip() queue_name = request.POST.get("queue_name","").strip() if op =="list_workers": context["queues"] = app.control.inspect(timeout=timeout).active_queues() elif op == "list": t = app.send_task('manage_host', args=[op, ], exchange='qmanager') t.wait(timeout=timeout) elif op == "gpuinfo": t = app.send_task('manage_host', args=[op, ], exchange='qmanager') t.wait(timeout=timeout) elif op == "launch": t = app.send_task('manage_host', args=[op,host_name,queue_name],exchange='qmanager') t.wait(timeout=timeout) return render(request, 'management.html', context) @user_passes_test(user_check) def training(request): context = {} context["videos"] = Video.objects.all().filter() context["detectors"] = Detector.objects.all() return render(request, 'training.html', context) @user_passes_test(user_check) def textsearch(request): context = {'results': {}, "videos": Video.objects.all().filter()} q = request.GET.get('q') if q: offset = int(request.GET.get('offset',0)) delta = int(request.GET.get('delta',25)) limit = offset + delta context['q'] = q context['next'] = limit context['delta'] = delta context['offset'] = offset context['limit'] = limit if request.GET.get('regions'): context['results']['regions_meta'] = Region.objects.filter(text__search=q)[offset:limit] context['results']['regions_name'] = Region.objects.filter(object_name__search=q)[offset:limit] if request.GET.get('frames'): context['results']['frames_name'] = Frame.objects.filter(name__search=q)[offset:limit] context['results']['frames_subdir'] = Frame.objects.filter(subdir__search=q)[offset:limit] if request.GET.get('labels'): context['results']['labels'] = Label.objects.filter(name__search=q)[offset:limit] return render(request, 'textsearch.html', context) @user_passes_test(user_check) def retrievers(request): context = {} context['algorithms'] = {k.name for k in Indexer.objects.all()} context['index_entries'] = IndexEntries.objects.all() context['retrievers'] = Retriever.objects.all() return render(request, 'retrievers.html', context) @user_passes_test(user_check) def create_retriever(request): if request.method == 'POST': spec = {} if request.POST.get('retriever_type') == Retriever.LOPQ: v = request.POST.get('v') m = request.POST.get('m') components = request.POST.get('components') sub = request.POST.get('sub') spec['name'] = request.POST.get('name') spec['algorithm'] = Retriever.LOPQ args = {} args['components']= components args['sub']= sub args['m']= m args['v']= v spec['arguments'] = args if request.POST.get('source_filters',None): spec['source_filters'] = json.loads(request.POST.get('source_filter','{}')) else: spec['source_filters'] = {'indexer_shasum':Indexer.objects.get(name=request.POST.get('algorithm')).shasum} next_tasks = [{'operation': "perform_retriever_creation",'arguments': {'retriever_pk':'__pk__'},},] elif request.POST.get('retriever_type') == Retriever.EXACT: spec['name'] = request.POST.get('name') spec['last_built'] = '__timezone.now__' spec['source_filters'] = json.loads(request.POST.get('source_filters', '{}')) spec['algorithm'] = Retriever.EXACT next_tasks = [] else: raise ValueError if spec: p = DVAPQLProcess() p.create_from_json(j={"process_type": DVAPQL.PROCESS, "create":[{'MODEL':'Retriever','spec':spec,'tasks': next_tasks}], }, user=request.user if request.user.is_authenticated else None) p.launch() return redirect('retrievers') @user_passes_test(user_check) def submit_process(request): if request.method == 'POST': process_pk = request.POST.get('process_pk',None) if process_pk is None: p = DVAPQLProcess() p.create_from_json(j=json.loads(request.POST.get('script')), user=request.user if request.user.is_authenticated else None) p.launch() else: p = DVAPQLProcess(process=DVAPQL.objects.get(pk=process_pk)) p.launch() return redirect("process_detail",pk=p.process.pk) @user_passes_test(user_check) def validate_process(request): if request.method == 'POST': p = DVAPQLProcess() p.create_from_json(j=json.loads(request.POST.get('script')), user=request.user if request.user.is_authenticated else None) p.validate() return redirect("process_detail",pk=p.process.pk) @user_passes_test(user_check) def delete_object(request): if request.method == 'POST': pk = request.POST.get('pk') if request.POST.get('object_type') == 'annotation': annotation = Region.objects.get(pk=pk) if annotation.region_type == Region.ANNOTATION: annotation.delete() return JsonResponse({'status': True}) @user_passes_test(force_user_check) def security(request): context = {} context['username'] = request.user.username token, created = Token.objects.get_or_create(user=request.user if request.user.is_authenticated else None) context['token'] = token return render(request, 'security.html', context=context) @user_passes_test(force_user_check) def expire_token(request): # TODO Check if this is correct if request.method == 'POST': if request.POST.get('expire',False): token, created = Token.objects.get_or_create(user=request.user if request.user.is_authenticated else None) if not created: token.delete() return redirect("security") @user_passes_test(user_check) def import_dataset(request): if request.method == 'POST': url = request.POST.get('dataset_url') server = VDNServer.objects.get(pk=request.POST.get('server_pk')) user = request.user if request.user.is_authenticated else None cached_response = server.last_response_datasets[int(request.POST.get('dindex'))] import_vdn_dataset_url(server, url, user, cached_response) else: raise NotImplementedError return redirect('video_list') @user_passes_test(user_check) def import_detector(request): if request.method == 'POST': url = request.POST.get('detector_url') server = VDNServer.objects.get(pk=request.POST.get('server_pk')) user = request.user if request.user.is_authenticated else None cached_response = server.last_response_detectors[int(request.POST.get('dindex'))] import_vdn_detector_url(server, url, user, cached_response) else: raise NotImplementedError return redirect('models') @user_passes_test(user_check) def import_s3(request): if request.method == 'POST': keys = request.POST.get('key') region = request.POST.get('region') bucket = request.POST.get('bucket') rate = request.POST.get('rate',defaults.DEFAULT_RATE) rescale = request.POST.get('rescale',defaults.DEFAULT_RESCALE) user = request.user if request.user.is_authenticated else None create = [] for key in keys.strip().split('\n'): tasks =[] key = key.strip() if key: extract_task = {'arguments': {'rate': rate, 'rescale': rescale, 'next_tasks': defaults.DEFAULT_PROCESSING_PLAN_DATASET}, 'operation': 'perform_dataset_extraction'} segment_decode_task = {'operation': 'perform_video_segmentation', 'arguments': { 'next_tasks': [ {'operation': 'perform_video_decode', 'arguments': { 'rate': rate, 'rescale': rescale, 'segments_batch_size':defaults.DEFAULT_SEGMENTS_BATCH_SIZE, 'next_tasks': defaults.DEFAULT_PROCESSING_PLAN_VIDEO } } ]}, } if key.endswith('.dva_export.zip'): next_tasks = [] elif key.endswith('.zip'): next_tasks = [extract_task,] else: next_tasks = [segment_decode_task,] tasks.append({'video_id':'__pk__', 'operation':'perform_import', 'arguments':{'key':key, 'bucket':bucket, 'region':region, 'source':'S3', 'next_tasks':next_tasks} }) create.append({'MODEL': 'Video', 'spec': {'uploader_id': user.pk if user else None, 'name': "pending S3 import {} s3://{}/{}".format(region, bucket, key)}, 'tasks': tasks }) process_spec = {'process_type': DVAPQL.PROCESS, 'create':create} p = DVAPQLProcess() p.create_from_json(process_spec,user) p.launch() else: raise NotImplementedError return redirect('video_list') @user_passes_test(user_check) def external(request): if request.method == 'POST': pk = request.POST.get('server_pk') try: pull_vdn_list(pk) except: pass context = { 'servers': VDNServer.objects.all(), 'available_datasets': {server: server.last_response_datasets for server in VDNServer.objects.all()}, 'available_detectors': {server: server.last_response_detectors for server in VDNServer.objects.all()}, } return render(request, 'external_data.html', context) @user_passes_test(user_check) def retry_task(request): pk = request.POST.get('pk') event = TEvent.objects.get(pk=int(pk)) spec = { 'process_type':DVAPQL.PROCESS, 'tasks':[ { 'operation':event.operation, 'arguments':event.arguments } ] } p = DVAPQLProcess() p.create_from_json(spec) p.launch() return redirect('/processes/') @user_passes_test(user_check) def delete_video(request): if request.user.is_staff: # currently only staff can delete video_pk = request.POST.get('video_id') delete_video_object(video_pk,request.user) return redirect('video_list') else: return redirect('accounts/login/') @user_passes_test(user_check) def rename_video(request): if request.user.is_staff: # currently only staff can rename video_pk = request.POST.get('video_id') name = request.POST.get('name') v = Video.objects.get(pk=int(video_pk)) v.name = name v.save() return redirect('video_list') else: return redirect('accounts/login/') @user_passes_test(user_check) def models(request): context = { 'visual_index_list': Indexer.objects.all(), 'index_entries': IndexEntries.objects.all(), "videos": Video.objects.all().filter(), "region_types": Region.REGION_TYPES, "detectors": Detector.objects.all() } detector_stats = [] context["detectors"] = Detector.objects.all() return render(request, 'models.html', context) @user_passes_test(user_check) def index_video(request): if request.method == 'POST': filters = { 'region_type__in': request.POST.getlist('region_type__in', []), 'w__gte': int(request.POST.get('w__gte')), 'h__gte': int(request.POST.get('h__gte')) } for optional_key in ['text__contains', 'object_name__contains', 'object_name']: if request.POST.get(optional_key, None): filters[optional_key] = request.POST.get(optional_key) for optional_key in ['h__lte', 'w__lte']: if request.POST.get(optional_key, None): filters[optional_key] = int(request.POST.get(optional_key)) args = {'filters':filters,'index':request.POST.get('visual_index_name')} p = DVAPQLProcess() spec = { 'process_type':DVAPQL.PROCESS, 'tasks':[ { 'operation':'perform_indexing', 'arguments':args, 'video_id':request.POST.get('video_id') } ] } user = request.user if request.user.is_authenticated else None p.create_from_json(spec,user) p.launch() redirect('process_detail',pk=p.process.pk) else: raise ValueError @user_passes_test(user_check) def detect_objects(request): if request.method == 'POST': detector_pk = request.POST.get('detector_pk') video_pk = request.POST.get('video_pk') p = DVAPQLProcess() p.create_from_json(j={ "process_type":DVAPQL.PROCESS, "tasks":[{'operation':"perform_detection", 'arguments':{'detector_pk': int(detector_pk),'detector':"custom"}, 'video_id':video_pk}] },user=request.user if request.user.is_authenticated else None) p.launch() return redirect('process_detail',pk=p.process.pk) else: raise ValueError @user_passes_test(user_check) def train_detector(request): if request.method == 'POST': args = request.POST.get('args') args = json.loads(args) if args.strip() else {} args['name'] = request.POST.get('name') args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()] args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()] args['excluded_videos'] = request.POST.getlist('excluded_videos') args['detector_pk'] = '__pk__' p = DVAPQLProcess() p.create_from_json(j={ "process_type":DVAPQL.PROCESS, "create":[ { 'MODEL':'Detector', 'spec':{ 'name':args['name'], 'arguments':json.dumps(args), 'algorithm':'yolo' }, "tasks":[ {'operation':"perform_detector_training", 'arguments':args, }] } ] },user=request.user if request.user.is_authenticated else None) p.launch() return redirect('process_detail', pk=p.process.pk) # elif request.POST.get('action') == 'estimate': # args = request.POST.get('args') # args = json.loads(args) if args.strip() else {} # args['name'] = request.POST.get('name') # args['labels'] = [k.strip() for k in request.POST.get('labels').split(',') if k.strip()] # args['object_names'] = [k.strip() for k in request.POST.get('object_names').split(',') if k.strip()] # args['excluded_videos'] = request.POST.getlist('excluded_videos') # labels = set(args['labels']) if 'labels' in args else set() # object_names = set(args['object_names']) if 'object_names' in args else set() # class_distribution, class_names, rboxes, rboxes_set, # frames, i_class_names = create_detector_dataset(object_names, labels) # context["estimate"] = { # 'args':args, # 'class_distribution':class_distribution, # 'class_names':class_names, # 'rboxes':rboxes, # 'rboxes_set':rboxes_set, # 'frames':frames, # 'i_class_names':i_class_names # } else: raise ValueError

[ "[email protected]" ]

[email protected]

c2374a4c2a0f8125c15d4daa87dc1bee036aa59b

ef54d37f8a3303013ca7469871a320d303957ed7

/robo4.2/fusion/tests/wpst_crm/ci_fit/config/I11/CI-FIT-16-CI-FIT-17_old.py

573ae00df59b8aacdb29e3a48d49f44cd672ae90

[]

no_license

richa92/Jenkin_Regression_Testing

d18badfcf16bda682dfe7bcbbd66f54a9a27a58d

24a74926170cbdfafa47e972644e2fe5b627d8ff

refs/heads/master

2020-07-12T10:01:59.099137

2019-08-27T12:14:53

null

0

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py

def make_range_list(vrange): rlist = [] for x in xrange(vrange['start'], (vrange['end'] + 1)): rlist.append(vrange['prefix'] + str(x) + vrange['suffix']) return rlist admin_credentials = {'userName': 'Administrator', 'password': 'hpvse123'} vcenter = {'server': '15.186.4.110', 'user': 'Administrator', 'password': 'Compaq123'} appliance = {'type': 'ApplianceNetworkConfiguration', 'applianceNetworks': [{'device': 'eth0', 'macAddress': None, 'interfaceName': 'hpqcorpnet', 'activeNode': '1', 'unconfigure': False, 'ipv4Type': 'STATIC', 'ipv4Subnet': '255.255.224.0', 'ipv4Gateway': '15.186.0.1', 'ipv4NameServers': ['16.110.135.51', '16.110.135.52'], 'app1Ipv4Addr': '15.186.7.140', 'ipv6Type': 'UNCONFIGURE', 'hostname': 'I11-CI-FIT-16-17.austin.hp.com', 'confOneNode': True, 'domainName': 'usa.hp.com', 'aliasDisabled': True, }], } timeandlocale = {'type': 'TimeAndLocale', 'dateTime': None, 'timezone': 'UTC', 'ntpServers': ['ntp.hp.net'], 'locale': 'en_US.UTF-8'} ranges = [{'name': 'CI-FIT-16-MAC', 'type': 'Range', 'category': 'id-range-VMAC', 'rangeCategory': 'CUSTOM', 'startAddress': 'A2:66:66:00:00:00', 'endAddress': 'A2:66:66:0F:FF:FF', 'enabled': True}, {'name': 'CI-FIT-16-WWN', 'type': 'Range', 'category': 'id-range-VWWN', 'rangeCategory': 'CUSTOM', 'startAddress': '26:66:6C:66:00:0F:FF:FF', 'endAddress': '26:66:6C:66:00:0F:FF:FF', 'enabled': True}, {'name': 'CI-FIT-16-SN', 'type': 'Range', 'category': 'id-range-VSN', 'rangeCategory': 'CUSTOM', 'startAddress': 'VCUGGG0000', 'endAddress': 'VCUGGG0ZZZ', 'enabled': True}] users = [{'userName': 'Serveradmin', 'password': 'Serveradmin', 'fullName': 'Serveradmin', 'roles': ['Server administrator'], 'emailAddress': '[email protected]', 'officePhone': '512-555-0004', 'mobilePhone': '512-500-0004', 'type': 'UserAndRoles'}, {'userName': 'Networkadmin', 'password': 'Networkadmin', 'fullName': 'Networkadmin', 'roles': ['Network administrator'], 'emailAddress': '[email protected]', 'officePhone': '512-555-0003', 'mobilePhone': '512-500-0003', 'type': 'UserAndRoles'}, {'userName': 'Backupadmin', 'password': 'Backupadmin', 'fullName': 'Backupadmin', 'roles': ['Backup administrator'], 'emailAddress': '[email protected]', 'officePhone': '512-555-0003', 'mobilePhone': '512-500-0003', 'type': 'UserAndRoles'}, {'userName': 'Noprivledge', 'password': 'Noprivledge', 'fullName': 'Noprivledge', 'roles': ['Read only'], 'emailAddress': '[email protected]', 'officePhone': '512-555-0003', 'mobilePhone': '512-500-0003', 'type': 'UserAndRoles'} ] licenses = [{'key': 'YCDE D9MA H9P9 8HUZ U7B5 HWW5 Y9JL KMPL MHND 7AJ9 DXAU 2CSM GHTG L762 LFH6 F4R4 KJVT D5KM EFVW DT5J 83HJ 8VC6 AK2P 3EW2 L9YE HUNJ TZZ7 MB5X 82Z5 WHEF GE4C LUE3 BKT8 WXDG NK6Y C4GA HZL4 XBE7 3VJ6 2MSU 4ZU9 9WGG CZU7 WE4X YN44 CH55 KZLG 2F4N A8RJ UKEG 3F9V JQY5 "423207356 HPOV-NFR2 HP_OneView_w/o_iLO_16_Seat_NFR H3TCJHCGAYAY"'}, {'key': 'QC3C A9MA H9PQ GHVZ U7B5 HWW5 Y9JL KMPL 2HVF 4FZ9 DXAU 2CSM GHTG L762 7JX5 V5FU KJVT D5KM EFVW DV5J 43LL PSS6 AK2P 3EW2 T9YE XUNJ TZZ7 MB5X 82Z5 WHEF GE4C LUE3 BKT8 WXDG NK6Y C4GA HZL4 XBE7 3VJ6 2MSU 4ZU9 9WGG CZU7 WE4X YN44 CH55 KZLG 2F4N A8RJ UKEG 3F9V JQY5 "423207566 HPOV-NFR2 HP_OneView_w/o_iLO_48_Seat_NFR 6H72JHCGY5AU"'} ] ethernet_networks = [{'name': 'IC', 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': True, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Untagged'}, {'name': 'Tunnel1', 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': True, 'privateNetwork': True, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tunnel'}, {'name': 'Tunnel2', 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': True, 'privateNetwork': True, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tunnel'}, ] ethernet_ranges = [{'prefix': 'net_', 'suffix': '', 'start': 2, 'end': 45, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'}, {'prefix': 'net_', 'suffix': '-O', 'start': 2, 'end': 45, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'}, {'prefix': 'net_', 'suffix': '-E', 'start': 2, 'end': 45, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'}, {'prefix': 'net_', 'suffix': '', 'start': 3985, 'end': 4000, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'}, {'prefix': 'net_', 'suffix': '-O', 'start': 3985, 'end': 4000, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'}, {'prefix': 'net_', 'suffix': '-E', 'start': 3985, 'end': 4000, 'name': None, 'type': 'ethernet-networkV3', 'vlanId': None, 'purpose': 'General', 'smartLink': False, 'privateNetwork': False, 'connectionTemplateUri': None, 'ethernetNetworkType': 'Tagged'} ] fc_networks = [{'name': 'SAN-3-A', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-4-B', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-1-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-1-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-1-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-1-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-2-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-2-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-2-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-2-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-3-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-3-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-3-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-3-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-4-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-4-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-4-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-4-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-5-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-5-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-5-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-5-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-6-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-6-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-6-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-6-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-7-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-7-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-7-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-7-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-8-1-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-8-2-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-8-3-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': False, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, {'name': 'SAN-8-4-DA', 'type': 'fc-networkV2', 'linkStabilityTime': 30, 'autoLoginRedistribution': True, 'connectionTemplateUri': None, 'managedSanUri': None, 'fabricType': 'DirectAttach'}, ] network_sets = [{'name': 'NS_4000', 'type': 'network-set', 'networkUris': ['net_4000'], 'nativeNetworkUri': 'net_4000'}, {'name': 'NS_3999', 'type': 'network-set', 'networkUris': ['net_3999'], 'nativeNetworkUri': 'net_3999'}, {'name': 'NS_3998', 'type': 'network-set', 'networkUris': ['net_3998'], 'nativeNetworkUri': 'net_3998'}, {'name': 'NS_3997', 'type': 'network-set', 'networkUris': ['net_3997'], 'nativeNetworkUri': 'net_3997'}, {'name': 'NS_3996', 'type': 'network-set', 'networkUris': ['net_3996'], 'nativeNetworkUri': 'net_3996'}, {'name': 'NS_3995', 'type': 'network-set', 'networkUris': ['net_3995'], 'nativeNetworkUri': 'net_3995'}, {'name': 'NS_3994', 'type': 'network-set', 'networkUris': ['net_3994'], 'nativeNetworkUri': 'net_3994'}, {'name': 'NS_3993', 'type': 'network-set', 'networkUris': ['net_3993'], 'nativeNetworkUri': 'net_3993'}, {'name': 'NS_3992', 'type': 'network-set', 'networkUris': ['net_3992'], 'nativeNetworkUri': 'net_3992'}, {'name': 'NS_3991', 'type': 'network-set', 'networkUris': ['net_3991'], 'nativeNetworkUri': 'net_3991'}, {'name': 'NS_3990', 'type': 'network-set', 'networkUris': ['net_3990'], 'nativeNetworkUri': 'net_3990'}, {'name': 'NS_3989', 'type': 'network-set', 'networkUris': ['net_3989'], 'nativeNetworkUri': 'net_3989'}, {'name': 'NS_3988', 'type': 'network-set', 'networkUris': ['net_3988'], 'nativeNetworkUri': 'net_3988'}, {'name': 'NS_3987', 'type': 'network-set', 'networkUris': ['net_3987'], 'nativeNetworkUri': 'net_3987'}, {'name': 'NS_3986', 'type': 'network-set', 'networkUris': ['net_3986'], 'nativeNetworkUri': 'net_3986'}, {'name': 'NS_3985', 'type': 'network-set', 'networkUris': ['net_3985'], 'nativeNetworkUri': 'net_3985'}, {'name': 'NS_4000-O', 'type': 'network-set', 'networkUris': ['net_4000-O'], 'nativeNetworkUri': 'net_4000-O'}, {'name': 'NS_3999-O', 'type': 'network-set', 'networkUris': ['net_3999-O'], 'nativeNetworkUri': 'net_3999-O'}, {'name': 'NS_3998-O', 'type': 'network-set', 'networkUris': ['net_3998-O'], 'nativeNetworkUri': 'net_3998-O'}, {'name': 'NS_3997-O', 'type': 'network-set', 'networkUris': ['net_3997-O'], 'nativeNetworkUri': 'net_3997-O'}, {'name': 'NS_3996-O', 'type': 'network-set', 'networkUris': ['net_3996-O'], 'nativeNetworkUri': 'net_3996-O'}, {'name': 'NS_3995-O', 'type': 'network-set', 'networkUris': ['net_3995-O'], 'nativeNetworkUri': 'net_3995-O'}, {'name': 'NS_3994-O', 'type': 'network-set', 'networkUris': ['net_3994-O'], 'nativeNetworkUri': 'net_3994-O'}, {'name': 'NS_3993-O', 'type': 'network-set', 'networkUris': ['net_3993-O'], 'nativeNetworkUri': 'net_3993-O'}, {'name': 'NS_3992-O', 'type': 'network-set', 'networkUris': ['net_3992-O'], 'nativeNetworkUri': 'net_3992-O'}, {'name': 'NS_3991-O', 'type': 'network-set', 'networkUris': ['net_3991-O'], 'nativeNetworkUri': 'net_3991-O'}, {'name': 'NS_3990-O', 'type': 'network-set', 'networkUris': ['net_3990-O'], 'nativeNetworkUri': 'net_3990-O'}, {'name': 'NS_3989-O', 'type': 'network-set', 'networkUris': ['net_3989-O'], 'nativeNetworkUri': 'net_3989-O'}, {'name': 'NS_3988-O', 'type': 'network-set', 'networkUris': ['net_3988-O'], 'nativeNetworkUri': 'net_3988-O'}, {'name': 'NS_3987-O', 'type': 'network-set', 'networkUris': ['net_3987-O'], 'nativeNetworkUri': 'net_3987-O'}, {'name': 'NS_3986-O', 'type': 'network-set', 'networkUris': ['net_3986-O'], 'nativeNetworkUri': 'net_3986-O'}, {'name': 'NS_3985-O', 'type': 'network-set', 'networkUris': ['net_3985-O'], 'nativeNetworkUri': 'net_3985-O'}, {'name': 'NS_4000-E', 'type': 'network-set', 'networkUris': ['net_4000-E'], 'nativeNetworkUri': 'net_4000-E'}, {'name': 'NS_3999-E', 'type': 'network-set', 'networkUris': ['net_3999-E'], 'nativeNetworkUri': 'net_3999-E'}, {'name': 'NS_3998-E', 'type': 'network-set', 'networkUris': ['net_3998-E'], 'nativeNetworkUri': 'net_3998-E'}, {'name': 'NS_3997-E', 'type': 'network-set', 'networkUris': ['net_3997-E'], 'nativeNetworkUri': 'net_3997-E'}, {'name': 'NS_3996-E', 'type': 'network-set', 'networkUris': ['net_3996-E'], 'nativeNetworkUri': 'net_3996-E'}, {'name': 'NS_3995-E', 'type': 'network-set', 'networkUris': ['net_3995-E'], 'nativeNetworkUri': 'net_3995-E'}, {'name': 'NS_3994-E', 'type': 'network-set', 'networkUris': ['net_3994-E'], 'nativeNetworkUri': 'net_3994-E'}, {'name': 'NS_3993-E', 'type': 'network-set', 'networkUris': ['net_3993-E'], 'nativeNetworkUri': 'net_3993-E'}, {'name': 'NS_3992-E', 'type': 'network-set', 'networkUris': ['net_3992-E'], 'nativeNetworkUri': 'net_3992-E'}, {'name': 'NS_3991-E', 'type': 'network-set', 'networkUris': ['net_3991-E'], 'nativeNetworkUri': 'net_3991-E'}, {'name': 'NS_3990-E', 'type': 'network-set', 'networkUris': ['net_3990-E'], 'nativeNetworkUri': 'net_3990-E'}, {'name': 'NS_3989-E', 'type': 'network-set', 'networkUris': ['net_3989-E'], 'nativeNetworkUri': 'net_3989-E'}, {'name': 'NS_3988-E', 'type': 'network-set', 'networkUris': ['net_3988-E'], 'nativeNetworkUri': 'net_3988-E'}, {'name': 'NS_3987-E', 'type': 'network-set', 'networkUris': ['net_3987-E'], 'nativeNetworkUri': 'net_3987-E'}, {'name': 'NS_3986-E', 'type': 'network-set', 'networkUris': ['net_3986-E'], 'nativeNetworkUri': 'net_3986-E'}, {'name': 'NS_3985-E', 'type': 'network-set', 'networkUris': ['net_3985-E'], 'nativeNetworkUri': 'net_3985-E'} ] #network_set_ranges = [{'prefix': 'net_', 'suffix': '-O', 'start': 2502, 'end': 2515, 'name': 'VlanTrunk1-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2502-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2502, 'end': 2515, 'name': 'VlanTrunk1-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2502-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2516, 'end': 2530, 'name': 'VlanTrunk2-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2516-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2516, 'end': 2530, 'name': 'VlanTrunk2-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2516-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2531, 'end': 2545, 'name': 'VlanTrunk3-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2531-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2531, 'end': 2545, 'name': 'VlanTrunk3-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2531-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2546, 'end': 2560, 'name': 'VlanTrunk4-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2546-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2546, 'end': 2560, 'name': 'VlanTrunk4-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2546-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2561, 'end': 2575, 'name': 'VlanTrunk5-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2561-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2561, 'end': 2575, 'name': 'VlanTrunk5-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2561-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2576, 'end': 2590, 'name': 'VlanTrunk6-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2576-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2576, 'end': 2590, 'name': 'VlanTrunk6-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2576-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2591, 'end': 2605, 'name': 'VlanTrunk7-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2591-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2591, 'end': 2605, 'name': 'VlanTrunk7-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2591-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2606, 'end': 2620, 'name': 'VlanTrunk8-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2606-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2606, 'end': 2620, 'name': 'VlanTrunk8-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2606-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2621, 'end': 2635, 'name': 'VlanTrunk9-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2621-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2621, 'end': 2635, 'name': 'VlanTrunk9-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2621-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2636, 'end': 2650, 'name': 'VlanTrunk10-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2636-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2636, 'end': 2650, 'name': 'VlanTrunk10-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2636-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2651, 'end': 2665, 'name': 'VlanTrunk11-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2651-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2651, 'end': 2665, 'name': 'VlanTrunk11-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2651-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2666, 'end': 2680, 'name': 'VlanTrunk12-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2666-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2666, 'end': 2680, 'name': 'VlanTrunk12-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2666-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2681, 'end': 2695, 'name': 'VlanTrunk13-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2681-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2681, 'end': 2695, 'name': 'VlanTrunk13-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2681-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2696, 'end': 2710, 'name': 'VlanTrunk14-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2696-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2696, 'end': 2710, 'name': 'VlanTrunk14-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2696-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2711, 'end': 2725, 'name': 'VlanTrunk15-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2711-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2711, 'end': 2725, 'name': 'VlanTrunk15-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2711-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2726, 'end': 2750, 'name': 'VlanTrunk16-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2726-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2726, 'end': 2750, 'name': 'VlanTrunk16-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2726-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2751, 'end': 2777, 'name': 'VlanTrunk17-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2751-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2751, 'end': 2777, 'name': 'VlanTrunk17-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2751-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2778, 'end': 2804, 'name': 'VlanTrunk18-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2778-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2778, 'end': 2804, 'name': 'VlanTrunk18-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2778-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2805, 'end': 2831, 'name': 'VlanTrunk19-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2805-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2805, 'end': 2831, 'name': 'VlanTrunk19-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2805-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2832, 'end': 2858, 'name': 'VlanTrunk20-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2832-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2832, 'end': 2858, 'name': 'VlanTrunk20-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2832-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2859, 'end': 2885, 'name': 'VlanTrunk21-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2859-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2859, 'end': 2885, 'name': 'VlanTrunk21-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2859-E'}, # {'prefix': 'net_', 'suffix': '-O', 'start': 2886, 'end': 2992, 'name': 'VlanTrunk22-O', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2886-O'}, # {'prefix': 'net_', 'suffix': '-E', 'start': 2886, 'end': 2992, 'name': 'VlanTrunk22-E', 'type': 'network-set', 'networkUris': None, 'nativeNetworkUri': 'net_2886-E'} # ] enc_group = [{'name': 'F10D-F10D-F10D-F10D', 'type': 'EnclosureGroupV200', 'enclosureTypeUri': '/rest/enclosure-types/c7000', 'stackingMode': 'Enclosure', 'interconnectBayMappingCount': 8, 'configurationScript': None, 'interconnectBayMappings': [{'interconnectBay': 1, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 2, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 3, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 4, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 5, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 6, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 7, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}, {'interconnectBay': 8, 'logicalInterconnectGroupUri': 'LIG:LIG-F10D-F10D-F10D-F10D'}]}, {'name': 'FF-8FC20', 'type': 'EnclosureGroupV200', 'enclosureTypeUri': '/rest/enclosure-types/c7000', 'stackingMode': 'Enclosure', 'interconnectBayMappingCount': 8, 'configurationScript': None, 'interconnectBayMappings': [{'interconnectBay': 1, 'logicalInterconnectGroupUri': 'LIG:LIG-FF'}, {'interconnectBay': 2, 'logicalInterconnectGroupUri': 'LIG:LIG-FF'}, {'interconnectBay': 3, 'logicalInterconnectGroupUri': 'LIG:LIG-FF'}, {'interconnectBay': 4, 'logicalInterconnectGroupUri': 'LIG:LIG-FF'}]}, ] enc = [{'hostname': '15.186.2.186', 'username': 'Administrator', 'password': 'compaq', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'force': False, 'licensingIntent': 'OneView'}, {'hostname': '15.186.2.137', 'username': 'Administrator', 'password': 'compaq', 'enclosureGroupUri': 'EG:FF-8FC20', 'force': False, 'licensingIntent': 'OneView'}, ] uplink_sets = {'IC-a': {'name': 'IC', 'ethernetNetworkType': 'Untagged', 'networkType': 'Ethernet', 'networkUris': ['IC'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X10', 'speed': 'Auto'}]}, 'IC-b': {'name': 'IC', 'ethernetNetworkType': 'Untagged', 'networkType': 'Ethernet', 'networkUris': ['IC'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X6', 'speed': 'Auto'}]}, 'SAN-3-A': {'name': 'SAN-3-A', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-3-A'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '3', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-1-2-DA': {'name': 'SAN-1-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-1-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-1-1-DA': {'name': 'SAN-1-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-1-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-1-2-DA': {'name': 'SAN-1-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-1-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-1-3-DA': {'name': 'SAN-1-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-1-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-1-4-DA': {'name': 'SAN-1-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-1-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-2-1-DA': {'name': 'SAN-2-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-2-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '2', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-2-2-DA': {'name': 'SAN-2-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-2-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '2', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-2-3-DA': {'name': 'SAN-2-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-2-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '2', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-2-4-DA': {'name': 'SAN-2-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-2-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '2', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-3-1-DA': {'name': 'SAN-3-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-3-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '3', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-3-2-DA': {'name': 'SAN-3-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-3-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '3', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-3-3-DA': {'name': 'SAN-3-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-3-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '3', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-3-4-DA': {'name': 'SAN-3-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-3-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '3', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-4-1-DA': {'name': 'SAN-4-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-4-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '4', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-4-2-DA': {'name': 'SAN-4-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-4-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '4', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-4-3-DA': {'name': 'SAN-4-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-4-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '4', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-4-4-DA': {'name': 'SAN-4-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-4-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '4', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-5-1-DA': {'name': 'SAN-5-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-5-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '5', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-5-2-DA': {'name': 'SAN-5-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-5-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '5', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-5-3-DA': {'name': 'SAN-5-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-5-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '5', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-5-4-DA': {'name': 'SAN-5-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-5-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '5', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-6-1-DA': {'name': 'SAN-6-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-6-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '6', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-6-2-DA': {'name': 'SAN-6-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-6-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '6', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-6-3-DA': {'name': 'SAN-6-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-6-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '6', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-6-4-DA': {'name': 'SAN-6-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-6-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '6', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-7-1-DA': {'name': 'SAN-7-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-7-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '7', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-7-2-DA': {'name': 'SAN-7-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-7-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '7', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-7-3-DA': {'name': 'SAN-7-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-7-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '7', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-7-4-DA': {'name': 'SAN-7-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-7-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '7', 'port': 'X4', 'speed': 'Auto'}]}, 'SAN-8-1-DA': {'name': 'SAN-8-1-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-8-1-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '8', 'port': 'X1', 'speed': 'Auto'}]}, 'SAN-8-2-DA': {'name': 'SAN-8-2-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-8-2-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '8', 'port': 'X2', 'speed': 'Auto'}]}, 'SAN-8-3-DA': {'name': 'SAN-8-3-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-8-3-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '8', 'port': 'X3', 'speed': 'Auto'}]}, 'SAN-8-4-DA': {'name': 'SAN-8-4-DA', 'ethernetNetworkType': 'NotApplicable', 'networkType': 'FibreChannel', 'networkUris': ['SAN-8-4-DA'], 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '8', 'port': 'X4', 'speed': 'Auto'}]}, 'BigPipe16': {'name': 'BigPipe16', 'ethernetNetworkType': 'Tagged', 'networkType': 'Ethernet', 'networkUris': make_range_list({'start': 1, 'end': 44, 'prefix':'net_', 'suffix':''}), 'nativeNetworkUri': None, 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X5', 'speed': 'Auto'}, {'bay': '2', 'port': 'X5', 'speed': 'Auto'}, {'bay': '1', 'port': 'X6', 'speed': 'Auto'}, {'bay': '2', 'port': 'X6', 'speed': 'Auto'}]}, 'BigPipe17': {'name': 'BigPipe17', 'ethernetNetworkType': 'Tagged', 'networkType': 'Ethernet', 'networkUris': make_range_list({'start': 1, 'end': 44, 'prefix':'net_', 'suffix':''}), 'nativeNetworkUri': 'net_1', 'mode': 'Auto', 'logicalPortConfigInfos': [{'bay': '1', 'port': 'X1', 'speed': 'Auto'}, {'bay': '2', 'port': 'X1', 'speed': 'Auto'}]} } ligs = [{'name': 'LIG-F10D-F10D-F10D-F10D', 'type': 'logical-interconnect-groupV3', 'enclosureType': 'C7000', 'interconnectMapTemplate': [{'bay': 1, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 2, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 3, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 4, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 5, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 6, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 7, 'type': 'HP VC Flex-10/10D Module'}, {'bay': 8, 'type': 'HP VC Flex-10/10D Module'}, ], 'uplinkSets': [uplink_sets['IC'], # uplink_sets['SAN-1-1-DA'], # uplink_sets['SAN-1-2-DA'], # uplink_sets['SAN-1-3-DA'], # uplink_sets['SAN-1-4-DA'], # uplink_sets['SAN-2-1-DA'], # uplink_sets['SAN-2-2-DA'], # uplink_sets['SAN-2-3-DA'], # uplink_sets['SAN-2-4-DA'], # uplink_sets['SAN-3-1-DA'], # uplink_sets['SAN-3-2-DA'], # uplink_sets['SAN-3-3-DA'], # uplink_sets['SAN-3-4-DA'], # uplink_sets['SAN-4-1-DA'], # uplink_sets['SAN-4-2-DA'], # uplink_sets['SAN-4-3-DA'], # uplink_sets['SAN-4-4-DA'], # uplink_sets['SAN-5-1-DA'], # uplink_sets['SAN-5-2-DA'], # uplink_sets['SAN-5-3-DA'], # uplink_sets['SAN-5-4-DA'], # uplink_sets['SAN-6-1-DA'], # uplink_sets['SAN-6-2-DA'], # uplink_sets['SAN-6-3-DA'], # uplink_sets['SAN-6-4-DA'], # uplink_sets['SAN-7-1-DA'], # uplink_sets['SAN-7-2-DA'], # uplink_sets['SAN-7-3-DA'], # uplink_sets['SAN-7-4-DA'], # uplink_sets['SAN-8-1-DA'], # uplink_sets['SAN-8-2-DA'], # uplink_sets['SAN-8-3-DA'], # uplink_sets['SAN-8-4-DA'], # uplink_sets['BigPipe15-a'], uplink_sets['BigPipe16']], 'stackingMode': None, 'ethernetSettings': None, 'state': 'Active', 'telemetryConfiguration': None, 'snmpConfiguration': None}, {'name': 'LIG-FF-8FC20', 'type': 'logical-interconnect-groupV3', 'enclosureType': 'C7000', 'interconnectMapTemplate': [{'bay': 1, 'type': 'HP VC FlexFabric 10Gb/24-Port Module'}, {'bay': 2, 'type': 'HP VC FlexFabric 10Gb/24-Port Module'}, {'bay': 3, 'type': 'HP VC 8Gb 20-Port FC Module'}, {'bay': 4, 'type': 'HP VC 8Gb 20-Port FC Module'}, ], 'uplinkSets': [uplink_sets['IC'], uplink_sets['BigPipe17']], 'stackingMode': None, 'ethernetSettings': None, 'state': 'Active', 'telemetryConfiguration': None, 'snmpConfiguration': None}, ] telemetry = {'enableTelemetry': True, 'sampleInterval': 400, 'sampleCount': 20} trapDestinations = [{'trapSeverities': ['Major'], 'enetTrapCategories': ['Other'], 'fcTrapCategories': ['Other'], 'vcmTrapCategories': ['Legacy'], 'trapFormat': 'SNMPv1', 'trapDestination': '192.168.99.99', 'communityString': 'public'}] snmp = {'snmpAccess': ['192.168.1.0/24'], 'trapDestinations': trapDestinations} enet = {'enableFastMacCacheFailover': False} server_profiles = [{'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 1', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay1-BL620cG7', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_9', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_9', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_10', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_10', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_11', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_11', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 2', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay2-BL685cG7', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3996', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3996', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_12', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_12', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Lom 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_13', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Lom 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_13', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_14', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_14', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3995', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3995', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_15', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_15', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_16', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_16', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_17', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_17', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3994', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3994', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_18', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_18', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_19', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_19', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_20', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_20', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_21', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_21', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_22', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_22', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 3', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay3-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3992', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3992', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_23', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_23', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_24', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_24', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_25', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_25', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3991', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3991', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_26', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_26', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_27', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_27', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_28', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_28', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3990', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3990', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_30', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_30', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_31', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_31', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_32', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_32', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_33', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_33', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 4', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay4-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3988', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3988', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_34', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_34', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_35', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_35', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_36', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_36', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3987', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3987', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_37', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_37', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_38', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_38', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_39', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_39', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3986', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3986', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_40', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_40', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_41', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_41', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_42', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_42', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_43', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_43', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_44', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_44', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 5', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay5-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_9', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_9', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_10', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_10', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_11', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_11', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 6', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay6-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3996', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3996', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_12', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_12', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_13', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_13', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_14', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_14', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3995', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3995', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_15', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_15', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_16', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_16', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_17', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_17', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3994', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3994', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_18', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_18', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_19', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_19', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_20', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_20', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_21', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_21', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_22', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_22', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 7', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay7-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3992', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3992', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_23', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_23', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_24', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_24', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_25', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_25', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3991', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3991', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_26', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_26', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_27', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_27', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_28', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_28', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3990', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3990', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_30', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_30', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_31', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_31', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_32', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_32', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_33', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_33', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-16, bay 8', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-16', 'enclosureGroupUri': 'EG:F10D-F10D-F10D-F10D', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-16_Bay8-BL660cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3988', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3988', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_34', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_34', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_35', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_35', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_36', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_36', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 10, 'name': '10', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3987', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 11, 'name': '11', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3987', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 12, 'name': '12', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_37', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 13, 'name': '13', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_37', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 14, 'name': '14', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_38', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 15, 'name': '15', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_38', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 16, 'name': '16', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_39', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 17, 'name': '17', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_39', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 18, 'name': '18', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3986', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 19, 'name': '19', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3986', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 20, 'name': '20', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_40', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 21, 'name': '21', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_40', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 22, 'name': '22', 'functionType': 'Ethernet', 'portId': 'Mezz 2:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_41', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 23, 'name': '23', 'functionType': 'Ethernet', 'portId': 'Mezz 2:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_41', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 24, 'name': '24', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_42', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 25, 'name': '25', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:net_42', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 26, 'name': '26', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel1', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 27, 'name': '27', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-b', 'requestedMbps': '2500', 'networkUri': 'ETH:Tunnel2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 28, 'name': '28', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_43', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 29, 'name': '29', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_43', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 30, 'name': '30', 'functionType': 'Ethernet', 'portId': 'Mezz 3:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_44', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 31, 'name': '31', 'functionType': 'Ethernet', 'portId': 'Mezz 3:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_44', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 1', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay1-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 2', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay2-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 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'', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 3', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 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{'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 8', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay8-BL420cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 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'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 11', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay11-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 12', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay12-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 13', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay13-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_4000', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_29', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_2', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 14', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay14-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3999', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_3', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_4', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 15', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay15-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3998', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_5', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_6', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, {'type': 'ServerProfileV5', 'serverHardwareUri': 'CI-FIT-17, bay 16', 'serverHardwareTypeUri': '', 'enclosureUri': 'ENC:CI-FIT-17', 'enclosureGroupUri': 'EG:FF-8FC20', 'serialNumberType': 'Virtual', 'macType': 'Virtual', 'wwnType': 'Virtual', 'name': 'CI-FIT-17_Bay16-BL465cGen8', 'description': '', 'affinity': 'Bay', 'boot': {'manageBoot': True, 'order': ['CD', 'Floppy', 'USB', 'PXE', 'HardDisk']}, 'connections': [{'id': 1, 'name': '1', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-a', 'requestedMbps': '2500', 'networkUri': 'ETH:IC', 'boot': {'priority': 'Primary'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 2, 'name': '2', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 3, 'name': '3', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-b', 'requestedMbps': '2500', 'networkUri': 'NS:NS_3997', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 4, 'name': '4', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 5, 'name': '5', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-c', 'requestedMbps': '2500', 'networkUri': 'ETH:net_7', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 6, 'name': '6', 'functionType': 'Ethernet', 'portId': 'Flb 1:1-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 7, 'name': '7', 'functionType': 'Ethernet', 'portId': 'Flb 1:2-d', 'requestedMbps': '2500', 'networkUri': 'ETH:net_8', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 8, 'name': '8', 'functionType': 'Ethernet', 'portId': 'Mezz 1:1', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-3-A', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, {'id': 9, 'name': '9', 'functionType': 'Ethernet', 'portId': 'Mezz 1:2', 'requestedMbps': '2500', 'networkUri': 'FC:SAN-4-B', 'boot': {'priority': 'NotBootable'}, 'mac': None, 'wwpn': '', 'wwnn': ''}, ]}, ] true = True false = False rc = {'200': 200, '201': 201, '202': 202, '400': 400, '401': 401, '403': 403, '412': 412, '500': 500} ######################################## default_variables = {'admin_credentials': admin_credentials, 'appliance': appliance, 'enc': enc, 'enc_group': enc_group, 'ethernet_networks': ethernet_networks, 'ethernet_ranges': ethernet_ranges, 'fc_networks': fc_networks, 'licenses': licenses, 'ligs': ligs, 'network_sets': network_sets, # 'network_set_ranges': network_set_ranges, 'ranges': ranges, 'rc': rc, 'server_profiles': server_profiles, 'uplink_sets': uplink_sets, 'users': users, 'timeandlocale': timeandlocale, 'true': true, 'false': false, 'vcenter': vcenter} def get_variables(): variables = default_variables return variables

[ "[email protected]" ]

[email protected]

243e12cea1928f4024ea0d1bbd062a8b27808eb6

6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4

/avBHBMAwf9ibDpfNM_17.py

90a9250d618305bcb7990c7a48831401227015ca

[]

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daniel-reich/ubiquitous-fiesta

26e80f0082f8589e51d359ce7953117a3da7d38c

9af2700dbe59284f5697e612491499841a6c126f

refs/heads/master

2023-04-05T06:40:37.328213

2021-04-06T20:17:44

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0

null

UTF-8

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false

122

py

import requests def content_type(url): r = requests.get('https://edabit.com/') return r.headers['content-type']

[ "[email protected]" ]

[email protected]

e4da1c60a852bd610107e481b15b04c840883e61

306a4c0c7ed32e879f76e6c101da70c46679f6bc

/copying_files_folders.py

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[]

no_license

ksoh512/automatetheboringstuff

3552f803d73644862e2e31d307b50aff82b6a839

0d9ee8de7927dbe0e0f08dbfb73867ffd9bf563c

refs/heads/master

2021-01-20T03:02:14.554780

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import shutil, os os.chdir('C:\\') ''' COPY FILES ''' shutil.copy('C:\\spam.txt', 'C:\\Users\\koh\\Documents\\codes\\automattheboringstuff') shutil.copy('C:\\eggs.txt', 'C:\\Users\\koh\\Documents\\codes\\automattheboringstuff') '''COPY FOLDERS AND FILES CONTAINED IN IT''' shutil.copytree('C:\\delicious', 'C:\\Users\\koh\\Documents\\codes\\automattheboringstuff')

[ "[email protected]" ]

[email protected]

c0642e90ddb142bbe67af2cbb81148287054d3d3

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/Data Set/bug-fixing-5/530bd27ed2c4c8e3f6a44b332569c3f73dfcb332-<test_np_mixed_precision_binary_funcs>-fix.py

8e9d3b67eb8f1b2c486e7784f60da806ef689c24

[]

no_license

wsgan001/PyFPattern

e0fe06341cc5d51b3ad0fe29b84098d140ed54d1

cc347e32745f99c0cd95e79a18ddacc4574d7faa

refs/heads/main

2023-08-25T23:48:26.112133

2021-10-23T14:11:22

null

0

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UTF-8

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py

@with_seed() @use_np def test_np_mixed_precision_binary_funcs(): def check_mixed_precision_binary_func(func, low, high, lshape, rshape, ltype, rtype): class TestMixedBinary(HybridBlock): def __init__(self, func): super(TestMixedBinary, self).__init__() self._func = func def hybrid_forward(self, F, a, b, *args, **kwargs): return getattr(F.np, self._func)(a, b) np_func = getattr(_np, func) mx_func = TestMixedBinary(func) np_test_x1 = _np.random.uniform(low, high, lshape).astype(ltype) np_test_x2 = _np.random.uniform(low, high, rshape).astype(rtype) mx_test_x1 = mx.numpy.array(np_test_x1, dtype=ltype) mx_test_x2 = mx.numpy.array(np_test_x2, dtype=rtype) rtol = (0.01 if ((ltype is np.float16) or (rtype is np.float16)) else 0.001) atol = (0.001 if ((ltype is np.float16) or (rtype is np.float16)) else 1e-05) for hybridize in [True, False]: if hybridize: mx_func.hybridize() np_out = np_func(np_test_x1, np_test_x2) with mx.autograd.record(): y = mx_func(mx_test_x1, mx_test_x2) assert (y.shape == np_out.shape) assert_almost_equal(y.asnumpy(), np_out.astype(y.dtype), rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True) np_out = getattr(_np, func)(np_test_x1, np_test_x2) mx_out = getattr(mx.np, func)(mx_test_x1, mx_test_x2) assert (mx_out.shape == np_out.shape) assert_almost_equal(mx_out.asnumpy(), np_out.astype(mx_out.dtype), rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True) funcs = { 'add': ((- 1.0), 1.0), 'subtract': ((- 1.0), 1.0), 'multiply': ((- 1.0), 1.0), } shape_pairs = [((3, 2), (3, 2)), ((3, 2), (3, 1)), ((3, 1), (3, 0)), ((0, 2), (1, 2)), ((2, 3, 4), (3, 1)), ((2, 3), ()), ((), (2, 3))] itypes = [np.bool, np.int8, np.int32, np.int64] ftypes = [np.float16, np.float32, np.float64] for (func, func_data) in funcs.items(): (low, high) = func_data for (lshape, rshape) in shape_pairs: for (type1, type2) in itertools.product(itypes, ftypes): check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2) check_mixed_precision_binary_func(func, low, high, lshape, rshape, type2, type1) for (type1, type2) in itertools.product(ftypes, ftypes): if (type1 == type2): continue check_mixed_precision_binary_func(func, low, high, lshape, rshape, type1, type2)

[ "[email protected]" ]

[email protected]

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2022-05-20T07:27:10

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{ 'variables': { 'openssl_sources': [ 'openssl/ssl/bio_ssl.c', 'openssl/ssl/d1_lib.c', 'openssl/ssl/d1_msg.c', 'openssl/ssl/d1_srtp.c', 'openssl/ssl/methods.c', 'openssl/ssl/packet.c', 'openssl/ssl/pqueue.c', 'openssl/ssl/record/dtls1_bitmap.c', 'openssl/ssl/record/rec_layer_d1.c', 'openssl/ssl/record/rec_layer_s3.c', 'openssl/ssl/record/ssl3_buffer.c', 'openssl/ssl/record/ssl3_record.c', 'openssl/ssl/record/ssl3_record_tls13.c', 'openssl/ssl/s3_cbc.c', 'openssl/ssl/s3_enc.c', 'openssl/ssl/s3_lib.c', 'openssl/ssl/s3_msg.c', 'openssl/ssl/ssl_asn1.c', 'openssl/ssl/ssl_cert.c', 'openssl/ssl/ssl_ciph.c', 'openssl/ssl/ssl_conf.c', 'openssl/ssl/ssl_err.c', 'openssl/ssl/ssl_init.c', 'openssl/ssl/ssl_lib.c', 'openssl/ssl/ssl_mcnf.c', 'openssl/ssl/ssl_rsa.c', 'openssl/ssl/ssl_sess.c', 'openssl/ssl/ssl_stat.c', 'openssl/ssl/ssl_txt.c', 'openssl/ssl/ssl_utst.c', 'openssl/ssl/statem/extensions.c', 'openssl/ssl/statem/extensions_clnt.c', 'openssl/ssl/statem/extensions_cust.c', 'openssl/ssl/statem/extensions_srvr.c', 'openssl/ssl/statem/statem.c', 'openssl/ssl/statem/statem_clnt.c', 'openssl/ssl/statem/statem_dtls.c', 'openssl/ssl/statem/statem_lib.c', 'openssl/ssl/statem/statem_srvr.c', 'openssl/ssl/t1_enc.c', 'openssl/ssl/t1_lib.c', 'openssl/ssl/t1_trce.c', 'openssl/ssl/tls13_enc.c', 'openssl/ssl/tls_srp.c', 'openssl/crypto/aes/aes_cbc.c', 'openssl/crypto/aes/aes_cfb.c', 'openssl/crypto/aes/aes_core.c', 'openssl/crypto/aes/aes_ecb.c', 'openssl/crypto/aes/aes_ige.c', 'openssl/crypto/aes/aes_misc.c', 'openssl/crypto/aes/aes_ofb.c', 'openssl/crypto/aes/aes_wrap.c', 'openssl/crypto/aria/aria.c', 'openssl/crypto/asn1/a_bitstr.c', 'openssl/crypto/asn1/a_d2i_fp.c', 'openssl/crypto/asn1/a_digest.c', 'openssl/crypto/asn1/a_dup.c', 'openssl/crypto/asn1/a_gentm.c', 'openssl/crypto/asn1/a_i2d_fp.c', 'openssl/crypto/asn1/a_int.c', 'openssl/crypto/asn1/a_mbstr.c', 'openssl/crypto/asn1/a_object.c', 'openssl/crypto/asn1/a_octet.c', 'openssl/crypto/asn1/a_print.c', 'openssl/crypto/asn1/a_sign.c', 'openssl/crypto/asn1/a_strex.c', 'openssl/crypto/asn1/a_strnid.c', 'openssl/crypto/asn1/a_time.c', 'openssl/crypto/asn1/a_type.c', 'openssl/crypto/asn1/a_utctm.c', 'openssl/crypto/asn1/a_utf8.c', 'openssl/crypto/asn1/a_verify.c', 'openssl/crypto/asn1/ameth_lib.c', 'openssl/crypto/asn1/asn1_err.c', 'openssl/crypto/asn1/asn1_gen.c', 'openssl/crypto/asn1/asn1_item_list.c', 'openssl/crypto/asn1/asn1_lib.c', 'openssl/crypto/asn1/asn1_par.c', 'openssl/crypto/asn1/asn_mime.c', 'openssl/crypto/asn1/asn_moid.c', 'openssl/crypto/asn1/asn_mstbl.c', 'openssl/crypto/asn1/asn_pack.c', 'openssl/crypto/asn1/bio_asn1.c', 'openssl/crypto/asn1/bio_ndef.c', 'openssl/crypto/asn1/d2i_pr.c', 'openssl/crypto/asn1/d2i_pu.c', 'openssl/crypto/asn1/evp_asn1.c', 'openssl/crypto/asn1/f_int.c', 'openssl/crypto/asn1/f_string.c', 'openssl/crypto/asn1/i2d_pr.c', 'openssl/crypto/asn1/i2d_pu.c', 'openssl/crypto/asn1/n_pkey.c', 'openssl/crypto/asn1/nsseq.c', 'openssl/crypto/asn1/p5_pbe.c', 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'openssl/crypto/rc2/rc2_cbc.c', 'openssl/crypto/rc2/rc2_ecb.c', 'openssl/crypto/rc2/rc2_skey.c', 'openssl/crypto/rc2/rc2cfb64.c', 'openssl/crypto/rc2/rc2ofb64.c', 'openssl/crypto/ripemd/rmd_dgst.c', 'openssl/crypto/ripemd/rmd_one.c', 'openssl/crypto/rsa/rsa_ameth.c', 'openssl/crypto/rsa/rsa_asn1.c', 'openssl/crypto/rsa/rsa_chk.c', 'openssl/crypto/rsa/rsa_crpt.c', 'openssl/crypto/rsa/rsa_depr.c', 'openssl/crypto/rsa/rsa_err.c', 'openssl/crypto/rsa/rsa_gen.c', 'openssl/crypto/rsa/rsa_lib.c', 'openssl/crypto/rsa/rsa_meth.c', 'openssl/crypto/rsa/rsa_mp.c', 'openssl/crypto/rsa/rsa_none.c', 'openssl/crypto/rsa/rsa_oaep.c', 'openssl/crypto/rsa/rsa_ossl.c', 'openssl/crypto/rsa/rsa_pk1.c', 'openssl/crypto/rsa/rsa_pmeth.c', 'openssl/crypto/rsa/rsa_prn.c', 'openssl/crypto/rsa/rsa_pss.c', 'openssl/crypto/rsa/rsa_saos.c', 'openssl/crypto/rsa/rsa_sign.c', 'openssl/crypto/rsa/rsa_ssl.c', 'openssl/crypto/rsa/rsa_x931.c', 'openssl/crypto/rsa/rsa_x931g.c', 'openssl/crypto/seed/seed.c', 'openssl/crypto/seed/seed_cbc.c', 'openssl/crypto/seed/seed_cfb.c', 'openssl/crypto/seed/seed_ecb.c', 'openssl/crypto/seed/seed_ofb.c', 'openssl/crypto/sha/keccak1600.c', 'openssl/crypto/sha/sha1_one.c', 'openssl/crypto/sha/sha1dgst.c', 'openssl/crypto/sha/sha256.c', 'openssl/crypto/sha/sha512.c', 'openssl/crypto/siphash/siphash.c', 'openssl/crypto/siphash/siphash_ameth.c', 'openssl/crypto/siphash/siphash_pmeth.c', 'openssl/crypto/sm2/sm2_crypt.c', 'openssl/crypto/sm2/sm2_err.c', 'openssl/crypto/sm2/sm2_pmeth.c', 'openssl/crypto/sm2/sm2_sign.c', 'openssl/crypto/sm3/m_sm3.c', 'openssl/crypto/sm3/sm3.c', 'openssl/crypto/sm4/sm4.c', 'openssl/crypto/srp/srp_lib.c', 'openssl/crypto/srp/srp_vfy.c', 'openssl/crypto/stack/stack.c', 'openssl/crypto/store/loader_file.c', 'openssl/crypto/store/store_err.c', 'openssl/crypto/store/store_init.c', 'openssl/crypto/store/store_lib.c', 'openssl/crypto/store/store_register.c', 'openssl/crypto/store/store_strings.c', 'openssl/crypto/threads_none.c', 'openssl/crypto/threads_pthread.c', 'openssl/crypto/threads_win.c', 'openssl/crypto/ts/ts_asn1.c', 'openssl/crypto/ts/ts_conf.c', 'openssl/crypto/ts/ts_err.c', 'openssl/crypto/ts/ts_lib.c', 'openssl/crypto/ts/ts_req_print.c', 'openssl/crypto/ts/ts_req_utils.c', 'openssl/crypto/ts/ts_rsp_print.c', 'openssl/crypto/ts/ts_rsp_sign.c', 'openssl/crypto/ts/ts_rsp_utils.c', 'openssl/crypto/ts/ts_rsp_verify.c', 'openssl/crypto/ts/ts_verify_ctx.c', 'openssl/crypto/txt_db/txt_db.c', 'openssl/crypto/ui/ui_err.c', 'openssl/crypto/ui/ui_lib.c', 'openssl/crypto/ui/ui_null.c', 'openssl/crypto/ui/ui_openssl.c', 'openssl/crypto/ui/ui_util.c', 'openssl/crypto/uid.c', 'openssl/crypto/whrlpool/wp_block.c', 'openssl/crypto/whrlpool/wp_dgst.c', 'openssl/crypto/x509/by_dir.c', 'openssl/crypto/x509/by_file.c', 'openssl/crypto/x509/t_crl.c', 'openssl/crypto/x509/t_req.c', 'openssl/crypto/x509/t_x509.c', 'openssl/crypto/x509/x509_att.c', 'openssl/crypto/x509/x509_cmp.c', 'openssl/crypto/x509/x509_d2.c', 'openssl/crypto/x509/x509_def.c', 'openssl/crypto/x509/x509_err.c', 'openssl/crypto/x509/x509_ext.c', 'openssl/crypto/x509/x509_lu.c', 'openssl/crypto/x509/x509_meth.c', 'openssl/crypto/x509/x509_obj.c', 'openssl/crypto/x509/x509_r2x.c', 'openssl/crypto/x509/x509_req.c', 'openssl/crypto/x509/x509_set.c', 'openssl/crypto/x509/x509_trs.c', 'openssl/crypto/x509/x509_txt.c', 'openssl/crypto/x509/x509_v3.c', 'openssl/crypto/x509/x509_vfy.c', 'openssl/crypto/x509/x509_vpm.c', 'openssl/crypto/x509/x509cset.c', 'openssl/crypto/x509/x509name.c', 'openssl/crypto/x509/x509rset.c', 'openssl/crypto/x509/x509spki.c', 'openssl/crypto/x509/x509type.c', 'openssl/crypto/x509/x_all.c', 'openssl/crypto/x509/x_attrib.c', 'openssl/crypto/x509/x_crl.c', 'openssl/crypto/x509/x_exten.c', 'openssl/crypto/x509/x_name.c', 'openssl/crypto/x509/x_pubkey.c', 'openssl/crypto/x509/x_req.c', 'openssl/crypto/x509/x_x509.c', 'openssl/crypto/x509/x_x509a.c', 'openssl/crypto/x509v3/pcy_cache.c', 'openssl/crypto/x509v3/pcy_data.c', 'openssl/crypto/x509v3/pcy_lib.c', 'openssl/crypto/x509v3/pcy_map.c', 'openssl/crypto/x509v3/pcy_node.c', 'openssl/crypto/x509v3/pcy_tree.c', 'openssl/crypto/x509v3/v3_addr.c', 'openssl/crypto/x509v3/v3_admis.c', 'openssl/crypto/x509v3/v3_akey.c', 'openssl/crypto/x509v3/v3_akeya.c', 'openssl/crypto/x509v3/v3_alt.c', 'openssl/crypto/x509v3/v3_asid.c', 'openssl/crypto/x509v3/v3_bcons.c', 'openssl/crypto/x509v3/v3_bitst.c', 'openssl/crypto/x509v3/v3_conf.c', 'openssl/crypto/x509v3/v3_cpols.c', 'openssl/crypto/x509v3/v3_crld.c', 'openssl/crypto/x509v3/v3_enum.c', 'openssl/crypto/x509v3/v3_extku.c', 'openssl/crypto/x509v3/v3_genn.c', 'openssl/crypto/x509v3/v3_ia5.c', 'openssl/crypto/x509v3/v3_info.c', 'openssl/crypto/x509v3/v3_int.c', 'openssl/crypto/x509v3/v3_lib.c', 'openssl/crypto/x509v3/v3_ncons.c', 'openssl/crypto/x509v3/v3_pci.c', 'openssl/crypto/x509v3/v3_pcia.c', 'openssl/crypto/x509v3/v3_pcons.c', 'openssl/crypto/x509v3/v3_pku.c', 'openssl/crypto/x509v3/v3_pmaps.c', 'openssl/crypto/x509v3/v3_prn.c', 'openssl/crypto/x509v3/v3_purp.c', 'openssl/crypto/x509v3/v3_skey.c', 'openssl/crypto/x509v3/v3_sxnet.c', 'openssl/crypto/x509v3/v3_tlsf.c', 'openssl/crypto/x509v3/v3_utl.c', 'openssl/crypto/x509v3/v3err.c', 'openssl/engines/e_capi.c', 'openssl/engines/e_padlock.c', ], 'openssl_sources_solaris-x86-gcc': [ './config/archs/solaris-x86-gcc/asm/crypto/aes/aesni-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/aes/vpaes-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/bf/bf-586.s', './config/archs/solaris-x86-gcc/asm/crypto/bn/bn-586.s', './config/archs/solaris-x86-gcc/asm/crypto/bn/co-586.s', './config/archs/solaris-x86-gcc/asm/crypto/bn/x86-gf2m.s', './config/archs/solaris-x86-gcc/asm/crypto/bn/x86-mont.s', './config/archs/solaris-x86-gcc/asm/crypto/camellia/cmll-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/chacha/chacha-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/des/crypt586.s', './config/archs/solaris-x86-gcc/asm/crypto/des/des-586.s', './config/archs/solaris-x86-gcc/asm/crypto/ec/ecp_nistz256-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/md5/md5-586.s', './config/archs/solaris-x86-gcc/asm/crypto/modes/ghash-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/poly1305/poly1305-x86.s', './config/archs/solaris-x86-gcc/asm/crypto/rc4/rc4-586.s', './config/archs/solaris-x86-gcc/asm/crypto/ripemd/rmd-586.s', './config/archs/solaris-x86-gcc/asm/crypto/sha/sha1-586.s', './config/archs/solaris-x86-gcc/asm/crypto/sha/sha256-586.s', './config/archs/solaris-x86-gcc/asm/crypto/sha/sha512-586.s', './config/archs/solaris-x86-gcc/asm/crypto/whrlpool/wp-mmx.s', './config/archs/solaris-x86-gcc/asm/crypto/x86cpuid.s', './config/archs/solaris-x86-gcc/asm/engines/e_padlock-x86.s', ], 'openssl_defines_solaris-x86-gcc': [ 'NDEBUG', 'FILIO_H', 'L_ENDIAN', 'OPENSSL_PIC', 'OPENSSL_CPUID_OBJ', 'OPENSSL_BN_ASM_PART_WORDS', 'OPENSSL_IA32_SSE2', 'OPENSSL_BN_ASM_MONT', 'OPENSSL_BN_ASM_GF2m', 'SHA1_ASM', 'SHA256_ASM', 'SHA512_ASM', 'RC4_ASM', 'MD5_ASM', 'RMD160_ASM', 'AESNI_ASM', 'VPAES_ASM', 'WHIRLPOOL_ASM', 'GHASH_ASM', 'ECP_NISTZ256_ASM', 'POLY1305_ASM', ], 'openssl_cflags_solaris-x86-gcc': [ '-Wa,--noexecstack', '-Wall -O3 -fomit-frame-pointer', '-pthread', '-Wall -O3 -fomit-frame-pointer', ], 'openssl_ex_libs_solaris-x86-gcc': [ '-lsocket -lnsl -ldl -pthread', ], }, 'include_dirs': [ '.', './include', './crypto', './crypto/include/internal', ], 'defines': ['<@(openssl_defines_solaris-x86-gcc)'], 'cflags' : ['<@(openssl_cflags_solaris-x86-gcc)'], 'libraries': ['<@(openssl_ex_libs_solaris-x86-gcc)'], 'sources': ['<@(openssl_sources)', '<@(openssl_sources_solaris-x86-gcc)'], }

[ "[email protected]" ]

[email protected]

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HeshamSayed/ElectroBekia

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from django.shortcuts import render, get_object_or_404 from .models import * from cart.forms import CartAddProductForm def product_list(request): categories = Category.objects.all() products = Product.objects.all() context = { 'categories': categories, 'products': products, } return render(request, 'products/list.html', context) def product_detail(request, pk): product = get_object_or_404(Product, pk=pk) cart_product_form = CartAddProductForm() context = { 'product': product, 'cart_product_form': cart_product_form, } return render(request, 'products/detail.html', context)

[ "[email protected]" ]

[email protected]

360817c27d99ca21241781e95372efb461f4a4b0

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lancelote/advent_of_code

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"""2021 - Day 5 Part 2: Hydrothermal Venture.""" from src.year2021.day05a import Floor from src.year2021.day05a import Segment def solve(task: str) -> int: segments = [Segment.from_line(line) for line in task.splitlines()] floor = Floor() floor.draw(segments) return floor.num_overlap

[ "[email protected]" ]

[email protected]

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gabriellaec/desoft-analise-exercicios

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conta= float(input("Valor da conta com 10%: R$ ")) conta += conta*10/100 print("Valor da conta com 10%: R$ {0:.2f}".format(conta))

[ "[email protected]" ]

[email protected]

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""" Copyright (C) 2018-2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import logging import os import cv2 import numpy as np import sys from glob import glob from random import choice from .logging import logger IMAGE_EXTENSIONS = ['JPEG', 'JPG', 'PNG', 'BMP'] BINARY_EXTENSIONS = ['BIN'] def isImage(blob): if (blob.layout != "NCHW"): return False channels = blob.shape[1] return (channels == 3) def isImageInfo(blob): if (blob.layout != "NC"): return False channels = blob.shape[1] return (channels >= 2) def getInputs(path_to_input, batch_size, input_info, requests): input_image_sizes = {} for key in input_info.keys(): if (isImage(input_info[key])): input_image_sizes[key] = (input_info[key].shape[2], input_info[key].shape[3]) logger.info("Network input '{}' precision {}, dimensions ({}): {}".format(key, input_info[key].precision, input_info[key].layout, " ".join(str(x) for x in input_info[key].shape))) images_count = len(input_image_sizes.keys()) binaries_count = len(input_info) - images_count image_files = list() binary_files = list() if (path_to_input): image_files = get_files_by_extensions(path_to_input, IMAGE_EXTENSIONS) image_files.sort() binary_files = get_files_by_extensions(path_to_input, BINARY_EXTENSIONS) binary_files.sort() if (len(image_files) == 0) and (len(binary_files) == 0): logger.warn("No input files were given: all inputs will be filled with random values!") else: binary_to_be_used = binaries_count*batch_size*len(requests) if binary_to_be_used > 0 and len(binary_files) == 0: logger.warn("No supported binary inputs found! Please check your file extensions: {}".format(",".join(BINARY_EXTENSIONS))) elif binary_to_be_used > len(binary_files): logger.warn("Some binary input files will be duplicated: {} files are required, but only {} were provided".format(binary_to_be_used, len(binary_files))) elif binary_to_be_used < len(binary_files): logger.warn("Some binary input files will be ignored: only {} files are required from {}".format(binary_to_be_used, len(binary_files))) images_to_be_used = images_count*batch_size*len(requests) if images_to_be_used > 0 and len(image_files) == 0: logger.warn("No supported image inputs found! Please check your file extensions: {}".format(",".join(IMAGE_EXTENSIONS))) elif images_to_be_used > len(image_files): logger.warn("Some image input files will be duplicated: {} files are required, but only {} were provided".format(images_to_be_used, len(image_files))) elif images_to_be_used < len(image_files): logger.warn("Some image input files will be ignored: only {} files are required from {}".format(images_to_be_used, len(image_files))) requests_input_data = [] for request_id in range(0, len(requests)): logger.info("Infer Request {} filling".format(request_id)) input_data = {} keys = list(input_info.keys()) for key in keys: if isImage(input_info[key]): # input is image if (len(image_files) > 0): input_data[key] = fill_blob_with_image(image_files, request_id, batch_size, keys.index(key), len(keys), input_info[key].shape) continue # input is binary if (len(binary_files) > 0): input_data[key] = fill_blob_with_binary(binary_files, input_info[key].shape) continue # most likely input is image info if isImageInfo(input_info[key]) and len(input_image_sizes) == 1: image_size = input_image_sizes[list(input_image_sizes.keys()).pop()] logger.info("Fill input '" + key + "' with image size " + str(image_size[0]) + "x" + str(image_size[1])) input_data[key] = fill_blob_with_image_info(image_size, input_info[key].shape) continue # fill with random data logger.info("Fill input '{}' with random values ({} is expected)".format(key, "image" if isImage(input_info[key]) else "some binary data")) input_data[key] = fill_blob_with_random(input_info[key].precision, input_info[key].shape) requests_input_data.append(input_data) return requests_input_data def get_files_by_extensions(path_to_input, extensions): input_files = list() if os.path.isfile(path_to_input): input_files.append(path_to_input) else: path = os.path.join(path_to_input, '*') files = glob(path, recursive=True) for file in files: file_extension = file.rsplit('.').pop().upper() if file_extension in extensions: input_files.append(file) return input_files def fill_blob_with_image(image_paths, request_id, batch_size, input_id, input_size, shape): images = np.ndarray(shape) image_index = request_id*batch_size*input_size + input_id for b in range(batch_size): image_index %= len(image_paths) image_filename = image_paths[image_index] image = cv2.imread(image_filename) new_im_size = tuple(shape[2:]) if image.shape[:-1] != new_im_size: logger.warn("Image {} is resized from ({}) to ({})".format(image_filename, image.shape[:-1], new_im_size)) image = cv2.resize(image, new_im_size) image = image.transpose((2, 1, 0)) images[b] = image image_index += input_size return images def fill_blob_with_binary(binary_paths, request_id, batch_size, input_id, input_size, shape): binaries = np.ndarray(shape) binary_index = request_id*batch_size*input_size + input_id for b in range(batch_size): binary_index %= len(image_paths) binary_filename = binary_paths[binary_index] binary_file_size = os.path.getsize(binary_file) input_size = np.prod(shape)/batch_size if (input_size != binary_file_size): raise Exception("File " + binary_filename + " contains " << str(binary_file_size) + " bytes " + "but network expects " + str(input_size)) with open(binary_file, 'r') as f: binary_data = f.read() binaries[b] = binary_data binary_index += input_size return binaries def fill_blob_with_image_info(image_size, shape): im_info = np.ndarray(shape) for b in range(shape[0]): for i in range(shape[1]): im_info[b][i] = image_size[i] if i in [0, 1] else 1 return im_info def fill_blob_with_random(precision, shape): if precision == "FP32": return np.random.rand(*shape).astype(np.float32) elif precision == "FP16": return np.random.rand(*shape).astype(np.float16) elif precision == "I32": return np.random.rand(*shape).astype(np.int32) elif precision == "U8": return np.random.rand(*shape).astype(np.uint8) elif precision == "I8": return np.random.rand(*shape).astype(np.int8) elif precision == "U16": return np.random.rand(*shape).astype(np.uint16) elif precision == "I16": return np.random.rand(*shape).astype(np.int16) else: raise Exception("Input precision is not supported: " + precision)

[ "[email protected]" ]

[email protected]

b11132f9a28ade952b2c9bb6c536a6194a591483

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sun3shines/web

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# -*- coding: utf-8 -*- from urllib import unquote from cloudweb.db.db_record import record_put # msgPut -> db_message_object_put # msgGet -> db_message_object_get # msgHead -> db_message_object_head # msgMeta -> db_message_object_meta # msgDelete -> db_message_object_delete # msgDeleteRecycle -> db_message_object_deleterecycle # msgMove -> db_message_object_move # msgCopy -> db_message_object_copy # msgMoveRecycle -> db_message_object_moverecycle # msgPost -> db_message_object_post def db_message_object_put(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s PUT OBJECT %s' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_get(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s DOWNLOAD OBJECT %s' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_head(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s GET OBJECT %s INFO' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_meta(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s GET OBJECT %s METADATA' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_delete(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s DELETE OBJECT %s' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_deleterecycle(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s MOVE OBJECT %s TO RECYCLE' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_move(db,objPath,dstName): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s MOVE OBJECT %s TO %s' % (urName,objName,dstName) return record_put(db, msg, urName, objPath) def db_message_object_copy(db,objPath,dstName): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s COPY OBJECT %s TO %s' % (urName,objName,dstName) return record_put(db, msg, urName, objPath) def db_message_object_moverecycle(db,objPath): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s MOVE OBJECT %s FROM RECYCLE' % (urName,objName) return record_put(db, msg, urName, objPath) def db_message_object_post(db,objPath,header): # objPath = unquote(path) # objPath = '/'.join(objPath.split('/')[3:]) urName = objPath.split('/')[0] objName = objPath.split('/')[-1] msg = ' %s UPDATE OBJECT METADATA %s' % (urName,objName,header) return record_put(db, msg, urName, objPath)

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twitter_api_key = 'twitter API key of your registered twitter application' twitter_api_secret = 'consumer secret' twitter_access_token = 'Access token' twitter_access_secret = 'Access token secret' twitter_stream_filter = {'track': 'python'}

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prettyirrelevant/snippets-backend

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""" Django settings for snippets_backend project. Generated by 'django-admin startproject' using Django 3.1.5. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '@w*wfy5)lp19)4-zf&0y^je9wc8=)ljqjcwoj82xsujxfi&o-1' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition AUTH_USER_MODEL = 'api.User' INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'rest_framework', 'knox', 'api' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'snippets_backend.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'snippets_backend.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ['knox.auth.TokenAuthentication'], 'DEFAULT_PERMISSION_CLASSES': ['rest_framework.permissions.AllowAny'] } REST_KNOX = { 'USER_SERIALIZER': 'api.serializers.UserSerializer', 'EXPIRY_DATETIME_FORMAT': None } CORS_ALLOWED_ORIGINS = ["http://localhost:3000"]

[ "[email protected]" ]

[email protected]

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sinjorjob/django-chat

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""" Django settings for config project. Generated by 'django-admin startproject' using Django 3.2.5. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-^dal#)b$5x8j2)2(osq^d^i-tt*=7pux8$i$(-pjd%bi+ia9@n' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'chat.apps.ChatConfig', #追加 'accounts.apps.AccountsConfig', #追加 'widget_tweaks', #追加 ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'config.urls' import os TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'config.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'ja' TIME_ZONE = 'Asia/Tokyo' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static') # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' # カスタムユーザモデル AUTH_USER_MODEL = 'accounts.CustomUser' #ログイン後のリダイレクトURL設定 LOGIN_REDIRECT_URL = '/chat_room/' #ログアウト後のリダイレクト先 LOGOUT_REDIRECT_URL = '/' # ファイルアップロード用 MEDIA_ROOT = os.path.join(BASE_DIR, 'uploads') MEDIA_URL = '/media/'

[ "[email protected]" ]

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#!/Users/raymondmbp/makeschool/BEW-2.4-Decentralized-Apps-Distributed-Protocols/generative-structures/venv/bin/python3 # -*- coding: utf-8 -*- import re import sys from libpasteurize.main import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# (C) Datadog, Inc. 2018-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import click from ..console import CONTEXT_SETTINGS from .changelog import changelog from .integrations import integrations from .integrations_changelog import integrations_changelog from .requirements import requirements ALL_COMMANDS = (changelog, requirements, integrations, integrations_changelog) @click.group(context_settings=CONTEXT_SETTINGS, short_help='A collection of tasks related to the Datadog Agent') def agent(): pass for command in ALL_COMMANDS: agent.add_command(command)

[ "[email protected]" ]

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def agrupa_por_idade(dicio): dicio={[nome]:idade} key=nome dic={} if idade<=11: dic.update={criança:[nome]} return dic if idade>=12 and idade>=17: dic.update={adolescente:[nome]} return dic if idade>=18 and idade<=59: dic.update={adulto:[nome]} return dic else: dic.update={idoso:[nome]} return dic

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# Generated by Django 3.0.7 on 2021-03-23 12:36 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('foodcartapp', '0042_auto_20210317_1251'), ] operations = [ migrations.AddField( model_name='order', name='products', field=models.ManyToManyField(through='foodcartapp.OrderPosition', to='foodcartapp.Product'), ), ]

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''' Faça um Programa que peça as quatro notas de 10 alunos, calcule e armazene num vetor a média de cada aluno, imprima o número de alunos com média maior ou igual a 7.0. ''' alunos = 2 nota = 0 soma = 0 for i in range(1,3): notas = [] for j in range(1,3): nota += float(input("Digite a %iª nota do aluno %i: " %(i, j))) nota /= 2 notas.append(nota) for media in notas: if media > 7: soma += 1 print("O número de alunos com média maior que 7.00 foi de %i." %soma)

[ "[email protected]" ]

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import time from .primitives import extend_towards from .rrt import TreeNode, configs from .utils import irange, RRT_ITERATIONS, INF, elapsed_time def wrap_collision_fn(collision_fn): # TODO: joint limits # import inspect # print(inspect.getargspec(collision_fn)) # print(dir(collision_fn)) def fn(q1, q2): try: return collision_fn(q1, q2) except TypeError: return collision_fn(q2) return fn def rrt_connect(start, goal, distance_fn, sample_fn, extend_fn, collision_fn, max_iterations=RRT_ITERATIONS, max_time=INF, **kwargs): """ :param start: Start configuration - conf :param goal: End configuration - conf :param distance_fn: Distance function - distance_fn(q1, q2)->float :param sample_fn: Sample function - sample_fn()->conf :param extend_fn: Extension function - extend_fn(q1, q2)->[q', ..., q"] :param collision_fn: Collision function - collision_fn(q)->bool :param max_iterations: Maximum number of iterations - int :param max_time: Maximum runtime - float :param kwargs: Keyword arguments :return: Path [q', ..., q"] or None if unable to find a solution """ # TODO: goal sampling function connected to a None node start_time = time.time() if collision_fn(start) or collision_fn(goal): return None # TODO: support continuous collision_fn with two arguments #collision_fn = wrap_collision_fn(collision_fn) nodes1, nodes2 = [TreeNode(start)], [TreeNode(goal)] # TODO: allow a tree to be prespecified (possibly as start) for iteration in irange(max_iterations): if elapsed_time(start_time) >= max_time: break swap = len(nodes1) > len(nodes2) tree1, tree2 = nodes1, nodes2 if swap: tree1, tree2 = nodes2, nodes1 target = sample_fn() last1, _ = extend_towards(tree1, target, distance_fn, extend_fn, collision_fn, swap, **kwargs) last2, success = extend_towards(tree2, last1.config, distance_fn, extend_fn, collision_fn, not swap, **kwargs) if success: path1, path2 = last1.retrace(), last2.retrace() if swap: path1, path2 = path2, path1 #print('{} max_iterations, {} nodes'.format(iteration, len(nodes1) + len(nodes2))) path = configs(path1[:-1] + path2[::-1]) # TODO: return the trees return path return None ################################################################# def birrt(start, goal, distance_fn, sample_fn, extend_fn, collision_fn, **kwargs): """ :param start: Start configuration - conf :param goal: End configuration - conf :param distance_fn: Distance function - distance_fn(q1, q2)->float :param sample_fn: Sample function - sample_fn()->conf :param extend_fn: Extension function - extend_fn(q1, q2)->[q', ..., q"] :param collision_fn: Collision function - collision_fn(q)->bool :param kwargs: Keyword arguments :return: Path [q', ..., q"] or None if unable to find a solution """ # TODO: deprecate from .meta import random_restarts solutions = random_restarts(rrt_connect, start, goal, distance_fn, sample_fn, extend_fn, collision_fn, max_solutions=1, **kwargs) if not solutions: return None return solutions[0]

[ "[email protected]" ]

[email protected]

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""" (C) Copyright 2019 IBM Corp. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Created on Apr 25, 2018 """ import pandas as pd from .base_weight import WeightEstimator from .base_estimator import PopulationOutcomeEstimator class MarginalOutcomeEstimator(WeightEstimator, PopulationOutcomeEstimator): """ A marginal outcome predictor. Assumes the sample is marginally exchangeable, and therefore does not correct (adjust, control) for covariates. Predicts the outcome/effect as if the sample came from a randomized control trial: $\\Pr[Y|A]$. """ def compute_weight_matrix(self, X, a, use_stabilized=None, **kwargs): # Another way to view this is that Uncorrected is basically an IPW-like with all individuals equally weighted. treatment_values = a.unique() treatment_values = treatment_values.sort() weights = pd.DataFrame(data=1, index=a.index, columns=treatment_values) return weights def compute_weights(self, X, a, treatment_values=None, use_stabilized=None, **kwargs): # Another way to view this is that Uncorrected is basically an IPW-like with all individuals equally weighted. weights = pd.Series(data=1, index=a.index) return weights def fit(self, X=None, a=None, y=None): """ Dummy implementation to match the API. MarginalOutcomeEstimator acts as a WeightEstimator that weights each sample as 1 Args: X (pd.DataFrame): Covariate matrix of size (num_subjects, num_features). a (pd.Series): Treatment assignment of size (num_subjects,). y (pd.Series): Observed outcome of size (num_subjects,). Returns: MarginalOutcomeEstimator: a fitted model. """ return self def estimate_population_outcome(self, X, a, y, w=None, treatment_values=None): """ Calculates potential population outcome for each treatment value. Args: X (pd.DataFrame): Covariate matrix of size (num_subjects, num_features). a (pd.Series): Treatment assignment of size (num_subjects,). y (pd.Series): Observed outcome of size (num_subjects,). w (pd.Series | None): Individual (sample) weights calculated. Used to achieved unbiased average outcome. If not provided, will be calculated on the data. treatment_values (Any): Desired treatment value/s to stratify upon before aggregating individual into population outcome. If not supplied, calculates for all available treatment values. Returns: pd.Series[Any, float]: Series which index are treatment values, and the values are numbers - the aggregated outcome for the strata of people whose assigned treatment is the key. """ if w is None: w = self.compute_weights(X, a) res = self._compute_stratified_weighted_aggregate(y, sample_weight=w, stratify_by=a, treatment_values=treatment_values) return res

[ "[email protected]" ]

[email protected]

9e2d106caf576c763e11e32eb14eb27cc379899f

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/src/core/geom/data/transform.py

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no_license

jorjuato/panda3dstudio

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refs/heads/master

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from ...base import * class GeomTransformBase(BaseObject): def __init__(self): self._verts_to_transf = {"vert": {}, "edge": {}, "poly": {}} self._rows_to_transf = {"vert": None, "edge": None, "poly": None} self._transf_start_data = {"bbox": None, "pos_array": None} def _update_verts_to_transform(self, subobj_lvl): selected_subobj_ids = self._selected_subobj_ids[subobj_lvl] verts = self._subobjs["vert"] self._verts_to_transf[subobj_lvl] = verts_to_transf = {} self._rows_to_transf[ subobj_lvl] = rows_to_transf = SparseArray.allOff() merged_verts = self._merged_verts merged_verts_to_transf = set() if subobj_lvl == "vert": for vert_id in selected_subobj_ids: merged_verts_to_transf.add(merged_verts[vert_id]) elif subobj_lvl == "edge": edges = self._subobjs["edge"] for edge_id in selected_subobj_ids: edge = edges[edge_id] for vert_id in edge: merged_verts_to_transf.add(merged_verts[vert_id]) elif subobj_lvl == "poly": polys = self._subobjs["poly"] for poly_id in selected_subobj_ids: poly = polys[poly_id] for vert_ids in poly: for vert_id in vert_ids: merged_verts_to_transf.add(merged_verts[vert_id]) for merged_vert in merged_verts_to_transf: rows = merged_vert.get_row_indices() verts_to_transf[merged_vert] = rows for row in rows: rows_to_transf.set_bit(row) def init_transform(self): geom_node_top = self._geoms["top"]["shaded"].node() start_data = self._transf_start_data start_data["bbox"] = geom_node_top.get_bounds() start_data["pos_array"] = geom_node_top.get_geom( 0).get_vertex_data().get_array(0) def transform_selection(self, subobj_lvl, transf_type, value): geom_node_top = self._geoms["top"]["shaded"].node() vertex_data_top = geom_node_top.modify_geom(0).modify_vertex_data() tmp_vertex_data = GeomVertexData(vertex_data_top) if transf_type == "translate": grid_origin = Mgr.get(("grid", "origin")) vec = self._origin.get_relative_vector(grid_origin, value) rows = self._rows_to_transf[subobj_lvl] start_data = self._transf_start_data tmp_vertex_data.set_array(0, start_data["pos_array"]) mat = Mat4.translate_mat(vec) tmp_vertex_data.transform_vertices(mat, rows) elif transf_type == "rotate": grid_origin = Mgr.get(("grid", "origin")) tc_pos = self._origin.get_relative_point( self.world, Mgr.get("transf_center_pos")) quat = self._origin.get_quat( grid_origin) * value * grid_origin.get_quat(self._origin) rows = self._rows_to_transf[subobj_lvl] start_data = self._transf_start_data tmp_vertex_data.set_array(0, start_data["pos_array"]) quat_mat = Mat4() quat.extract_to_matrix(quat_mat) offset_mat = Mat4.translate_mat(-tc_pos) mat = offset_mat * quat_mat offset_mat = Mat4.translate_mat(tc_pos) mat *= offset_mat tmp_vertex_data.transform_vertices(mat, rows) elif transf_type == "scale": grid_origin = Mgr.get(("grid", "origin")) tc_pos = self._origin.get_relative_point( self.world, Mgr.get("transf_center_pos")) scale_mat = Mat4.scale_mat(value) mat = self._origin.get_mat( grid_origin) * scale_mat * grid_origin.get_mat(self._origin) # remove translation component mat.set_row(3, VBase3()) rows = self._rows_to_transf[subobj_lvl] start_data = self._transf_start_data tmp_vertex_data.set_array(0, start_data["pos_array"]) offset_mat = Mat4.translate_mat(-tc_pos) mat = offset_mat * mat offset_mat = Mat4.translate_mat(tc_pos) mat *= offset_mat tmp_vertex_data.transform_vertices(mat, rows) array = tmp_vertex_data.get_array(0) vertex_data_top.set_array(0, array) for subobj_type in ("vert", "poly"): vertex_data = self._vertex_data[subobj_type] vertex_data.set_array(0, array) array = GeomVertexArrayData(array) handle = array.modify_handle() handle.set_data(handle.get_data() * 2) self._vertex_data["edge"].set_array(0, array) def finalize_transform(self, cancelled=False): start_data = self._transf_start_data geom_node_top = self._geoms["top"]["shaded"].node() vertex_data_top = geom_node_top.modify_geom(0).modify_vertex_data() if cancelled: bounds = start_data["bbox"] pos_array = start_data["pos_array"] vertex_data_top.set_array(0, pos_array) for subobj_type in ("vert", "poly"): self._vertex_data[subobj_type].set_array(0, pos_array) pos_array = GeomVertexArrayData(pos_array) handle = pos_array.modify_handle() handle.set_data(handle.get_data() * 2) self._vertex_data["edge"].set_array(0, pos_array) else: bounds = geom_node_top.get_bounds() pos_reader = GeomVertexReader(vertex_data_top, "vertex") subobj_lvl = Mgr.get_global("active_obj_level") polys = self._subobjs["poly"] poly_ids = set() for merged_vert, indices in self._verts_to_transf[subobj_lvl].iteritems(): pos_reader.set_row(indices[0]) pos = Point3(pos_reader.get_data3f()) merged_vert.set_pos(pos) poly_ids.update(merged_vert.get_polygon_ids()) vert_ids = [] for poly_id in poly_ids: poly = polys[poly_id] poly.update_center_pos() poly.update_normal() vert_ids.extend(poly.get_vertex_ids()) merged_verts = set(self._merged_verts[ vert_id] for vert_id in vert_ids) self._update_vertex_normals(merged_verts) self._origin.node().set_bounds(bounds) self.get_toplevel_object().get_bbox().update(*self._origin.get_tight_bounds()) start_data.clear() def _restore_subobj_transforms(self, old_time_id, new_time_id): obj_id = self.get_toplevel_object().get_id() prop_id = "subobj_transform" prev_time_ids = Mgr.do("load_last_from_history", obj_id, prop_id, old_time_id) new_time_ids = Mgr.do("load_last_from_history", obj_id, prop_id, new_time_id) if prev_time_ids is None: prev_time_ids = () if new_time_ids is None: new_time_ids = () if not (prev_time_ids or new_time_ids): return if prev_time_ids and new_time_ids: i = 0 for time_id in new_time_ids: if time_id not in prev_time_ids: break i += 1 common_time_ids = prev_time_ids[:i] prev_time_ids = prev_time_ids[i:] new_time_ids = new_time_ids[i:] verts = self._subobjs["vert"] polys = self._subobjs["poly"] data_id = "vert_pos_data" time_ids_to_restore = {} prev_prop_times = {} positions = {} # to undo transformations, determine the time IDs of the transforms that # need to be restored by checking the data that was stored when transforms # occurred, at times leading up to the time that is being replaced (the old # time) for time_id in prev_time_ids[::-1]: # time_id is a Time ID to update time_ids_to_restore with subobj_data = Mgr.do("load_from_history", obj_id, data_id, time_id) # subobj_data.get("prev", {}) yields previous transform times time_ids_to_restore.update(subobj_data.get("prev", {})) data_for_loading = {} # time_ids_to_restore.keys() are the IDs of vertices that need a # transform update for vert_id, time_id in time_ids_to_restore.iteritems(): if vert_id in verts: prev_prop_times[vert_id] = time_id # since multiple vertex positions might have to be loaded from the same # datafile, make sure each datafile is loaded only once data_for_loading.setdefault(time_id, []).append(vert_id) for time_id, vert_ids in data_for_loading.iteritems(): pos_data = Mgr.do("load_from_history", obj_id, data_id, time_id)["pos"] for vert_id in vert_ids: if vert_id in pos_data: positions[vert_id] = pos_data[vert_id] # to redo transformations, retrieve the transforms that need to be restored # from the data that was stored when transforms occurred, at times leading # up to the time that is being restored (the new time) for time_id in new_time_ids: subobj_data = Mgr.do("load_from_history", obj_id, data_id, time_id) positions.update(subobj_data.get("pos", {})) for vert_id in subobj_data.get("prev", {}): if vert_id in verts: prev_prop_times[vert_id] = time_id # restore the verts' previous transform time IDs for vert_id, time_id in prev_prop_times.iteritems(): verts[vert_id].set_previous_property_time("transform", time_id) polys_to_update = set() vertex_data_top = self._geoms["top"][ "shaded"].node().modify_geom(0).modify_vertex_data() pos_writer = GeomVertexWriter(vertex_data_top, "vertex") for vert_id, pos in positions.iteritems(): if vert_id in verts: vert = verts[vert_id] poly = polys[vert.get_polygon_id()] polys_to_update.add(poly) vert.set_pos(pos) row = vert.get_row_index() pos_writer.set_row(row) pos_writer.set_data3f(pos) pos_array = vertex_data_top.get_array(0) self._vertex_data["vert"].set_array(0, pos_array) self._vertex_data["poly"].set_array(0, pos_array) pos_array = GeomVertexArrayData(pos_array) handle = pos_array.modify_handle() handle.set_data(handle.get_data() * 2) self._vertex_data["edge"].set_array(0, pos_array) vert_ids = [] for poly in polys_to_update: poly.update_center_pos() poly.update_normal() vert_ids.extend(poly.get_vertex_ids()) self._vert_normal_change.update(vert_ids) self.get_toplevel_object().get_bbox().update(*self._origin.get_tight_bounds())

[ "[email protected]" ]

[email protected]

d627be799d34ca09b15dbb8a8ba4999497693d40

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""" 무조건 하나만 홀수가 발생하니 마지막 index는 짝수일 수밖에 없다.(0부터 시작이니) [조건] 1. A의 크기가 1인 경우 2. 홀수가 중간에 있는 경우 3. 홀수가 맨 마지막에 있는 경우 """ def solution(A): A.sort() for i in range(0, len(A)-1, 2): if A[i] != A[i+1]: # 조건2 - 홀수가 1개밖에 없으니 답이 아니라면 짝수개이므로 앞에 것이 틀리다. return A[i] # 조건1, 3 - 조건2에서 끝나지 않았다면 맨 마지막 값이 답 return A[-1] """ [처음 풀이] 시도를 해본 문제 문제를 이해를 잘못한 부분도 한몫하였고, 효율성을 가장 크게 생각해야했던 문제 처음에는 set으로 감싸서 중복을 없앤 후, 해당 set내용으로 A.count를 하였으나 N^2이 나와 실패 Dict형태도 퍼포먼스에서는 좋지 않았다. 어떻게 짜면 효율적일지 다른 방도로 생각해보면 좋을듯한 문제. 현재 방법은 100점이나, 더 좋은 방도가 없을까? """ def solution(A): A.sort() if len(A) < 2: return A[0] cnt = 1 for i in range(1, len(A)): if A[i-1] == A[i]: cnt += 1 else: if cnt%2: return A[i-1] else: cnt = 1 return A[i]

[ "[email protected]" ]

[email protected]

92ea82d00e3baa47f0708f8943155310bef045d0

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/python3_base/exception.py

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HuiZhaozh/python_tutorials

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# -*- coding:utf-8 -*- # /usr/bin/python ''' Author:Yan Errol Email:[email protected] Wechat:qq260187357 Date:2019-04-29--21:59 Describe:异常诊断 ''' import time def func(): try: for i in range(5): if i >3: raise Exception("数字大于3了==") except Exception as ret: print (ret) func() import re a = "张明 99分" ret = re.sub(r"\d+","100",a) print (ret) a = [1,2,3] b = [4,5,6] print(a+b)

[ "[email protected]" ]

[email protected]

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#!/usr/bin/env python # -*- coding: utf-8 -*- # author: houzhiwei # time: 2020/1/4 16:10 from rdflib import BNode, Graph, RDF, Namespace, Literal from rdflib.namespace import DCTERMS g = Graph() # namespaces data = Namespace("http://www.egc.org/ont/data#") saga = Namespace("http://www.egc.org/ont/process/saga#") sh = Namespace("http://www.w3.org/ns/shacl#") process = Namespace('http://www.egc.org/ont/gis/process#') # prefixes g.bind('data', data) g.bind('sh', sh) g.bind('saga', saga) g.bind('process', process) g.bind('dcterms', DCTERMS) # SHACL shape graph ds = saga.FlowAccumulationTopDownShape g.add((ds, RDF.type, sh.NodeShape)) # [tool]_[parameter] g.add((ds, sh.targetNode, saga.method_of_flow_accumulation_top_down)) p1 = BNode() g.add((p1, sh.path, process.hasData)) g.add((p1, sh.minCount, Literal(0))) g.add((p1, sh.maxCount, Literal(1))) g.add((p1, sh.message, Literal('Must has at most one input value for option ‘Method’ of tool ‘Flow Accumulation (Top-Down)’', lang='en'))) g.add((ds, sh.property, p1)) # save as turtle file g.serialize('../shapes/L2_FunctionalityLevelShape.ttl', format='turtle')

[ "[email protected]" ]

[email protected]

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# -*- coding: utf-8 -*- """ caching local handlers package. """ from pyrin.packaging.base import Package class CachingLocalHandlersPackage(Package): """ caching local handlers package class. """ NAME = __name__ DEPENDS = ['pyrin.configuration', 'pyrin.globalization.datetime', 'pyrin.logging']

[ "[email protected]" ]

[email protected]

8f8808d79b13456226c20d29fa09308ae24382df

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/qstrader/utils/console.py

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BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8) def string_colour(text, colour=WHITE): """ Create string text in a particular colour to the terminal. """ seq = "\x1b[1;%dm" % (30 + colour) + text + "\x1b[0m" return seq

[ "[email protected]" ]

[email protected]

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/Advent of code/Day2_Part1.py

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ritesh-deshmukh/Algorithms-and-Data-Structures

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# f = open("elves_input", "r") # if f.mode == "r": # input_task = f.read() # input_task = f.readlines() # for symbol in input_task: # dimensions = symbol.split("x") # print(dimensions) with open('elves_input') as f: dimensions_data = [] for line in f: line = line.split('x') # to deal with blank if line: # lines (ie skip them) line = [int(i) for i in line] dimensions_data.append(line) # product = dimensions_data[0][0] # print(dimensions_data[0]) total_area = 0 for dimensions in dimensions_data: # sorted = sorted(dimensions) # small_side_1 = sorted[0] # small_side_2 = sorted[1] area = ((2* dimensions[0] * dimensions[1]) + (2* dimensions[1] * dimensions[2]) + (2* dimensions[0] * dimensions[2])) total_area += area # print(sorted) print(f"Area total: {total_area}") total_small_side = 0 for dimensions1 in dimensions_data: area1 = sorted(dimensions1) # print(area1[0] * area1[1]) small_side = area1[0] * area1[1] total_small_side += small_side print(f"Small side total: {total_small_side}") answer = total_area + total_small_side print(f"Total Square feet: {answer}")

[ "[email protected]" ]

[email protected]

1e68f4426a5b3835594ad8792a036f353f9b5734

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/15_pj_pdf_merger.py

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ilmoi/ATBS

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"""Finds all pdfs in cur dir > sorts alphabetically > merges together taking the first page only once.""" import PyPDF2 import os import re # prep the files list files = os.listdir() chosen = [] r = re.compile(r'.*\.pdf') for file in files: try: mo = r.search(file) # print(mo.group()) chosen.append(mo.group()) except: pass chosen.sort() # manually removing the encrypted file (cba) chosen.pop(1) chosen.pop(1) print(chosen) # create writer writer = PyPDF2.PdfFileWriter() # iterate through files and pages and write them all down for i, file in enumerate(chosen): with open(file, 'rb') as f: reader = PyPDF2.PdfFileReader(f) # for first doc - add the first page too if i == 0: pageObj = reader.getPage(0) writer.addPage(pageObj) # for all docs for p in range(1, reader.numPages): pageObj = reader.getPage(p) writer.addPage(pageObj) # finally write # NOTE this one needs to sit inside of the with open statement or the pages will be blank! with open('longfile.pdf', 'wb') as f: writer.write(f) # lets check number of pages matches for file in chosen: with open(file, 'rb') as f: reader = PyPDF2.PdfFileReader(f) print(reader.numPages) print('compare that to ----->') with open('longfile.pdf', 'rb') as f: reader = PyPDF2.PdfFileReader(f) print(reader.numPages) # sounds correct!

[ "[email protected]" ]

[email protected]

9dedd846ed49f891c3ea2109f26b3eed81fcdf88

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/simplemooc/core/urls.py

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MIT

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from django.conf.urls import include, url from django.contrib import admin admin.autodiscover() import simplemooc.core.views urlpatterns = [ url(r'^$', simplemooc.core.views.home, name='home'), url(r'^contact/$',simplemooc.core.views.contact, name='contact'), url(r'^about/$',simplemooc.core.views.about, name='about'), ] #urlpatterns = patterns('simplemooc.core.views', # url(r'^$','home', name='home'), # url(r'^contact/$','contact', name='contact'), # url(r'^about/$','about', name='about'), #)

[ "[email protected]" ]

[email protected]

c2cfda99592ea8ed25c13139448162753c8e3e09

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/migrations/versions/f8f3d3338933_initial.py

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melardev/FlaskApiCrud

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"""'initial' Revision ID: f8f3d3338933 Revises: Create Date: 2019-03-08 21:02:46.699000 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'f8f3d3338933' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('todos', sa.Column('id', sa.Integer(), nullable=False), sa.Column('title', sa.String(length=100), nullable=False), sa.Column('description', sa.Text(), nullable=True), sa.Column('completed', sa.Boolean(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=False), sa.Column('updated_at', sa.DateTime(), nullable=False), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('todos') # ### end Alembic commands ###

[ "[email protected]" ]

[email protected]

dfbba26851a42e9ca1b1a62230992475e7e16da9

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/moodledata/vpl_data/12/usersdata/76/5514/submittedfiles/impedimento.py

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rafaelperazzo/programacao-web

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refs/heads/master

2021-01-12T14:06:25.773146

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# -*- coding: utf-8 -*- from __future__ import division import math #COMECE SEU CÓDIGO AQUI L = input('Digite o valor de L:') R = input('Digite o valor de R:') D = input('Digite o valor de D:') if R>50 and L<R and R>D: print('S') if R>50 and L<R and R<D: print('N')

[ "[email protected]" ]

[email protected]

aad8c4965b91dbc1a68802b5dc45aa593d98d20a

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/Suanfa/天际线问题Suanfa1_3.py

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QiuHongHao123/Algorithm-Practise

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def getSkyline(buildings): if not buildings: return [] if len(buildings) == 1: return [[buildings[0][0], buildings[0][2]], [buildings[0][1], 0]] mid = len(buildings) // 2 left = getSkyline(buildings[:mid]) right = getSkyline(buildings[mid:]) return merge(left, right) def merge(left, right): # 记录目前左右建筑物的高度 lheight = rheight = 0 # 位置 l = r = 0 res = [] while l < len(left) and r < len(right): if left[l][0] < right[r][0]: cp = [left[l][0], max(left[l][1], rheight)] lheight = left[l][1] l += 1 elif left[l][0] > right[r][0]: cp = [right[r][0], max(right[r][1], lheight)] rheight = right[r][1] r += 1 else: cp = [left[l][0], max(left[l][1], right[r][1])] lheight = left[l][1] rheight = right[r][1] l += 1 r += 1 # 和前面高度比较，不一样才加入 if len(res) == 0 or res[-1][1] != cp[1]: res.append(cp) # 剩余部分添加进去 res.extend(left[l:] or right[r:]) return res print(getSkyline([[1,5,11], [2,7,6], [3,9,13], [12,16,7], [14,25,3], [19,22,18], [23,29,13],[24,28,4]]))

[ "[email protected]" ]

[email protected]

869d7f8aec582f9c09dfa15e9791d99d7c9c617d

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/EQ5D.py

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2021-05-09T22:56:44.996009

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# EQ-5D regression coefficients Constant = 0.081 N3 = 0.269 dictCoefficients = {'Mobility': [0, 0.069, 0.314], 'Self-Care': [0, 0.104, 0.214], 'Usual Activity': [0, 0.036, 0.094], 'Pain/Discomfort': [0, 0.123, 0.386], 'Anxiety/Depression': [0, 0.071, 0.236]};

[ "[email protected]" ]

[email protected]

49681f30a6612dac501c48d0b1e070e630b6bf72

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/build/spdlog/catkin_generated/generate_cached_setup.py

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Zauberr/KAL

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# -*- coding: utf-8 -*- from __future__ import print_function import argparse import os import stat import sys # find the import for catkin's python package - either from source space or from an installed underlay if os.path.exists(os.path.join('/opt/mrtros/share/catkin/cmake', 'catkinConfig.cmake.in')): sys.path.insert(0, os.path.join('/opt/mrtros/share/catkin/cmake', '..', 'python')) try: from catkin.environment_cache import generate_environment_script except ImportError: # search for catkin package in all workspaces and prepend to path for workspace in "/home/kal5-2/rammbo/devel;/opt/mrtros;/opt/mrtsoftware/local;/opt/mrtsoftware/release".split(';'): python_path = os.path.join(workspace, 'lib/python2.7/dist-packages') if os.path.isdir(os.path.join(python_path, 'catkin')): sys.path.insert(0, python_path) break from catkin.environment_cache import generate_environment_script code = generate_environment_script('/home/kal5-2/rammbo/devel/.private/spdlog/env.sh') output_filename = '/home/kal5-2/rammbo/build/spdlog/catkin_generated/setup_cached.sh' with open(output_filename, 'w') as f: #print('Generate script for cached setup "%s"' % output_filename) f.write('\n'.join(code)) mode = os.stat(output_filename).st_mode os.chmod(output_filename, mode | stat.S_IXUSR)

[ "[email protected]" ]

[email protected]

37b973fff2fbbb70adca43095f7713a15f44886e

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/000989letpy/letpy_059_is_20200503.py

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[ "Apache-2.0" ]

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SafonovMikhail/python_000577

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a = "Hello, world!" b = "Hello, world!" print(a is b)

[ "[email protected]" ]

[email protected]

4c69aba309858501551b000e6236b893e0d8f7f7

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/leetcode/p0461/solve.py

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b1ueskydragon/PythonGround

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refs/heads/master

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class Solution: def hammingDistance(self, x: int, y: int) -> int: xor = x ^ y ones = 0 while xor: if (xor | 1) == xor: ones += 1 xor >>= 1 return ones if __name__ == '__main__': s = Solution() """ 011 101 --- 110 count = 2 """ print(s.hammingDistance(3, 5))

[ "[email protected]" ]

[email protected]

61d86514b4f788b34ca4fa7e4b2e2c9fe7d8d723

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/Section A/group10/task_1/part_1.py

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Medhashah/Big-DataProject

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refs/heads/master

2020-10-01T07:30:25.637471

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import json import subprocess import os from dateutil import parser from pyspark.sql import SparkSession from pyspark.sql.functions import * import pyspark.sql.functions as F import time import sys def main(start_index, end_index): spark = SparkSession \ .builder \ .appName("big_data_prof") \ .config("spark.some.config.option", "some-value") \ .getOrCreate() # conf = spark.sparkContext._conf.setAll( # [('spark.executor.memory', '8g'), ('spark.app.name', 'big_data_proj'), ('spark.executor.cores', '4'), # ('spark.cores.max', '4'), ('spark.driver.memory', '8g')]) # spark.sparkContext.stop() # spark = SparkSession.builder.config(conf=conf).getOrCreate() # new_conf = spark.sparkContext._conf.getAll() # print(new_conf) cmd = "hadoop fs -ls /user/hm74/NYCOpenData" files = subprocess.check_output(cmd, shell=True).decode().strip().split('\n') pfiles = [(x.split()[7], int(x.split()[4])) for x in files[1:]] pfiles_sorted = sorted(pfiles, key=lambda x: x[1]) if not os.path.exists('job_{}_{}'.format(start_index, end_index)): os.mkdir('job_{}_{}'.format(start_index, end_index)) for i, nyc_open_datafile in enumerate(pfiles_sorted[start_index:end_index]): print("processing number {} of {}".format(i+start_index, end_index)) # pretty hacky preprocessing but it will work for now # could maybe use pathlib library or get it with hdfs processed_path = nyc_open_datafile[0] df_nod = spark.read.option("header", "true").option("delimiter", "\t").csv(processed_path) try: file_name = processed_path.split('/')[-1].replace('.tsv.gz', '') print(file_name) if os.path.exists("job_{}_{}/{}.json".format(start_index, end_index, file_name)): continue start_process = time.time() bp = BasicProfiling(processed_path, df_nod) table_dict = bp.process() json_type = json.dumps(table_dict) #write to hdfs # spark.parallelize([json_type]).toDF().coalesce(1).write.json('/user/gl758/big_data/job_{}_{}/{}'.format(start_index, end_index, file_name)) with open("job_{}_{}/{}.json".format(start_index, end_index, file_name), 'w+', encoding="utf-8") as f: f.write(json_type) end_process = time.time() print("total process time {}".format(end_process - start_process)) except Exception as e: print("unable to process because {}".format(e)) # We should put this in it's on package, but submitting with packages is kind of annoying so # I moved it out for now look at --py-files #https://spark.apache.org/docs/latest/submitting-applications.html class BasicProfiling: """ Class for data profiling basic schema and statistics on a dataframe """ def __init__(self, dataset_name, df_nod): self.dataset_name = dataset_name self.df_nod = df_nod self.table_dict = dict() self.columns = self.df_nod.columns self.spec_types = ['INT', 'REAL', 'DATE', 'TEXT'] # self.column_dict = None # the currently processed column dict # self.column = None # the currently processed column dataframe def __set_up_dictionary(self): self.table_dict['dataset_name'] = self.dataset_name self.table_dict['columns'] = [] def __add_column_general_info(self, column, column_name): general_count = column.agg(lit(column_name).alias("name"), count(when(col(column_name).isNotNull(), True)).alias("count_not_null"), countDistinct(col(column_name)).alias("distinct"), count(when(col(column_name).isNull(), True)).alias("count_null")) general_fre = column.groupBy(column_name).agg(count(column_name).alias("count_col")).orderBy(desc("count_col")).limit(5).agg(collect_list(column_name).alias('fre')) return general_count, general_fre def _add_datatype_columns(self, column, column_name): """ Adds a type column to add every column we currently have, obviously this doubles the size :return: """ get_column_type_udf = udf(self.get_column_type) column = column.withColumn("dtype", get_column_type_udf(column_name)) return column def __get_stats_int(self, column, column_name): int_info = column.filter("dtype = 'INT'").withColumn(column_name[1:-1], col(column_name).cast('int'))\ .select(array(count(col(column_name)), F.max(col(column_name)), F.min(col(column_name)), mean(col(column_name)), stddev(col(column_name))).alias('stats_int')) return int_info def __get_stats_double(self, column, column_name): double_info = column.filter("dtype = 'REAL'").withColumn(column_name[1:-1], column[column_name].cast('double')).\ select(array(count(column_name), max(column_name), min(column_name), mean(column_name), stddev(column_name)).alias('stats_double')) return double_info def __get_stats_date(self, column, column_name): udf_cast_date = udf(BasicProfiling.__get_datetime) date_info = column.filter("dtype = 'DATE'").select(array(count(column_name), max(udf_cast_date(column_name)), min(udf_cast_date(column_name))).alias('stats_date')) return date_info def __get_stats_text(self, column, column_name): df_len = column.filter("dtype = 'TEXT'").withColumn("len", length(column_name)) text_info = df_len.select(array(count(column_name), mean("len")).alias('stats_text')) shortest = df_len.orderBy(asc("len")).limit(5).agg(collect_list(column_name).alias('shortest_values')).select('shortest_values') longest = df_len.orderBy(desc("len")).limit(5).agg(collect_list(column_name).alias('longest_values')).select('longest_values') return text_info, shortest, longest def __convert_df_to_dict(self, integer, real, date, text, shortest, longest, count, fre): stats_int = integer.collect() stats_double = real.collect() stats_date = date.collect() stats_text = text.collect() stats_shortest = shortest.collect() stats_longest = longest.collect() general_count = count.collect() # general_empty = empty.collect() general_fre = fre.collect() for i in range(len(stats_int)): column_dict = {} column_stats = [general_count[i][0], stats_int[i][0], stats_double[i][0], stats_date[i][0], stats_text[i][0], stats_shortest[i][0], stats_longest[i][0]] column_dict['column_name'] = column_stats[0] column_dict['number_empty_cells'] = general_count[i][3] column_dict['number_non_empty_cells'] = general_count[i][1] column_dict['number_distinct_values'] = general_count[i][2] column_dict['frequent_values'] = general_fre[i][0] column_dict['data_type'] = [] if column_stats[1][0] != 0: type_dict = {} type_dict['type'] = "INTERGER(LONG)" type_dict['count'] = int(column_stats[1][0]) type_dict['max_value'] = int(column_stats[1][1]) type_dict['min_value'] = int(column_stats[1][2]) type_dict['mean'] = float(column_stats[1][3]) type_dict['stddev'] = float(column_stats[1][4]) column_dict['data_type'].append(type_dict) if column_stats[2][0] != 0: type_dict = {} type_dict['type'] = 'REAL' type_dict['count'] = int(column_stats[2][0]) type_dict['max_value'] = float(column_stats[2][1]) type_dict['min_value'] = float(column_stats[2][2]) type_dict['mean'] = float(column_stats[2][3]) type_dict['stddev'] = float(column_stats[2][4]) column_dict['data_type'].append(type_dict) if column_stats[3][0] != '0': type_dict = {} type_dict['type'] = "DATE/TIME" type_dict['count'] = int(column_stats[3][0]) type_dict['max_value'] = column_stats[3][1] type_dict['min_value'] = column_stats[3][2] column_dict['data_type'].append(type_dict) if column_stats[4][0] != 0: type_dict = {} type_dict['type'] = "TEXT" type_dict['count'] = column_stats[4][0] type_dict['shortest_value'] = column_stats[5] type_dict['longest_value'] = column_stats[6] type_dict['average_length'] = column_stats[4][1] column_dict['data_type'].append(type_dict) self.table_dict['columns'].append(column_dict) @staticmethod def get_column_type(val): """ Returns the type of the value :param val: :return: """ if BasicProfiling.__is_int(val): return 'INT' elif BasicProfiling.__is_real(val): return 'REAL' elif BasicProfiling.__is_datetime(val): return 'DATE' elif val is None: return None else: return 'TEXT' @staticmethod def __is_int(val): try: int(val) return True except (ValueError, TypeError): return False @staticmethod def __is_real(val): try: float(val) return True except (ValueError, TypeError): return False @staticmethod def __is_datetime(val): try: parser.parse(val) return True # raw exception here, I tried to catch none raw dateutil error exception, but it's giving some errors # not sure I will need to fix up. except: return False @staticmethod def __get_datetime(val): #tried to give actual timestamp, but then we can't put it into array, so instead I'm giving iso format return parser.parse(val).isoformat() def process(self): start = time.time() self.__set_up_dictionary() for i, column_name in enumerate(self.columns): column_name = "`{}`".format(column_name) column = self.df_nod.select(column_name) general_count, general_fre = self.__add_column_general_info(column, column_name) # # generate type_dict column = self._add_datatype_columns(column, column_name) stats_int = self.__get_stats_int(column, column_name) stats_double = self.__get_stats_double(column, column_name) stats_date = self.__get_stats_date(column, column_name) stats_text, shortest, longest = self.__get_stats_text(column, column_name) if i == 0: stats_table_int = stats_int stats_table_double = stats_double stats_table_date = stats_date stats_table_text = stats_text table_shortest = shortest table_longest = longest general_table_count = general_count general_table_fre = general_fre else: stats_table_int = stats_table_int.union(stats_int) stats_table_double = stats_table_double.union(stats_double) stats_table_date = stats_table_date.union(stats_date) stats_table_text = stats_table_text.union(stats_text) table_shortest = table_shortest.union(shortest) table_longest = table_longest.union(longest) general_table_count = general_table_count.union(general_count) general_table_fre = general_table_fre.union(general_fre) self.__convert_df_to_dict(stats_table_int, stats_table_double, stats_table_date, stats_table_text, table_shortest, table_longest, general_table_count, general_table_fre) return self.table_dict if __name__ == "__main__": start_index = int(sys.argv[1]) end_index = int(sys.argv[2]) main(start_index, end_index)

[ "[email protected]" ]

[email protected]

5493b2f3a565402852a6d878c4d63e0d4b1c5509

3263139017e2e3cc253e93a9fb92604b00176466

/pias/pias_logging.py

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[]

no_license

saalfeldlab/pias

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acc7c19dc0ca81b846816ec0d0edf7ff87d46665

refs/heads/master

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2019-03-10T19:01:53

2019-03-10T19:01:56

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import logging print(logging) trace = logging.DEBUG- 5 logging.TRACE = trace logging.addLevelName(trace, 'TRACE') class PiasLogger(logging.getLoggerClass()): def trace(self, msg, *args, **kwargs): self.log(trace, msg, *args, **kwargs) logging.setLoggerClass(PiasLogger) levels = ('NOTSET', 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL', 'FATAL', 'TRACE')

[ "[email protected]" ]

[email protected]

ba81708752f0fb17ace59645543fa3e7548bc1cb

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permissive

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2,423

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import requests import pandas as pd import io import math TMPL = 'http://glamod2.ceda.ac.uk/select/?domain=land&frequency=monthly&variable=accumulated_precipitation,air_temperature&intended_use=non-commercial&data_quality=quality_controlled&column_selection=detailed_metadata&year=1974&month=03&bbox={w}.0,{s}.0,{e}.0,{n}.0&compress=false' def _assert_in_range(df, w, s, e, n, to_nearest_degree=False): if len(df) == 0: print('Empty df') return lats, lons = df.latitude, df.longitude min_lat, max_lat = lats.min(), lats.max() min_lon, max_lon = lons.min(), lons.max() print(f'Wanted lons: {w} to {e}; lats: {s} to {n}') print(f'Actual lons: {min_lon} to {max_lon}; lats: {min_lat} to {max_lat}') def fix(n): if n < 0: return math.ceil(n) else: return math.floor(n) if to_nearest_degree: min_lat, max_lat, min_lon, max_lon = [fix(_) for _ in [min_lat, max_lat, min_lon, max_lon]] # print(lats, lats.max(), lats.min()) assert(min_lat >= s), 'min_lat >= s' assert(max_lat <= n), 'max_lat <= n' if min_lat == max_lat and min_lat == -90 or min_lat == 90: print('Longitude checks are meaningless at the north/south pole') return if 90 in list(lats) or -90 in list(lats): print('Some lats are north/south pole - so ignore longitude checks') assert(min_lon >= w), 'min_lon >= w' assert(max_lon <= e), 'max_lon <= e' def _fetch_as_df(w, s, e, n): url = TMPL.format(**vars()) print(f'{url}') content = requests.get(url).text if content.startswith('Exception raised'): print(f'[ERROR] Fetch error: {content}') return content return pd.read_csv(io.StringIO(content)) def test_bbox_in_range(): for w in range(-180, 160, 30): e = w + 30 for s in range(-90, 61, 30): n = s + 30 df = _fetch_as_df(w, s, e, n) _assert_in_range(df, w, s, e, n, True) def test_bbox_full_range(): bboxes = ['-180,-90,180,90'] #, '-90,90,-180,180', '-90,-180,90,180'] for bbox in bboxes: w, s, e, n = [int(_) for _ in bbox.split(',')] df = _fetch_as_df(w, s, e, n) if type(df) == str: continue _assert_in_range(df, w, s, e, n, True) if __name__ == '__main__': test_bbox_full_range() test_bbox_in_range()

[ "[email protected]" ]

[email protected]

1e4c3dc8648edeb0f51d861b4003419811ebc27a

28b6e6a35b6591f36140b6cb907ac60c71dbcab1

/app/migrations/0001_initial.py

b9dba1404818a767036d64bf7989c45046f5bdcf

[]

no_license

mahmud-sajib/Social-Profile-Rest-Api

97c89af42439d08e730b3901fc76ac21cc3a7280

5c84ad847ce3303d63284a4363a2b1b4aaf76319

refs/heads/master

2023-03-22T09:24:00.439550

2021-03-15T08:18:44

347,887,887

0

null

UTF-8

Python

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989

py

# Generated by Django 3.1 on 2021-02-28 06:31 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Status', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('content', models.TextField(blank=True, null=True)), ('image', models.ImageField(blank=True, null=True, upload_to='uploads/%Y/%m/%d')), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "[email protected]" ]

[email protected]

0a6291eaae1de1fc9b8321ad436642d3776c3ae5

d48dfa622e07d346a91be3aa8e8657e409faf552

/RozbudowaKodu/lab_files/lab_file_2.py

6b25fe1a7a19217f18835bf54768e39c3fa1b477

[]

no_license

sineczek/PythonSrednioZaawansowany

71c8c94f7cdc193482a50b94315b86e1f0ab0039

75823b36de99ef9ac487672cf131a0b84ce23d2b

refs/heads/main

2023-03-14T04:33:26.853500

2021-03-06T18:13:02

332,524,333

0

null

UTF-8

Python

false

250

py

import math argument_list = [] results_list = [] for i in range (1000000): argument_list.append(i/10) for x in argument_list: results_list.append(abs(x**3 - x**0.5)) print('min = {} max = {}'.format(min(results_list), max(results_list)))

[ "[email protected]" ]

[email protected]

c668614ba1c31b9ddada5697bd9bd9833931bd3e

d28a65d23c204a9736b597ae510d9dd54d2ffd0f

/bin/newdb

cbffe8f0ede31ac97c8ea7393d309dee7b9fa505

[ "BSD-3-Clause" ]

permissive

cts2/rf2db

99ba327611e620fc5533245064afcc1daff7c164

985cd7ad84c8907306a0d7d309d4a1c0fb422ba4

refs/heads/master

2020-05-17T22:37:25.476553

2015-08-24T22:18:19

15,264,407

0

null

UTF-8

Python

false

4,053

#!/usr/local/bin/python # -*- coding: utf-8 -*- # Copyright (c) 2013, Mayo Clinic # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # Neither the name of the Mayo Clinic nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED # OF THE POSSIBILITY OF SUCH DAMAGE. import sys import argparse import os # Assuming that we are running in the bin directory _curdir = os.path.join(os.getcwd(), os.path.dirname(__file__)) sys.path.append(os.path.join(_curdir, '..')) # TODO: Make this into a package sys.path.append(os.path.join(_curdir, '..', '..', 'ConfigManager')) from rf2db.db.RF2DBConnection import config_parms, debug_parms, cp_values, db_values, RF2DBConnection helpargs = ['-h', '-?'] def main(argv): """ Create a MySQL database for RF2 files and/or manage the connection parameters. Example sequence: * python newdb --upd ../../rf2service/settings.conf --host localhost --db rf220140731 --charset utf8 --user root --passwd pw * python newdb --show ../../rf2service/settings.conf * python newdb --create ../../rf2service/settings.conf """ parser = argparse.ArgumentParser(description="Set up RF2 DB parameters and optionally create a database") parser.add_argument('configfile', help="configuration file location") parser.add_argument('--show', dest='show', action="store_true", help="show current configuration") parser.add_argument('--upd', dest='update', action="store_true", help="update configuration file") parser.add_argument('--create', action="store_true", help="create database if it doesn't exist") # Can't do a lot more if there isn't configuration file if len(argv) == 0 or (len(argv) == 1 and argv[0] in helpargs): config_parms.add_to_parser(parser) debug_parms.add_to_parser(parser) parser.parse_args(argv) return # There is (or should be) a configuration file -- pick it out of the arguments and then reparse args = [e for e in argv if e not in helpargs] fileopt, _ = parser.parse_known_args(args) # Open the existing configuration file so we know what the defaults should be cp_values.set_configfile(fileopt.configfile) config_parms.add_to_parser(parser, cp_values) debug_parms.add_to_parser(parser, db_values) opts = parser.parse_args(argv) cp_values.update(vars(opts)) if opts.show: print(str(cp_values)) if opts.update or not opts.show: if cp_values.flush(): print("\nConfiguration file updated") if opts.create: RF2DBConnection().newDB() print("Database %s created in %s" % (cp_values.db, cp_values.host + ((':' + cp_values.port) if cp_values.port else ''))) if __name__ == '__main__': main(sys.argv[1:])

[ "[email protected]" ]

[email protected]

852065b653ca396ea321c7ff5ad1faeaba1cebe6

88b4b883c1a262b5f9ca2c97bf1835d6d73d9f0b

/src/api/python/hce/ftests/ftest_exit_code_simple.py

b2c4a8d9a82b0002258fc983f2ffd5611aca4435

[]

no_license

hce-project/hce-bundle

2f93dc219d717b9983c4bb534884e4a4b95e9b7b

856a6df2acccd67d7af640ed09f05b2c99895f2e

refs/heads/master

2021-09-07T22:55:20.964266

2018-03-02T12:00:42

104,993,955

1

0

null

UTF-8

Python

false

551

py

#!/usr/bin/python """ HCE project, Python bindings, Distributed Tasks Manager application. RTCFinalizer Class content main functional for finalize realtime crawling. @package: dc @file rtc-finalizer.py @author Oleksii <[email protected]>, bgv, Alexander Vybornyh <[email protected]> @link: http://hierarchical-cluster-engine.com/ @copyright: Copyright © 2013-2015 IOIX Ukraine @license: http://hierarchical-cluster-engine.com/license/ @since: 0.1 """ import ppath from ppath import sys import os import sys os._exit(11)

[ "bgv@bgv-d9" ]

bgv@bgv-d9

65e1d34a1f7f96955c2ec68156d6da06d5ed4e7f

9c38f3a354844f5080632005630d249d6487ebb3

/haspy/test/simple/classdef_4.py

e8b6676653624320671c3245c2ff360d34222689

[ "MIT" ]

permissive

keithroe/keithscode

ee7247ad6bdd844279f29a56718992cb886f9215

470c6b833b9b8bc2c78d1b43aac896b0ce9c9a7c

refs/heads/master

2021-01-10T08:48:12.729594

2018-10-16T17:48:31

51,531,627

0

null

UTF-8

Python

false

25

py

@b(c) @d class a: pass

[ "keithroe@cba41313-dcc7-113d-0d3d-2a2d30939b49" ]

keithroe@cba41313-dcc7-113d-0d3d-2a2d30939b49

8025ba35b9d424317c8728eb00872d51f226b847

5fe083b1082dd960dda5789b1cac7287be1d882b

/bin/parse_oneway.py

ade40fc8bd3d76417799a19345007a36ee098b97

[ "MIT" ]

permissive

single-cell-rna-sequencing/scanorama

d412a98386354483a7ae768cb314731084c36431

60d21e5f71722baedc1cc0c2f0bff0338116b16a

refs/heads/master

2020-05-18T19:03:02.178470

2018-12-11T23:14:55

184,600,314

0

1

null

2019-05-02T14:55:33

null

UTF-8

Python

false

1,088

py

import numpy as np from scanorama import plt plt.rcParams.update({'font.size': 25}) import sys scano, uncor = {}, {} in_scano = True for line in open(sys.argv[1]): fields = line.rstrip().split() if len(fields) > 3: continue try: F = float(fields[1]) except ValueError: continue if in_scano: scano[fields[0]] = F else: uncor[fields[0]] = F if fields[0] == 'ZZZ3': in_scano = False scanorama, uncorrected = [], [] for gene in set(scano.keys()) & set(uncor.keys()): scanorama.append(scano[gene]) uncorrected.append(uncor[gene]) scanorama = np.array(scanorama) uncorrected = np.array(uncorrected) below = sum(scanorama > uncorrected + 50) above = sum(scanorama < uncorrected - 50) print('{}% above line'.format(float(above) / float(above + below) * 100)) name = sys.argv[1].split('.')[0] line = min(max(scanorama), max(uncorrected)) plt.figure() plt.scatter(scanorama, uncorrected, s=10) plt.plot([0, line], [0, line], 'r--') plt.tight_layout() plt.savefig('oneway_{}.png'.format(name))

[ "[email protected]" ]

[email protected]

32f4c462ec8097a34c1519e066a80a65f1a14c8f

4f3a4c194451eae32f1ff7cf3b0db947e3892365

/contest24/matrix.py

6a654f89bbe393517b379bdacf7311c9a7f2387e

[]

no_license

szhongren/leetcode

84dd848edbfd728b344927f4f3c376b89b6a81f4

8cda0518440488992d7e2c70cb8555ec7b34083f

refs/heads/master

2021-12-01T01:34:54.639508

2021-11-30T05:54:45

83,624,410

0

null

UTF-8

Python

false

2,146

py

""" Given a matrix consists of 0 and 1, find the distance of the nearest 0 for each cell. The distance between two adjacent cells is 1. Example 1: Input: 0 0 0 0 1 0 0 0 0 Output: 0 0 0 0 1 0 0 0 0 Example 2: Input: 0 0 0 0 1 0 1 1 1 Output: 0 0 0 0 1 0 1 2 1 Note: The number of elements of the given matrix will not exceed 10,000. There are at least one 0 in the given matrix. The cells are adjacent in only four directions: up, down, left and right. """ class Solution(object): def updateMatrix(self, matrix): """ :type matrix: List[List[int]] :rtype: List[List[int]] """ indexes = [(i, j) for i in range(len(matrix)) for j in range(len(matrix[0])) if matrix[i][j] == 1] matrix = [[0 if val == 0 else -1 for val in row]for row in matrix] curr_level = 0 while len(indexes) > 0: new_indexes = [] for index in indexes: done = False x = index[0] y = index[1] if x > 0: if matrix[x - 1][y] == curr_level: done = True matrix[x][y] = curr_level + 1 if y > 0: if matrix[x][y - 1] == curr_level: done = True matrix[x][y] = curr_level + 1 if x < len(matrix) - 1: if matrix[x + 1][y] == curr_level: done = True matrix[x][y] = curr_level + 1 if y < len(matrix[0]) - 1: if matrix[x][y + 1] == curr_level: done = True matrix[x][y] = curr_level + 1 if not done: new_indexes.append(index) curr_level += 1 indexes = new_indexes return matrix ans = Solution() print(ans.updateMatrix([ [0, 0, 0], [0, 1, 0], [0, 0, 0] ])) print(ans.updateMatrix([ [1, 1, 1], [0, 1, 0], [0, 0, 0] ]))

[ "[email protected]" ]

[email protected]

86c74660e97fcfad69873b6025a9430d87b3496f

492d3e666b87eff971628a74fe13facde01e2949

/htmlcov/_python_Django_My Projects_student-portal_Lib_site-packages_django_db_migrations_graph_py.html.py

05c7ac90efdcb13c551ee039f2457e7c75ee6996

[]

no_license

OmarFateh/Student-Portal

42050da15327aa01944dc79b5e00ca34deb51531

167ffd3a4183529c0cbc5db4ab232026711ea915

refs/heads/master

2023-06-13T01:03:16.475588

2021-07-08T11:09:09

382,895,837

0

null

UTF-8

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false

96,031

py

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XXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXX XX XXXXXXXXX XXXXXXXXXX XXXXXXXXXXXXXXX XXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXX XX XXXXXXXXX XXXXXXXXXXXXXXXXX XXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXX XX XXXXXXXXX XXXXXXXXXXXXXXXXX XXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXX XX XXXXXXXXX XXXXXXXXXXXXXXXXX XXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXX XXXXXX XXXX XXXXXXXXXXXX XXXX XXXXXXXXXXXXXXXX XXX XX XXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXX XXXXXX XXXXXX XX XXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXXXX XXXXX XXXXX XXXX XXXXXX XXXXXX XXXXXXX XXXXXXX

[ "[email protected]" ]

[email protected]

dbee469da3d768ac8bd9b40a106f32df70d98ae3

069dafce9f495f09bf8c2f76dbf5c045b7551721

/run_size_V1_inhibition_overlapping.py

2445079f234bb3bce526b0f73ebe9143a77a5600

[]

no_license

dguarino/T2

26b1bc640812aa5438b09f9fab2bc73096cd7eef

66b786928508089492f5f696c7c1576e098c6615

refs/heads/master

2020-04-03T22:39:06.059845

2020-03-13T15:43:02

41,812,819

1

0

null

UTF-8

Python

false

2,661

py

# -*- coding: utf-8 -*- """ This is """ from pyNN import nest import sys import mozaik import mozaik.controller from mozaik.controller import run_workflow, setup_logging from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from parameters import ParameterSet from model_V1_full import ThalamoCorticalModel from experiments import create_experiments_size_V1_inactivated_overlapping from analysis_and_visualization import perform_analysis_test from analysis_and_visualization import perform_analysis_and_visualization from analysis_and_visualization import perform_analysis_and_visualization_radius try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop # Model execution if True: data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size_V1_inactivated_overlapping ) data_store.save() # or only load pickled data else: setup_logging() # data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'Deliverable/ThalamoCorticalModel_data_size_overlapping_____', 'store_stimuli' : False}),replace=True) data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'ThalamoCorticalModel_data_size_overlapping_____', 'store_stimuli' : False}),replace=True) logger.info('Loaded data store') # Analysis and Plotting if mpi_comm.rank == MPI_ROOT: # perform_analysis_test( data_store ) # perform_analysis_and_visualization( data_store, 'luminance', withPGN, withV1 ) # perform_analysis_and_visualization( data_store, 'contrast', withPGN, withV1 ) # perform_analysis_and_visualization( data_store, 'spatial_frequency', withPGN, withV1 ) # perform_analysis_and_visualization( data_store, 'temporal_frequency', withPGN, withV1 ) perform_analysis_and_visualization( data_store, 'size', withPGN, withV1 ) # perform_analysis_and_visualization( data_store, 'size_radius', withPGN, withV1 ) # perform_analysis_and_visualization( data_store, 'orientation', withPGN, withV1 ) # import numpy # step = .2 # for i in numpy.arange(step, 2.+step, step): # perform_analysis_and_visualization_radius( data_store, 'size_radius', [i-step,i], withPGN, withV1 ) data_store.save()

[ "[email protected]" ]

[email protected]

72624296abb72df145e8940e711e87d4f531eab1

09ae3f372d1000f118ad80874870ae420a4be66f

/scikit-learn-master/sklearn/datasets/tests/test_base.py

08a6ba29413cf8bbe4e7c2cdcbf499993f650548

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permissive

lqkweb/learnMLflow

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13c5decaebba95b1b90f92021be35e343b4764af

refs/heads/master

2022-10-18T06:17:23.584172

2019-01-18T09:51:38

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import os import shutil import tempfile import warnings import numpy from pickle import loads from pickle import dumps from functools import partial import pytest from sklearn.datasets import get_data_home from sklearn.datasets import clear_data_home from sklearn.datasets import load_files from sklearn.datasets import load_sample_images from sklearn.datasets import load_sample_image from sklearn.datasets import load_digits from sklearn.datasets import load_diabetes from sklearn.datasets import load_linnerud from sklearn.datasets import load_iris from sklearn.datasets import load_breast_cancer from sklearn.datasets import load_boston from sklearn.datasets import load_wine from sklearn.datasets.base import Bunch from sklearn.datasets.tests.test_common import check_return_X_y from sklearn.externals._pilutil import pillow_installed from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises def _remove_dir(path): if os.path.isdir(path): shutil.rmtree(path) @pytest.fixture(scope="module") def data_home(tmpdir_factory): tmp_file = str(tmpdir_factory.mktemp("scikit_learn_data_home_test")) yield tmp_file _remove_dir(tmp_file) @pytest.fixture(scope="module") def load_files_root(tmpdir_factory): tmp_file = str(tmpdir_factory.mktemp("scikit_learn_load_files_test")) yield tmp_file _remove_dir(tmp_file) @pytest.fixture def test_category_dir_1(load_files_root): test_category_dir1 = tempfile.mkdtemp(dir=load_files_root) sample_file = tempfile.NamedTemporaryFile(dir=test_category_dir1, delete=False) sample_file.write(b"Hello World!\n") sample_file.close() yield str(test_category_dir1) _remove_dir(test_category_dir1) @pytest.fixture def test_category_dir_2(load_files_root): test_category_dir2 = tempfile.mkdtemp(dir=load_files_root) yield str(test_category_dir2) _remove_dir(test_category_dir2) def test_data_home(data_home): # get_data_home will point to a pre-existing folder data_home = get_data_home(data_home=data_home) assert_equal(data_home, data_home) assert os.path.exists(data_home) # clear_data_home will delete both the content and the folder it-self clear_data_home(data_home=data_home) assert not os.path.exists(data_home) # if the folder is missing it will be created again data_home = get_data_home(data_home=data_home) assert os.path.exists(data_home) def test_default_empty_load_files(load_files_root): res = load_files(load_files_root) assert_equal(len(res.filenames), 0) assert_equal(len(res.target_names), 0) assert_equal(res.DESCR, None) def test_default_load_files(test_category_dir_1, test_category_dir_2, load_files_root): res = load_files(load_files_root) assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 2) assert_equal(res.DESCR, None) assert_equal(res.data, [b"Hello World!\n"]) def test_load_files_w_categories_desc_and_encoding( test_category_dir_1, test_category_dir_2, load_files_root): category = os.path.abspath(test_category_dir_1).split('/').pop() res = load_files(load_files_root, description="test", categories=category, encoding="utf-8") assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 1) assert_equal(res.DESCR, "test") assert_equal(res.data, ["Hello World!\n"]) def test_load_files_wo_load_content( test_category_dir_1, test_category_dir_2, load_files_root): res = load_files(load_files_root, load_content=False) assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 2) assert_equal(res.DESCR, None) assert_equal(res.get('data'), None) def test_load_sample_images(): try: res = load_sample_images() assert_equal(len(res.images), 2) assert_equal(len(res.filenames), 2) assert res.DESCR except ImportError: warnings.warn("Could not load sample images, PIL is not available.") def test_load_digits(): digits = load_digits() assert_equal(digits.data.shape, (1797, 64)) assert_equal(numpy.unique(digits.target).size, 10) # test return_X_y option check_return_X_y(digits, partial(load_digits)) def test_load_digits_n_class_lt_10(): digits = load_digits(9) assert_equal(digits.data.shape, (1617, 64)) assert_equal(numpy.unique(digits.target).size, 9) def test_load_sample_image(): try: china = load_sample_image('china.jpg') assert_equal(china.dtype, 'uint8') assert_equal(china.shape, (427, 640, 3)) except ImportError: warnings.warn("Could not load sample images, PIL is not available.") def test_load_missing_sample_image_error(): if pillow_installed: assert_raises(AttributeError, load_sample_image, 'blop.jpg') else: warnings.warn("Could not load sample images, PIL is not available.") def test_load_diabetes(): res = load_diabetes() assert_equal(res.data.shape, (442, 10)) assert res.target.size, 442 assert_equal(len(res.feature_names), 10) assert res.DESCR # test return_X_y option check_return_X_y(res, partial(load_diabetes)) def test_load_linnerud(): res = load_linnerud() assert_equal(res.data.shape, (20, 3)) assert_equal(res.target.shape, (20, 3)) assert_equal(len(res.target_names), 3) assert res.DESCR assert os.path.exists(res.data_filename) assert os.path.exists(res.target_filename) # test return_X_y option check_return_X_y(res, partial(load_linnerud)) def test_load_iris(): res = load_iris() assert_equal(res.data.shape, (150, 4)) assert_equal(res.target.size, 150) assert_equal(res.target_names.size, 3) assert res.DESCR assert os.path.exists(res.filename) # test return_X_y option check_return_X_y(res, partial(load_iris)) def test_load_wine(): res = load_wine() assert_equal(res.data.shape, (178, 13)) assert_equal(res.target.size, 178) assert_equal(res.target_names.size, 3) assert res.DESCR # test return_X_y option check_return_X_y(res, partial(load_wine)) def test_load_breast_cancer(): res = load_breast_cancer() assert_equal(res.data.shape, (569, 30)) assert_equal(res.target.size, 569) assert_equal(res.target_names.size, 2) assert res.DESCR assert os.path.exists(res.filename) # test return_X_y option check_return_X_y(res, partial(load_breast_cancer)) def test_load_boston(): res = load_boston() assert_equal(res.data.shape, (506, 13)) assert_equal(res.target.size, 506) assert_equal(res.feature_names.size, 13) assert res.DESCR assert os.path.exists(res.filename) # test return_X_y option check_return_X_y(res, partial(load_boston)) def test_loads_dumps_bunch(): bunch = Bunch(x="x") bunch_from_pkl = loads(dumps(bunch)) bunch_from_pkl.x = "y" assert_equal(bunch_from_pkl['x'], bunch_from_pkl.x) def test_bunch_pickle_generated_with_0_16_and_read_with_0_17(): bunch = Bunch(key='original') # This reproduces a problem when Bunch pickles have been created # with scikit-learn 0.16 and are read with 0.17. Basically there # is a surprising behaviour because reading bunch.key uses # bunch.__dict__ (which is non empty for 0.16 Bunch objects) # whereas assigning into bunch.key uses bunch.__setattr__. See # https://github.com/scikit-learn/scikit-learn/issues/6196 for # more details bunch.__dict__['key'] = 'set from __dict__' bunch_from_pkl = loads(dumps(bunch)) # After loading from pickle the __dict__ should have been ignored assert_equal(bunch_from_pkl.key, 'original') assert_equal(bunch_from_pkl['key'], 'original') # Making sure that changing the attr does change the value # associated with __getitem__ as well bunch_from_pkl.key = 'changed' assert_equal(bunch_from_pkl.key, 'changed') assert_equal(bunch_from_pkl['key'], 'changed') def test_bunch_dir(): # check that dir (important for autocomplete) shows attributes data = load_iris() assert "data" in dir(data)

[ "[email protected]" ]

[email protected]

d74fff88ba05004f13b29253044811a8d2b7d787

3249577773cf18e5c09ea36de62477ddb43b662b

/Python/flask_fundamentals/Disappearing Ninja/server.py

91bc33c8de93b14123c980e0d252bf8f7f89d6c4

[]

no_license

HollinRoberts/code

5394abe2a7c42bbbe83d8f64a99c50a52f05792b

8026522ab169c4174037fdf1b271de60b75d79bf

refs/heads/master

2021-01-01T16:12:11.674680

2017-10-18T21:08:10

97,786,418

0

null

UTF-8

Python

false

671

py

from flask import Flask, render_template, request, redirect app = Flask(__name__) @app.route("/") def index(): return render_template("index.html") @app.route("/ninja") def ninja(): return render_template("ninja.html") @app.route("/ninja/<color>") def ninja_color(color): if color=="blue": return render_template("leonardo.html" ) elif color=="orange": return render_template("michelangelo.html") elif color=="red": return render_template("raphael.html") elif color=="purple": return render_template("donatello.html") else: return render_template("notapril.html") app.run(debug=True)

[ "[email protected]" ]

[email protected]

d59c7349c687bb89df6ffe6c91d0cb52724efdaa

d4eb113c44c86322b3811513a7286d176f106eb6

/experiments/variational_autoencoder/validation/compare_results.py

9533452ba1c680b701a373947b1b8279453615c6

[]

no_license

philip-brohan/Machine-Learning

67a2eb780383b3436da4fef1d763f39d255ae696

dc53b9c336d5f12272257f327abe49dec436ea04

refs/heads/master

2021-03-27T12:33:07.518279

2020-04-30T19:38:02

56,614,781

0

null

UTF-8

Python

false

6,113

py

#!/usr/bin/env python # Model training results plot import tensorflow as tf tf.enable_eager_execution() import numpy import IRData.twcr as twcr import iris import datetime import argparse import os import math import pickle import Meteorographica as mg import matplotlib from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure import cartopy import cartopy.crs as ccrs # Function to resize and rotate pole def rr_cube(cbe): # Use the Cassini projection (boundary is the equator) cs=iris.coord_systems.RotatedGeogCS(0.0,60.0,270.0) # Latitudes cover -90 to 90 with 79 values lat_values=numpy.arange(-90,91,180/78) latitude = iris.coords.DimCoord(lat_values, standard_name='latitude', units='degrees_north', coord_system=cs) # Longitudes cover -180 to 180 with 159 values lon_values=numpy.arange(-180,181,360/158) longitude = iris.coords.DimCoord(lon_values, standard_name='longitude', units='degrees_east', coord_system=cs) dummy_data = numpy.zeros((len(lat_values), len(lon_values))) dummy_cube = iris.cube.Cube(dummy_data, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]) n_cube=cbe.regrid(dummy_cube,iris.analysis.Linear()) return(n_cube) # Get the 20CR data ic=twcr.load('prmsl',datetime.datetime(2009,3,12,18), version='2c') ic=rr_cube(ic.extract(iris.Constraint(member=1))) # Get the autoencoder model_save_file=("%s/Machine-Learning-experiments/"+ "variational_autoencoder/"+ "/saved_models/Epoch_%04d/autoencoder") % ( os.getenv('SCRATCH'),500) autoencoder=tf.keras.models.load_model(model_save_file,compile=False) # Normalisation - Pa to mean=0, sd=1 - and back def normalise(x): x -= 101325 x /= 3000 return x def unnormalise(x): x *= 3000 x += 101325 return x fig=Figure(figsize=(9.6,10.8), # 1/2 HD dpi=100, facecolor=(0.88,0.88,0.88,1), edgecolor=None, linewidth=0.0, frameon=False, subplotpars=None, tight_layout=None) canvas=FigureCanvas(fig) # Top - map showing original and reconstructed fields projection=ccrs.RotatedPole(pole_longitude=60.0, pole_latitude=0.0, central_rotated_longitude=270.0) ax_map=fig.add_axes([0.01,0.51,0.98,0.48],projection=projection) ax_map.set_axis_off() extent=[-180,180,-90,90] ax_map.set_extent(extent, crs=projection) matplotlib.rc('image',aspect='auto') # Run the data through the autoencoder and convert back to iris cube pm=ic.copy() pm.data=normalise(pm.data) ict=tf.convert_to_tensor(pm.data, numpy.float32) ict=tf.reshape(ict,[1,79,159,1]) result=autoencoder.predict_on_batch(ict) result=tf.reshape(result,[79,159]) pm.data=unnormalise(result) # Background, grid and land ax_map.background_patch.set_facecolor((0.88,0.88,0.88,1)) #mg.background.add_grid(ax_map) land_img_orig=ax_map.background_img(name='GreyT', resolution='low') # original pressures as red contours mg.pressure.plot(ax_map,ic, scale=0.01, resolution=0.25, levels=numpy.arange(870,1050,7), colors='red', label=False, linewidths=1) # Encoded pressures as blue contours mg.pressure.plot(ax_map,pm, scale=0.01, resolution=0.25, levels=numpy.arange(870,1050,7), colors='blue', label=False, linewidths=1) mg.utils.plot_label(ax_map, '%04d-%02d-%02d:%02d' % (2009,3,12,6), facecolor=(0.88,0.88,0.88,0.9), fontsize=8, x_fraction=0.98, y_fraction=0.03, verticalalignment='bottom', horizontalalignment='right') # Scatterplot of encoded v original ax=fig.add_axes([0.08,0.05,0.45,0.4]) aspect=.225/.4*16/9 # Axes ranges from data dmin=min(ic.data.min(),pm.data.min()) dmax=max(ic.data.max(),pm.data.max()) dmean=(dmin+dmax)/2 dmax=dmean+(dmax-dmean)*1.05 dmin=dmean-(dmean-dmin)*1.05 if aspect<1: ax.set_xlim(dmin/100,dmax/100) ax.set_ylim((dmean-(dmean-dmin)*aspect)/100, (dmean+(dmax-dmean)*aspect)/100) else: ax.set_ylim(dmin/100,dmax/100) ax.set_xlim((dmean-(dmean-dmin)*aspect)/100, (dmean+(dmax-dmean)*aspect)/100) ax.scatter(x=pm.data.flatten()/100, y=ic.data.flatten()/100, c='black', alpha=0.25, marker='.', s=2) ax.set(ylabel='Original', xlabel='Encoded') ax.grid(color='black', alpha=0.2, linestyle='-', linewidth=0.5) # Plot the training history history_save_file=("%s/Machine-Learning-experiments/"+ "variational_autoencoder/"+ "saved_models/history_to_%04d.pkl") % ( os.getenv('SCRATCH'),500) history=pickle.load( open( history_save_file, "rb" ) ) ax=fig.add_axes([0.62,0.05,0.35,0.4]) # Axes ranges from data ax.set_xlim(0,len(history['loss'])) ax.set_ylim(0,numpy.max(numpy.concatenate((history['loss'], history['val_loss'])))) ax.set(xlabel='Epochs', ylabel='Loss (grey) and validation loss (black)') ax.grid(color='black', alpha=0.2, linestyle='-', linewidth=0.5) ax.plot(range(len(history['loss'])), history['loss'], color='grey', linestyle='-', linewidth=2) ax.plot(range(len(history['val_loss'])), history['val_loss'], color='black', linestyle='-', linewidth=2) # Render the figure as a png fig.savefig("comparison_results.png")

[ "[email protected]" ]

[email protected]

a2010a39af08d72a34b058f92fd12104c0aa8d29

aa0cc19eedf38baca2ecef3de6f2a4c69ce68675

/clld/scripts/postgres2sqlite.py

168f94320038122afe286f29dcc8c331998e4f23

[]

no_license

mitcho/clld

de84c54247138efa53ee5f68a87edc2a0ab06bbf

dcf5f063a44ac5167f677f05b2c66b0d094d4ff3

refs/heads/master

2021-01-18T09:56:18.486647

2013-08-23T15:13:18

null

0

null

UTF-8

Python

false

3,229

py

""" python postgres2sqlite.py apics 2>&1 >/dev/null | less Unfortunately this approach does not seem to work, thus, our only option is intialize_db and making sure all db changes are done via alembic migrations. """ from subprocess import call from importlib import import_module import pkg_resources import re from tempfile import mktemp from path import path from sqlalchemy import create_engine from clld.db.meta import Base def replace_booleans(line): """replaces postgres boolean literals with 0|1 within the values in an INSERT statement as created by pg_dump. .. note:: - we rely on the INSERT statements not containing newlines. - we somewhat naively split the values at commas and assume that if a single token equals "true" or false", it was a boolean value in postgres. Obviously this assumption does not hold for a text value like "..., true, ...". We may switch to using sqlparse for a more robust detection of booleans. >>> assert replace_booleans('INSERT (true, false);').strip() == 'INSERT (1, 0);' """ insert, values = line.split('(', 1) assert values.endswith(');') values = values[:-2] clean_values = [] for token in values.split(', '): if token == 'true': token = "1" elif token == 'false': token = "0" clean_values.append(token) return '%s(%s);\n' % (insert, ', '.join(clean_values)) STMT_END = re.compile("([^\']\'|\, [0-9]+)\)\;$") def inserts(iterator): """ >>> assert list(inserts(["INSERT (1, 1);"])) == ['INSERT (1, 1);'] >>> assert list(inserts(["INSERT ('a", "b');"])) == ["INSERT ('a__newline__b');"] """ insert = [] for line in iterator: line = line.strip() if line.startswith('INSERT '): if STMT_END.search(line): yield line else: insert = [line] else: if insert: # a continuation line! insert.append(line) if STMT_END.search(line): c = '__newline__'.join(insert) insert = [] yield c def convert_dump(i, o): # pragma: no cover _insert = False with file(o, 'w') as fp: fp.write('.echo OFF\n.bail ON\n') fp.write('BEGIN;\n') for n, insert in enumerate(inserts(file(i))): fp.write(replace_booleans(insert)) fp.write('END;\n') def postgres2sqlite(name): # pragma: no cover pg_sql = path(mktemp('.sql')) sqlite_sql = path(mktemp('.sql')) sqlite = mktemp('.sqlite') call("pg_dump -f {0} --data-only --inserts {1}".format(pg_sql, name), shell=True) convert_dump(pg_sql, sqlite_sql) engine = create_engine('sqlite:////{0}'.format(sqlite)) m = import_module('{0}.models'.format(name)) Base.metadata.create_all(engine) call('sqlite3 -bail -init {0} {1} ".exit"'.format(sqlite_sql, sqlite), shell=True) if pg_sql.exists(): pg_sql.remove() if sqlite_sql.exists(): sqlite_sql.remove() return sqlite if __name__ == '__main__': # pragma: no cover import sys postgres2sqlite(sys.argv[1]) sys.exit(0)

[ "[email protected]" ]

[email protected]

f60880e5d4192b5bcbd9bd669c188d6935c9d098

4bee31f6a823fb1aebbd3dfe1d163aa0b1d41a7c

/seata/registry/FileRegistry.py

460f4982eb6b95f9f7bcc623f50e55a313c15d63

[ "Apache-2.0" ]

permissive

rohankumardubey/seata-python

92532d1e8f8c961f2317aa8c23e2f53fe07711e9

66fb3382217a43effa3d1bc5ec2b62204d499dba

refs/heads/master

2023-08-17T08:29:12.603412

2021-09-27T06:04:56

null

0

null

UTF-8

Python

false

1,249

py

#!/usr/bin/env python3 # -*- coding:utf-8 -*- # @author jsbxyyx # @since 1.0 from seata.config.Config import ConfigFactory from seata.core.rpc.Address import Address from seata.registry.Registry import Registry class FileRegistry(Registry): config = ConfigFactory.get_config() def __init__(self): pass def register(self, address): pass def unregister(self, address): pass def subscribe(self, cluster, listener): pass def unsubscribe(self, cluster, listener): pass def lookup(self, key): cluster_name = super(FileRegistry, self).get_service_group(key) if cluster_name is None: return None endpoint_str = self.config.get('service.grouplist.' + cluster_name) endpoints = endpoint_str.split(';') addresses = [] for endpoint in endpoints: if endpoint is None or len(endpoint.strip()) == 0: continue ip_port_arr = endpoint.split(':') if len(ip_port_arr) != 2: raise ValueError('endpoint format should like ip:port') addresses.append(Address(ip_port_arr[0], int(ip_port_arr[1]))) return addresses def close(self): pass

[ "[email protected]" ]

[email protected]

a06e048a185a9d0251f8d18dda29718efb09a160

462e52636f351a30da5bf2159bc9b30719bbe79b

/stuff/finished libraries/pytorch/_torch_docs.py

5737af3d182faaa9e7a7cddb16dc5de740dcd382

[]

no_license

SoutiRini/Top-20-Python-Libraries

c27b1ae77e11209bfe97405f8e324c54d4d49db4

1adcc6255dc59b25a831df2d23fa759e1e7c3264

refs/heads/master

2022-12-08T11:09:19.587218

2020-08-28T22:16:41

291,153,411

4

1

null

UTF-8

Python

false

267,122

py

"""Adds docstrings to functions defined in the torch._C""" import re import torch._C from torch._C import _add_docstr as add_docstr def parse_kwargs(desc): """Maps a description of args to a dictionary of {argname: description}. Input: (' weight (Tensor): a weight tensor\n' + ' Some optional description') Output: { 'weight': \ 'weight (Tensor): a weight tensor\n Some optional description' } """ # Split on exactly 4 spaces after a newline regx = re.compile(r"\n\s{4}(?!\s)") kwargs = [section.strip() for section in regx.split(desc)] kwargs = [section for section in kwargs if len(section) > 0] return {desc.split(' ')[0]: desc for desc in kwargs} def merge_dicts(*dicts): return {x: d[x] for d in dicts for x in d} common_args = parse_kwargs(""" input (Tensor): the input tensor. generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling out (Tensor, optional): the output tensor. """) reduceops_common_args = merge_dicts(common_args, parse_kwargs(""" dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. keepdim (bool): whether the output tensor has :attr:`dim` retained or not. """)) multi_dim_common = merge_dicts(reduceops_common_args, parse_kwargs(""" dim (int or tuple of ints): the dimension or dimensions to reduce. """), {'keepdim_details': """ If :attr:`keepdim` is ``True``, the output tensor is of the same size as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the output tensor having 1 (or ``len(dim)``) fewer dimension(s). """}) single_dim_common = merge_dicts(reduceops_common_args, parse_kwargs(""" dim (int): the dimension to reduce. """), {'keepdim_details': """If :attr:`keepdim` is ``True``, the output tensor is of the same size as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the output tensor having 1 fewer dimension than :attr:`input`."""}) factory_common_args = merge_dicts(common_args, parse_kwargs(""" dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`). layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. Default: ``torch.strided``. device (:class:`torch.device`, optional): the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: ``False``. pin_memory (bool, optional): If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. memory_format (:class:`torch.memory_format`, optional): the desired memory format of returned Tensor. Default: ``torch.contiguous_format``. """)) factory_like_common_args = parse_kwargs(""" input (Tensor): the size of :attr:`input` will determine size of the output tensor. layout (:class:`torch.layout`, optional): the desired layout of returned tensor. Default: if ``None``, defaults to the layout of :attr:`input`. dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of :attr:`input`. device (:class:`torch.device`, optional): the desired device of returned tensor. Default: if ``None``, defaults to the device of :attr:`input`. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: ``False``. pin_memory (bool, optional): If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. memory_format (:class:`torch.memory_format`, optional): the desired memory format of returned Tensor. Default: ``torch.preserve_format``. """) factory_data_common_args = parse_kwargs(""" data (array_like): Initial data for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if ``None``, infers data type from :attr:`data`. device (:class:`torch.device`, optional): the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: ``False``. pin_memory (bool, optional): If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. """) add_docstr(torch.abs, r""" abs(input, out=None) -> Tensor Computes the element-wise absolute value of the given :attr:`input` tensor. .. math:: \text{out}_{i} = |\text{input}_{i}| """ + r""" Args: {input} {out} Example:: >>> torch.abs(torch.tensor([-1, -2, 3])) tensor([ 1, 2, 3]) """.format(**common_args)) add_docstr(torch.absolute, r""" absolute(input, out=None) -> Tensor Alias for :func:`torch.abs` """.format(**common_args)) add_docstr(torch.acos, r""" acos(input, out=None) -> Tensor Returns a new tensor with the arccosine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \cos^{-1}(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.3348, -0.5889, 0.2005, -0.1584]) >>> torch.acos(a) tensor([ 1.2294, 2.2004, 1.3690, 1.7298]) """.format(**common_args)) add_docstr(torch.acosh, r""" acosh(input, out=None) -> Tensor Returns a new tensor with the inverse hyperbolic cosine of the elements of :attr:`input`. Note: The domain of the inverse hyperbolic cosine is `[1, inf)` and values outside this range will be mapped to ``NaN``, except for `+ INF` for which the output is mapped to `+ INF`. .. math:: \text{out}_{i} = \cosh^{-1}(\text{input}_{i}) """ + r""" Args: {input} Keyword arguments: {out} Example:: >>> a = torch.randn(4).uniform_(1, 2) >>> a tensor([ 1.3192, 1.9915, 1.9674, 1.7151 ]) >>> torch.acosh(a) tensor([ 0.7791, 1.3120, 1.2979, 1.1341 ]) """.format(**common_args)) add_docstr(torch.add, r""" add(input, other, out=None) Adds the scalar :attr:`other` to each element of the input :attr:`input` and returns a new resulting tensor. .. math:: \text{{out}} = \text{{input}} + \text{{other}} If :attr:`input` is of type FloatTensor or DoubleTensor, :attr:`other` must be a real number, otherwise it should be an integer. Args: {input} value (Number): the number to be added to each element of :attr:`input` Keyword arguments: {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) >>> torch.add(a, 20) tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) .. function:: add(input, other, *, alpha=1, out=None) Each element of the tensor :attr:`other` is multiplied by the scalar :attr:`alpha` and added to each element of the tensor :attr:`input`. The resulting tensor is returned. The shapes of :attr:`input` and :attr:`other` must be :ref:`broadcastable <broadcasting-semantics>`. .. math:: \text{{out}} = \text{{input}} + \text{{alpha}} \times \text{{other}} If :attr:`other` is of type FloatTensor or DoubleTensor, :attr:`alpha` must be a real number, otherwise it should be an integer. Args: input (Tensor): the first input tensor other (Tensor): the second input tensor alpha (Number): the scalar multiplier for :attr:`other` Keyword arguments: {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.9732, -0.3497, 0.6245, 0.4022]) >>> b = torch.randn(4, 1) >>> b tensor([[ 0.3743], [-1.7724], [-0.5811], [-0.8017]]) >>> torch.add(a, b, alpha=10) tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], [-18.6971, -18.0736, -17.0994, -17.3216], [ -6.7845, -6.1610, -5.1868, -5.4090], [ -8.9902, -8.3667, -7.3925, -7.6147]]) """.format(**common_args)) add_docstr(torch.addbmm, r""" addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor Performs a batch matrix-matrix product of matrices stored in :attr:`batch1` and :attr:`batch2`, with a reduced add step (all matrix multiplications get accumulated along the first dimension). :attr:`input` is added to the final result. :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same number of matrices. If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a :math:`(b \times m \times p)` tensor, :attr:`input` must be :ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor and :attr:`out` will be a :math:`(n \times p)` tensor. .. math:: out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) """ + r""" For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` must be real numbers, otherwise they should be integers. Args: batch1 (Tensor): the first batch of matrices to be multiplied batch2 (Tensor): the second batch of matrices to be multiplied beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) input (Tensor): matrix to be added alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) {out} Example:: >>> M = torch.randn(3, 5) >>> batch1 = torch.randn(10, 3, 4) >>> batch2 = torch.randn(10, 4, 5) >>> torch.addbmm(M, batch1, batch2) tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) """.format(**common_args)) add_docstr(torch.addcdiv, r""" addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, multiply the result by the scalar :attr:`value` and add it to :attr:`input`. .. warning:: Integer division with addcdiv is no longer supported, and in a future release addcdiv will perform a true division of :attr:`tensor1` and :attr:`tensor2`. The historic addcdiv behavior can be implemented using :func:`floor_divide` for integral inputs (:attr:`input` + :attr:`value` * :attr:`tensor1` // :attr:`tensor2`) and :func:`div` for float inputs (:attr:`input` + :attr:`value` * :attr:`tensor1` / :attr:`tensor2`). The future addcdiv behavior can be implemented with :func:`true_divide` (:attr:`input` + :attr:`value` * torch.true_divide(:attr:`tensor1`, :attr:`tensor2`). .. math:: \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} """ + r""" The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be :ref:`broadcastable <broadcasting-semantics>`. For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be a real number, otherwise an integer. Args: input (Tensor): the tensor to be added tensor1 (Tensor): the numerator tensor tensor2 (Tensor): the denominator tensor value (Number, optional): multiplier for :math:`\text{{tensor1}} / \text{{tensor2}}` {out} Example:: >>> t = torch.randn(1, 3) >>> t1 = torch.randn(3, 1) >>> t2 = torch.randn(1, 3) >>> torch.addcdiv(t, t1, t2, value=0.1) tensor([[-0.2312, -3.6496, 0.1312], [-1.0428, 3.4292, -0.1030], [-0.5369, -0.9829, 0.0430]]) """.format(**common_args)) add_docstr(torch.addcmul, r""" addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor Performs the element-wise multiplication of :attr:`tensor1` by :attr:`tensor2`, multiply the result by the scalar :attr:`value` and add it to :attr:`input`. .. math:: \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i """ + r""" The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be :ref:`broadcastable <broadcasting-semantics>`. For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be a real number, otherwise an integer. Args: input (Tensor): the tensor to be added tensor1 (Tensor): the tensor to be multiplied tensor2 (Tensor): the tensor to be multiplied value (Number, optional): multiplier for :math:`tensor1 .* tensor2` {out} Example:: >>> t = torch.randn(1, 3) >>> t1 = torch.randn(3, 1) >>> t2 = torch.randn(1, 3) >>> torch.addcmul(t, t1, t2, value=0.1) tensor([[-0.8635, -0.6391, 1.6174], [-0.7617, -0.5879, 1.7388], [-0.8353, -0.6249, 1.6511]]) """.format(**common_args)) add_docstr(torch.addmm, r""" addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. The matrix :attr:`input` is added to the final result. If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, then :attr:`input` must be :ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor and :attr:`out` will be a :math:`(n \times p)` tensor. :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. .. math:: \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) """ + r""" For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` must be real numbers, otherwise they should be integers. Args: input (Tensor): matrix to be added mat1 (Tensor): the first matrix to be multiplied mat2 (Tensor): the second matrix to be multiplied beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) {out} Example:: >>> M = torch.randn(2, 3) >>> mat1 = torch.randn(2, 3) >>> mat2 = torch.randn(3, 3) >>> torch.addmm(M, mat1, mat2) tensor([[-4.8716, 1.4671, -1.3746], [ 0.7573, -3.9555, -2.8681]]) """.format(**common_args)) add_docstr(torch.addmv, r""" addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor Performs a matrix-vector product of the matrix :attr:`mat` and the vector :attr:`vec`. The vector :attr:`input` is added to the final result. If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of size `m`, then :attr:`input` must be :ref:`broadcastable <broadcasting-semantics>` with a 1-D tensor of size `n` and :attr:`out` will be 1-D tensor of size `n`. :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. .. math:: \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) """ + r""" For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` must be real numbers, otherwise they should be integers Args: input (Tensor): vector to be added mat (Tensor): matrix to be multiplied vec (Tensor): vector to be multiplied beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) {out} Example:: >>> M = torch.randn(2) >>> mat = torch.randn(2, 3) >>> vec = torch.randn(3) >>> torch.addmv(M, mat, vec) tensor([-0.3768, -5.5565]) """.format(**common_args)) add_docstr(torch.addr, r""" addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` and adds it to the matrix :attr:`input`. Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the outer product between :attr:`vec1` and :attr:`vec2` and the added matrix :attr:`input` respectively. .. math:: \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) """ + r""" If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector of size `m`, then :attr:`input` must be :ref:`broadcastable <broadcasting-semantics>` with a matrix of size :math:`(n \times m)` and :attr:`out` will be a matrix of size :math:`(n \times m)`. For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` must be real numbers, otherwise they should be integers Args: input (Tensor): matrix to be added vec1 (Tensor): the first vector of the outer product vec2 (Tensor): the second vector of the outer product beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`\text{{vec1}} \otimes \text{{vec2}}` (:math:`\alpha`) {out} Example:: >>> vec1 = torch.arange(1., 4.) >>> vec2 = torch.arange(1., 3.) >>> M = torch.zeros(3, 2) >>> torch.addr(M, vec1, vec2) tensor([[ 1., 2.], [ 2., 4.], [ 3., 6.]]) """.format(**common_args)) add_docstr(torch.allclose, r""" allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool This function checks if all :attr:`input` and :attr:`other` satisfy the condition: .. math:: \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert """ + r""" elementwise, for all elements of :attr:`input` and :attr:`other`. The behaviour of this function is analogous to `numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>`_ Args: input (Tensor): first tensor to compare other (Tensor): second tensor to compare atol (float, optional): absolute tolerance. Default: 1e-08 rtol (float, optional): relative tolerance. Default: 1e-05 equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` Example:: >>> torch.allclose(torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08])) False >>> torch.allclose(torch.tensor([10000., 1e-08]), torch.tensor([10000.1, 1e-09])) True >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')])) False >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]), equal_nan=True) True """) add_docstr(torch.angle, r""" angle(input, out=None) -> Tensor Computes the element-wise angle (in radians) of the given :attr:`input` tensor. .. math:: \text{out}_{i} = angle(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> torch.angle(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))*180/3.14159 tensor([ 135., 135, -45]) """.format(**common_args)) add_docstr(torch.as_strided, r""" as_strided(input, size, stride, storage_offset=0) -> Tensor Create a view of an existing `torch.Tensor` :attr:`input` with specified :attr:`size`, :attr:`stride` and :attr:`storage_offset`. .. warning:: More than one element of a created tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first. Many PyTorch functions, which return a view of a tensor, are internally implemented with this function. Those functions, like :meth:`torch.Tensor.expand`, are easier to read and are therefore more advisable to use. Args: {input} size (tuple or ints): the shape of the output tensor stride (tuple or ints): the stride of the output tensor storage_offset (int, optional): the offset in the underlying storage of the output tensor Example:: >>> x = torch.randn(3, 3) >>> x tensor([[ 0.9039, 0.6291, 1.0795], [ 0.1586, 2.1939, -0.4900], [-0.1909, -0.7503, 1.9355]]) >>> t = torch.as_strided(x, (2, 2), (1, 2)) >>> t tensor([[0.9039, 1.0795], [0.6291, 0.1586]]) >>> t = torch.as_strided(x, (2, 2), (1, 2), 1) tensor([[0.6291, 0.1586], [1.0795, 2.1939]]) """.format(**common_args)) add_docstr(torch.as_tensor, r""" as_tensor(data, dtype=None, device=None) -> Tensor Convert the data into a `torch.Tensor`. If the data is already a `Tensor` with the same `dtype` and `device`, no copy will be performed, otherwise a new `Tensor` will be returned with computational graph retained if data `Tensor` has ``requires_grad=True``. Similarly, if the data is an ``ndarray`` of the corresponding `dtype` and the `device` is the cpu, no copy will be performed. Args: {data} {dtype} {device} Example:: >>> a = numpy.array([1, 2, 3]) >>> t = torch.as_tensor(a) >>> t tensor([ 1, 2, 3]) >>> t[0] = -1 >>> a array([-1, 2, 3]) >>> a = numpy.array([1, 2, 3]) >>> t = torch.as_tensor(a, device=torch.device('cuda')) >>> t tensor([ 1, 2, 3]) >>> t[0] = -1 >>> a array([1, 2, 3]) """.format(**factory_data_common_args)) add_docstr(torch.asin, r""" asin(input, out=None) -> Tensor Returns a new tensor with the arcsine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \sin^{-1}(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.5962, 1.4985, -0.4396, 1.4525]) >>> torch.asin(a) tensor([-0.6387, nan, -0.4552, nan]) """.format(**common_args)) add_docstr(torch.asinh, r""" asinh(input, out=None) -> Tensor Returns a new tensor with the inverse hyperbolic sine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \sinh^{-1}(\text{input}_{i}) """ + r""" Args: {input} Keyword arguments: {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.1606, -1.4267, -1.0899, -1.0250 ]) >>> torch.asinh(a) tensor([ 0.1599, -1.1534, -0.9435, -0.8990 ]) """.format(**common_args)) add_docstr(torch.atan, r""" atan(input, out=None) -> Tensor Returns a new tensor with the arctangent of the elements of :attr:`input`. .. math:: \text{out}_{i} = \tan^{-1}(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.2341, 0.2539, -0.6256, -0.6448]) >>> torch.atan(a) tensor([ 0.2299, 0.2487, -0.5591, -0.5727]) """.format(**common_args)) add_docstr(torch.atan2, r""" atan2(input, other, out=None) -> Tensor Element-wise arctangent of :math:`\text{{input}}_{{i}} / \text{{other}}_{{i}}` with consideration of the quadrant. Returns a new tensor with the signed angles in radians between vector :math:`(\text{{other}}_{{i}}, \text{{input}}_{{i}})` and vector :math:`(1, 0)`. (Note that :math:`\text{{other}}_{{i}}`, the second parameter, is the x-coordinate, while :math:`\text{{input}}_{{i}}`, the first parameter, is the y-coordinate.) The shapes of ``input`` and ``other`` must be :ref:`broadcastable <broadcasting-semantics>`. Args: input (Tensor): the first input tensor other (Tensor): the second input tensor {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.9041, 0.0196, -0.3108, -2.4423]) >>> torch.atan2(a, torch.randn(4)) tensor([ 0.9833, 0.0811, -1.9743, -1.4151]) """.format(**common_args)) add_docstr(torch.atanh, r""" atanh(input, out=None) -> Tensor Returns a new tensor with the inverse hyperbolic tangent of the elements of :attr:`input`. Note: The domain of the inverse hyperbolic tangent is `(-1, 1)` and values outside this range will be mapped to ``NaN``, except for the values `1` and `-1` for which the output is mapped to `+/-INF` respectively. .. math:: \text{out}_{i} = \tanh^{-1}(\text{input}_{i}) """ + r""" Args: {input} Keyword arguments: {out} Example:: >>> a = torch.randn(4).uniform_(-1, 1) >>> a tensor([ -0.9385, 0.2968, -0.8591, -0.1871 ]) >>> torch.atanh(a) tensor([ -1.7253, 0.3060, -1.2899, -0.1893 ]) """.format(**common_args)) add_docstr(torch.baddbmm, r""" baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor Performs a batch matrix-matrix product of matrices in :attr:`batch1` and :attr:`batch2`. :attr:`input` is added to the final result. :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same number of matrices. If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a :math:`(b \times m \times p)` tensor, then :attr:`input` must be :ref:`broadcastable <broadcasting-semantics>` with a :math:`(b \times n \times p)` tensor and :attr:`out` will be a :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the same as the scaling factors used in :meth:`torch.addbmm`. .. math:: \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) """ + r""" For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` must be real numbers, otherwise they should be integers. Args: input (Tensor): the tensor to be added batch1 (Tensor): the first batch of matrices to be multiplied batch2 (Tensor): the second batch of matrices to be multiplied beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`\text{{batch1}} \mathbin{{@}} \text{{batch2}}` (:math:`\alpha`) {out} Example:: >>> M = torch.randn(10, 3, 5) >>> batch1 = torch.randn(10, 3, 4) >>> batch2 = torch.randn(10, 4, 5) >>> torch.baddbmm(M, batch1, batch2).size() torch.Size([10, 3, 5]) """.format(**common_args)) add_docstr(torch.bernoulli, r""" bernoulli(input, *, generator=None, out=None) -> Tensor Draws binary random numbers (0 or 1) from a Bernoulli distribution. The :attr:`input` tensor should be a tensor containing probabilities to be used for drawing the binary random number. Hence, all values in :attr:`input` have to be in the range: :math:`0 \leq \text{input}_i \leq 1`. The :math:`\text{i}^{th}` element of the output tensor will draw a value :math:`1` according to the :math:`\text{i}^{th}` probability value given in :attr:`input`. .. math:: \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) """ + r""" The returned :attr:`out` tensor only has values 0 or 1 and is of the same shape as :attr:`input`. :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating point ``dtype``. Args: input (Tensor): the input tensor of probability values for the Bernoulli distribution {generator} {out} Example:: >>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1] >>> a tensor([[ 0.1737, 0.0950, 0.3609], [ 0.7148, 0.0289, 0.2676], [ 0.9456, 0.8937, 0.7202]]) >>> torch.bernoulli(a) tensor([[ 1., 0., 0.], [ 0., 0., 0.], [ 1., 1., 1.]]) >>> a = torch.ones(3, 3) # probability of drawing "1" is 1 >>> torch.bernoulli(a) tensor([[ 1., 1., 1.], [ 1., 1., 1.], [ 1., 1., 1.]]) >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0 >>> torch.bernoulli(a) tensor([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) """.format(**common_args)) add_docstr(torch.bincount, r""" bincount(input, weights=None, minlength=0) -> Tensor Count the frequency of each value in an array of non-negative ints. The number of bins (size 1) is one larger than the largest value in :attr:`input` unless :attr:`input` is empty, in which case the result is a tensor of size 0. If :attr:`minlength` is specified, the number of bins is at least :attr:`minlength` and if :attr:`input` is empty, then the result is tensor of size :attr:`minlength` filled with zeros. If ``n`` is the value at position ``i``, ``out[n] += weights[i]`` if :attr:`weights` is specified else ``out[n] += 1``. Note: In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting ``torch.backends.cudnn.deterministic = True``. Please see the notes on :doc:`/notes/randomness` for background. Arguments: input (Tensor): 1-d int tensor weights (Tensor): optional, weight for each value in the input tensor. Should be of same size as input tensor. minlength (int): optional, minimum number of bins. Should be non-negative. Returns: output (Tensor): a tensor of shape ``Size([max(input) + 1])`` if :attr:`input` is non-empty, else ``Size(0)`` Example:: >>> input = torch.randint(0, 8, (5,), dtype=torch.int64) >>> weights = torch.linspace(0, 1, steps=5) >>> input, weights (tensor([4, 3, 6, 3, 4]), tensor([ 0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) >>> torch.bincount(input) tensor([0, 0, 0, 2, 2, 0, 1]) >>> input.bincount(weights) tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000]) """) add_docstr(torch.bitwise_not, r""" bitwise_not(input, out=None) -> Tensor Computes the bitwise NOT of the given input tensor. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical NOT. Args: {input} {out} Example: >>> torch.bitwise_not(torch.tensor([-1, -2, 3], dtype=torch.int8)) tensor([ 0, 1, -4], dtype=torch.int8) """.format(**common_args)) add_docstr(torch.bmm, r""" bmm(input, mat2, deterministic=False, out=None) -> Tensor Performs a batch matrix-matrix product of matrices stored in :attr:`input` and :attr:`mat2`. :attr:`input` and :attr:`mat2` must be 3-D tensors each containing the same number of matrices. If :attr:`input` is a :math:`(b \times n \times m)` tensor, :attr:`mat2` is a :math:`(b \times m \times p)` tensor, :attr:`out` will be a :math:`(b \times n \times p)` tensor. .. math:: \text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i """ + r""" .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. For broadcasting matrix products, see :func:`torch.matmul`. Args: input (Tensor): the first batch of matrices to be multiplied mat2 (Tensor): the second batch of matrices to be multiplied deterministic (bool, optional): flag to choose between a faster non-deterministic calculation, or a slower deterministic calculation. This argument is only available for sparse-dense CUDA bmm. Default: ``False`` {out} Example:: >>> input = torch.randn(10, 3, 4) >>> mat2 = torch.randn(10, 4, 5) >>> res = torch.bmm(input, mat2) >>> res.size() torch.Size([10, 3, 5]) """.format(**common_args)) add_docstr(torch.bitwise_and, r""" bitwise_and(input, other, out=None) -> Tensor Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical AND. Args: input: the first input tensor other: the second input tensor {out} Example: >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) tensor([1, 0, 3], dtype=torch.int8) >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) tensor([ False, True, False]) """.format(**common_args)) add_docstr(torch.bitwise_or, r""" bitwise_or(input, other, out=None) -> Tensor Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical OR. Args: input: the first input tensor other: the second input tensor {out} Example: >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) tensor([-1, -2, 3], dtype=torch.int8) >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) tensor([ True, True, False]) """.format(**common_args)) add_docstr(torch.bitwise_xor, r""" bitwise_xor(input, other, out=None) -> Tensor Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical XOR. Args: input: the first input tensor other: the second input tensor {out} Example: >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) tensor([-2, -2, 0], dtype=torch.int8) >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) tensor([ True, False, False]) """.format(**common_args)) add_docstr(torch.stack, r""" stack(tensors, dim=0, out=None) -> Tensor Concatenates sequence of tensors along a new dimension. All tensors need to be of the same size. Arguments: tensors (sequence of Tensors): sequence of tensors to concatenate dim (int): dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive) {out} """.format(**common_args)) add_docstr(torch.chunk, r""" chunk(input, chunks, dim=0) -> List of Tensors Splits a tensor into a specific number of chunks. Each chunk is a view of the input tensor. Last chunk will be smaller if the tensor size along the given dimension :attr:`dim` is not divisible by :attr:`chunks`. Arguments: input (Tensor): the tensor to split chunks (int): number of chunks to return dim (int): dimension along which to split the tensor """) add_docstr(torch.unsafe_chunk, r""" unsafe_chunk(input, chunks, dim=0) -> List of Tensors Works like :func:`torch.chunk` but without enforcing the autograd restrictions on inplace modification of the outputs. .. warning:: This function is safe to use as long as only the input, or only the outputs are modified inplace after calling this function. It is user's responsibility to ensure that is the case. If both the input and one or more of the outputs are modified inplace, gradients computed by autograd will be silently incorrect. """) add_docstr(torch.unsafe_split, r""" unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors Works like :func:`torch.split` but without enforcing the autograd restrictions on inplace modification of the outputs. .. warning:: This function is safe to use as long as only the input, or only the outputs are modified inplace after calling this function. It is user's responsibility to ensure that is the case. If both the input and one or more of the outputs are modified inplace, gradients computed by autograd will be silently incorrect. """) add_docstr(torch.can_cast, r""" can_cast(from, to) -> bool Determines if a type conversion is allowed under PyTorch casting rules described in the type promotion :ref:`documentation <type-promotion-doc>`. Args: from (dtype): The original :class:`torch.dtype`. to (dtype): The target :class:`torch.dtype`. Example:: >>> torch.can_cast(torch.double, torch.float) True >>> torch.can_cast(torch.float, torch.int) False """) add_docstr(torch.cat, r""" cat(tensors, dim=0, out=None) -> Tensor Concatenates the given sequence of :attr:`seq` tensors in the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be empty. :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split` and :func:`torch.chunk`. :func:`torch.cat` can be best understood via examples. Args: tensors (sequence of Tensors): any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension. dim (int, optional): the dimension over which the tensors are concatenated {out} Example:: >>> x = torch.randn(2, 3) >>> x tensor([[ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497]]) >>> torch.cat((x, x, x), 0) tensor([[ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497], [ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497], [ 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497]]) >>> torch.cat((x, x, x), 1) tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614], [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497]]) """.format(**common_args)) add_docstr(torch.ceil, r""" ceil(input, out=None) -> Tensor Returns a new tensor with the ceil of the elements of :attr:`input`, the smallest integer greater than or equal to each element. .. math:: \text{out}_{i} = \left\lceil \text{input}_{i} \right\rceil = \left\lfloor \text{input}_{i} \right\rfloor + 1 """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.6341, -1.4208, -1.0900, 0.5826]) >>> torch.ceil(a) tensor([-0., -1., -1., 1.]) """.format(**common_args)) add_docstr(torch.real, r""" real(input) -> Tensor Returns a new tensor containing real values of the :attr:`self` tensor. The returned tensor and :attr:`self` share the same underlying storage. .. warning:: :func:`real` is only supported for tensors with complex dtypes. Args: {input} Example:: >>> x=torch.randn(4, dtype=torch.cfloat) >>> x tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) >>> x.real tensor([ 0.3100, -0.5445, -1.6492, -0.0638]) """.format(**common_args)) add_docstr(torch.imag, r""" imag(input) -> Tensor Returns a new tensor containing imaginary values of the :attr:`self` tensor. The returned tensor and :attr:`self` share the same underlying storage. .. warning:: :func:`imag` is only supported for tensors with complex dtypes. Args: {input} Example:: >>> x=torch.randn(4, dtype=torch.cfloat) >>> x tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) >>> x.imag tensor([ 0.3553, -0.7896, -0.0633, -0.8119]) """.format(**common_args)) add_docstr(torch.view_as_real, r""" view_as_real(input) -> Tensor Returns a view of :attr:`input` as a real tensor. For an input complex tensor of :attr:`size` :math:`m1, m2, \dots, mi`, this function returns a new real tensor of size :math:`m1, m2, \dots, mi, 2`, where the last dimension of size 2 represents the real and imaginary components of complex numbers. .. warning:: :func:`view_as_real` is only supported for tensors with ``complex dtypes``. Args: {input} Example:: >>> x=torch.randn(4, dtype=torch.cfloat) >>> x tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)]) >>> torch.view_as_real(x) tensor([[ 0.4737, -0.3839], [-0.2098, -0.6699], [ 0.3470, -0.9451], [-0.5174, -1.3136]]) """.format(**common_args)) add_docstr(torch.view_as_complex, r""" view_as_complex(input) -> Tensor Returns a view of :attr:`input` as a complex tensor. For an input complex tensor of :attr:`size` :math:`m1, m2, \dots, mi, 2`, this function returns a new complex tensor of :attr:`size` :math:`m1, m2, \dots, mi` where the last dimension of the input tensor is expected to represent the real and imaginary components of complex numbers. .. warning:: :func:`view_as_complex` is only supported for tensors with :class:`torch.dtype` ``torch.float64`` and ``torch.float32``. The input is expected to have the last dimension of :attr:`size` 2. In addition, the tensor must have a `stride` of 1 for its last dimension. The strides of all other dimensions must be even numbers. Args: {input} Example:: >>> x=torch.randn(4, 2) >>> x tensor([[ 1.6116, -0.5772], [-1.4606, -0.9120], [ 0.0786, -1.7497], [-0.6561, -1.6623]]) >>> torch.view_as_complex(x) tensor([(1.6116-0.5772j), (-1.4606-0.9120j), (0.0786-1.7497j), (-0.6561-1.6623j)]) """.format(**common_args)) add_docstr(torch.reciprocal, r""" reciprocal(input, out=None) -> Tensor Returns a new tensor with the reciprocal of the elements of :attr:`input` .. math:: \text{out}_{i} = \frac{1}{\text{input}_{i}} """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.4595, -2.1219, -1.4314, 0.7298]) >>> torch.reciprocal(a) tensor([-2.1763, -0.4713, -0.6986, 1.3702]) """.format(**common_args)) add_docstr(torch.cholesky, r""" cholesky(input, upper=False, out=None) -> Tensor Computes the Cholesky decomposition of a symmetric positive-definite matrix :math:`A` or for batches of symmetric positive-definite matrices. If :attr:`upper` is ``True``, the returned matrix ``U`` is upper-triangular, and the decomposition has the form: .. math:: A = U^TU If :attr:`upper` is ``False``, the returned matrix ``L`` is lower-triangular, and the decomposition has the form: .. math:: A = LL^T If :attr:`upper` is ``True``, and :math:`A` is a batch of symmetric positive-definite matrices, then the returned tensor will be composed of upper-triangular Cholesky factors of each of the individual matrices. Similarly, when :attr:`upper` is ``False``, the returned tensor will be composed of lower-triangular Cholesky factors of each of the individual matrices. Args: input (Tensor): the input tensor :math:`A` of size :math:`(*, n, n)` where `*` is zero or more batch dimensions consisting of symmetric positive-definite matrices. upper (bool, optional): flag that indicates whether to return a upper or lower triangular matrix. Default: ``False`` out (Tensor, optional): the output matrix Example:: >>> a = torch.randn(3, 3) >>> a = torch.mm(a, a.t()) # make symmetric positive-definite >>> l = torch.cholesky(a) >>> a tensor([[ 2.4112, -0.7486, 1.4551], [-0.7486, 1.3544, 0.1294], [ 1.4551, 0.1294, 1.6724]]) >>> l tensor([[ 1.5528, 0.0000, 0.0000], [-0.4821, 1.0592, 0.0000], [ 0.9371, 0.5487, 0.7023]]) >>> torch.mm(l, l.t()) tensor([[ 2.4112, -0.7486, 1.4551], [-0.7486, 1.3544, 0.1294], [ 1.4551, 0.1294, 1.6724]]) >>> a = torch.randn(3, 2, 2) >>> a = torch.matmul(a, a.transpose(-1, -2)) + 1e-03 # make symmetric positive-definite >>> l = torch.cholesky(a) >>> z = torch.matmul(l, l.transpose(-1, -2)) >>> torch.max(torch.abs(z - a)) # Max non-zero tensor(2.3842e-07) """) add_docstr(torch.cholesky_solve, r""" cholesky_solve(input, input2, upper=False, out=None) -> Tensor Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix :math:`u`. If :attr:`upper` is ``False``, :math:`u` is and lower triangular and `c` is returned such that: .. math:: c = (u u^T)^{{-1}} b If :attr:`upper` is ``True`` or not provided, :math:`u` is upper triangular and `c` is returned such that: .. math:: c = (u^T u)^{{-1}} b `torch.cholesky_solve(b, u)` can take in 2D inputs `b, u` or inputs that are batches of 2D matrices. If the inputs are batches, then returns batched outputs `c` Args: input (Tensor): input matrix :math:`b` of size :math:`(*, m, k)`, where :math:`*` is zero or more batch dimensions input2 (Tensor): input matrix :math:`u` of size :math:`(*, m, m)`, where :math:`*` is zero of more batch dimensions composed of upper or lower triangular Cholesky factor upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: ``False``. out (Tensor, optional): the output tensor for `c` Example:: >>> a = torch.randn(3, 3) >>> a = torch.mm(a, a.t()) # make symmetric positive definite >>> u = torch.cholesky(a) >>> a tensor([[ 0.7747, -1.9549, 1.3086], [-1.9549, 6.7546, -5.4114], [ 1.3086, -5.4114, 4.8733]]) >>> b = torch.randn(3, 2) >>> b tensor([[-0.6355, 0.9891], [ 0.1974, 1.4706], [-0.4115, -0.6225]]) >>> torch.cholesky_solve(b, u) tensor([[ -8.1625, 19.6097], [ -5.8398, 14.2387], [ -4.3771, 10.4173]]) >>> torch.mm(a.inverse(), b) tensor([[ -8.1626, 19.6097], [ -5.8398, 14.2387], [ -4.3771, 10.4173]]) """) add_docstr(torch.cholesky_inverse, r""" cholesky_inverse(input, upper=False, out=None) -> Tensor Computes the inverse of a symmetric positive-definite matrix :math:`A` using its Cholesky factor :math:`u`: returns matrix ``inv``. The inverse is computed using LAPACK routines ``dpotri`` and ``spotri`` (and the corresponding MAGMA routines). If :attr:`upper` is ``False``, :math:`u` is lower triangular such that the returned tensor is .. math:: inv = (uu^{{T}})^{{-1}} If :attr:`upper` is ``True`` or not provided, :math:`u` is upper triangular such that the returned tensor is .. math:: inv = (u^T u)^{{-1}} Args: input (Tensor): the input 2-D tensor :math:`u`, a upper or lower triangular Cholesky factor upper (bool, optional): whether to return a lower (default) or upper triangular matrix out (Tensor, optional): the output tensor for `inv` Example:: >>> a = torch.randn(3, 3) >>> a = torch.mm(a, a.t()) + 1e-05 * torch.eye(3) # make symmetric positive definite >>> u = torch.cholesky(a) >>> a tensor([[ 0.9935, -0.6353, 1.5806], [ -0.6353, 0.8769, -1.7183], [ 1.5806, -1.7183, 10.6618]]) >>> torch.cholesky_inverse(u) tensor([[ 1.9314, 1.2251, -0.0889], [ 1.2251, 2.4439, 0.2122], [-0.0889, 0.2122, 0.1412]]) >>> a.inverse() tensor([[ 1.9314, 1.2251, -0.0889], [ 1.2251, 2.4439, 0.2122], [-0.0889, 0.2122, 0.1412]]) """) add_docstr(torch.clamp, r""" clamp(input, min, max, out=None) -> Tensor Clamp all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]` and return a resulting tensor: .. math:: y_i = \begin{cases} \text{min} & \text{if } x_i < \text{min} \\ x_i & \text{if } \text{min} \leq x_i \leq \text{max} \\ \text{max} & \text{if } x_i > \text{max} \end{cases} """ + r""" If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, args :attr:`min` and :attr:`max` must be real numbers, otherwise they should be integers. Args: {input} min (Number): lower-bound of the range to be clamped to max (Number): upper-bound of the range to be clamped to {out} Example:: >>> a = torch.randn(4) >>> a tensor([-1.7120, 0.1734, -0.0478, -0.0922]) >>> torch.clamp(a, min=-0.5, max=0.5) tensor([-0.5000, 0.1734, -0.0478, -0.0922]) .. function:: clamp(input, *, min, out=None) -> Tensor Clamps all elements in :attr:`input` to be larger or equal :attr:`min`. If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value` should be a real number, otherwise it should be an integer. Args: {input} value (Number): minimal value of each element in the output {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.0299, -2.3184, 2.1593, -0.8883]) >>> torch.clamp(a, min=0.5) tensor([ 0.5000, 0.5000, 2.1593, 0.5000]) .. function:: clamp(input, *, max, out=None) -> Tensor Clamps all elements in :attr:`input` to be smaller or equal :attr:`max`. If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value` should be a real number, otherwise it should be an integer. Args: {input} value (Number): maximal value of each element in the output {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.7753, -0.4702, -0.4599, 1.1899]) >>> torch.clamp(a, max=0.5) tensor([ 0.5000, -0.4702, -0.4599, 0.5000]) """.format(**common_args)) add_docstr(torch.conj, r""" conj(input, out=None) -> Tensor Computes the element-wise conjugate of the given :attr:`input` tensor. .. math:: \text{out}_{i} = conj(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> torch.conj(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])) tensor([-1 - 1j, -2 - 2j, 3 + 3j]) """.format(**common_args)) add_docstr(torch.cos, r""" cos(input, out=None) -> Tensor Returns a new tensor with the cosine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \cos(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 1.4309, 1.2706, -0.8562, 0.9796]) >>> torch.cos(a) tensor([ 0.1395, 0.2957, 0.6553, 0.5574]) """.format(**common_args)) add_docstr(torch.cosh, r""" cosh(input, out=None) -> Tensor Returns a new tensor with the hyperbolic cosine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \cosh(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.1632, 1.1835, -0.6979, -0.7325]) >>> torch.cosh(a) tensor([ 1.0133, 1.7860, 1.2536, 1.2805]) """.format(**common_args)) add_docstr(torch.cross, r""" cross(input, other, dim=-1, out=None) -> Tensor Returns the cross product of vectors in dimension :attr:`dim` of :attr:`input` and :attr:`other`. :attr:`input` and :attr:`other` must have the same size, and the size of their :attr:`dim` dimension should be 3. If :attr:`dim` is not given, it defaults to the first dimension found with the size 3. Args: {input} other (Tensor): the second input tensor dim (int, optional): the dimension to take the cross-product in. {out} Example:: >>> a = torch.randn(4, 3) >>> a tensor([[-0.3956, 1.1455, 1.6895], [-0.5849, 1.3672, 0.3599], [-1.1626, 0.7180, -0.0521], [-0.1339, 0.9902, -2.0225]]) >>> b = torch.randn(4, 3) >>> b tensor([[-0.0257, -1.4725, -1.2251], [-1.1479, -0.7005, -1.9757], [-1.3904, 0.3726, -1.1836], [-0.9688, -0.7153, 0.2159]]) >>> torch.cross(a, b, dim=1) tensor([[ 1.0844, -0.5281, 0.6120], [-2.4490, -1.5687, 1.9792], [-0.8304, -1.3037, 0.5650], [-1.2329, 1.9883, 1.0551]]) >>> torch.cross(a, b) tensor([[ 1.0844, -0.5281, 0.6120], [-2.4490, -1.5687, 1.9792], [-0.8304, -1.3037, 0.5650], [-1.2329, 1.9883, 1.0551]]) """.format(**common_args)) add_docstr(torch.logcumsumexp, r""" logcumsumexp(input, dim, out=None) -> Tensor Returns the logarithm of the cumulative summation of the exponentiation of elements of :attr:`input` in the dimension :attr:`dim`. For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is .. math:: \text{{logcumsumexp}}(x)_{{ij}} = \log \sum\limits_{{j=0}}^{{i}} \exp(x_{{ij}}) Args: {input} dim (int): the dimension to do the operation over {out} Example:: >>> a = torch.randn(10) >>> torch.logcumsumexp(a, dim=0) tensor([-0.42296738, -0.04462666, 0.86278635, 0.94622083, 1.05277811, 1.39202815, 1.83525007, 1.84492621, 2.06084887, 2.06844475])) """.format(**reduceops_common_args)) add_docstr(torch.cummax, r""" cummax(input, dim, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index location of each maximum value found in the dimension :attr:`dim`. .. math:: y_i = max(x_1, x_2, x_3, \dots, x_i) Args: {input} dim (int): the dimension to do the operation over out (tuple, optional): the result tuple of two output tensors (values, indices) Example:: >>> a = torch.randn(10) >>> a tensor([-0.3449, -1.5447, 0.0685, -1.5104, -1.1706, 0.2259, 1.4696, -1.3284, 1.9946, -0.8209]) >>> torch.cummax(a, dim=0) torch.return_types.cummax( values=tensor([-0.3449, -0.3449, 0.0685, 0.0685, 0.0685, 0.2259, 1.4696, 1.4696, 1.9946, 1.9946]), indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8])) """.format(**reduceops_common_args)) add_docstr(torch.cummin, r""" cummin(input, dim, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index location of each maximum value found in the dimension :attr:`dim`. .. math:: y_i = min(x_1, x_2, x_3, \dots, x_i) Args: {input} dim (int): the dimension to do the operation over out (tuple, optional): the result tuple of two output tensors (values, indices) Example:: >>> a = torch.randn(10) >>> a tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220, -0.3885, 1.1762, 0.9165, 1.6684]) >>> torch.cummin(a, dim=0) torch.return_types.cummin( values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298, -1.3298, -1.3298]), indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4])) """.format(**reduceops_common_args)) add_docstr(torch.cumprod, r""" cumprod(input, dim, out=None, dtype=None) -> Tensor Returns the cumulative product of elements of :attr:`input` in the dimension :attr:`dim`. For example, if :attr:`input` is a vector of size N, the result will also be a vector of size N, with elements. .. math:: y_i = x_1 \times x_2\times x_3\times \dots \times x_i Args: {input} dim (int): the dimension to do the operation over {dtype} {out} Example:: >>> a = torch.randn(10) >>> a tensor([ 0.6001, 0.2069, -0.1919, 0.9792, 0.6727, 1.0062, 0.4126, -0.2129, -0.4206, 0.1968]) >>> torch.cumprod(a, dim=0) tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065, 0.0014, -0.0006, -0.0001]) >>> a[5] = 0.0 >>> torch.cumprod(a, dim=0) tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000, 0.0000, -0.0000, -0.0000]) """.format(**reduceops_common_args)) add_docstr(torch.cumsum, r""" cumsum(input, dim, out=None, dtype=None) -> Tensor Returns the cumulative sum of elements of :attr:`input` in the dimension :attr:`dim`. For example, if :attr:`input` is a vector of size N, the result will also be a vector of size N, with elements. .. math:: y_i = x_1 + x_2 + x_3 + \dots + x_i Args: {input} dim (int): the dimension to do the operation over {dtype} {out} Example:: >>> a = torch.randn(10) >>> a tensor([-0.8286, -0.4890, 0.5155, 0.8443, 0.1865, -0.1752, -2.0595, 0.1850, -1.1571, -0.4243]) >>> torch.cumsum(a, dim=0) tensor([-0.8286, -1.3175, -0.8020, 0.0423, 0.2289, 0.0537, -2.0058, -1.8209, -2.9780, -3.4022]) """.format(**reduceops_common_args)) add_docstr(torch.count_nonzero, r""" count_nonzero(input, dim=None) -> Tensor Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`. If no dim is specified then all non-zeros in the tensor are counted. Args: {input} dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros. Example:: >>> x = torch.zeros(3,3) >>> x[torch.randn(3,3) > 0.5] = 1 >>> x tensor([[0., 1., 1.], [0., 0., 0.], [0., 0., 1.]]) >>> torch.count_nonzero(x) tensor(3) >>> torch.count_nonzero(x, dim=0) tensor([0, 1, 2]) """.format(**reduceops_common_args)) add_docstr(torch.dequantize, r""" dequantize(tensor) -> Tensor Given a quantized Tensor, dequantize it and return an fp32 Tensor Args: tensor (Tensor): A quantized Tensor .. function:: dequantize(tensors) -> sequence of Tensors Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors Args: tensors (sequence of Tensors): A list of quantized Tensors """) add_docstr(torch.diag, r""" diag(input, diagonal=0, out=None) -> Tensor - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor with the elements of :attr:`input` as the diagonal. - If :attr:`input` is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of :attr:`input`. The argument :attr:`diagonal` controls which diagonal to consider: - If :attr:`diagonal` = 0, it is the main diagonal. - If :attr:`diagonal` > 0, it is above the main diagonal. - If :attr:`diagonal` < 0, it is below the main diagonal. Args: {input} diagonal (int, optional): the diagonal to consider {out} .. seealso:: :func:`torch.diagonal` always returns the diagonal of its input. :func:`torch.diagflat` always constructs a tensor with diagonal elements specified by the input. Examples: Get the square matrix where the input vector is the diagonal:: >>> a = torch.randn(3) >>> a tensor([ 0.5950,-0.0872, 2.3298]) >>> torch.diag(a) tensor([[ 0.5950, 0.0000, 0.0000], [ 0.0000,-0.0872, 0.0000], [ 0.0000, 0.0000, 2.3298]]) >>> torch.diag(a, 1) tensor([[ 0.0000, 0.5950, 0.0000, 0.0000], [ 0.0000, 0.0000,-0.0872, 0.0000], [ 0.0000, 0.0000, 0.0000, 2.3298], [ 0.0000, 0.0000, 0.0000, 0.0000]]) Get the k-th diagonal of a given matrix:: >>> a = torch.randn(3, 3) >>> a tensor([[-0.4264, 0.0255,-0.1064], [ 0.8795,-0.2429, 0.1374], [ 0.1029,-0.6482,-1.6300]]) >>> torch.diag(a, 0) tensor([-0.4264,-0.2429,-1.6300]) >>> torch.diag(a, 1) tensor([ 0.0255, 0.1374]) """.format(**common_args)) add_docstr(torch.diag_embed, r""" diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor Creates a tensor whose diagonals of certain 2D planes (specified by :attr:`dim1` and :attr:`dim2`) are filled by :attr:`input`. To facilitate creating batched diagonal matrices, the 2D planes formed by the last two dimensions of the returned tensor are chosen by default. The argument :attr:`offset` controls which diagonal to consider: - If :attr:`offset` = 0, it is the main diagonal. - If :attr:`offset` > 0, it is above the main diagonal. - If :attr:`offset` < 0, it is below the main diagonal. The size of the new matrix will be calculated to make the specified diagonal of the size of the last input dimension. Note that for :attr:`offset` other than :math:`0`, the order of :attr:`dim1` and :attr:`dim2` matters. Exchanging them is equivalent to changing the sign of :attr:`offset`. Applying :meth:`torch.diagonal` to the output of this function with the same arguments yields a matrix identical to input. However, :meth:`torch.diagonal` has different default dimensions, so those need to be explicitly specified. Args: {input} Must be at least 1-dimensional. offset (int, optional): which diagonal to consider. Default: 0 (main diagonal). dim1 (int, optional): first dimension with respect to which to take diagonal. Default: -2. dim2 (int, optional): second dimension with respect to which to take diagonal. Default: -1. Example:: >>> a = torch.randn(2, 3) >>> torch.diag_embed(a) tensor([[[ 1.5410, 0.0000, 0.0000], [ 0.0000, -0.2934, 0.0000], [ 0.0000, 0.0000, -2.1788]], [[ 0.5684, 0.0000, 0.0000], [ 0.0000, -1.0845, 0.0000], [ 0.0000, 0.0000, -1.3986]]]) >>> torch.diag_embed(a, offset=1, dim1=0, dim2=2) tensor([[[ 0.0000, 1.5410, 0.0000, 0.0000], [ 0.0000, 0.5684, 0.0000, 0.0000]], [[ 0.0000, 0.0000, -0.2934, 0.0000], [ 0.0000, 0.0000, -1.0845, 0.0000]], [[ 0.0000, 0.0000, 0.0000, -2.1788], [ 0.0000, 0.0000, 0.0000, -1.3986]], [[ 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000]]]) """.format(**common_args)) add_docstr(torch.diagflat, r""" diagflat(input, offset=0) -> Tensor - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor with the elements of :attr:`input` as the diagonal. - If :attr:`input` is a tensor with more than one dimension, then returns a 2-D tensor with diagonal elements equal to a flattened :attr:`input`. The argument :attr:`offset` controls which diagonal to consider: - If :attr:`offset` = 0, it is the main diagonal. - If :attr:`offset` > 0, it is above the main diagonal. - If :attr:`offset` < 0, it is below the main diagonal. Args: {input} offset (int, optional): the diagonal to consider. Default: 0 (main diagonal). Examples:: >>> a = torch.randn(3) >>> a tensor([-0.2956, -0.9068, 0.1695]) >>> torch.diagflat(a) tensor([[-0.2956, 0.0000, 0.0000], [ 0.0000, -0.9068, 0.0000], [ 0.0000, 0.0000, 0.1695]]) >>> torch.diagflat(a, 1) tensor([[ 0.0000, -0.2956, 0.0000, 0.0000], [ 0.0000, 0.0000, -0.9068, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.1695], [ 0.0000, 0.0000, 0.0000, 0.0000]]) >>> a = torch.randn(2, 2) >>> a tensor([[ 0.2094, -0.3018], [-0.1516, 1.9342]]) >>> torch.diagflat(a) tensor([[ 0.2094, 0.0000, 0.0000, 0.0000], [ 0.0000, -0.3018, 0.0000, 0.0000], [ 0.0000, 0.0000, -0.1516, 0.0000], [ 0.0000, 0.0000, 0.0000, 1.9342]]) """.format(**common_args)) add_docstr(torch.diagonal, r""" diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor Returns a partial view of :attr:`input` with the its diagonal elements with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension at the end of the shape. The argument :attr:`offset` controls which diagonal to consider: - If :attr:`offset` = 0, it is the main diagonal. - If :attr:`offset` > 0, it is above the main diagonal. - If :attr:`offset` < 0, it is below the main diagonal. Applying :meth:`torch.diag_embed` to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input. However, :meth:`torch.diag_embed` has different default dimensions, so those need to be explicitly specified. Args: {input} Must be at least 2-dimensional. offset (int, optional): which diagonal to consider. Default: 0 (main diagonal). dim1 (int, optional): first dimension with respect to which to take diagonal. Default: 0. dim2 (int, optional): second dimension with respect to which to take diagonal. Default: 1. .. note:: To take a batch diagonal, pass in dim1=-2, dim2=-1. Examples:: >>> a = torch.randn(3, 3) >>> a tensor([[-1.0854, 1.1431, -0.1752], [ 0.8536, -0.0905, 0.0360], [ 0.6927, -0.3735, -0.4945]]) >>> torch.diagonal(a, 0) tensor([-1.0854, -0.0905, -0.4945]) >>> torch.diagonal(a, 1) tensor([ 1.1431, 0.0360]) >>> x = torch.randn(2, 5, 4, 2) >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2) tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], [-1.1065, 1.0401, -0.2235, -0.7938]], [[-1.7325, -0.3081, 0.6166, 0.2335], [ 1.0500, 0.7336, -0.3836, -1.1015]]]) """.format(**common_args)) add_docstr(torch.digamma, r""" digamma(input, out=None) -> Tensor Computes the logarithmic derivative of the gamma function on `input`. .. math:: \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)} Args: input (Tensor): the tensor to compute the digamma function on Example:: >>> a = torch.tensor([1, 0.5]) >>> torch.digamma(a) tensor([-0.5772, -1.9635]) """) add_docstr(torch.dist, r""" dist(input, other, p=2) -> Tensor Returns the p-norm of (:attr:`input` - :attr:`other`) The shapes of :attr:`input` and :attr:`other` must be :ref:`broadcastable <broadcasting-semantics>`. Args: {input} other (Tensor): the Right-hand-side input tensor p (float, optional): the norm to be computed Example:: >>> x = torch.randn(4) >>> x tensor([-1.5393, -0.8675, 0.5916, 1.6321]) >>> y = torch.randn(4) >>> y tensor([ 0.0967, -1.0511, 0.6295, 0.8360]) >>> torch.dist(x, y, 3.5) tensor(1.6727) >>> torch.dist(x, y, 3) tensor(1.6973) >>> torch.dist(x, y, 0) tensor(inf) >>> torch.dist(x, y, 1) tensor(2.6537) """.format(**common_args)) add_docstr(torch.div, r""" div(input, other, out=None) -> Tensor Divides each element of the input ``input`` with the scalar ``other`` and returns a new resulting tensor. .. warning:: Integer division using div is no longer supported, and in a future release div will perform true division as in Python 3. Use :func:`torch.true_divide` or :func:`torch.floor_divide` (// in Python), instead. .. math:: \text{{out}}_i = \frac{{\text{{input}}_i}}{{\text{{other}}}} If the :class:`torch.dtype` of ``input`` and ``other`` differ, the :class:`torch.dtype` of the result tensor is determined following rules described in the type promotion :ref:`documentation <type-promotion-doc>`. If ``out`` is specified, the result must be :ref:`castable <type-promotion-doc>` to the :class:`torch.dtype` of the specified output tensor. Integral division by zero leads to undefined behavior. Args: {input} other (Number): the number to be divided to each element of ``input`` Keyword args: {out} Example:: >>> a = torch.randn(5) >>> a tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637]) >>> torch.div(a, 0.5) tensor([ 0.7620, 2.5548, -0.5944, -0.7439, 0.9275]) .. function:: div(input, other, out=None) -> Tensor Each element of the tensor ``input`` is divided by each element of the tensor ``other``. The resulting tensor is returned. .. math:: \text{{out}}_i = \frac{{\text{{input}}_i}}{{\text{{other}}_i}} The shapes of ``input`` and ``other`` must be :ref:`broadcastable <broadcasting-semantics>`. If the :class:`torch.dtype` of ``input`` and ``other`` differ, the :class:`torch.dtype` of the result tensor is determined following rules described in the type promotion :ref:`documentation <type-promotion-doc>`. If ``out`` is specified, the result must be :ref:`castable <type-promotion-doc>` to the :class:`torch.dtype` of the specified output tensor. Integral division by zero leads to undefined behavior. Args: input (Tensor): the numerator tensor other (Tensor): the denominator tensor Keyword args: {out} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-0.3711, -1.9353, -0.4605, -0.2917], [ 0.1815, -1.0111, 0.9805, -1.5923], [ 0.1062, 1.4581, 0.7759, -1.2344], [-0.1830, -0.0313, 1.1908, -1.4757]]) >>> b = torch.randn(4) >>> b tensor([ 0.8032, 0.2930, -0.8113, -0.2308]) >>> torch.div(a, b) tensor([[-0.4620, -6.6051, 0.5676, 1.2637], [ 0.2260, -3.4507, -1.2086, 6.8988], [ 0.1322, 4.9764, -0.9564, 5.3480], [-0.2278, -0.1068, -1.4678, 6.3936]]) """.format(**common_args)) add_docstr(torch.dot, r""" dot(input, tensor) -> Tensor Computes the dot product (inner product) of two tensors. .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. Example:: >>> torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1])) tensor(7) """) add_docstr(torch.eig, r""" eig(input, eigenvectors=False, out=None) -> (Tensor, Tensor) Computes the eigenvalues and eigenvectors of a real square matrix. .. note:: Since eigenvalues and eigenvectors might be complex, backward pass is supported only for :func:`torch.symeig` Args: input (Tensor): the square matrix of shape :math:`(n \times n)` for which the eigenvalues and eigenvectors will be computed eigenvectors (bool): ``True`` to compute both eigenvalues and eigenvectors; otherwise, only eigenvalues will be computed out (tuple, optional): the output tensors Returns: (Tensor, Tensor): A namedtuple (eigenvalues, eigenvectors) containing - **eigenvalues** (*Tensor*): Shape :math:`(n \times 2)`. Each row is an eigenvalue of ``input``, where the first element is the real part and the second element is the imaginary part. The eigenvalues are not necessarily ordered. - **eigenvectors** (*Tensor*): If ``eigenvectors=False``, it's an empty tensor. Otherwise, this tensor of shape :math:`(n \times n)` can be used to compute normalized (unit length) eigenvectors of corresponding eigenvalues as follows. If the corresponding `eigenvalues[j]` is a real number, column `eigenvectors[:, j]` is the eigenvector corresponding to `eigenvalues[j]`. If the corresponding `eigenvalues[j]` and `eigenvalues[j + 1]` form a complex conjugate pair, then the true eigenvectors can be computed as :math:`\text{true eigenvector}[j] = eigenvectors[:, j] + i \times eigenvectors[:, j + 1]`, :math:`\text{true eigenvector}[j + 1] = eigenvectors[:, j] - i \times eigenvectors[:, j + 1]`. """) add_docstr(torch.eq, r""" eq(input, other, out=None) -> Tensor Computes element-wise equality The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare {out} Must be a `ByteTensor` Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location where comparison is true Example:: >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[ True, False], [False, True]]) """.format(**common_args)) add_docstr(torch.equal, r""" equal(input, other) -> bool ``True`` if two tensors have the same size and elements, ``False`` otherwise. Example:: >>> torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2])) True """) add_docstr(torch.erf, r""" erf(input, out=None) -> Tensor Computes the error function of each element. The error function is defined as follows: .. math:: \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt """ + r""" Args: {input} {out} Example:: >>> torch.erf(torch.tensor([0, -1., 10.])) tensor([ 0.0000, -0.8427, 1.0000]) """.format(**common_args)) add_docstr(torch.erfc, r""" erfc(input, out=None) -> Tensor Computes the complementary error function of each element of :attr:`input`. The complementary error function is defined as follows: .. math:: \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt """ + r""" Args: {input} {out} Example:: >>> torch.erfc(torch.tensor([0, -1., 10.])) tensor([ 1.0000, 1.8427, 0.0000]) """.format(**common_args)) add_docstr(torch.erfinv, r""" erfinv(input, out=None) -> Tensor Computes the inverse error function of each element of :attr:`input`. The inverse error function is defined in the range :math:`(-1, 1)` as: .. math:: \mathrm{erfinv}(\mathrm{erf}(x)) = x """ + r""" Args: {input} {out} Example:: >>> torch.erfinv(torch.tensor([0, 0.5, -1.])) tensor([ 0.0000, 0.4769, -inf]) """.format(**common_args)) add_docstr(torch.exp, r""" exp(input, out=None) -> Tensor Returns a new tensor with the exponential of the elements of the input tensor :attr:`input`. .. math:: y_{i} = e^{x_{i}} """ + r""" Args: {input} {out} Example:: >>> torch.exp(torch.tensor([0, math.log(2.)])) tensor([ 1., 2.]) """.format(**common_args)) add_docstr(torch.expm1, r""" expm1(input, out=None) -> Tensor Returns a new tensor with the exponential of the elements minus 1 of :attr:`input`. .. math:: y_{i} = e^{x_{i}} - 1 """ + r""" Args: {input} {out} Example:: >>> torch.expm1(torch.tensor([0, math.log(2.)])) tensor([ 0., 1.]) """.format(**common_args)) add_docstr(torch.eye, r""" eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. Args: n (int): the number of rows m (int, optional): the number of columns with default being :attr:`n` {out} {dtype} {layout} {device} {requires_grad} Returns: Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere Example:: >>> torch.eye(3) tensor([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) """.format(**factory_common_args)) add_docstr(torch.floor, r""" floor(input, out=None) -> Tensor Returns a new tensor with the floor of the elements of :attr:`input`, the largest integer less than or equal to each element. .. math:: \text{out}_{i} = \left\lfloor \text{input}_{i} \right\rfloor """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.8166, 1.5308, -0.2530, -0.2091]) >>> torch.floor(a) tensor([-1., 1., -1., -1.]) """.format(**common_args)) add_docstr(torch.floor_divide, r""" floor_divide(input, other, out=None) -> Tensor Return the division of the inputs rounded down to the nearest integer. See :func:`torch.div` for type promotion and broadcasting rules. .. math:: \text{{out}}_i = \left\lfloor \frac{{\text{{input}}_i}}{{\text{{other}}_i}} \right\rfloor """ + r""" Args: input (Tensor): the numerator tensor other (Tensor or Scalar): the denominator Keyword args: {out} Example:: >>> a = torch.tensor([4.0, 3.0]) >>> b = torch.tensor([2.0, 2.0]) >>> torch.floor_divide(a, b) tensor([2.0, 1.0]) >>> torch.floor_divide(a, 1.4) tensor([2.0, 2.0]) """.format(**common_args)) add_docstr(torch.fmod, r""" fmod(input, other, out=None) -> Tensor Computes the element-wise remainder of division. The dividend and divisor may contain both for integer and floating point numbers. The remainder has the same sign as the dividend :attr:`input`. When :attr:`other` is a tensor, the shapes of :attr:`input` and :attr:`other` must be :ref:`broadcastable <broadcasting-semantics>`. Args: input (Tensor): the dividend other (Tensor or float): the divisor, which may be either a number or a tensor of the same shape as the dividend {out} Example:: >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) tensor([-1., -0., -1., 1., 0., 1.]) >>> torch.fmod(torch.tensor([1., 2, 3, 4, 5]), 1.5) tensor([ 1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) """.format(**common_args)) add_docstr(torch.frac, r""" frac(input, out=None) -> Tensor Computes the fractional portion of each element in :attr:`input`. .. math:: \text{out}_{i} = \text{input}_{i} - \left\lfloor |\text{input}_{i}| \right\rfloor * \operatorname{sgn}(\text{input}_{i}) Example:: >>> torch.frac(torch.tensor([1, 2.5, -3.2])) tensor([ 0.0000, 0.5000, -0.2000]) """) add_docstr(torch.from_numpy, r""" from_numpy(ndarray) -> Tensor Creates a :class:`Tensor` from a :class:`numpy.ndarray`. The returned tensor and :attr:`ndarray` share the same memory. Modifications to the tensor will be reflected in the :attr:`ndarray` and vice versa. The returned tensor is not resizable. It currently accepts :attr:`ndarray` with dtypes of ``numpy.float64``, ``numpy.float32``, ``numpy.float16``, ``numpy.complex64``, ``numpy.complex128``, ``numpy.int64``, ``numpy.int32``, ``numpy.int16``, ``numpy.int8``, ``numpy.uint8``, and ``numpy.bool``. Example:: >>> a = numpy.array([1, 2, 3]) >>> t = torch.from_numpy(a) >>> t tensor([ 1, 2, 3]) >>> t[0] = -1 >>> a array([-1, 2, 3]) """) add_docstr(torch.flatten, r""" flatten(input, start_dim=0, end_dim=-1) -> Tensor Flattens a contiguous range of dims in a tensor. Args: {input} start_dim (int): the first dim to flatten end_dim (int): the last dim to flatten Example:: >>> t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) >>> torch.flatten(t) tensor([1, 2, 3, 4, 5, 6, 7, 8]) >>> torch.flatten(t, start_dim=1) tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) """.format(**common_args)) add_docstr(torch.gather, r""" gather(input, dim, index, out=None, sparse_grad=False) -> Tensor Gathers values along an axis specified by `dim`. For a 3-D tensor the output is specified by:: out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 If :attr:`input` is an n-dimensional tensor with size :math:`(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})` and ``dim = i``, then :attr:`index` must be an :math:`n`-dimensional tensor with size :math:`(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})` where :math:`y \geq 1` and :attr:`out` will have the same size as :attr:`index`. """ + r""" Args: input (Tensor): the source tensor dim (int): the axis along which to index index (LongTensor): the indices of elements to gather out (Tensor, optional): the destination tensor sparse_grad(bool,optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor. Example:: >>> t = torch.tensor([[1,2],[3,4]]) >>> torch.gather(t, 1, torch.tensor([[0,0],[1,0]])) tensor([[ 1, 1], [ 4, 3]]) """) add_docstr(torch.gcd, r""" gcd(input, other, out=None) -> Tensor Computes the element-wise greatest common divisor (GCD) of :attr:`input` and :attr:`other`. Both :attr:`input` and :attr:`other` must have integer types. .. note:: This defines :math:`gcd(0, 0) = 0`. Args: {input} other (Tensor): the second input tensor Keyword arguments: {out} Example:: >>> a = torch.tensor([5, 10, 15]) >>> b = torch.tensor([3, 4, 5]) >>> torch.gcd(a, b) tensor([1, 2, 5]) >>> c = torch.tensor([3]) >>> torch.gcd(a, c) tensor([1, 1, 3]) """.format(**common_args)) add_docstr(torch.ge, r""" ge(input, other, out=None) -> Tensor Computes :math:`\text{input} \geq \text{other}` element-wise. The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a `BoolTensor` Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location where comparison is true Example:: >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[True, True], [False, True]]) """) add_docstr(torch.geqrf, r""" geqrf(input, out=None) -> (Tensor, Tensor) This is a low-level function for calling LAPACK directly. This function returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ . You'll generally want to use :func:`torch.qr` instead. Computes a QR decomposition of :attr:`input`, but without constructing :math:`Q` and :math:`R` as explicit separate matrices. Rather, this directly calls the underlying LAPACK function `?geqrf` which produces a sequence of 'elementary reflectors'. See `LAPACK documentation for geqrf`_ for further details. Args: input (Tensor): the input matrix out (tuple, optional): the output tuple of (Tensor, Tensor) .. _LAPACK documentation for geqrf: https://software.intel.com/en-us/node/521004 """) add_docstr(torch.ger, r""" ger(input, vec2, out=None) -> Tensor Outer product of :attr:`input` and :attr:`vec2`. If :attr:`input` is a vector of size :math:`n` and :attr:`vec2` is a vector of size :math:`m`, then :attr:`out` must be a matrix of size :math:`(n \times m)`. .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. Args: input (Tensor): 1-D input vector vec2 (Tensor): 1-D input vector out (Tensor, optional): optional output matrix Example:: >>> v1 = torch.arange(1., 5.) >>> v2 = torch.arange(1., 4.) >>> torch.ger(v1, v2) tensor([[ 1., 2., 3.], [ 2., 4., 6.], [ 3., 6., 9.], [ 4., 8., 12.]]) """) add_docstr(torch.solve, r""" torch.solve(input, A, out=None) -> (Tensor, Tensor) This function returns the solution to the system of linear equations represented by :math:`AX = B` and the LU factorization of A, in order as a namedtuple `solution, LU`. `LU` contains `L` and `U` factors for LU factorization of `A`. `torch.solve(B, A)` can take in 2D inputs `B, A` or inputs that are batches of 2D matrices. If the inputs are batches, then returns batched outputs `solution, LU`. .. note:: Irrespective of the original strides, the returned matrices `solution` and `LU` will be transposed, i.e. with strides like `B.contiguous().transpose(-1, -2).stride()` and `A.contiguous().transpose(-1, -2).stride()` respectively. Args: input (Tensor): input matrix :math:`B` of size :math:`(*, m, k)` , where :math:`*` is zero or more batch dimensions. A (Tensor): input square matrix of size :math:`(*, m, m)`, where :math:`*` is zero or more batch dimensions. out ((Tensor, Tensor), optional): optional output tuple. Example:: >>> A = torch.tensor([[6.80, -2.11, 5.66, 5.97, 8.23], [-6.05, -3.30, 5.36, -4.44, 1.08], [-0.45, 2.58, -2.70, 0.27, 9.04], [8.32, 2.71, 4.35, -7.17, 2.14], [-9.67, -5.14, -7.26, 6.08, -6.87]]).t() >>> B = torch.tensor([[4.02, 6.19, -8.22, -7.57, -3.03], [-1.56, 4.00, -8.67, 1.75, 2.86], [9.81, -4.09, -4.57, -8.61, 8.99]]).t() >>> X, LU = torch.solve(B, A) >>> torch.dist(B, torch.mm(A, X)) tensor(1.00000e-06 * 7.0977) >>> # Batched solver example >>> A = torch.randn(2, 3, 1, 4, 4) >>> B = torch.randn(2, 3, 1, 4, 6) >>> X, LU = torch.solve(B, A) >>> torch.dist(B, A.matmul(X)) tensor(1.00000e-06 * 3.6386) """) add_docstr(torch.get_default_dtype, r""" get_default_dtype() -> torch.dtype Get the current default floating point :class:`torch.dtype`. Example:: >>> torch.get_default_dtype() # initial default for floating point is torch.float32 torch.float32 >>> torch.set_default_dtype(torch.float64) >>> torch.get_default_dtype() # default is now changed to torch.float64 torch.float64 >>> torch.set_default_tensor_type(torch.FloatTensor) # setting tensor type also affects this >>> torch.get_default_dtype() # changed to torch.float32, the dtype for torch.FloatTensor torch.float32 """) add_docstr(torch.get_num_threads, r""" get_num_threads() -> int Returns the number of threads used for parallelizing CPU operations """) add_docstr(torch.get_num_interop_threads, r""" get_num_interop_threads() -> int Returns the number of threads used for inter-op parallelism on CPU (e.g. in JIT interpreter) """) add_docstr(torch.gt, r""" gt(input, other, out=None) -> Tensor Computes :math:`\text{input} > \text{other}` element-wise. The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a `BoolTensor` Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location where comparison is true Example:: >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[False, True], [False, False]]) """) add_docstr(torch.histc, r""" histc(input, bins=100, min=0, max=0, out=None) -> Tensor Computes the histogram of a tensor. The elements are sorted into equal width bins between :attr:`min` and :attr:`max`. If :attr:`min` and :attr:`max` are both zero, the minimum and maximum values of the data are used. Elements lower than min and higher than max are ignored. Args: {input} bins (int): number of histogram bins min (int): lower end of the range (inclusive) max (int): upper end of the range (inclusive) {out} Returns: Tensor: Histogram represented as a tensor Example:: >>> torch.histc(torch.tensor([1., 2, 1]), bins=4, min=0, max=3) tensor([ 0., 2., 1., 0.]) """.format(**common_args)) add_docstr(torch.index_select, r""" index_select(input, dim, index, out=None) -> Tensor Returns a new tensor which indexes the :attr:`input` tensor along dimension :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`. The returned tensor has the same number of dimensions as the original tensor (:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length of :attr:`index`; other dimensions have the same size as in the original tensor. .. note:: The returned tensor does **not** use the same storage as the original tensor. If :attr:`out` has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary. Args: {input} dim (int): the dimension in which we index index (LongTensor): the 1-D tensor containing the indices to index {out} Example:: >>> x = torch.randn(3, 4) >>> x tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], [-0.4664, 0.2647, -0.1228, -1.1068], [-1.1734, -0.6571, 0.7230, -0.6004]]) >>> indices = torch.tensor([0, 2]) >>> torch.index_select(x, 0, indices) tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], [-1.1734, -0.6571, 0.7230, -0.6004]]) >>> torch.index_select(x, 1, indices) tensor([[ 0.1427, -0.5414], [-0.4664, -0.1228], [-1.1734, 0.7230]]) """.format(**common_args)) add_docstr(torch.inverse, r""" inverse(input, out=None) -> Tensor Takes the inverse of the square matrix :attr:`input`. :attr:`input` can be batches of 2D square tensors, in which case this function would return a tensor composed of individual inverses. .. note:: Irrespective of the original strides, the returned tensors will be transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()` Args: input (Tensor): the input tensor of size :math:`(*, n, n)` where `*` is zero or more batch dimensions {out} Example:: >>> x = torch.rand(4, 4) >>> y = torch.inverse(x) >>> z = torch.mm(x, y) >>> z tensor([[ 1.0000, -0.0000, -0.0000, 0.0000], [ 0.0000, 1.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 1.0000, 0.0000], [ 0.0000, -0.0000, -0.0000, 1.0000]]) >>> torch.max(torch.abs(z - torch.eye(4))) # Max non-zero tensor(1.1921e-07) >>> # Batched inverse example >>> x = torch.randn(2, 3, 4, 4) >>> y = torch.inverse(x) >>> z = torch.matmul(x, y) >>> torch.max(torch.abs(z - torch.eye(4).expand_as(x))) # Max non-zero tensor(1.9073e-06) """.format(**common_args)) add_docstr(torch.isinf, r""" Returns a new tensor with boolean elements representing if each element is `+/-INF` or not. Complex values are infinite when their real and/or imaginary part is infinite. Arguments: tensor (Tensor): A tensor to check Returns: Tensor: ``A torch.Tensor with dtype torch.bool`` containing a True at each location of `+/-INF` elements and False otherwise Example:: >>> torch.isinf(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) tensor([False, True, False, True, False]) """) add_docstr(torch.isclose, r""" isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor Returns a new tensor with boolean elements representing if each element of :attr:`input` is "close" to the corresponding element of :attr:`other`. Closeness is defined as: .. math:: \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert """ + r""" where :attr:`input` and :attr:`other` are finite. Where :attr:`input` and/or :attr:`other` are nonfinite they are close if and only if they are equal, with NaNs being considered equal to each other when :attr:`equal_nan` is True. Args: input (Tensor): first tensor to compare other (Tensor): second tensor to compare atol (float, optional): absolute tolerance. Default: 1e-08 rtol (float, optional): relative tolerance. Default: 1e-05 equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` Examples:: >>> torch.isclose(torch.tensor((1., 2, 3)), torch.tensor((1 + 1e-10, 3, 4))) tensor([ True, False, False]) >>> torch.isclose(torch.tensor((float('inf'), 4)), torch.tensor((float('inf'), 6)), rtol=.5) tensor([True, True]) """) add_docstr(torch.isfinite, r""" Returns a new tensor with boolean elements representing if each element is `finite` or not. Real values are finite when they are not NaN, negative infinity, or infinity. Complex values are finite when both their real and imaginary parts are finite. Arguments: tensor (Tensor): A tensor to check Returns: Tensor: ``A torch.Tensor with dtype torch.bool`` containing a True at each location of finite elements and False otherwise Example:: >>> torch.isfinite(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) tensor([True, False, True, False, False]) """) add_docstr(torch.isnan, r""" Returns a new tensor with boolean elements representing if each element is `NaN` or not. Complex values are considered `NaN` when either their real and/or imaginary part is NaN. Arguments: input (Tensor): A tensor to check Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location of `NaN` elements. Example:: >>> torch.isnan(torch.tensor([1, float('nan'), 2])) tensor([False, True, False]) """) add_docstr(torch.is_floating_point, r""" is_floating_point(input) -> (bool) Returns True if the data type of :attr:`input` is a floating point data type i.e., one of ``torch.float64``, ``torch.float32`` and ``torch.float16``. Args: input (Tensor): the PyTorch tensor to test """) add_docstr(torch.is_complex, r""" is_complex(input) -> (bool) Returns True if the data type of :attr:`input` is a complex data type i.e., one of ``torch.complex64``, and ``torch.complex128``. Args: input (Tensor): the PyTorch tensor to test """) add_docstr(torch.is_nonzero, r""" is_nonzero(input) -> (bool) Returns True if the :attr:`input` is a single element tensor which is not equal to zero after type conversions. i.e. not equal to ``torch.tensor([0.])`` or ``torch.tensor([0])`` or ``torch.tensor([False])``. Throws a ``RuntimeError`` if ``torch.numel() != 1`` (even in case of sparse tensors). Args: input (Tensor): the PyTorch tensor to test Example:: >>> torch.is_nonzero(torch.tensor([0.])) False >>> torch.is_nonzero(torch.tensor([1.5])) True >>> torch.is_nonzero(torch.tensor([False])) False >>> torch.is_nonzero(torch.tensor([3])) True >>> torch.is_nonzero(torch.tensor([1, 3, 5])) Traceback (most recent call last): ... RuntimeError: bool value of Tensor with more than one value is ambiguous >>> torch.is_nonzero(torch.tensor([])) Traceback (most recent call last): ... RuntimeError: bool value of Tensor with no values is ambiguous """) add_docstr(torch.kthvalue, r""" kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th smallest element of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. And ``indices`` is the index location of each element found. If :attr:`dim` is not given, the last dimension of the `input` is chosen. If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors are the same size as :attr:`input`, except in the dimension :attr:`dim` where they are of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in both the :attr:`values` and :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor. Args: {input} k (int): k for the k-th smallest element dim (int, optional): the dimension to find the kth value along {keepdim} out (tuple, optional): the output tuple of (Tensor, LongTensor) can be optionally given to be used as output buffers Example:: >>> x = torch.arange(1., 6.) >>> x tensor([ 1., 2., 3., 4., 5.]) >>> torch.kthvalue(x, 4) torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3)) >>> x=torch.arange(1.,7.).resize_(2,3) >>> x tensor([[ 1., 2., 3.], [ 4., 5., 6.]]) >>> torch.kthvalue(x, 2, 0, True) torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]])) """.format(**single_dim_common)) add_docstr(torch.lcm, r""" lcm(input, other, out=None) -> Tensor Computes the element-wise least common multiple (LCM) of :attr:`input` and :attr:`other`. Both :attr:`input` and :attr:`other` must have integer types. .. note:: This defines :math:`lcm(0, 0) = 0` and :math:`lcm(0, a) = 0`. Args: {input} other (Tensor): the second input tensor Keyword arguments: {out} Example:: >>> a = torch.tensor([5, 10, 15]) >>> b = torch.tensor([3, 4, 5]) >>> torch.lcm(a, b) tensor([15, 20, 15]) >>> c = torch.tensor([3]) >>> torch.lcm(a, c) tensor([15, 30, 15]) """.format(**common_args)) add_docstr(torch.le, r""" le(input, other, out=None) -> Tensor Computes :math:`\text{input} \leq \text{other}` element-wise. The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a `BoolTensor` Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location where comparison is true Example:: >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[True, False], [True, True]]) """) add_docstr(torch.lerp, r""" lerp(input, end, weight, out=None) Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor. .. math:: \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) """ + r""" The shapes of :attr:`start` and :attr:`end` must be :ref:`broadcastable <broadcasting-semantics>`. If :attr:`weight` is a tensor, then the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable <broadcasting-semantics>`. Args: input (Tensor): the tensor with the starting points end (Tensor): the tensor with the ending points weight (float or tensor): the weight for the interpolation formula {out} Example:: >>> start = torch.arange(1., 5.) >>> end = torch.empty(4).fill_(10) >>> start tensor([ 1., 2., 3., 4.]) >>> end tensor([ 10., 10., 10., 10.]) >>> torch.lerp(start, end, 0.5) tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) >>> torch.lerp(start, end, torch.full_like(start, 0.5)) tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) """.format(**common_args)) add_docstr(torch.lgamma, r""" lgamma(input, out=None) -> Tensor Computes the logarithm of the gamma function on :attr:`input`. .. math:: \text{out}_{i} = \log \Gamma(\text{input}_{i}) """ + """ Args: {input} {out} Example:: >>> a = torch.arange(0.5, 2, 0.5) >>> torch.lgamma(a) tensor([ 0.5724, 0.0000, -0.1208]) """.format(**common_args)) add_docstr(torch.linspace, r""" linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a one-dimensional tensor of :attr:`steps` equally spaced points between :attr:`start` and :attr:`end`. The output tensor is 1-D of size :attr:`steps`. Args: start (float): the starting value for the set of points end (float): the ending value for the set of points steps (int): number of points to sample between :attr:`start` and :attr:`end`. Default: ``100``. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.linspace(3, 10, steps=5) tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) >>> torch.linspace(-10, 10, steps=5) tensor([-10., -5., 0., 5., 10.]) >>> torch.linspace(start=-10, end=10, steps=5) tensor([-10., -5., 0., 5., 10.]) >>> torch.linspace(start=-10, end=10, steps=1) tensor([-10.]) """.format(**factory_common_args)) add_docstr(torch.log, r""" log(input, out=None) -> Tensor Returns a new tensor with the natural logarithm of the elements of :attr:`input`. .. math:: y_{i} = \log_{e} (x_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(5) >>> a tensor([-0.7168, -0.5471, -0.8933, -1.4428, -0.1190]) >>> torch.log(a) tensor([ nan, nan, nan, nan, nan]) """.format(**common_args)) add_docstr(torch.log10, r""" log10(input, out=None) -> Tensor Returns a new tensor with the logarithm to the base 10 of the elements of :attr:`input`. .. math:: y_{i} = \log_{10} (x_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.rand(5) >>> a tensor([ 0.5224, 0.9354, 0.7257, 0.1301, 0.2251]) >>> torch.log10(a) tensor([-0.2820, -0.0290, -0.1392, -0.8857, -0.6476]) """.format(**common_args)) add_docstr(torch.log1p, r""" log1p(input, out=None) -> Tensor Returns a new tensor with the natural logarithm of (1 + :attr:`input`). .. math:: y_i = \log_{e} (x_i + 1) """ + r""" .. note:: This function is more accurate than :func:`torch.log` for small values of :attr:`input` Args: {input} {out} Example:: >>> a = torch.randn(5) >>> a tensor([-1.0090, -0.9923, 1.0249, -0.5372, 0.2492]) >>> torch.log1p(a) tensor([ nan, -4.8653, 0.7055, -0.7705, 0.2225]) """.format(**common_args)) add_docstr(torch.log2, r""" log2(input, out=None) -> Tensor Returns a new tensor with the logarithm to the base 2 of the elements of :attr:`input`. .. math:: y_{i} = \log_{2} (x_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.rand(5) >>> a tensor([ 0.8419, 0.8003, 0.9971, 0.5287, 0.0490]) >>> torch.log2(a) tensor([-0.2483, -0.3213, -0.0042, -0.9196, -4.3504]) """.format(**common_args)) add_docstr(torch.logaddexp, r""" logaddexp(input, other, out=None) -> Tensor Logarithm of the sum of exponentiations of the inputs. Calculates pointwise :math:`\log\left(e^x + e^y\right)`. This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the logarithm of the calculated probability is stored. This function allows adding probabilities stored in such a fashion. This op should be disambiguated with :func:`torch.logsumexp` which performs a reduction on a single tensor. Args: {input} other (Tensor): the second input tensor Keyword arguments: {out} Example:: >>> torch.logaddexp(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3])) tensor([-0.3069, -0.6867, -0.8731]) >>> torch.logaddexp(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3])) tensor([-1., -2., -3.]) >>> torch.logaddexp(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3])) tensor([1.1269e+00, 2.0000e+03, 3.0000e+04]) """.format(**common_args)) add_docstr(torch.logaddexp2, r""" logaddexp2(input, other, out=None) -> Tensor Logarithm of the sum of exponentiations of the inputs in base-2. Calculates pointwise :math:`\log_2\left(2^x + 2^y\right)`. See :func:`torch.logaddexp` for more details. Args: {input} other (Tensor): the second input tensor Keyword arguments: {out} """.format(**common_args)) add_docstr(torch.logical_and, r""" logical_and(input, other, out=None) -> Tensor Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are treated as ``True``. Args: {input} other (Tensor): the tensor to compute AND with {out} Example:: >>> torch.logical_and(torch.tensor([True, False, True]), torch.tensor([True, False, False])) tensor([ True, False, False]) >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) >>> torch.logical_and(a, b) tensor([False, False, True, False]) >>> torch.logical_and(a.double(), b.double()) tensor([False, False, True, False]) >>> torch.logical_and(a.double(), b) tensor([False, False, True, False]) >>> torch.logical_and(a, b, out=torch.empty(4, dtype=torch.bool)) tensor([False, False, True, False]) """.format(**common_args)) add_docstr(torch.logical_not, r""" logical_not(input, out=None) -> Tensor Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool dtype. If the input tensor is not a bool tensor, zeros are treated as ``False`` and non-zeros are treated as ``True``. Args: {input} {out} Example:: >>> torch.logical_not(torch.tensor([True, False])) tensor([False, True]) >>> torch.logical_not(torch.tensor([0, 1, -10], dtype=torch.int8)) tensor([ True, False, False]) >>> torch.logical_not(torch.tensor([0., 1.5, -10.], dtype=torch.double)) tensor([ True, False, False]) >>> torch.logical_not(torch.tensor([0., 1., -10.], dtype=torch.double), out=torch.empty(3, dtype=torch.int16)) tensor([1, 0, 0], dtype=torch.int16) """.format(**common_args)) add_docstr(torch.logical_or, r""" logical_or(input, other, out=None) -> Tensor Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are treated as ``True``. Args: {input} other (Tensor): the tensor to compute OR with {out} Example:: >>> torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False])) tensor([ True, False, True]) >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) >>> torch.logical_or(a, b) tensor([ True, True, True, False]) >>> torch.logical_or(a.double(), b.double()) tensor([ True, True, True, False]) >>> torch.logical_or(a.double(), b) tensor([ True, True, True, False]) >>> torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool)) tensor([ True, True, True, False]) """.format(**common_args)) add_docstr(torch.logical_xor, r""" logical_xor(input, other, out=None) -> Tensor Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are treated as ``True``. Args: {input} other (Tensor): the tensor to compute XOR with {out} Example:: >>> torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False])) tensor([False, False, True]) >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) >>> torch.logical_xor(a, b) tensor([ True, True, False, False]) >>> torch.logical_xor(a.double(), b.double()) tensor([ True, True, False, False]) >>> torch.logical_xor(a.double(), b) tensor([ True, True, False, False]) >>> torch.logical_xor(a, b, out=torch.empty(4, dtype=torch.bool)) tensor([ True, True, False, False]) """.format(**common_args)) add_docstr(torch.logspace, r""" logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a one-dimensional tensor of :attr:`steps` points logarithmically spaced with base :attr:`base` between :math:`{{\text{{base}}}}^{{\text{{start}}}}` and :math:`{{\text{{base}}}}^{{\text{{end}}}}`. The output tensor is 1-D of size :attr:`steps`. Args: start (float): the starting value for the set of points end (float): the ending value for the set of points steps (int): number of points to sample between :attr:`start` and :attr:`end`. Default: ``100``. base (float): base of the logarithm function. Default: ``10.0``. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.logspace(start=-10, end=10, steps=5) tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) >>> torch.logspace(start=0.1, end=1.0, steps=5) tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) >>> torch.logspace(start=0.1, end=1.0, steps=1) tensor([1.2589]) >>> torch.logspace(start=2, end=2, steps=1, base=2) tensor([4.0]) """.format(**factory_common_args)) add_docstr(torch.logsumexp, r""" logsumexp(input, dim, keepdim=False, out=None) Returns the log of summed exponentials of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. The computation is numerically stabilized. For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is .. math:: \text{{logsumexp}}(x)_{{i}} = \log \sum_j \exp(x_{{ij}}) {keepdim_details} Args: {input} {dim} {keepdim} {out} Example:: >>> a = torch.randn(3, 3) >>> torch.logsumexp(a, 1) tensor([ 0.8442, 1.4322, 0.8711]) """.format(**multi_dim_common)) add_docstr(torch.lstsq, r""" lstsq(input, A, out=None) -> Tensor Computes the solution to the least squares and least norm problems for a full rank matrix :math:`A` of size :math:`(m \times n)` and a matrix :math:`B` of size :math:`(m \times k)`. If :math:`m \geq n`, :func:`lstsq` solves the least-squares problem: .. math:: \begin{array}{ll} \min_X & \|AX-B\|_2. \end{array} If :math:`m < n`, :func:`lstsq` solves the least-norm problem: .. math:: \begin{array}{ll} \min_X & \|X\|_2 & \text{subject to} & AX = B. \end{array} Returned tensor :math:`X` has shape :math:`(\max(m, n) \times k)`. The first :math:`n` rows of :math:`X` contains the solution. If :math:`m \geq n`, the residual sum of squares for the solution in each column is given by the sum of squares of elements in the remaining :math:`m - n` rows of that column. .. note:: The case when :math:`m < n` is not supported on the GPU. Args: input (Tensor): the matrix :math:`B` A (Tensor): the :math:`m` by :math:`n` matrix :math:`A` out (tuple, optional): the optional destination tensor Returns: (Tensor, Tensor): A namedtuple (solution, QR) containing: - **solution** (*Tensor*): the least squares solution - **QR** (*Tensor*): the details of the QR factorization .. note:: The returned matrices will always be transposed, irrespective of the strides of the input matrices. That is, they will have stride `(1, m)` instead of `(m, 1)`. Example:: >>> A = torch.tensor([[1., 1, 1], [2, 3, 4], [3, 5, 2], [4, 2, 5], [5, 4, 3]]) >>> B = torch.tensor([[-10., -3], [ 12, 14], [ 14, 12], [ 16, 16], [ 18, 16]]) >>> X, _ = torch.lstsq(B, A) >>> X tensor([[ 2.0000, 1.0000], [ 1.0000, 1.0000], [ 1.0000, 2.0000], [ 10.9635, 4.8501], [ 8.9332, 5.2418]]) """) add_docstr(torch.lt, r""" lt(input, other, out=None) -> Tensor Computes :math:`\text{input} < \text{other}` element-wise. The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a `BoolTensor` Returns: Tensor: A `torch.BoolTensor` containing a True at each location where comparison is true Example:: >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[False, False], [True, False]]) """) add_docstr(torch.lu_solve, r""" lu_solve(input, LU_data, LU_pivots, out=None) -> Tensor Returns the LU solve of the linear system :math:`Ax = b` using the partially pivoted LU factorization of A from :meth:`torch.lu`. Arguments: b (Tensor): the RHS tensor of size :math:`(*, m, k)`, where :math:`*` is zero or more batch dimensions. LU_data (Tensor): the pivoted LU factorization of A from :meth:`torch.lu` of size :math:`(*, m, m)`, where :math:`*` is zero or more batch dimensions. LU_pivots (IntTensor): the pivots of the LU factorization from :meth:`torch.lu` of size :math:`(*, m)`, where :math:`*` is zero or more batch dimensions. The batch dimensions of :attr:`LU_pivots` must be equal to the batch dimensions of :attr:`LU_data`. {out} Example:: >>> A = torch.randn(2, 3, 3) >>> b = torch.randn(2, 3, 1) >>> A_LU = torch.lu(A) >>> x = torch.lu_solve(b, *A_LU) >>> torch.norm(torch.bmm(A, x) - b) tensor(1.00000e-07 * 2.8312) """.format(**common_args)) add_docstr(torch.masked_select, r""" masked_select(input, mask, out=None) -> Tensor Returns a new 1-D tensor which indexes the :attr:`input` tensor according to the boolean mask :attr:`mask` which is a `BoolTensor`. The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need to match, but they must be :ref:`broadcastable <broadcasting-semantics>`. .. note:: The returned tensor does **not** use the same storage as the original tensor Args: {input} mask (BoolTensor): the tensor containing the binary mask to index with {out} Example:: >>> x = torch.randn(3, 4) >>> x tensor([[ 0.3552, -2.3825, -0.8297, 0.3477], [-1.2035, 1.2252, 0.5002, 0.6248], [ 0.1307, -2.0608, 0.1244, 2.0139]]) >>> mask = x.ge(0.5) >>> mask tensor([[False, False, False, False], [False, True, True, True], [False, False, False, True]]) >>> torch.masked_select(x, mask) tensor([ 1.2252, 0.5002, 0.6248, 2.0139]) """.format(**common_args)) add_docstr(torch.matrix_rank, r""" matrix_rank(input, tol=None, symmetric=False) -> Tensor Returns the numerical rank of a 2-D tensor. The method to compute the matrix rank is done using SVD by default. If :attr:`symmetric` is ``True``, then :attr:`input` is assumed to be symmetric, and the computation of the rank is done by obtaining the eigenvalues. :attr:`tol` is the threshold below which the singular values (or the eigenvalues when :attr:`symmetric` is ``True``) are considered to be 0. If :attr:`tol` is not specified, :attr:`tol` is set to ``S.max() * max(S.size()) * eps`` where `S` is the singular values (or the eigenvalues when :attr:`symmetric` is ``True``), and ``eps`` is the epsilon value for the datatype of :attr:`input`. Args: input (Tensor): the input 2-D tensor tol (float, optional): the tolerance value. Default: ``None`` symmetric(bool, optional): indicates whether :attr:`input` is symmetric. Default: ``False`` Example:: >>> a = torch.eye(10) >>> torch.matrix_rank(a) tensor(10) >>> b = torch.eye(10) >>> b[0, 0] = 0 >>> torch.matrix_rank(b) tensor(9) """) add_docstr(torch.matrix_power, r""" matrix_power(input, n) -> Tensor Returns the matrix raised to the power :attr:`n` for square matrices. For batch of matrices, each individual matrix is raised to the power :attr:`n`. If :attr:`n` is negative, then the inverse of the matrix (if invertible) is raised to the power :attr:`n`. For a batch of matrices, the batched inverse (if invertible) is raised to the power :attr:`n`. If :attr:`n` is 0, then an identity matrix is returned. Args: {input} n (int): the power to raise the matrix to Example:: >>> a = torch.randn(2, 2, 2) >>> a tensor([[[-1.9975, -1.9610], [ 0.9592, -2.3364]], [[-1.2534, -1.3429], [ 0.4153, -1.4664]]]) >>> torch.matrix_power(a, 3) tensor([[[ 3.9392, -23.9916], [ 11.7357, -0.2070]], [[ 0.2468, -6.7168], [ 2.0774, -0.8187]]]) """.format(**common_args)) add_docstr(torch.max, r""" max(input) -> Tensor Returns the maximum value of all elements in the ``input`` tensor. .. warning:: This function produces deterministic (sub)gradients unlike ``max(dim=0)`` Args: {input} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[ 0.6763, 0.7445, -2.2369]]) >>> torch.max(a) tensor(0.7445) .. function:: max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum value of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. And ``indices`` is the index location of each maximum value found (argmax). .. warning:: ``indices`` does not necessarily contain the first occurrence of each maximal value found, unless it is unique. The exact implementation details are device-specific. Do not expect the same result when run on CPU and GPU in general. For the same reason do not expect the gradients to be deterministic. If ``keepdim`` is ``True``, the output tensors are of the same size as ``input`` except in the dimension ``dim`` where they are of size 1. Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting in the output tensors having 1 fewer dimension than ``input``. Args: {input} {dim} {keepdim} Default: ``False``. out (tuple, optional): the result tuple of two output tensors (max, max_indices) Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-1.2360, -0.2942, -0.1222, 0.8475], [ 1.1949, -1.1127, -2.2379, -0.6702], [ 1.5717, -0.9207, 0.1297, -1.8768], [-0.6172, 1.0036, -0.6060, -0.2432]]) >>> torch.max(a, 1) torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) .. function:: max(input, other, out=None) -> Tensor Each element of the tensor ``input`` is compared with the corresponding element of the tensor ``other`` and an element-wise maximum is taken. The shapes of ``input`` and ``other`` don't need to match, but they must be :ref:`broadcastable <broadcasting-semantics>`. .. math:: \text{{out}}_i = \max(\text{{tensor}}_i, \text{{other}}_i) .. note:: When the shapes do not match, the shape of the returned output tensor follows the :ref:`broadcasting rules <broadcasting-semantics>`. Args: {input} other (Tensor): the second input tensor {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.2942, -0.7416, 0.2653, -0.1584]) >>> b = torch.randn(4) >>> b tensor([ 0.8722, -1.7421, -0.4141, -0.5055]) >>> torch.max(a, b) tensor([ 0.8722, -0.7416, 0.2653, -0.1584]) """.format(**single_dim_common)) add_docstr(torch.argmax, r""" argmax(input) -> LongTensor Returns the indices of the maximum value of all elements in the :attr:`input` tensor. This is the second value returned by :meth:`torch.max`. See its documentation for the exact semantics of this method. Args: {input} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], [-0.7401, -0.8805, -0.3402, -1.1936], [ 0.4907, -1.3948, -1.0691, -0.3132], [-1.6092, 0.5419, -0.2993, 0.3195]]) >>> torch.argmax(a) tensor(0) .. function:: argmax(input, dim, keepdim=False) -> LongTensor Returns the indices of the maximum values of a tensor across a dimension. This is the second value returned by :meth:`torch.max`. See its documentation for the exact semantics of this method. Args: {input} {dim} If ``None``, the argmax of the flattened input is returned. {keepdim} Ignored if ``dim=None``. Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], [-0.7401, -0.8805, -0.3402, -1.1936], [ 0.4907, -1.3948, -1.0691, -0.3132], [-1.6092, 0.5419, -0.2993, 0.3195]]) >>> torch.argmax(a, dim=1) tensor([ 0, 2, 0, 1]) """.format(**single_dim_common)) add_docstr(torch.mean, r""" mean(input) -> Tensor Returns the mean value of all elements in the :attr:`input` tensor. Args: {input} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[ 0.2294, -0.5481, 1.3288]]) >>> torch.mean(a) tensor(0.3367) .. function:: mean(input, dim, keepdim=False, out=None) -> Tensor Returns the mean value of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. {keepdim_details} Args: {input} {dim} {keepdim} {out} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-0.3841, 0.6320, 0.4254, -0.7384], [-0.9644, 1.0131, -0.6549, -1.4279], [-0.2951, -1.3350, -0.7694, 0.5600], [ 1.0842, -0.9580, 0.3623, 0.2343]]) >>> torch.mean(a, 1) tensor([-0.0163, -0.5085, -0.4599, 0.1807]) >>> torch.mean(a, 1, True) tensor([[-0.0163], [-0.5085], [-0.4599], [ 0.1807]]) """.format(**multi_dim_common)) add_docstr(torch.median, r""" median(input) -> Tensor Returns the median value of all elements in the :attr:`input` tensor. .. warning:: This function produces deterministic (sub)gradients unlike ``median(dim=0)`` Args: {input} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[ 1.5219, -1.5212, 0.2202]]) >>> torch.median(a) tensor(0.2202) .. function:: median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the median value of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. And ``indices`` is the index location of each median value found. By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. If :attr:`keepdim` is ``True``, the output tensors are of the same size as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the outputs tensor having 1 fewer dimension than :attr:`input`. .. warning:: ``indices`` does not necessarily contain the first occurrence of each median value found, unless it is unique. The exact implementation details are device-specific. Do not expect the same result when run on CPU and GPU in general. For the same reason do not expect the gradients to be deterministic. Args: {input} {dim} {keepdim} out (tuple, optional): the result tuple of two output tensors (max, max_indices) Example:: >>> a = torch.randn(4, 5) >>> a tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) >>> torch.median(a, 1) torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) """.format(**single_dim_common)) add_docstr(torch.min, r""" min(input) -> Tensor Returns the minimum value of all elements in the :attr:`input` tensor. .. warning:: This function produces deterministic (sub)gradients unlike ``min(dim=0)`` Args: {input} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[ 0.6750, 1.0857, 1.7197]]) >>> torch.min(a) tensor(0.6750) .. function:: min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum value of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. And ``indices`` is the index location of each minimum value found (argmin). .. warning:: ``indices`` does not necessarily contain the first occurrence of each minimal value found, unless it is unique. The exact implementation details are device-specific. Do not expect the same result when run on CPU and GPU in general. For the same reason do not expect the gradients to be deterministic. If :attr:`keepdim` is ``True``, the output tensors are of the same size as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the output tensors having 1 fewer dimension than :attr:`input`. Args: {input} {dim} {keepdim} out (tuple, optional): the tuple of two output tensors (min, min_indices) Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-0.6248, 1.1334, -1.1899, -0.2803], [-1.4644, -0.2635, -0.3651, 0.6134], [ 0.2457, 0.0384, 1.0128, 0.7015], [-0.1153, 2.9849, 2.1458, 0.5788]]) >>> torch.min(a, 1) torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) .. function:: min(input, other, out=None) -> Tensor Each element of the tensor :attr:`input` is compared with the corresponding element of the tensor :attr:`other` and an element-wise minimum is taken. The resulting tensor is returned. The shapes of :attr:`input` and :attr:`other` don't need to match, but they must be :ref:`broadcastable <broadcasting-semantics>`. .. math:: \text{{out}}_i = \min(\text{{tensor}}_i, \text{{other}}_i) .. note:: When the shapes do not match, the shape of the returned output tensor follows the :ref:`broadcasting rules <broadcasting-semantics>`. Args: {input} other (Tensor): the second input tensor {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.8137, -1.1740, -0.6460, 0.6308]) >>> b = torch.randn(4) >>> b tensor([-0.1369, 0.1555, 0.4019, -0.1929]) >>> torch.min(a, b) tensor([-0.1369, -1.1740, -0.6460, -0.1929]) """.format(**single_dim_common)) add_docstr(torch.argmin, r""" argmin(input) -> LongTensor Returns the indices of the minimum value of all elements in the :attr:`input` tensor. This is the second value returned by :meth:`torch.min`. See its documentation for the exact semantics of this method. Args: {input} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.1139, 0.2254, -0.1381, 0.3687], [ 1.0100, -1.1975, -0.0102, -0.4732], [-0.9240, 0.1207, -0.7506, -1.0213], [ 1.7809, -1.2960, 0.9384, 0.1438]]) >>> torch.argmin(a) tensor(13) .. function:: argmin(input, dim, keepdim=False, out=None) -> LongTensor Returns the indices of the minimum values of a tensor across a dimension. This is the second value returned by :meth:`torch.min`. See its documentation for the exact semantics of this method. Args: {input} {dim} If ``None``, the argmin of the flattened input is returned. {keepdim} Ignored if ``dim=None``. Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.1139, 0.2254, -0.1381, 0.3687], [ 1.0100, -1.1975, -0.0102, -0.4732], [-0.9240, 0.1207, -0.7506, -1.0213], [ 1.7809, -1.2960, 0.9384, 0.1438]]) >>> torch.argmin(a, dim=1) tensor([ 2, 1, 3, 1]) """.format(**single_dim_common)) add_docstr(torch.mm, r""" mm(input, mat2, out=None) -> Tensor Performs a matrix multiplication of the matrices :attr:`input` and :attr:`mat2`. If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, :attr:`out` will be a :math:`(n \times p)` tensor. .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. For broadcasting matrix products, see :func:`torch.matmul`. Args: input (Tensor): the first matrix to be multiplied mat2 (Tensor): the second matrix to be multiplied {out} Example:: >>> mat1 = torch.randn(2, 3) >>> mat2 = torch.randn(3, 3) >>> torch.mm(mat1, mat2) tensor([[ 0.4851, 0.5037, -0.3633], [-0.0760, -3.6705, 2.4784]]) """.format(**common_args)) add_docstr(torch.matmul, r""" matmul(input, other, out=None) -> Tensor Matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows: - If both tensors are 1-dimensional, the dot product (scalar) is returned. - If both arguments are 2-dimensional, the matrix-matrix product is returned. - If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed. - If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned. - If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e. batch) dimensions are :ref:`broadcasted <broadcasting-semantics>` (and thus must be broadcastable). For example, if :attr:`input` is a :math:`(j \times 1 \times n \times m)` tensor and :attr:`other` is a :math:`(k \times m \times p)` tensor, :attr:`out` will be an :math:`(j \times k \times n \times p)` tensor. .. note:: The 1-dimensional dot product version of this function does not support an :attr:`out` parameter. Arguments: input (Tensor): the first tensor to be multiplied other (Tensor): the second tensor to be multiplied {out} Example:: >>> # vector x vector >>> tensor1 = torch.randn(3) >>> tensor2 = torch.randn(3) >>> torch.matmul(tensor1, tensor2).size() torch.Size([]) >>> # matrix x vector >>> tensor1 = torch.randn(3, 4) >>> tensor2 = torch.randn(4) >>> torch.matmul(tensor1, tensor2).size() torch.Size([3]) >>> # batched matrix x broadcasted vector >>> tensor1 = torch.randn(10, 3, 4) >>> tensor2 = torch.randn(4) >>> torch.matmul(tensor1, tensor2).size() torch.Size([10, 3]) >>> # batched matrix x batched matrix >>> tensor1 = torch.randn(10, 3, 4) >>> tensor2 = torch.randn(10, 4, 5) >>> torch.matmul(tensor1, tensor2).size() torch.Size([10, 3, 5]) >>> # batched matrix x broadcasted matrix >>> tensor1 = torch.randn(10, 3, 4) >>> tensor2 = torch.randn(4, 5) >>> torch.matmul(tensor1, tensor2).size() torch.Size([10, 3, 5]) """.format(**common_args)) add_docstr(torch.mode, r""" mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) Returns a namedtuple ``(values, indices)`` where ``values`` is the mode value of each row of the :attr:`input` tensor in the given dimension :attr:`dim`, i.e. a value which appears most often in that row, and ``indices`` is the index location of each mode value found. By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. If :attr:`keepdim` is ``True``, the output tensors are of the same size as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the output tensors having 1 fewer dimension than :attr:`input`. .. note:: This function is not defined for ``torch.cuda.Tensor`` yet. Args: {input} {dim} {keepdim} out (tuple, optional): the result tuple of two output tensors (values, indices) Example:: >>> a = torch.randint(10, (5,)) >>> a tensor([6, 5, 1, 0, 2]) >>> b = a + (torch.randn(50, 1) * 5).long() >>> torch.mode(b, 0) torch.return_types.mode(values=tensor([6, 5, 1, 0, 2]), indices=tensor([2, 2, 2, 2, 2])) """.format(**single_dim_common)) add_docstr(torch.mul, r""" mul(input, other, out=None) Multiplies each element of the input :attr:`input` with the scalar :attr:`other` and returns a new resulting tensor. .. math:: \text{out}_i = \text{other} \times \text{input}_i """ + r""" If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`other` should be a real number, otherwise it should be an integer Args: {input} value (Number): the number to be multiplied to each element of :attr:`input` {out} Example:: >>> a = torch.randn(3) >>> a tensor([ 0.2015, -0.4255, 2.6087]) >>> torch.mul(a, 100) tensor([ 20.1494, -42.5491, 260.8663]) .. function:: mul(input, other, out=None) Each element of the tensor :attr:`input` is multiplied by the corresponding element of the Tensor :attr:`other`. The resulting tensor is returned. The shapes of :attr:`input` and :attr:`other` must be :ref:`broadcastable <broadcasting-semantics>`. .. math:: \text{out}_i = \text{input}_i \times \text{other}_i """ + r""" Args: input (Tensor): the first multiplicand tensor other (Tensor): the second multiplicand tensor {out} Example:: >>> a = torch.randn(4, 1) >>> a tensor([[ 1.1207], [-0.3137], [ 0.0700], [ 0.8378]]) >>> b = torch.randn(1, 4) >>> b tensor([[ 0.5146, 0.1216, -0.5244, 2.2382]]) >>> torch.mul(a, b) tensor([[ 0.5767, 0.1363, -0.5877, 2.5083], [-0.1614, -0.0382, 0.1645, -0.7021], [ 0.0360, 0.0085, -0.0367, 0.1567], [ 0.4312, 0.1019, -0.4394, 1.8753]]) """.format(**common_args)) add_docstr(torch.multinomial, r""" multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor Returns a tensor where each row contains :attr:`num_samples` indices sampled from the multinomial probability distribution located in the corresponding row of tensor :attr:`input`. .. note:: The rows of :attr:`input` do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. Indices are ordered from left to right according to when each was sampled (first samples are placed in first column). If :attr:`input` is a vector, :attr:`out` is a vector of size :attr:`num_samples`. If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape :math:`(m \times \text{{num\_samples}})`. If replacement is ``True``, samples are drawn with replacement. If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row. .. note:: When drawn without replacement, :attr:`num_samples` must be lower than number of non-zero elements in :attr:`input` (or the min number of non-zero elements in each row of :attr:`input` if it is a matrix). Args: input (Tensor): the input tensor containing probabilities num_samples (int): number of samples to draw replacement (bool, optional): whether to draw with replacement or not {generator} {out} Example:: >>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights >>> torch.multinomial(weights, 2) tensor([1, 2]) >>> torch.multinomial(weights, 4) # ERROR! RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False, not enough non-negative category to sample) at ../aten/src/TH/generic/THTensorRandom.cpp:320 >>> torch.multinomial(weights, 4, replacement=True) tensor([ 2, 1, 1, 1]) """.format(**common_args)) add_docstr(torch.mv, r""" mv(input, vec, out=None) -> Tensor Performs a matrix-vector product of the matrix :attr:`input` and the vector :attr:`vec`. If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of size :math:`m`, :attr:`out` will be 1-D of size :math:`n`. .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. Args: input (Tensor): matrix to be multiplied vec (Tensor): vector to be multiplied {out} Example:: >>> mat = torch.randn(2, 3) >>> vec = torch.randn(3) >>> torch.mv(mat, vec) tensor([ 1.0404, -0.6361]) """.format(**common_args)) add_docstr(torch.mvlgamma, r""" mvlgamma(input, p) -> Tensor Computes the `multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>`_) with dimension :math:`p` element-wise, given by .. math:: \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right) where :math:`C = \log(\pi) \times \frac{p (p - 1)}{4}` and :math:`\Gamma(\cdot)` is the Gamma function. All elements must be greater than :math:`\frac{p - 1}{2}`, otherwise an error would be thrown. Args: input (Tensor): the tensor to compute the multivariate log-gamma function p (int): the number of dimensions Example:: >>> a = torch.empty(2, 3).uniform_(1, 2) >>> a tensor([[1.6835, 1.8474, 1.1929], [1.0475, 1.7162, 1.4180]]) >>> torch.mvlgamma(a, 2) tensor([[0.3928, 0.4007, 0.7586], [1.0311, 0.3901, 0.5049]]) """) add_docstr(torch.narrow, r""" narrow(input, dim, start, length) -> Tensor Returns a new tensor that is a narrowed version of :attr:`input` tensor. The dimension :attr:`dim` is input from :attr:`start` to :attr:`start + length`. The returned tensor and :attr:`input` tensor share the same underlying storage. Args: input (Tensor): the tensor to narrow dim (int): the dimension along which to narrow start (int): the starting dimension length (int): the distance to the ending dimension Example:: >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> torch.narrow(x, 0, 0, 2) tensor([[ 1, 2, 3], [ 4, 5, 6]]) >>> torch.narrow(x, 1, 1, 2) tensor([[ 2, 3], [ 5, 6], [ 8, 9]]) """) add_docstr(torch.ne, r""" ne(input, other, out=None) -> Tensor Computes :math:`input \neq other` element-wise. The second argument can be a number or a tensor whose shape is :ref:`broadcastable <broadcasting-semantics>` with the first argument. Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a `BoolTensor` Returns: Tensor: A ``torch.BoolTensor`` containing a True at each location where comparison is true. Example:: >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) tensor([[False, True], [True, False]]) """) add_docstr(torch.neg, r""" neg(input, out=None) -> Tensor Returns a new tensor with the negative of the elements of :attr:`input`. .. math:: \text{out} = -1 \times \text{input} """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(5) >>> a tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) >>> torch.neg(a) tensor([-0.0090, 0.2262, 0.0682, 0.2866, -0.3940]) """.format(**common_args)) add_docstr(torch.nonzero, r""" nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors .. note:: :func:`torch.nonzero(..., as_tuple=False) <torch.nonzero>` (default) returns a 2-D tensor where each row is the index for a nonzero value. :func:`torch.nonzero(..., as_tuple=True) <torch.nonzero>` returns a tuple of 1-D index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor contains nonzero indices for a certain dimension. See below for more details on the two behaviors. **When** :attr:`as_tuple` **is ``False`` (default)**: Returns a tensor containing the indices of all non-zero elements of :attr:`input`. Each row in the result contains the indices of a non-zero element in :attr:`input`. The result is sorted lexicographically, with the last index changing the fastest (C-style). If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of non-zero elements in the :attr:`input` tensor. **When** :attr:`as_tuple` **is ``True``**: Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`, each containing the indices (in that dimension) of all non-zero elements of :attr:`input` . If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n` tensors of size :math:`z`, where :math:`z` is the total number of non-zero elements in the :attr:`input` tensor. As a special case, when :attr:`input` has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element. Args: {input} out (LongTensor, optional): the output tensor containing indices Returns: LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for each dimension, containing the indices of each nonzero element along that dimension. Example:: >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1])) tensor([[ 0], [ 1], [ 2], [ 4]]) >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], [0.0, 0.4, 0.0, 0.0], [0.0, 0.0, 1.2, 0.0], [0.0, 0.0, 0.0,-0.4]])) tensor([[ 0, 0], [ 1, 1], [ 2, 2], [ 3, 3]]) >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True) (tensor([0, 1, 2, 4]),) >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], [0.0, 0.4, 0.0, 0.0], [0.0, 0.0, 1.2, 0.0], [0.0, 0.0, 0.0,-0.4]]), as_tuple=True) (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3])) >>> torch.nonzero(torch.tensor(5), as_tuple=True) (tensor([0]),) """.format(**common_args)) add_docstr(torch.normal, r""" normal(mean, std, *, generator=None, out=None) -> Tensor Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given. The :attr:`mean` is a tensor with the mean of each output element's normal distribution The :attr:`std` is a tensor with the standard deviation of each output element's normal distribution The shapes of :attr:`mean` and :attr:`std` don't need to match, but the total number of elements in each tensor need to be the same. .. note:: When the shapes do not match, the shape of :attr:`mean` is used as the shape for the returned output tensor Args: mean (Tensor): the tensor of per-element means std (Tensor): the tensor of per-element standard deviations {generator} {out} Example:: >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, 8.0505, 8.1408, 9.0563, 10.0566]) .. function:: normal(mean=0.0, std, out=None) -> Tensor Similar to the function above, but the means are shared among all drawn elements. Args: mean (float, optional): the mean for all distributions std (Tensor): the tensor of per-element standard deviations {out} Example:: >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) .. function:: normal(mean, std=1.0, out=None) -> Tensor Similar to the function above, but the standard-deviations are shared among all drawn elements. Args: mean (Tensor): the tensor of per-element means std (float, optional): the standard deviation for all distributions out (Tensor, optional): the output tensor Example:: >>> torch.normal(mean=torch.arange(1., 6.)) tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) .. function:: normal(mean, std, size, *, out=None) -> Tensor Similar to the function above, but the means and standard deviations are shared among all drawn elements. The resulting tensor has size given by :attr:`size`. Args: mean (float): the mean for all distributions std (float): the standard deviation for all distributions size (int...): a sequence of integers defining the shape of the output tensor. {out} Example:: >>> torch.normal(2, 3, size=(1, 4)) tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) """.format(**common_args)) add_docstr(torch.numel, r""" numel(input) -> int Returns the total number of elements in the :attr:`input` tensor. Args: {input} Example:: >>> a = torch.randn(1, 2, 3, 4, 5) >>> torch.numel(a) 120 >>> a = torch.zeros(4,4) >>> torch.numel(a) 16 """.format(**common_args)) add_docstr(torch.ones, r""" ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor filled with the scalar value `1`, with the shape defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.ones(2, 3) tensor([[ 1., 1., 1.], [ 1., 1., 1.]]) >>> torch.ones(5) tensor([ 1., 1., 1., 1., 1.]) """.format(**factory_common_args)) add_docstr(torch.ones_like, r""" ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor Returns a tensor filled with the scalar value `1`, with the same size as :attr:`input`. ``torch.ones_like(input)`` is equivalent to ``torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. .. warning:: As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, the old ``torch.ones_like(input, out=output)`` is equivalent to ``torch.ones(input.size(), out=output)``. Args: {input} {dtype} {layout} {device} {requires_grad} {memory_format} Example:: >>> input = torch.empty(2, 3) >>> torch.ones_like(input) tensor([[ 1., 1., 1.], [ 1., 1., 1.]]) """.format(**factory_like_common_args)) add_docstr(torch.orgqr, r""" orgqr(input, input2) -> Tensor Computes the orthogonal matrix `Q` of a QR factorization, from the `(input, input2)` tuple returned by :func:`torch.geqrf`. This directly calls the underlying LAPACK function `?orgqr`. See `LAPACK documentation for orgqr`_ for further details. Args: input (Tensor): the `a` from :func:`torch.geqrf`. input2 (Tensor): the `tau` from :func:`torch.geqrf`. .. _LAPACK documentation for orgqr: https://software.intel.com/en-us/mkl-developer-reference-c-orgqr """) add_docstr(torch.ormqr, r""" ormqr(input, input2, input3, left=True, transpose=False) -> Tensor Multiplies `mat` (given by :attr:`input3`) by the orthogonal `Q` matrix of the QR factorization formed by :func:`torch.geqrf` that is represented by `(a, tau)` (given by (:attr:`input`, :attr:`input2`)). This directly calls the underlying LAPACK function `?ormqr`. See `LAPACK documentation for ormqr`_ for further details. Args: input (Tensor): the `a` from :func:`torch.geqrf`. input2 (Tensor): the `tau` from :func:`torch.geqrf`. input3 (Tensor): the matrix to be multiplied. .. _LAPACK documentation for ormqr: https://software.intel.com/en-us/mkl-developer-reference-c-ormqr """) add_docstr(torch.poisson, r""" poisson(input *, generator=None) -> Tensor Returns a tensor of the same size as :attr:`input` with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in :attr:`input` i.e., .. math:: \text{{out}}_i \sim \text{{Poisson}}(\text{{input}}_i) Args: input (Tensor): the input tensor containing the rates of the Poisson distribution {generator} Example:: >>> rates = torch.rand(4, 4) * 5 # rate parameter between 0 and 5 >>> torch.poisson(rates) tensor([[9., 1., 3., 5.], [8., 6., 6., 0.], [0., 4., 5., 3.], [2., 1., 4., 2.]]) """.format(**common_args)) add_docstr(torch.polygamma, r""" polygamma(n, input, out=None) -> Tensor Computes the :math:`n^{th}` derivative of the digamma function on :attr:`input`. :math:`n \geq 0` is called the order of the polygamma function. .. math:: \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x) .. note:: This function is not implemented for :math:`n \geq 2`. """ + """ Args: n (int): the order of the polygamma function {input} {out} Example:: >>> a = torch.tensor([1, 0.5]) >>> torch.polygamma(1, a) tensor([1.64493, 4.9348]) """.format(**common_args)) add_docstr(torch.pow, r""" pow(input, exponent, out=None) -> Tensor Takes the power of each element in :attr:`input` with :attr:`exponent` and returns a tensor with the result. :attr:`exponent` can be either a single ``float`` number or a `Tensor` with the same number of elements as :attr:`input`. When :attr:`exponent` is a scalar value, the operation applied is: .. math:: \text{out}_i = x_i ^ \text{exponent} When :attr:`exponent` is a tensor, the operation applied is: .. math:: \text{out}_i = x_i ^ {\text{exponent}_i} """ + r""" When :attr:`exponent` is a tensor, the shapes of :attr:`input` and :attr:`exponent` must be :ref:`broadcastable <broadcasting-semantics>`. Args: {input} exponent (float or tensor): the exponent value {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) >>> torch.pow(a, 2) tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) >>> exp = torch.arange(1., 5.) >>> a = torch.arange(1., 5.) >>> a tensor([ 1., 2., 3., 4.]) >>> exp tensor([ 1., 2., 3., 4.]) >>> torch.pow(a, exp) tensor([ 1., 4., 27., 256.]) .. function:: pow(self, exponent, out=None) -> Tensor :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. The returned tensor :attr:`out` is of the same shape as :attr:`exponent` The operation applied is: .. math:: \text{{out}}_i = \text{{self}} ^ {{\text{{exponent}}_i}} Args: self (float): the scalar base value for the power operation exponent (Tensor): the exponent tensor {out} Example:: >>> exp = torch.arange(1., 5.) >>> base = 2 >>> torch.pow(base, exp) tensor([ 2., 4., 8., 16.]) """.format(**common_args)) add_docstr(torch.prod, r""" prod(input, dtype=None) -> Tensor Returns the product of all elements in the :attr:`input` tensor. Args: {input} {dtype} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[-0.8020, 0.5428, -1.5854]]) >>> torch.prod(a) tensor(0.6902) .. function:: prod(input, dim, keepdim=False, dtype=None) -> Tensor Returns the product of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. {keepdim_details} Args: {input} {dim} {keepdim} {dtype} Example:: >>> a = torch.randn(4, 2) >>> a tensor([[ 0.5261, -0.3837], [ 1.1857, -0.2498], [-1.1646, 0.0705], [ 1.1131, -1.0629]]) >>> torch.prod(a, 1) tensor([-0.2018, -0.2962, -0.0821, -1.1831]) """.format(**single_dim_common)) add_docstr(torch.promote_types, r""" promote_types(type1, type2) -> dtype Returns the :class:`torch.dtype` with the smallest size and scalar kind that is not smaller nor of lower kind than either `type1` or `type2`. See type promotion :ref:`documentation <type-promotion-doc>` for more information on the type promotion logic. Args: type1 (:class:`torch.dtype`) type2 (:class:`torch.dtype`) Example:: >>> torch.promote_types(torch.int32, torch.float32)) torch.float32 >>> torch.promote_types(torch.uint8, torch.long) torch.long """) add_docstr(torch.qr, r""" qr(input, some=True, out=None) -> (Tensor, Tensor) Computes the QR decomposition of a matrix or a batch of matrices :attr:`input`, and returns a namedtuple (Q, R) of tensors such that :math:`\text{input} = Q R` with :math:`Q` being an orthogonal matrix or batch of orthogonal matrices and :math:`R` being an upper triangular matrix or batch of upper triangular matrices. If :attr:`some` is ``True``, then this function returns the thin (reduced) QR factorization. Otherwise, if :attr:`some` is ``False``, this function returns the complete QR factorization. .. note:: precision may be lost if the magnitudes of the elements of :attr:`input` are large .. note:: While it should always give you a valid decomposition, it may not give you the same one across platforms - it will depend on your LAPACK implementation. Args: input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more batch dimensions consisting of matrices of dimension :math:`m \times n`. some (bool, optional): Set to ``True`` for reduced QR decomposition and ``False`` for complete QR decomposition. out (tuple, optional): tuple of `Q` and `R` tensors satisfying :code:`input = torch.matmul(Q, R)`. The dimensions of `Q` and `R` are :math:`(*, m, k)` and :math:`(*, k, n)` respectively, where :math:`k = \min(m, n)` if :attr:`some:` is ``True`` and :math:`k = m` otherwise. Example:: >>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]]) >>> q, r = torch.qr(a) >>> q tensor([[-0.8571, 0.3943, 0.3314], [-0.4286, -0.9029, -0.0343], [ 0.2857, -0.1714, 0.9429]]) >>> r tensor([[ -14.0000, -21.0000, 14.0000], [ 0.0000, -175.0000, 70.0000], [ 0.0000, 0.0000, -35.0000]]) >>> torch.mm(q, r).round() tensor([[ 12., -51., 4.], [ 6., 167., -68.], [ -4., 24., -41.]]) >>> torch.mm(q.t(), q).round() tensor([[ 1., 0., 0.], [ 0., 1., -0.], [ 0., -0., 1.]]) >>> a = torch.randn(3, 4, 5) >>> q, r = torch.qr(a, some=False) >>> torch.allclose(torch.matmul(q, r), a) True >>> torch.allclose(torch.matmul(q.transpose(-2, -1), q), torch.eye(5)) True """) add_docstr(torch.rad2deg, r""" rad2deg(input, out=None) -> Tensor Returns a new tensor with each of the elements of :attr:`input` converted from angles in radians to degrees. Args: {input} Keyword arguments: {out} Example:: >>> a = torch.tensor([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]]) >>> torch.rad2deg(a) tensor([[ 180.0233, -180.0233], [ 359.9894, -359.9894], [ 89.9544, -89.9544]]) """.format(**common_args)) add_docstr(torch.deg2rad, r""" deg2rad(input, out=None) -> Tensor Returns a new tensor with each of the elements of :attr:`input` converted from angles in degrees to radians. Args: {input} Keyword arguments: {out} Example:: >>> a = torch.tensor([[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]]) >>> torch.deg2rad(a) tensor([[ 3.1416, -3.1416], [ 6.2832, -6.2832], [ 1.5708, -1.5708]]) """.format(**common_args)) add_docstr(torch.rand, r""" rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor filled with random numbers from a uniform distribution on the interval :math:`[0, 1)` The shape of the tensor is defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.rand(4) tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) >>> torch.rand(2, 3) tensor([[ 0.8237, 0.5781, 0.6879], [ 0.3816, 0.7249, 0.0998]]) """.format(**factory_common_args)) add_docstr(torch.rand_like, r""" rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor Returns a tensor with the same size as :attr:`input` that is filled with random numbers from a uniform distribution on the interval :math:`[0, 1)`. ``torch.rand_like(input)`` is equivalent to ``torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. Args: {input} {dtype} {layout} {device} {requires_grad} {memory_format} """.format(**factory_like_common_args)) add_docstr(torch.randint, """ randint(low=0, high, size, \\*, generator=None, out=None, \ dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor filled with random integers generated uniformly between :attr:`low` (inclusive) and :attr:`high` (exclusive). The shape of the tensor is defined by the variable argument :attr:`size`. .. note: With the global dtype default (``torch.float32``), this function returns a tensor with dtype ``torch.int64``. Args: low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. high (int): One above the highest integer to be drawn from the distribution. size (tuple): a tuple defining the shape of the output tensor. {generator} {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.randint(3, 5, (3,)) tensor([4, 3, 4]) >>> torch.randint(10, (2, 2)) tensor([[0, 2], [5, 5]]) >>> torch.randint(3, 10, (2, 2)) tensor([[4, 5], [6, 7]]) """.format(**factory_common_args)) add_docstr(torch.randint_like, """ randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, \ memory_format=torch.preserve_format) -> Tensor Returns a tensor with the same shape as Tensor :attr:`input` filled with random integers generated uniformly between :attr:`low` (inclusive) and :attr:`high` (exclusive). .. note: With the global dtype default (``torch.float32``), this function returns a tensor with dtype ``torch.int64``. Args: {input} low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. high (int): One above the highest integer to be drawn from the distribution. {dtype} {layout} {device} {requires_grad} {memory_format} """.format(**factory_like_common_args)) add_docstr(torch.randn, r""" randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor filled with random numbers from a normal distribution with mean `0` and variance `1` (also called the standard normal distribution). .. math:: \text{{out}}_{{i}} \sim \mathcal{{N}}(0, 1) The shape of the tensor is defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.randn(4) tensor([-2.1436, 0.9966, 2.3426, -0.6366]) >>> torch.randn(2, 3) tensor([[ 1.5954, 2.8929, -1.0923], [ 1.1719, -0.4709, -0.1996]]) """.format(**factory_common_args)) add_docstr(torch.randn_like, r""" randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor Returns a tensor with the same size as :attr:`input` that is filled with random numbers from a normal distribution with mean 0 and variance 1. ``torch.randn_like(input)`` is equivalent to ``torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. Args: {input} {dtype} {layout} {device} {requires_grad} {memory_format} """.format(**factory_like_common_args)) add_docstr(torch.randperm, r""" randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor Returns a random permutation of integers from ``0`` to ``n - 1``. Args: n (int): the upper bound (exclusive) {out} dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: ``torch.int64``. {layout} {device} {requires_grad} Example:: >>> torch.randperm(4) tensor([2, 1, 0, 3]) """.format(**factory_common_args)) add_docstr(torch.tensor, r""" tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor Constructs a tensor with :attr:`data`. .. warning:: :func:`torch.tensor` always copies :attr:`data`. If you have a Tensor ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_` or :func:`torch.Tensor.detach`. If you have a NumPy ``ndarray`` and want to avoid a copy, use :func:`torch.as_tensor`. .. warning:: When data is a tensor `x`, :func:`torch.tensor` reads out 'the data' from whatever it is passed, and constructs a leaf variable. Therefore ``torch.tensor(x)`` is equivalent to ``x.clone().detach()`` and ``torch.tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``. The equivalents using ``clone()`` and ``detach()`` are recommended. Args: {data} {dtype} {device} {requires_grad} {pin_memory} Example:: >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]]) tensor([[ 0.1000, 1.2000], [ 2.2000, 3.1000], [ 4.9000, 5.2000]]) >>> torch.tensor([0, 1]) # Type inference on data tensor([ 0, 1]) >>> torch.tensor([[0.11111, 0.222222, 0.3333333]], dtype=torch.float64, device=torch.device('cuda:0')) # creates a torch.cuda.DoubleTensor tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') >>> torch.tensor(3.14159) # Create a scalar (zero-dimensional tensor) tensor(3.1416) >>> torch.tensor([]) # Create an empty tensor (of size (0,)) tensor([]) """.format(**factory_data_common_args)) add_docstr(torch.range, r""" range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a 1-D tensor of size :math:`\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1` with values from :attr:`start` to :attr:`end` with step :attr:`step`. Step is the gap between two values in the tensor. .. math:: \text{out}_{i+1} = \text{out}_i + \text{step}. """ + r""" .. warning:: This function is deprecated in favor of :func:`torch.arange`. Args: start (float): the starting value for the set of points. Default: ``0``. end (float): the ending value for the set of points step (float): the gap between each pair of adjacent points. Default: ``1``. {out} {dtype} If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. {layout} {device} {requires_grad} Example:: >>> torch.range(1, 4) tensor([ 1., 2., 3., 4.]) >>> torch.range(1, 4, 0.5) tensor([ 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000]) """.format(**factory_common_args)) add_docstr(torch.arange, r""" arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` with values from the interval ``[start, end)`` taken with common difference :attr:`step` beginning from `start`. Note that non-integer :attr:`step` is subject to floating point rounding errors when comparing against :attr:`end`; to avoid inconsistency, we advise adding a small epsilon to :attr:`end` in such cases. .. math:: \text{out}_{{i+1}} = \text{out}_{i} + \text{step} """ + r""" Args: start (Number): the starting value for the set of points. Default: ``0``. end (Number): the ending value for the set of points step (Number): the gap between each pair of adjacent points. Default: ``1``. {out} {dtype} If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. {layout} {device} {requires_grad} Example:: >>> torch.arange(5) tensor([ 0, 1, 2, 3, 4]) >>> torch.arange(1, 4) tensor([ 1, 2, 3]) >>> torch.arange(1, 2.5, 0.5) tensor([ 1.0000, 1.5000, 2.0000]) """.format(**factory_common_args)) add_docstr(torch.remainder, r""" remainder(input, other, out=None) -> Tensor Computes the element-wise remainder of division. The dividend and divisor may contain both for integer and floating point numbers. The remainder has the same sign as the divisor :attr:`other`. When :attr:`other` is a tensor, the shapes of :attr:`input` and :attr:`other` must be :ref:`broadcastable <broadcasting-semantics>`. Args: input (Tensor): the dividend other (Tensor or float): the divisor that may be either a number or a Tensor of the same shape as the dividend {out} Example:: >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) tensor([ 1., 0., 1., 1., 0., 1.]) >>> torch.remainder(torch.tensor([1., 2, 3, 4, 5]), 1.5) tensor([ 1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) .. seealso:: :func:`torch.fmod`, which computes the element-wise remainder of division equivalently to the C library function ``fmod()``. """.format(**common_args)) add_docstr(torch.renorm, r""" renorm(input, p, dim, maxnorm, out=None) -> Tensor Returns a tensor where each sub-tensor of :attr:`input` along dimension :attr:`dim` is normalized such that the `p`-norm of the sub-tensor is lower than the value :attr:`maxnorm` .. note:: If the norm of a row is lower than `maxnorm`, the row is unchanged Args: {input} p (float): the power for the norm computation dim (int): the dimension to slice over to get the sub-tensors maxnorm (float): the maximum norm to keep each sub-tensor under {out} Example:: >>> x = torch.ones(3, 3) >>> x[1].fill_(2) tensor([ 2., 2., 2.]) >>> x[2].fill_(3) tensor([ 3., 3., 3.]) >>> x tensor([[ 1., 1., 1.], [ 2., 2., 2.], [ 3., 3., 3.]]) >>> torch.renorm(x, 1, 0, 5) tensor([[ 1.0000, 1.0000, 1.0000], [ 1.6667, 1.6667, 1.6667], [ 1.6667, 1.6667, 1.6667]]) """.format(**common_args)) add_docstr(torch.reshape, r""" reshape(input, shape) -> Tensor Returns a tensor with the same data and number of elements as :attr:`input`, but with the specified shape. When possible, the returned tensor will be a view of :attr:`input`. Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior. See :meth:`torch.Tensor.view` on when it is possible to return a view. A single dimension may be -1, in which case it's inferred from the remaining dimensions and the number of elements in :attr:`input`. Args: input (Tensor): the tensor to be reshaped shape (tuple of ints): the new shape Example:: >>> a = torch.arange(4.) >>> torch.reshape(a, (2, 2)) tensor([[ 0., 1.], [ 2., 3.]]) >>> b = torch.tensor([[0, 1], [2, 3]]) >>> torch.reshape(b, (-1,)) tensor([ 0, 1, 2, 3]) """) add_docstr(torch.result_type, r""" result_type(tensor1, tensor2) -> dtype Returns the :class:`torch.dtype` that would result from performing an arithmetic operation on the provided input tensors. See type promotion :ref:`documentation <type-promotion-doc>` for more information on the type promotion logic. Args: tensor1 (Tensor or Number): an input tensor or number tensor2 (Tensor or Number): an input tensor or number Example:: >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) torch.float32 >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) torch.uint8 """) add_docstr(torch.round, r""" round(input, out=None) -> Tensor Returns a new tensor with each of the elements of :attr:`input` rounded to the closest integer. Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.9920, 0.6077, 0.9734, -1.0362]) >>> torch.round(a) tensor([ 1., 1., 1., -1.]) """.format(**common_args)) add_docstr(torch.rsqrt, r""" rsqrt(input, out=None) -> Tensor Returns a new tensor with the reciprocal of the square-root of each of the elements of :attr:`input`. .. math:: \text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}} """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.0370, 0.2970, 1.5420, -0.9105]) >>> torch.rsqrt(a) tensor([ nan, 1.8351, 0.8053, nan]) """.format(**common_args)) add_docstr(torch.set_flush_denormal, r""" set_flush_denormal(mode) -> bool Disables denormal floating numbers on CPU. Returns ``True`` if your system supports flushing denormal numbers and it successfully configures flush denormal mode. :meth:`~torch.set_flush_denormal` is only supported on x86 architectures supporting SSE3. Args: mode (bool): Controls whether to enable flush denormal mode or not Example:: >>> torch.set_flush_denormal(True) True >>> torch.tensor([1e-323], dtype=torch.float64) tensor([ 0.], dtype=torch.float64) >>> torch.set_flush_denormal(False) True >>> torch.tensor([1e-323], dtype=torch.float64) tensor(9.88131e-324 * [ 1.0000], dtype=torch.float64) """) add_docstr(torch.set_num_threads, r""" set_num_threads(int) Sets the number of threads used for intraop parallelism on CPU. WARNING: To ensure that the correct number of threads is used, set_num_threads must be called before running eager, JIT or autograd code. """) add_docstr(torch.set_num_interop_threads, r""" set_num_interop_threads(int) Sets the number of threads used for interop parallelism (e.g. in JIT interpreter) on CPU. WARNING: Can only be called once and before any inter-op parallel work is started (e.g. JIT execution). """) add_docstr(torch.sigmoid, r""" sigmoid(input, out=None) -> Tensor Returns a new tensor with the sigmoid of the elements of :attr:`input`. .. math:: \text{out}_{i} = \frac{1}{1 + e^{-\text{input}_{i}}} """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.9213, 1.0887, -0.8858, -1.7683]) >>> torch.sigmoid(a) tensor([ 0.7153, 0.7481, 0.2920, 0.1458]) """.format(**common_args)) add_docstr(torch.logit, r""" logit(input, eps=None, out=None) -> Tensor Returns a new tensor with the logit of the elements of :attr:`input`. :attr:`input` is clamped to [eps, 1 - eps] when eps is not None. When eps is None and :attr:`input` < 0 or :attr:`input` > 1, the function will yields NaN. .. math:: y_{i} = \ln(\frac{z_{i}}{1 - z_{i}}) \\ z_{i} = \begin{cases} x_{i} & \text{if eps is None} \\ \text{eps} & \text{if } x_{i} < \text{eps} \\ x_{i} & \text{if } \text{eps} \leq x_{i} \leq 1 - \text{eps} \\ 1 - \text{eps} & \text{if } x_{i} > 1 - \text{eps} \end{cases} """ + r""" Args: {input} eps (float, optional): the epsilon for input clamp bound. Default: ``None`` {out} Example:: >>> a = torch.rand(5) >>> a tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516]) >>> torch.logit(a, eps=1e-6) tensor([-0.9466, 2.6352, 0.6131, -1.7169, 0.6261]) """.format(**common_args)) add_docstr(torch.sign, r""" sign(input, out=None) -> Tensor Returns a new tensor with the signs of the elements of :attr:`input`. .. math:: \text{out}_{i} = \operatorname{sgn}(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.tensor([0.7, -1.2, 0., 2.3]) >>> a tensor([ 0.7000, -1.2000, 0.0000, 2.3000]) >>> torch.sign(a) tensor([ 1., -1., 0., 1.]) """.format(**common_args)) add_docstr(torch.sin, r""" sin(input, out=None) -> Tensor Returns a new tensor with the sine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \sin(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-0.5461, 0.1347, -2.7266, -0.2746]) >>> torch.sin(a) tensor([-0.5194, 0.1343, -0.4032, -0.2711]) """.format(**common_args)) add_docstr(torch.sinh, r""" sinh(input, out=None) -> Tensor Returns a new tensor with the hyperbolic sine of the elements of :attr:`input`. .. math:: \text{out}_{i} = \sinh(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.5380, -0.8632, -0.1265, 0.9399]) >>> torch.sinh(a) tensor([ 0.5644, -0.9744, -0.1268, 1.0845]) """.format(**common_args)) add_docstr(torch.sort, r""" sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) Sorts the elements of the :attr:`input` tensor along a given dimension in ascending order by value. If :attr:`dim` is not given, the last dimension of the `input` is chosen. If :attr:`descending` is ``True`` then the elements are sorted in descending order by value. A namedtuple of (values, indices) is returned, where the `values` are the sorted values and `indices` are the indices of the elements in the original `input` tensor. Args: {input} dim (int, optional): the dimension to sort along descending (bool, optional): controls the sorting order (ascending or descending) out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can be optionally given to be used as output buffers Example:: >>> x = torch.randn(3, 4) >>> sorted, indices = torch.sort(x) >>> sorted tensor([[-0.2162, 0.0608, 0.6719, 2.3332], [-0.5793, 0.0061, 0.6058, 0.9497], [-0.5071, 0.3343, 0.9553, 1.0960]]) >>> indices tensor([[ 1, 0, 2, 3], [ 3, 1, 0, 2], [ 0, 3, 1, 2]]) >>> sorted, indices = torch.sort(x, 0) >>> sorted tensor([[-0.5071, -0.2162, 0.6719, -0.5793], [ 0.0608, 0.0061, 0.9497, 0.3343], [ 0.6058, 0.9553, 1.0960, 2.3332]]) >>> indices tensor([[ 2, 0, 0, 1], [ 0, 1, 1, 2], [ 1, 2, 2, 0]]) """.format(**common_args)) add_docstr(torch.argsort, r""" argsort(input, dim=-1, descending=False) -> LongTensor Returns the indices that sort a tensor along a given dimension in ascending order by value. This is the second value returned by :meth:`torch.sort`. See its documentation for the exact semantics of this method. Args: {input} dim (int, optional): the dimension to sort along descending (bool, optional): controls the sorting order (ascending or descending) Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], [ 0.1598, 0.0788, -0.0745, -1.2700], [ 1.2208, 1.0722, -0.7064, 1.2564], [ 0.0669, -0.2318, -0.8229, -0.9280]]) >>> torch.argsort(a, dim=1) tensor([[2, 0, 3, 1], [3, 2, 1, 0], [2, 1, 0, 3], [3, 2, 1, 0]]) """.format(**common_args)) add_docstr(torch.sparse_coo_tensor, r""" sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given :attr:`indices` with the given :attr:`values`. A sparse tensor can be `uncoalesced`, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: `torch.sparse`_. Args: indices (array_like): Initial data for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. Will be cast to a :class:`torch.LongTensor` internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. values (array_like): Initial values for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. size (list, tuple, or :class:`torch.Size`, optional): Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if None, infers data type from :attr:`values`. device (:class:`torch.device`, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. {requires_grad} Example:: >>> i = torch.tensor([[0, 1, 1], [2, 0, 2]]) >>> v = torch.tensor([3, 4, 5], dtype=torch.float32) >>> torch.sparse_coo_tensor(i, v, [2, 4]) tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), size=(2, 4), nnz=3, layout=torch.sparse_coo) >>> torch.sparse_coo_tensor(i, v) # Shape inference tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), size=(2, 3), nnz=3, layout=torch.sparse_coo) >>> torch.sparse_coo_tensor(i, v, [2, 4], dtype=torch.float64, device=torch.device('cuda:0')) tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64, layout=torch.sparse_coo) # Create an empty sparse tensor with the following invariants: # 1. sparse_dim + dense_dim = len(SparseTensor.shape) # 2. SparseTensor._indices().shape = (sparse_dim, nnz) # 3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:]) # # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0)) >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1]) tensor(indices=tensor([], size=(1, 0)), values=tensor([], size=(0,)), size=(1,), nnz=0, layout=torch.sparse_coo) # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and # sparse_dim = 1 >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2]) tensor(indices=tensor([], size=(1, 0)), values=tensor([], size=(0, 2)), size=(1, 2), nnz=0, layout=torch.sparse_coo) .. _torch.sparse: https://pytorch.org/docs/stable/sparse.html """.format(**factory_common_args)) add_docstr(torch.sqrt, r""" sqrt(input, out=None) -> Tensor Returns a new tensor with the square-root of the elements of :attr:`input`. .. math:: \text{out}_{i} = \sqrt{\text{input}_{i}} """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-2.0755, 1.0226, 0.0831, 0.4806]) >>> torch.sqrt(a) tensor([ nan, 1.0112, 0.2883, 0.6933]) """.format(**common_args)) add_docstr(torch.square, r""" square(input, out=None) -> Tensor Returns a new tensor with the square of the elements of :attr:`input`. Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-2.0755, 1.0226, 0.0831, 0.4806]) >>> torch.square(a) tensor([ 4.3077, 1.0457, 0.0069, 0.2310]) """.format(**common_args)) add_docstr(torch.squeeze, r""" squeeze(input, dim=None, out=None) -> Tensor Returns a tensor with all the dimensions of :attr:`input` of size `1` removed. For example, if `input` is of shape: :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `out` tensor will be of shape: :math:`(A \times B \times C \times D)`. When :attr:`dim` is given, a squeeze operation is done only in the given dimension. If `input` is of shape: :math:`(A \times 1 \times B)`, ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` will squeeze the tensor to the shape :math:`(A \times B)`. .. note:: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other. .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` will also remove the batch dimension, which can lead to unexpected errors. Args: {input} dim (int, optional): if given, the input will be squeezed only in this dimension {out} Example:: >>> x = torch.zeros(2, 1, 2, 1, 2) >>> x.size() torch.Size([2, 1, 2, 1, 2]) >>> y = torch.squeeze(x) >>> y.size() torch.Size([2, 2, 2]) >>> y = torch.squeeze(x, 0) >>> y.size() torch.Size([2, 1, 2, 1, 2]) >>> y = torch.squeeze(x, 1) >>> y.size() torch.Size([2, 2, 1, 2]) """.format(**common_args)) add_docstr(torch.std, r""" std(input, unbiased=True) -> Tensor Returns the standard-deviation of all elements in the :attr:`input` tensor. If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} unbiased (bool): whether to use the unbiased estimation or not Example:: >>> a = torch.randn(1, 3) >>> a tensor([[-0.8166, -1.3802, -0.3560]]) >>> torch.std(a) tensor(0.5130) .. function:: std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor Returns the standard-deviation of each row of the :attr:`input` tensor in the dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. {keepdim_details} If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} {dim} unbiased (bool): whether to use the unbiased estimation or not {keepdim} {out} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.2035, 1.2959, 1.8101, -0.4644], [ 1.5027, -0.3270, 0.5905, 0.6538], [-1.5745, 1.3330, -0.5596, -0.6548], [ 0.1264, -0.5080, 1.6420, 0.1992]]) >>> torch.std(a, dim=1) tensor([ 1.0311, 0.7477, 1.2204, 0.9087]) """.format(**multi_dim_common)) add_docstr(torch.std_mean, r""" std_mean(input, unbiased=True) -> (Tensor, Tensor) Returns the standard-deviation and mean of all elements in the :attr:`input` tensor. If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} unbiased (bool): whether to use the unbiased estimation or not Example:: >>> a = torch.randn(1, 3) >>> a tensor([[0.3364, 0.3591, 0.9462]]) >>> torch.std_mean(a) (tensor(0.3457), tensor(0.5472)) .. function:: std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) Returns the standard-deviation and mean of each row of the :attr:`input` tensor in the dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. {keepdim_details} If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} {dim} unbiased (bool): whether to use the unbiased estimation or not {keepdim} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.5648, -0.5984, -1.2676, -1.4471], [ 0.9267, 1.0612, 1.1050, -0.6014], [ 0.0154, 1.9301, 0.0125, -1.0904], [-1.9711, -0.7748, -1.3840, 0.5067]]) >>> torch.std_mean(a, 1) (tensor([0.9110, 0.8197, 1.2552, 1.0608]), tensor([-0.6871, 0.6229, 0.2169, -0.9058])) """.format(**multi_dim_common)) add_docstr(torch.sum, r""" sum(input, dtype=None) -> Tensor Returns the sum of all elements in the :attr:`input` tensor. Args: {input} {dtype} Example:: >>> a = torch.randn(1, 3) >>> a tensor([[ 0.1133, -0.9567, 0.2958]]) >>> torch.sum(a) tensor(-0.5475) .. function:: sum(input, dim, keepdim=False, dtype=None) -> Tensor Returns the sum of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. {keepdim_details} Args: {input} {dim} {keepdim} {dtype} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], [-0.2993, 0.9138, 0.9337, -1.6864], [ 0.1132, 0.7892, -0.1003, 0.5688], [ 0.3637, -0.9906, -0.4752, -1.5197]]) >>> torch.sum(a, 1) tensor([-0.4598, -0.1381, 1.3708, -2.6217]) >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) >>> torch.sum(b, (2, 1)) tensor([ 435., 1335., 2235., 3135.]) """.format(**multi_dim_common)) add_docstr(torch.svd, r""" svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) This function returns a namedtuple ``(U, S, V)`` which is the singular value decomposition of a input real matrix or batches of real matrices :attr:`input` such that :math:`input = U \times diag(S) \times V^T`. If :attr:`some` is ``True`` (default), the method returns the reduced singular value decomposition i.e., if the last two dimensions of :attr:`input` are ``m`` and ``n``, then the returned `U` and `V` matrices will contain only :math:`min(n, m)` orthonormal columns. If :attr:`compute_uv` is ``False``, the returned `U` and `V` matrices will be zero matrices of shape :math:`(m \times m)` and :math:`(n \times n)` respectively. :attr:`some` will be ignored here. .. note:: The singular values are returned in descending order. If :attr:`input` is a batch of matrices, then the singular values of each matrix in the batch is returned in descending order. .. note:: The implementation of SVD on CPU uses the LAPACK routine `?gesdd` (a divide-and-conquer algorithm) instead of `?gesvd` for speed. Analogously, the SVD on GPU uses the MAGMA routine `gesdd` as well. .. note:: Irrespective of the original strides, the returned matrix `U` will be transposed, i.e. with strides :code:`U.contiguous().transpose(-2, -1).stride()` .. note:: Extra care needs to be taken when backward through `U` and `V` outputs. Such operation is really only stable when :attr:`input` is full rank with all distinct singular values. Otherwise, ``NaN`` can appear as the gradients are not properly defined. Also, notice that double backward will usually do an additional backward through `U` and `V` even if the original backward is only on `S`. .. note:: When :attr:`some` = ``False``, the gradients on :code:`U[..., :, min(m, n):]` and :code:`V[..., :, min(m, n):]` will be ignored in backward as those vectors can be arbitrary bases of the subspaces. .. note:: When :attr:`compute_uv` = ``False``, backward cannot be performed since `U` and `V` from the forward pass is required for the backward operation. Args: input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more batch dimensions consisting of :math:`m \times n` matrices. some (bool, optional): controls the shape of returned `U` and `V` compute_uv (bool, optional): option whether to compute `U` and `V` or not out (tuple, optional): the output tuple of tensors Example:: >>> a = torch.randn(5, 3) >>> a tensor([[ 0.2364, -0.7752, 0.6372], [ 1.7201, 0.7394, -0.0504], [-0.3371, -1.0584, 0.5296], [ 0.3550, -0.4022, 1.5569], [ 0.2445, -0.0158, 1.1414]]) >>> u, s, v = torch.svd(a) >>> u tensor([[ 0.4027, 0.0287, 0.5434], [-0.1946, 0.8833, 0.3679], [ 0.4296, -0.2890, 0.5261], [ 0.6604, 0.2717, -0.2618], [ 0.4234, 0.2481, -0.4733]]) >>> s tensor([2.3289, 2.0315, 0.7806]) >>> v tensor([[-0.0199, 0.8766, 0.4809], [-0.5080, 0.4054, -0.7600], [ 0.8611, 0.2594, -0.4373]]) >>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t())) tensor(8.6531e-07) >>> a_big = torch.randn(7, 5, 3) >>> u, s, v = torch.svd(a_big) >>> torch.dist(a_big, torch.matmul(torch.matmul(u, torch.diag_embed(s)), v.transpose(-2, -1))) tensor(2.6503e-06) """) add_docstr(torch.symeig, r""" symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) This function returns eigenvalues and eigenvectors of a real symmetric matrix :attr:`input` or a batch of real symmetric matrices, represented by a namedtuple (eigenvalues, eigenvectors). This function calculates all eigenvalues (and vectors) of :attr:`input` such that :math:`\text{input} = V \text{diag}(e) V^T`. The boolean argument :attr:`eigenvectors` defines computation of both eigenvectors and eigenvalues or eigenvalues only. If it is ``False``, only eigenvalues are computed. If it is ``True``, both eigenvalues and eigenvectors are computed. Since the input matrix :attr:`input` is supposed to be symmetric, only the upper triangular portion is used by default. If :attr:`upper` is ``False``, then lower triangular portion is used. .. note:: The eigenvalues are returned in ascending order. If :attr:`input` is a batch of matrices, then the eigenvalues of each matrix in the batch is returned in ascending order. .. note:: Irrespective of the original strides, the returned matrix `V` will be transposed, i.e. with strides `V.contiguous().transpose(-1, -2).stride()`. .. note:: Extra care needs to be taken when backward through outputs. Such operation is really only stable when all eigenvalues are distinct. Otherwise, ``NaN`` can appear as the gradients are not properly defined. Args: input (Tensor): the input tensor of size :math:`(*, n, n)` where `*` is zero or more batch dimensions consisting of symmetric matrices. eigenvectors(boolean, optional): controls whether eigenvectors have to be computed upper(boolean, optional): controls whether to consider upper-triangular or lower-triangular region out (tuple, optional): the output tuple of (Tensor, Tensor) Returns: (Tensor, Tensor): A namedtuple (eigenvalues, eigenvectors) containing - **eigenvalues** (*Tensor*): Shape :math:`(*, m)`. The eigenvalues in ascending order. - **eigenvectors** (*Tensor*): Shape :math:`(*, m, m)`. If ``eigenvectors=False``, it's an empty tensor. Otherwise, this tensor contains the orthonormal eigenvectors of the ``input``. Examples:: >>> a = torch.randn(5, 5) >>> a = a + a.t() # To make a symmetric >>> a tensor([[-5.7827, 4.4559, -0.2344, -1.7123, -1.8330], [ 4.4559, 1.4250, -2.8636, -3.2100, -0.1798], [-0.2344, -2.8636, 1.7112, -5.5785, 7.1988], [-1.7123, -3.2100, -5.5785, -2.6227, 3.1036], [-1.8330, -0.1798, 7.1988, 3.1036, -5.1453]]) >>> e, v = torch.symeig(a, eigenvectors=True) >>> e tensor([-13.7012, -7.7497, -2.3163, 5.2477, 8.1050]) >>> v tensor([[ 0.1643, 0.9034, -0.0291, 0.3508, 0.1817], [-0.2417, -0.3071, -0.5081, 0.6534, 0.4026], [-0.5176, 0.1223, -0.0220, 0.3295, -0.7798], [-0.4850, 0.2695, -0.5773, -0.5840, 0.1337], [ 0.6415, -0.0447, -0.6381, -0.0193, -0.4230]]) >>> a_big = torch.randn(5, 2, 2) >>> a_big = a_big + a_big.transpose(-2, -1) # To make a_big symmetric >>> e, v = a_big.symeig(eigenvectors=True) >>> torch.allclose(torch.matmul(v, torch.matmul(e.diag_embed(), v.transpose(-2, -1))), a_big) True """) add_docstr(torch.t, r""" t(input) -> Tensor Expects :attr:`input` to be <= 2-D tensor and transposes dimensions 0 and 1. 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this is equivalent to ``transpose(input, 0, 1)``. Args: {input} Example:: >>> x = torch.randn(()) >>> x tensor(0.1995) >>> torch.t(x) tensor(0.1995) >>> x = torch.randn(3) >>> x tensor([ 2.4320, -0.4608, 0.7702]) >>> torch.t(x) tensor([ 2.4320, -0.4608, 0.7702]) >>> x = torch.randn(2, 3) >>> x tensor([[ 0.4875, 0.9158, -0.5872], [ 0.3938, -0.6929, 0.6932]]) >>> torch.t(x) tensor([[ 0.4875, 0.3938], [ 0.9158, -0.6929], [-0.5872, 0.6932]]) """.format(**common_args)) add_docstr(torch.flip, r""" flip(input, dims) -> Tensor Reverse the order of a n-D tensor along given axis in dims. Args: {input} dims (a list or tuple): axis to flip on Example:: >>> x = torch.arange(8).view(2, 2, 2) >>> x tensor([[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]]]) >>> torch.flip(x, [0, 1]) tensor([[[ 6, 7], [ 4, 5]], [[ 2, 3], [ 0, 1]]]) """.format(**common_args)) add_docstr(torch.fliplr, r""" fliplr(input) -> Tensor Flip array in the left/right direction, returning a new tensor. Flip the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before. Note: Equivalent to input[:,::-1]. Requires the array to be at least 2-D. Args: input (Tensor): Must be at least 2-dimensional. Example:: >>> x = torch.arange(4).view(2, 2) >>> x tensor([[0, 1], [2, 3]]) >>> torch.fliplr(x) tensor([[1, 0], [3, 2]]) """.format(**common_args)) add_docstr(torch.flipud, r""" flipud(input) -> Tensor Flip array in the up/down direction, returning a new tensor. Flip the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before. Note: Equivalent to input[::-1,...]. Requires the array to be at least 1-D. Args: input (Tensor): Must be at least 1-dimensional. Example:: >>> x = torch.arange(4).view(2, 2) >>> x tensor([[0, 1], [2, 3]]) >>> torch.flipud(x) tensor([[2, 3], [0, 1]]) """.format(**common_args)) add_docstr(torch.roll, r""" roll(input, shifts, dims=None) -> Tensor Roll the tensor along the given dimension(s). Elements that are shifted beyond the last position are re-introduced at the first position. If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape. Args: {input} shifts (int or tuple of ints): The number of places by which the elements of the tensor are shifted. If shifts is a tuple, dims must be a tuple of the same size, and each dimension will be rolled by the corresponding value dims (int or tuple of ints): Axis along which to roll Example:: >>> x = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]).view(4, 2) >>> x tensor([[1, 2], [3, 4], [5, 6], [7, 8]]) >>> torch.roll(x, 1, 0) tensor([[7, 8], [1, 2], [3, 4], [5, 6]]) >>> torch.roll(x, -1, 0) tensor([[3, 4], [5, 6], [7, 8], [1, 2]]) >>> torch.roll(x, shifts=(2, 1), dims=(0, 1)) tensor([[6, 5], [8, 7], [2, 1], [4, 3]]) """.format(**common_args)) add_docstr(torch.rot90, r""" rot90(input, k, dims) -> Tensor Rotate a n-D tensor by 90 degrees in the plane specified by dims axis. Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0. Args: {input} k (int): number of times to rotate dims (a list or tuple): axis to rotate Example:: >>> x = torch.arange(4).view(2, 2) >>> x tensor([[0, 1], [2, 3]]) >>> torch.rot90(x, 1, [0, 1]) tensor([[1, 3], [0, 2]]) >>> x = torch.arange(8).view(2, 2, 2) >>> x tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) >>> torch.rot90(x, 1, [1, 2]) tensor([[[1, 3], [0, 2]], [[5, 7], [4, 6]]]) """.format(**common_args)) add_docstr(torch.take, r""" take(input, index) -> Tensor Returns a new tensor with the elements of :attr:`input` at the given indices. The input tensor is treated as if it were viewed as a 1-D tensor. The result takes the same shape as the indices. Args: {input} indices (LongTensor): the indices into tensor Example:: >>> src = torch.tensor([[4, 3, 5], [6, 7, 8]]) >>> torch.take(src, torch.tensor([0, 2, 5])) tensor([ 4, 5, 8]) """.format(**common_args)) add_docstr(torch.tan, r""" tan(input, out=None) -> Tensor Returns a new tensor with the tangent of the elements of :attr:`input`. .. math:: \text{out}_{i} = \tan(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([-1.2027, -1.7687, 0.4412, -1.3856]) >>> torch.tan(a) tensor([-2.5930, 4.9859, 0.4722, -5.3366]) """.format(**common_args)) add_docstr(torch.tanh, r""" tanh(input, out=None) -> Tensor Returns a new tensor with the hyperbolic tangent of the elements of :attr:`input`. .. math:: \text{out}_{i} = \tanh(\text{input}_{i}) """ + r""" Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 0.8986, -0.7279, 1.1745, 0.2611]) >>> torch.tanh(a) tensor([ 0.7156, -0.6218, 0.8257, 0.2553]) """.format(**common_args)) add_docstr(torch.topk, r""" topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) Returns the :attr:`k` largest elements of the given :attr:`input` tensor along a given dimension. If :attr:`dim` is not given, the last dimension of the `input` is chosen. If :attr:`largest` is ``False`` then the `k` smallest elements are returned. A namedtuple of `(values, indices)` is returned, where the `indices` are the indices of the elements in the original `input` tensor. The boolean option :attr:`sorted` if ``True``, will make sure that the returned `k` elements are themselves sorted Args: {input} k (int): the k in "top-k" dim (int, optional): the dimension to sort along largest (bool, optional): controls whether to return largest or smallest elements sorted (bool, optional): controls whether to return the elements in sorted order out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers Example:: >>> x = torch.arange(1., 6.) >>> x tensor([ 1., 2., 3., 4., 5.]) >>> torch.topk(x, 3) torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2])) """.format(**common_args)) add_docstr(torch.trace, r""" trace(input) -> Tensor Returns the sum of the elements of the diagonal of the input 2-D matrix. Example:: >>> x = torch.arange(1., 10.).view(3, 3) >>> x tensor([[ 1., 2., 3.], [ 4., 5., 6.], [ 7., 8., 9.]]) >>> torch.trace(x) tensor(15.) """) add_docstr(torch.transpose, r""" transpose(input, dim0, dim1) -> Tensor Returns a tensor that is a transposed version of :attr:`input`. The given dimensions :attr:`dim0` and :attr:`dim1` are swapped. The resulting :attr:`out` tensor shares it's underlying storage with the :attr:`input` tensor, so changing the content of one would change the content of the other. Args: {input} dim0 (int): the first dimension to be transposed dim1 (int): the second dimension to be transposed Example:: >>> x = torch.randn(2, 3) >>> x tensor([[ 1.0028, -0.9893, 0.5809], [-0.1669, 0.7299, 0.4942]]) >>> torch.transpose(x, 0, 1) tensor([[ 1.0028, -0.1669], [-0.9893, 0.7299], [ 0.5809, 0.4942]]) """.format(**common_args)) add_docstr(torch.triangular_solve, r""" triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) Solves a system of equations with a triangular coefficient matrix :math:`A` and multiple right-hand sides :math:`b`. In particular, solves :math:`AX = b` and assumes :math:`A` is upper-triangular with the default keyword arguments. `torch.triangular_solve(b, A)` can take in 2D inputs `b, A` or inputs that are batches of 2D matrices. If the inputs are batches, then returns batched outputs `X` Args: input (Tensor): multiple right-hand sides of size :math:`(*, m, k)` where :math:`*` is zero of more batch dimensions (:math:`b`) A (Tensor): the input triangular coefficient matrix of size :math:`(*, m, m)` where :math:`*` is zero or more batch dimensions upper (bool, optional): whether to solve the upper-triangular system of equations (default) or the lower-triangular system of equations. Default: ``True``. transpose (bool, optional): whether :math:`A` should be transposed before being sent into the solver. Default: ``False``. unitriangular (bool, optional): whether :math:`A` is unit triangular. If True, the diagonal elements of :math:`A` are assumed to be 1 and not referenced from :math:`A`. Default: ``False``. Returns: A namedtuple `(solution, cloned_coefficient)` where `cloned_coefficient` is a clone of :math:`A` and `solution` is the solution :math:`X` to :math:`AX = b` (or whatever variant of the system of equations, depending on the keyword arguments.) Examples:: >>> A = torch.randn(2, 2).triu() >>> A tensor([[ 1.1527, -1.0753], [ 0.0000, 0.7986]]) >>> b = torch.randn(2, 3) >>> b tensor([[-0.0210, 2.3513, -1.5492], [ 1.5429, 0.7403, -1.0243]]) >>> torch.triangular_solve(b, A) torch.return_types.triangular_solve( solution=tensor([[ 1.7841, 2.9046, -2.5405], [ 1.9320, 0.9270, -1.2826]]), cloned_coefficient=tensor([[ 1.1527, -1.0753], [ 0.0000, 0.7986]])) """) add_docstr(torch.tril, r""" tril(input, diagonal=0, out=None) -> Tensor Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. The lower triangular part of the matrix is defined as the elements on and below the diagonal. The argument :attr:`diagonal` controls which diagonal to consider. If :attr:`diagonal` = 0, all elements on and below the main diagonal are retained. A positive value includes just as many diagonals above the main diagonal, and similarly a negative value excludes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. """ + r""" Args: {input} diagonal (int, optional): the diagonal to consider {out} Example:: >>> a = torch.randn(3, 3) >>> a tensor([[-1.0813, -0.8619, 0.7105], [ 0.0935, 0.1380, 2.2112], [-0.3409, -0.9828, 0.0289]]) >>> torch.tril(a) tensor([[-1.0813, 0.0000, 0.0000], [ 0.0935, 0.1380, 0.0000], [-0.3409, -0.9828, 0.0289]]) >>> b = torch.randn(4, 6) >>> b tensor([[ 1.2219, 0.5653, -0.2521, -0.2345, 1.2544, 0.3461], [ 0.4785, -0.4477, 0.6049, 0.6368, 0.8775, 0.7145], [ 1.1502, 3.2716, -1.1243, -0.5413, 0.3615, 0.6864], [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0978]]) >>> torch.tril(b, diagonal=1) tensor([[ 1.2219, 0.5653, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.4785, -0.4477, 0.6049, 0.0000, 0.0000, 0.0000], [ 1.1502, 3.2716, -1.1243, -0.5413, 0.0000, 0.0000], [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0000]]) >>> torch.tril(b, diagonal=-1) tensor([[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.4785, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 1.1502, 3.2716, 0.0000, 0.0000, 0.0000, 0.0000], [-0.0614, -0.7344, -1.3164, 0.0000, 0.0000, 0.0000]]) """.format(**common_args)) # docstr is split in two parts to avoid format mis-captureing :math: braces '{}' # as common args. add_docstr(torch.tril_indices, r""" tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor Returns the indices of the lower triangular part of a :attr:`row`-by- :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and then columns. The lower triangular part of the matrix is defined as the elements on and below the diagonal. The argument :attr:`offset` controls which diagonal to consider. If :attr:`offset` = 0, all elements on and below the main diagonal are retained. A positive value includes just as many diagonals above the main diagonal, and similarly a negative value excludes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. .. note:: When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to prevent overflow during calculation. """ + r""" Args: row (``int``): number of rows in the 2-D matrix. col (``int``): number of columns in the 2-D matrix. offset (``int``): diagonal offset from the main diagonal. Default: if not provided, 0. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if ``None``, ``torch.long``. {device} layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. Example:: >>> a = torch.tril_indices(3, 3) >>> a tensor([[0, 1, 1, 2, 2, 2], [0, 0, 1, 0, 1, 2]]) >>> a = torch.tril_indices(4, 3, -1) >>> a tensor([[1, 2, 2, 3, 3, 3], [0, 0, 1, 0, 1, 2]]) >>> a = torch.tril_indices(4, 3, 1) >>> a tensor([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], [0, 1, 0, 1, 2, 0, 1, 2, 0, 1, 2]]) """.format(**factory_common_args)) add_docstr(torch.triu, r""" triu(input, diagonal=0, out=None) -> Tensor Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. The upper triangular part of the matrix is defined as the elements on and above the diagonal. The argument :attr:`diagonal` controls which diagonal to consider. If :attr:`diagonal` = 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. """ + r""" Args: {input} diagonal (int, optional): the diagonal to consider {out} Example:: >>> a = torch.randn(3, 3) >>> a tensor([[ 0.2309, 0.5207, 2.0049], [ 0.2072, -1.0680, 0.6602], [ 0.3480, -0.5211, -0.4573]]) >>> torch.triu(a) tensor([[ 0.2309, 0.5207, 2.0049], [ 0.0000, -1.0680, 0.6602], [ 0.0000, 0.0000, -0.4573]]) >>> torch.triu(a, diagonal=1) tensor([[ 0.0000, 0.5207, 2.0049], [ 0.0000, 0.0000, 0.6602], [ 0.0000, 0.0000, 0.0000]]) >>> torch.triu(a, diagonal=-1) tensor([[ 0.2309, 0.5207, 2.0049], [ 0.2072, -1.0680, 0.6602], [ 0.0000, -0.5211, -0.4573]]) >>> b = torch.randn(4, 6) >>> b tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.4333, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], [-0.9888, 1.0679, -1.3337, -1.6556, 0.4798, 0.2830]]) >>> torch.triu(b, diagonal=1) tensor([[ 0.0000, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [ 0.0000, 0.0000, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.0000, 0.0000, 0.0000, -1.0432, 0.9348, -0.4410], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.4798, 0.2830]]) >>> torch.triu(b, diagonal=-1) tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.0000, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], [ 0.0000, 0.0000, -1.3337, -1.6556, 0.4798, 0.2830]]) """.format(**common_args)) # docstr is split in two parts to avoid format mis-capturing :math: braces '{}' # as common args. add_docstr(torch.triu_indices, r""" triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor Returns the indices of the upper triangular part of a :attr:`row` by :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and then columns. The upper triangular part of the matrix is defined as the elements on and above the diagonal. The argument :attr:`offset` controls which diagonal to consider. If :attr:`offset` = 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where :math:`d_{1}, d_{2}` are the dimensions of the matrix. .. note:: When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to prevent overflow during calculation. """ + r""" Args: row (``int``): number of rows in the 2-D matrix. col (``int``): number of columns in the 2-D matrix. offset (``int``): diagonal offset from the main diagonal. Default: if not provided, 0. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if ``None``, ``torch.long``. {device} layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. Example:: >>> a = torch.triu_indices(3, 3) >>> a tensor([[0, 0, 0, 1, 1, 2], [0, 1, 2, 1, 2, 2]]) >>> a = torch.triu_indices(4, 3, -1) >>> a tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3], [0, 1, 2, 0, 1, 2, 1, 2, 2]]) >>> a = torch.triu_indices(4, 3, 1) >>> a tensor([[0, 0, 1], [1, 2, 2]]) """.format(**factory_common_args)) add_docstr(torch.true_divide, r""" true_divide(dividend, divisor) -> Tensor Performs "true division" that always computes the division in floating point. Analogous to division in Python 3 and equivalent to :func:`torch.div` except when both inputs have bool or integer scalar types, in which case they are cast to the default (floating) scalar type before the division. .. math:: \text{{out}}_i = \frac{{\text{{dividend}}_i}}{{\text{{divisor}}}} Args: dividend (Tensor): the dividend divisor (Tensor or Scalar): the divisor Keyword args: {out} Example:: >>> dividend = torch.tensor([5, 3], dtype=torch.int) >>> divisor = torch.tensor([3, 2], dtype=torch.int) >>> torch.true_divide(dividend, divisor) tensor([1.6667, 1.5000]) >>> torch.true_divide(dividend, 2) tensor([2.5000, 1.5000]) """.format(**common_args)) add_docstr(torch.trunc, r""" trunc(input, out=None) -> Tensor Returns a new tensor with the truncated integer values of the elements of :attr:`input`. Args: {input} {out} Example:: >>> a = torch.randn(4) >>> a tensor([ 3.4742, 0.5466, -0.8008, -0.9079]) >>> torch.trunc(a) tensor([ 3., 0., -0., -0.]) """.format(**common_args)) add_docstr(torch.unsqueeze, r""" unsqueeze(input, dim) -> Tensor Returns a new tensor with a dimension of size one inserted at the specified position. The returned tensor shares the same underlying data with this tensor. A :attr:`dim` value within the range ``[-input.dim() - 1, input.dim() + 1)`` can be used. Negative :attr:`dim` will correspond to :meth:`unsqueeze` applied at :attr:`dim` = ``dim + input.dim() + 1``. Args: {input} dim (int): the index at which to insert the singleton dimension Example:: >>> x = torch.tensor([1, 2, 3, 4]) >>> torch.unsqueeze(x, 0) tensor([[ 1, 2, 3, 4]]) >>> torch.unsqueeze(x, 1) tensor([[ 1], [ 2], [ 3], [ 4]]) """.format(**common_args)) add_docstr(torch.var, r""" var(input, unbiased=True) -> Tensor Returns the variance of all elements in the :attr:`input` tensor. If :attr:`unbiased` is ``False``, then the variance will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} unbiased (bool): whether to use the unbiased estimation or not Example:: >>> a = torch.randn(1, 3) >>> a tensor([[-0.3425, -1.2636, -0.4864]]) >>> torch.var(a) tensor(0.2455) .. function:: var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor Returns the variance of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. {keepdim_details} If :attr:`unbiased` is ``False``, then the variance will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} {dim} {keepdim} unbiased (bool): whether to use the unbiased estimation or not {out} Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-0.3567, 1.7385, -1.3042, 0.7423], [ 1.3436, -0.1015, -0.9834, -0.8438], [ 0.6056, 0.1089, -0.3112, -1.4085], [-0.7700, 0.6074, -0.1469, 0.7777]]) >>> torch.var(a, 1) tensor([ 1.7444, 1.1363, 0.7356, 0.5112]) """.format(**multi_dim_common)) add_docstr(torch.var_mean, r""" var_mean(input, unbiased=True) -> (Tensor, Tensor) Returns the variance and mean of all elements in the :attr:`input` tensor. If :attr:`unbiased` is ``False``, then the variance will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} unbiased (bool): whether to use the unbiased estimation or not Example:: >>> a = torch.randn(1, 3) >>> a tensor([[0.0146, 0.4258, 0.2211]]) >>> torch.var_mean(a) (tensor(0.0423), tensor(0.2205)) .. function:: var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) Returns the variance and mean of each row of the :attr:`input` tensor in the given dimension :attr:`dim`. {keepdim_details} If :attr:`unbiased` is ``False``, then the variance will be calculated via the biased estimator. Otherwise, Bessel's correction will be used. Args: {input} {dim} {keepdim} unbiased (bool): whether to use the unbiased estimation or not Example:: >>> a = torch.randn(4, 4) >>> a tensor([[-1.5650, 2.0415, -0.1024, -0.5790], [ 0.2325, -2.6145, -1.6428, -0.3537], [-0.2159, -1.1069, 1.2882, -1.3265], [-0.6706, -1.5893, 0.6827, 1.6727]]) >>> torch.var_mean(a, 1) (tensor([2.3174, 1.6403, 1.4092, 2.0791]), tensor([-0.0512, -1.0946, -0.3403, 0.0239])) """.format(**multi_dim_common)) add_docstr(torch.zeros, r""" zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor filled with the scalar value `0`, with the shape defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.zeros(2, 3) tensor([[ 0., 0., 0.], [ 0., 0., 0.]]) >>> torch.zeros(5) tensor([ 0., 0., 0., 0., 0.]) """.format(**factory_common_args)) add_docstr(torch.zeros_like, r""" zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor Returns a tensor filled with the scalar value `0`, with the same size as :attr:`input`. ``torch.zeros_like(input)`` is equivalent to ``torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. .. warning:: As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, the old ``torch.zeros_like(input, out=output)`` is equivalent to ``torch.zeros(input.size(), out=output)``. Args: {input} {dtype} {layout} {device} {requires_grad} {memory_format} Example:: >>> input = torch.empty(2, 3) >>> torch.zeros_like(input) tensor([[ 0., 0., 0.], [ 0., 0., 0.]]) """.format(**factory_like_common_args)) add_docstr(torch.empty, r""" empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. {out} {dtype} {layout} {device} {requires_grad} {pin_memory} {memory_format} Example:: >>> torch.empty(2, 3) tensor(1.00000e-08 * [[ 6.3984, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000]]) """.format(**factory_common_args)) add_docstr(torch.empty_like, r""" empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor Returns an uninitialized tensor with the same size as :attr:`input`. ``torch.empty_like(input)`` is equivalent to ``torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. Args: {input} {dtype} {layout} {device} {requires_grad} {memory_format} Example:: >>> torch.empty((2,3), dtype=torch.int64) tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) """.format(**factory_like_common_args)) add_docstr(torch.empty_strided, r""" empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor Returns a tensor filled with uninitialized data. The shape and strides of the tensor is defined by the variable argument :attr:`size` and :attr:`stride` respectively. ``torch.empty_strided(size, stride)`` is equivalent to ``torch.empty(size).as_strided(size, stride)``. .. warning:: More than one element of the created tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first. Args: size (tuple of ints): the shape of the output tensor stride (tuple of ints): the strides of the output tensor {dtype} {layout} {device} {requires_grad} {pin_memory} Example:: >>> a = torch.empty_strided((2, 3), (1, 2)) >>> a tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07], [0.0000e+00, 0.0000e+00, 3.0705e-41]]) >>> a.stride() (1, 2) >>> a.size() torch.Size([2, 3]) """.format(**factory_common_args)) add_docstr(torch.full, r""" full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor Returns a tensor of size :attr:`size` filled with :attr:`fill_value`. .. warning:: Providing a bool or integral :attr:`fill_value` without setting the optional :attr:`dtype` or :attr:`out` arguments is currently unsupported. In PyTorch 1.7, when :attr:`dtype` and :attr:`out` are not set a bool :attr:`fill_value` will return a tensor of torch.bool dtype, and an integral :attr:`fill_value` will return a tensor of torch.long dtype. Args: size (int...): a list, tuple, or :class:`torch.Size` of integers defining the shape of the output tensor. fill_value: the number to fill the output tensor with. {out} {dtype} {layout} {device} {requires_grad} Example:: >>> torch.full((2, 3), 3.141592) tensor([[ 3.1416, 3.1416, 3.1416], [ 3.1416, 3.1416, 3.1416]]) """.format(**factory_common_args)) add_docstr(torch.full_like, """ full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, \ memory_format=torch.preserve_format) -> Tensor Returns a tensor with the same size as :attr:`input` filled with :attr:`fill_value`. ``torch.full_like(input, fill_value)`` is equivalent to ``torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``. Args: {input} fill_value: the number to fill the output tensor with. {dtype} {layout} {device} {requires_grad} {memory_format} """.format(**factory_like_common_args)) add_docstr(torch.det, r""" det(input) -> Tensor Calculates determinant of a square matrix or batches of square matrices. .. note:: Backward through :meth:`det` internally uses SVD results when :attr:`input` is not invertible. In this case, double backward through :meth:`det` will be unstable in when :attr:`input` doesn't have distinct singular values. See :meth:`~torch.svd` for details. Arguments: input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. Example:: >>> A = torch.randn(3, 3) >>> torch.det(A) tensor(3.7641) >>> A = torch.randn(3, 2, 2) >>> A tensor([[[ 0.9254, -0.6213], [-0.5787, 1.6843]], [[ 0.3242, -0.9665], [ 0.4539, -0.0887]], [[ 1.1336, -0.4025], [-0.7089, 0.9032]]]) >>> A.det() tensor([1.1990, 0.4099, 0.7386]) """) add_docstr(torch.where, r""" where(condition, x, y) -> Tensor Return a tensor of elements selected from either :attr:`x` or :attr:`y`, depending on :attr:`condition`. The operation is defined as: .. math:: \text{out}_i = \begin{cases} \text{x}_i & \text{if } \text{condition}_i \\ \text{y}_i & \text{otherwise} \\ \end{cases} .. note:: The tensors :attr:`condition`, :attr:`x`, :attr:`y` must be :ref:`broadcastable <broadcasting-semantics>`. Arguments: condition (BoolTensor): When True (nonzero), yield x, otherwise yield y x (Tensor): values selected at indices where :attr:`condition` is ``True`` y (Tensor): values selected at indices where :attr:`condition` is ``False`` Returns: Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`x`, :attr:`y` Example:: >>> x = torch.randn(3, 2) >>> y = torch.ones(3, 2) >>> x tensor([[-0.4620, 0.3139], [ 0.3898, -0.7197], [ 0.0478, -0.1657]]) >>> torch.where(x > 0, x, y) tensor([[ 1.0000, 0.3139], [ 0.3898, 1.0000], [ 0.0478, 1.0000]]) .. function:: where(condition) -> tuple of LongTensor ``torch.where(condition)`` is identical to ``torch.nonzero(condition, as_tuple=True)``. .. note:: See also :func:`torch.nonzero`. """) add_docstr(torch.logdet, r""" logdet(input) -> Tensor Calculates log determinant of a square matrix or batches of square matrices. .. note:: Result is ``-inf`` if :attr:`input` has zero log determinant, and is ``nan`` if :attr:`input` has negative determinant. .. note:: Backward through :meth:`logdet` internally uses SVD results when :attr:`input` is not invertible. In this case, double backward through :meth:`logdet` will be unstable in when :attr:`input` doesn't have distinct singular values. See :meth:`~torch.svd` for details. Arguments: input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. Example:: >>> A = torch.randn(3, 3) >>> torch.det(A) tensor(0.2611) >>> torch.logdet(A) tensor(-1.3430) >>> A tensor([[[ 0.9254, -0.6213], [-0.5787, 1.6843]], [[ 0.3242, -0.9665], [ 0.4539, -0.0887]], [[ 1.1336, -0.4025], [-0.7089, 0.9032]]]) >>> A.det() tensor([1.1990, 0.4099, 0.7386]) >>> A.det().log() tensor([ 0.1815, -0.8917, -0.3031]) """) add_docstr(torch.slogdet, r""" slogdet(input) -> (Tensor, Tensor) Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices. .. note:: If ``input`` has zero determinant, this returns ``(0, -inf)``. .. note:: Backward through :meth:`slogdet` internally uses SVD results when :attr:`input` is not invertible. In this case, double backward through :meth:`slogdet` will be unstable in when :attr:`input` doesn't have distinct singular values. See :meth:`~torch.svd` for details. Arguments: input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. Returns: A namedtuple (sign, logabsdet) containing the sign of the determinant, and the log value of the absolute determinant. Example:: >>> A = torch.randn(3, 3) >>> A tensor([[ 0.0032, -0.2239, -1.1219], [-0.6690, 0.1161, 0.4053], [-1.6218, -0.9273, -0.0082]]) >>> torch.det(A) tensor(-0.7576) >>> torch.logdet(A) tensor(nan) >>> torch.slogdet(A) torch.return_types.slogdet(sign=tensor(-1.), logabsdet=tensor(-0.2776)) """) add_docstr(torch.pinverse, r""" pinverse(input, rcond=1e-15) -> Tensor Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor. Please look at `Moore-Penrose inverse`_ for more details .. note:: This method is implemented using the Singular Value Decomposition. .. note:: The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_. Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_. However, this method is backprop-able due to the implementation by using SVD results, and could be unstable. Double-backward will also be unstable due to the usage of SVD internally. See :meth:`~torch.svd` for more details. Arguments: input (Tensor): The input tensor of size :math:`(*, m, n)` where :math:`*` is zero or more batch dimensions rcond (float): A floating point value to determine the cutoff for small singular values. Default: 1e-15 Returns: The pseudo-inverse of :attr:`input` of dimensions :math:`(*, n, m)` Example:: >>> input = torch.randn(3, 5) >>> input tensor([[ 0.5495, 0.0979, -1.4092, -0.1128, 0.4132], [-1.1143, -0.3662, 0.3042, 1.6374, -0.9294], [-0.3269, -0.5745, -0.0382, -0.5922, -0.6759]]) >>> torch.pinverse(input) tensor([[ 0.0600, -0.1933, -0.2090], [-0.0903, -0.0817, -0.4752], [-0.7124, -0.1631, -0.2272], [ 0.1356, 0.3933, -0.5023], [-0.0308, -0.1725, -0.5216]]) >>> # Batched pinverse example >>> a = torch.randn(2,6,3) >>> b = torch.pinverse(a) >>> torch.matmul(b, a) tensor([[[ 1.0000e+00, 1.6391e-07, -1.1548e-07], [ 8.3121e-08, 1.0000e+00, -2.7567e-07], [ 3.5390e-08, 1.4901e-08, 1.0000e+00]], [[ 1.0000e+00, -8.9407e-08, 2.9802e-08], [-2.2352e-07, 1.0000e+00, 1.1921e-07], [ 0.0000e+00, 8.9407e-08, 1.0000e+00]]]) .. _Moore-Penrose inverse: https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse .. _[1]: https://epubs.siam.org/doi/10.1137/0117004 .. _[2]: https://www.jstor.org/stable/2156365 """) add_docstr(torch.fft, r""" fft(input, signal_ndim, normalized=False) -> Tensor Complex-to-complex Discrete Fourier Transform This method computes the complex-to-complex discrete Fourier transform. Ignoring the batch dimensions, it computes the following expression: .. math:: X[\omega_1, \dots, \omega_d] = \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d] e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}}, where :math:`d` = :attr:`signal_ndim` is number of dimensions for the signal, and :math:`N_i` is the size of signal dimension :math:`i`. This method supports 1D, 2D and 3D complex-to-complex transforms, indicated by :attr:`signal_ndim`. :attr:`input` must be a tensor with last dimension of size 2, representing the real and imaginary components of complex numbers, and should have at least ``signal_ndim + 1`` dimensions with optionally arbitrary number of leading batch dimensions. If :attr:`normalized` is set to ``True``, this normalizes the result by dividing it with :math:`\sqrt{\prod_{i=1}^K N_i}` so that the operator is unitary. Returns the real and the imaginary parts together as one tensor of the same shape of :attr:`input`. The inverse of this function is :func:`~torch.ifft`. .. note:: For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See :ref:`cufft-plan-cache` for more details on how to monitor and control the cache. .. warning:: Due to limited dynamic range of half datatype, performing this operation in half precision may cause the first element of result to overflow for certain inputs. .. warning:: For CPU tensors, this method is currently only available with MKL. Use :func:`torch.backends.mkl.is_available` to check if MKL is installed. Arguments: input (Tensor): the input tensor of at least :attr:`signal_ndim` ``+ 1`` dimensions signal_ndim (int): the number of dimensions in each signal. :attr:`signal_ndim` can only be 1, 2 or 3 normalized (bool, optional): controls whether to return normalized results. Default: ``False`` Returns: Tensor: A tensor containing the complex-to-complex Fourier transform result Example:: >>> # unbatched 2D FFT >>> x = torch.randn(4, 3, 2) >>> torch.fft(x, 2) tensor([[[-0.0876, 1.7835], [-2.0399, -2.9754], [ 4.4773, -5.0119]], [[-1.5716, 2.7631], [-3.8846, 5.2652], [ 0.2046, -0.7088]], [[ 1.9938, -0.5901], [ 6.5637, 6.4556], [ 2.9865, 4.9318]], [[ 7.0193, 1.1742], [-1.3717, -2.1084], [ 2.0289, 2.9357]]]) >>> # batched 1D FFT >>> torch.fft(x, 1) tensor([[[ 1.8385, 1.2827], [-0.1831, 1.6593], [ 2.4243, 0.5367]], [[-0.9176, -1.5543], [-3.9943, -2.9860], [ 1.2838, -2.9420]], [[-0.8854, -0.6860], [ 2.4450, 0.0808], [ 1.3076, -0.5768]], [[-0.1231, 2.7411], [-0.3075, -1.7295], [-0.5384, -2.0299]]]) >>> # arbitrary number of batch dimensions, 2D FFT >>> x = torch.randn(3, 3, 5, 5, 2) >>> y = torch.fft(x, 2) >>> y.shape torch.Size([3, 3, 5, 5, 2]) """) add_docstr(torch.ifft, r""" ifft(input, signal_ndim, normalized=False) -> Tensor Complex-to-complex Inverse Discrete Fourier Transform This method computes the complex-to-complex inverse discrete Fourier transform. Ignoring the batch dimensions, it computes the following expression: .. math:: X[\omega_1, \dots, \omega_d] = \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d] e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}}, where :math:`d` = :attr:`signal_ndim` is number of dimensions for the signal, and :math:`N_i` is the size of signal dimension :math:`i`. The argument specifications are almost identical with :func:`~torch.fft`. However, if :attr:`normalized` is set to ``True``, this instead returns the results multiplied by :math:`\sqrt{\prod_{i=1}^d N_i}`, to become a unitary operator. Therefore, to invert a :func:`~torch.fft`, the :attr:`normalized` argument should be set identically for :func:`~torch.fft`. Returns the real and the imaginary parts together as one tensor of the same shape of :attr:`input`. The inverse of this function is :func:`~torch.fft`. .. note:: For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See :ref:`cufft-plan-cache` for more details on how to monitor and control the cache. .. warning:: Due to limited dynamic range of half datatype, performing this operation in half precision may cause the first element of result to overflow for certain inputs. .. warning:: For CPU tensors, this method is currently only available with MKL. Use :func:`torch.backends.mkl.is_available` to check if MKL is installed. Arguments: input (Tensor): the input tensor of at least :attr:`signal_ndim` ``+ 1`` dimensions signal_ndim (int): the number of dimensions in each signal. :attr:`signal_ndim` can only be 1, 2 or 3 normalized (bool, optional): controls whether to return normalized results. Default: ``False`` Returns: Tensor: A tensor containing the complex-to-complex inverse Fourier transform result Example:: >>> x = torch.randn(3, 3, 2) >>> x tensor([[[ 1.2766, 1.3680], [-0.8337, 2.0251], [ 0.9465, -1.4390]], [[-0.1890, 1.6010], [ 1.1034, -1.9230], [-0.9482, 1.0775]], [[-0.7708, -0.8176], [-0.1843, -0.2287], [-1.9034, -0.2196]]]) >>> y = torch.fft(x, 2) >>> torch.ifft(y, 2) # recover x tensor([[[ 1.2766, 1.3680], [-0.8337, 2.0251], [ 0.9465, -1.4390]], [[-0.1890, 1.6010], [ 1.1034, -1.9230], [-0.9482, 1.0775]], [[-0.7708, -0.8176], [-0.1843, -0.2287], [-1.9034, -0.2196]]]) """) add_docstr(torch.rfft, r""" rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor Real-to-complex Discrete Fourier Transform This method computes the real-to-complex discrete Fourier transform. It is mathematically equivalent with :func:`~torch.fft` with differences only in formats of the input and output. This method supports 1D, 2D and 3D real-to-complex transforms, indicated by :attr:`signal_ndim`. :attr:`input` must be a tensor with at least ``signal_ndim`` dimensions with optionally arbitrary number of leading batch dimensions. If :attr:`normalized` is set to ``True``, this normalizes the result by dividing it with :math:`\sqrt{\prod_{i=1}^K N_i}` so that the operator is unitary, where :math:`N_i` is the size of signal dimension :math:`i`. The real-to-complex Fourier transform results follow conjugate symmetry: .. math:: X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d], where the index arithmetic is computed modulus the size of the corresponding dimension, :math:`\ ^*` is the conjugate operator, and :math:`d` = :attr:`signal_ndim`. :attr:`onesided` flag controls whether to avoid redundancy in the output results. If set to ``True`` (default), the output will not be full complex result of shape :math:`(*, 2)`, where :math:`*` is the shape of :attr:`input`, but instead the last dimension will be halfed as of size :math:`\lfloor \frac{N_d}{2} \rfloor + 1`. The inverse of this function is :func:`~torch.irfft`. .. note:: For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See :ref:`cufft-plan-cache` for more details on how to monitor and control the cache. .. warning:: Due to limited dynamic range of half datatype, performing this operation in half precision may cause the first element of result to overflow for certain inputs. .. warning:: For CPU tensors, this method is currently only available with MKL. Use :func:`torch.backends.mkl.is_available` to check if MKL is installed. Arguments: input (Tensor): the input tensor of at least :attr:`signal_ndim` dimensions signal_ndim (int): the number of dimensions in each signal. :attr:`signal_ndim` can only be 1, 2 or 3 normalized (bool, optional): controls whether to return normalized results. Default: ``False`` onesided (bool, optional): controls whether to return half of results to avoid redundancy. Default: ``True`` Returns: Tensor: A tensor containing the real-to-complex Fourier transform result Example:: >>> x = torch.randn(5, 5) >>> torch.rfft(x, 2).shape torch.Size([5, 3, 2]) >>> torch.rfft(x, 2, onesided=False).shape torch.Size([5, 5, 2]) """) add_docstr(torch.irfft, r""" irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor Complex-to-real Inverse Discrete Fourier Transform This method computes the complex-to-real inverse discrete Fourier transform. It is mathematically equivalent with :func:`ifft` with differences only in formats of the input and output. The argument specifications are almost identical with :func:`~torch.ifft`. Similar to :func:`~torch.ifft`, if :attr:`normalized` is set to ``True``, this normalizes the result by multiplying it with :math:`\sqrt{\prod_{i=1}^K N_i}` so that the operator is unitary, where :math:`N_i` is the size of signal dimension :math:`i`. .. note:: Due to the conjugate symmetry, :attr:`input` do not need to contain the full complex frequency values. Roughly half of the values will be sufficient, as is the case when :attr:`input` is given by :func:`~torch.rfft` with ``rfft(signal, onesided=True)``. In such case, set the :attr:`onesided` argument of this method to ``True``. Moreover, the original signal shape information can sometimes be lost, optionally set :attr:`signal_sizes` to be the size of the original signal (without the batch dimensions if in batched mode) to recover it with correct shape. Therefore, to invert an :func:`~torch.rfft`, the :attr:`normalized` and :attr:`onesided` arguments should be set identically for :func:`~torch.irfft`, and preferably a :attr:`signal_sizes` is given to avoid size mismatch. See the example below for a case of size mismatch. See :func:`~torch.rfft` for details on conjugate symmetry. The inverse of this function is :func:`~torch.rfft`. .. warning:: Generally speaking, input to this function should contain values following conjugate symmetry. Note that even if :attr:`onesided` is ``True``, often symmetry on some part is still needed. When this requirement is not satisfied, the behavior of :func:`~torch.irfft` is undefined. Since :func:`torch.autograd.gradcheck` estimates numerical Jacobian with point perturbations, :func:`~torch.irfft` will almost certainly fail the check. .. note:: For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See :ref:`cufft-plan-cache` for more details on how to monitor and control the cache. .. warning:: Due to limited dynamic range of half datatype, performing this operation in half precision may cause the first element of result to overflow for certain inputs. .. warning:: For CPU tensors, this method is currently only available with MKL. Use :func:`torch.backends.mkl.is_available` to check if MKL is installed. Arguments: input (Tensor): the input tensor of at least :attr:`signal_ndim` ``+ 1`` dimensions signal_ndim (int): the number of dimensions in each signal. :attr:`signal_ndim` can only be 1, 2 or 3 normalized (bool, optional): controls whether to return normalized results. Default: ``False`` onesided (bool, optional): controls whether :attr:`input` was halfed to avoid redundancy, e.g., by :func:`rfft`. Default: ``True`` signal_sizes (list or :class:`torch.Size`, optional): the size of the original signal (without batch dimension). Default: ``None`` Returns: Tensor: A tensor containing the complex-to-real inverse Fourier transform result Example:: >>> x = torch.randn(4, 4) >>> torch.rfft(x, 2, onesided=True).shape torch.Size([4, 3, 2]) >>> >>> # notice that with onesided=True, output size does not determine the original signal size >>> x = torch.randn(4, 5) >>> torch.rfft(x, 2, onesided=True).shape torch.Size([4, 3, 2]) >>> >>> # now we use the original shape to recover x >>> x tensor([[-0.8992, 0.6117, -1.6091, -0.4155, -0.8346], [-2.1596, -0.0853, 0.7232, 0.1941, -0.0789], [-2.0329, 1.1031, 0.6869, -0.5042, 0.9895], [-0.1884, 0.2858, -1.5831, 0.9917, -0.8356]]) >>> y = torch.rfft(x, 2, onesided=True) >>> torch.irfft(y, 2, onesided=True, signal_sizes=x.shape) # recover x tensor([[-0.8992, 0.6117, -1.6091, -0.4155, -0.8346], [-2.1596, -0.0853, 0.7232, 0.1941, -0.0789], [-2.0329, 1.1031, 0.6869, -0.5042, 0.9895], [-0.1884, 0.2858, -1.5831, 0.9917, -0.8356]]) """) add_docstr(torch.hann_window, """ hann_window(window_length, periodic=True, dtype=None, \ layout=torch.strided, device=None, requires_grad=False) -> Tensor """ + r""" Hann window function. .. math:: w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = \sin^2 \left( \frac{\pi n}{N - 1} \right), where :math:`N` is the full window size. The input :attr:`window_length` is a positive integer controlling the returned window size. :attr:`periodic` flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have ``torch.hann_window(L, periodic=True)`` equal to ``torch.hann_window(L + 1, periodic=False)[:-1])``. .. note:: If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. """ + r""" Arguments: window_length (int): the size of returned window periodic (bool, optional): If True, returns a window to be used as periodic function. If False, return a symmetric window. {dtype} Only floating point types are supported. layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only ``torch.strided`` (dense layout) is supported. {device} {requires_grad} Returns: Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window """.format(**factory_common_args)) add_docstr(torch.hamming_window, """ hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, \ layout=torch.strided, device=None, requires_grad=False) -> Tensor """ + r""" Hamming window function. .. math:: w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), where :math:`N` is the full window size. The input :attr:`window_length` is a positive integer controlling the returned window size. :attr:`periodic` flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have ``torch.hamming_window(L, periodic=True)`` equal to ``torch.hamming_window(L + 1, periodic=False)[:-1])``. .. note:: If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. .. note:: This is a generalized version of :meth:`torch.hann_window`. """ + r""" Arguments: window_length (int): the size of returned window periodic (bool, optional): If True, returns a window to be used as periodic function. If False, return a symmetric window. alpha (float, optional): The coefficient :math:`\alpha` in the equation above beta (float, optional): The coefficient :math:`\beta` in the equation above {dtype} Only floating point types are supported. layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only ``torch.strided`` (dense layout) is supported. {device} {requires_grad} Returns: Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window """.format(**factory_common_args)) add_docstr(torch.bartlett_window, """ bartlett_window(window_length, periodic=True, dtype=None, \ layout=torch.strided, device=None, requires_grad=False) -> Tensor """ + r""" Bartlett window function. .. math:: w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ \end{cases}, where :math:`N` is the full window size. The input :attr:`window_length` is a positive integer controlling the returned window size. :attr:`periodic` flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have ``torch.bartlett_window(L, periodic=True)`` equal to ``torch.bartlett_window(L + 1, periodic=False)[:-1])``. .. note:: If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. """ + r""" Arguments: window_length (int): the size of returned window periodic (bool, optional): If True, returns a window to be used as periodic function. If False, return a symmetric window. {dtype} Only floating point types are supported. layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only ``torch.strided`` (dense layout) is supported. {device} {requires_grad} Returns: Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window """.format(**factory_common_args)) add_docstr(torch.blackman_window, """ blackman_window(window_length, periodic=True, dtype=None, \ layout=torch.strided, device=None, requires_grad=False) -> Tensor """ + r""" Blackman window function. .. math:: w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) where :math:`N` is the full window size. The input :attr:`window_length` is a positive integer controlling the returned window size. :attr:`periodic` flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have ``torch.blackman_window(L, periodic=True)`` equal to ``torch.blackman_window(L + 1, periodic=False)[:-1])``. .. note:: If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. """ + r""" Arguments: window_length (int): the size of returned window periodic (bool, optional): If True, returns a window to be used as periodic function. If False, return a symmetric window. {dtype} Only floating point types are supported. layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only ``torch.strided`` (dense layout) is supported. {device} {requires_grad} Returns: Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window """.format(**factory_common_args)) add_docstr(torch.vander, """ vander(x, N=None, increasing=False) -> Tensor """ + r""" Generates a Vandermonde matrix. The columns of the output matrix are elementwise powers of the input vector :math:`x^{{(N-1)}}, x^{{(N-2)}}, ..., x^0`. If increasing is True, the order of the columns is reversed :math:`x^0, x^1, ..., x^{{(N-1)}}`. Such a matrix with a geometric progression in each row is named for Alexandre-Theophile Vandermonde. Arguments: x (Tensor): 1-D input tensor. N (int, optional): Number of columns in the output. If N is not specified, a square array is returned :math:`(N = len(x))`. increasing (bool, optional): Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. Returns: Tensor: Vandermonde matrix. If increasing is False, the first column is :math:`x^{{(N-1)}}`, the second :math:`x^{{(N-2)}}` and so forth. If increasing is True, the columns are :math:`x^0, x^1, ..., x^{{(N-1)}}`. Example:: >>> x = torch.tensor([1, 2, 3, 5]) >>> torch.vander(x) tensor([[ 1, 1, 1, 1], [ 8, 4, 2, 1], [ 27, 9, 3, 1], [125, 25, 5, 1]]) >>> torch.vander(x, N=3) tensor([[ 1, 1, 1], [ 4, 2, 1], [ 9, 3, 1], [25, 5, 1]]) >>> torch.vander(x, N=3, increasing=True) tensor([[ 1, 1, 1], [ 1, 2, 4], [ 1, 3, 9], [ 1, 5, 25]]) """.format(**factory_common_args)) add_docstr(torch.unbind, r""" unbind(input, dim=0) -> seq Removes a tensor dimension. Returns a tuple of all slices along a given dimension, already without it. Arguments: input (Tensor): the tensor to unbind dim (int): dimension to remove Example:: >>> torch.unbind(torch.tensor([[1, 2, 3], >>> [4, 5, 6], >>> [7, 8, 9]])) (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9])) """) add_docstr(torch.combinations, r""" combinations(input, r=2, with_replacement=False) -> seq Compute combinations of length :math:`r` of the given tensor. The behavior is similar to python's `itertools.combinations` when `with_replacement` is set to `False`, and `itertools.combinations_with_replacement` when `with_replacement` is set to `True`. Arguments: input (Tensor): 1D vector. r (int, optional): number of elements to combine with_replacement (boolean, optional): whether to allow duplication in combination Returns: Tensor: A tensor equivalent to converting all the input tensors into lists, do `itertools.combinations` or `itertools.combinations_with_replacement` on these lists, and finally convert the resulting list into tensor. Example:: >>> a = [1, 2, 3] >>> list(itertools.combinations(a, r=2)) [(1, 2), (1, 3), (2, 3)] >>> list(itertools.combinations(a, r=3)) [(1, 2, 3)] >>> list(itertools.combinations_with_replacement(a, r=2)) [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)] >>> tensor_a = torch.tensor(a) >>> torch.combinations(tensor_a) tensor([[1, 2], [1, 3], [2, 3]]) >>> torch.combinations(tensor_a, r=3) tensor([[1, 2, 3]]) >>> torch.combinations(tensor_a, with_replacement=True) tensor([[1, 1], [1, 2], [1, 3], [2, 2], [2, 3], [3, 3]]) """) add_docstr(torch.trapz, r""" trapz(y, x, *, dim=-1) -> Tensor Estimate :math:`\int y\,dx` along `dim`, using the trapezoid rule. Arguments: y (Tensor): The values of the function to integrate x (Tensor): The points at which the function `y` is sampled. If `x` is not in ascending order, intervals on which it is decreasing contribute negatively to the estimated integral (i.e., the convention :math:`\int_a^b f = -\int_b^a f` is followed). dim (int): The dimension along which to integrate. By default, use the last dimension. Returns: A Tensor with the same shape as the input, except with `dim` removed. Each element of the returned tensor represents the estimated integral :math:`\int y\,dx` along `dim`. Example:: >>> y = torch.randn((2, 3)) >>> y tensor([[-2.1156, 0.6857, -0.2700], [-1.2145, 0.5540, 2.0431]]) >>> x = torch.tensor([[1, 3, 4], [1, 2, 3]]) >>> torch.trapz(y, x) tensor([-1.2220, 0.9683]) .. function:: trapz(y, *, dx=1, dim=-1) -> Tensor As above, but the sample points are spaced uniformly at a distance of `dx`. Arguments: y (Tensor): The values of the function to integrate dx (float): The distance between points at which `y` is sampled. dim (int): The dimension along which to integrate. By default, use the last dimension. Returns: A Tensor with the same shape as the input, except with `dim` removed. Each element of the returned tensor represents the estimated integral :math:`\int y\,dx` along `dim`. """) add_docstr(torch.repeat_interleave, r""" repeat_interleave(input, repeats, dim=None) -> Tensor Repeat elements of a tensor. .. warning:: This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. Args: {input} repeats (Tensor or int): The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. dim (int, optional): The dimension along which to repeat values. By default, use the flattened input array, and return a flat output array. Returns: Tensor: Repeated tensor which has the same shape as input, except along the given axis. Example:: >>> x = torch.tensor([1, 2, 3]) >>> x.repeat_interleave(2) tensor([1, 1, 2, 2, 3, 3]) >>> y = torch.tensor([[1, 2], [3, 4]]) >>> torch.repeat_interleave(y, 2) tensor([1, 1, 2, 2, 3, 3, 4, 4]) >>> torch.repeat_interleave(y, 3, dim=1) tensor([[1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4]]) >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) tensor([[1, 2], [3, 4], [3, 4]]) .. function:: repeat_interleave(repeats) -> Tensor If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, `1` appears `n2` times, `2` appears `n3` times, etc. """.format(**common_args)) add_docstr(torch.quantize_per_tensor, r""" quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor Converts a float tensor to quantized tensor with given scale and zero point. Arguments: input (Tensor): float tensor to quantize scale (float): scale to apply in quantization formula zero_point (int): offset in integer value that maps to float zero dtype (:class:`torch.dtype`): the desired data type of returned tensor. Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` Returns: Tensor: A newly quantized tensor Example:: >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() tensor([ 0, 10, 20, 30], dtype=torch.uint8) """) add_docstr(torch.quantize_per_channel, r""" quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor Converts a float tensor to per-channel quantized tensor with given scales and zero points. Arguments: input (Tensor): float tensor to quantize scales (Tensor): float 1D tensor of scales to use, size should match ``input.size(axis)`` zero_points (int): integer 1D tensor of offset to use, size should match ``input.size(axis)`` axis (int): dimension on which apply per-channel quantization dtype (:class:`torch.dtype`): the desired data type of returned tensor. Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` Returns: Tensor: A newly quantized tensor Example:: >>> x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]]) >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8) tensor([[-1., 0.], [ 1., 2.]], size=(2, 2), dtype=torch.quint8, quantization_scheme=torch.per_channel_affine, scale=tensor([0.1000, 0.0100], dtype=torch.float64), zero_point=tensor([10, 0]), axis=0) >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8).int_repr() tensor([[ 0, 10], [100, 200]], dtype=torch.uint8) """) add_docstr(torch.Generator, r""" Generator(device='cpu') -> Generator Creates and returns a generator object which manages the state of the algorithm that produces pseudo random numbers. Used as a keyword argument in many :ref:`inplace-random-sampling` functions. Arguments: device (:class:`torch.device`, optional): the desired device for the generator. Returns: Generator: An torch.Generator object. Example:: >>> g_cpu = torch.Generator() >>> g_cuda = torch.Generator(device='cuda') """) add_docstr(torch.Generator.set_state, r""" Generator.set_state(new_state) -> void Sets the Generator state. Arguments: new_state (torch.ByteTensor): The desired state. Example:: >>> g_cpu = torch.Generator() >>> g_cpu_other = torch.Generator() >>> g_cpu.set_state(g_cpu_other.get_state()) """) add_docstr(torch.Generator.get_state, r""" Generator.get_state() -> Tensor Returns the Generator state as a ``torch.ByteTensor``. Returns: Tensor: A ``torch.ByteTensor`` which contains all the necessary bits to restore a Generator to a specific point in time. Example:: >>> g_cpu = torch.Generator() >>> g_cpu.get_state() """) add_docstr(torch.Generator.manual_seed, r""" Generator.manual_seed(seed) -> Generator Sets the seed for generating random numbers. Returns a `torch.Generator` object. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed. Arguments: seed (int): The desired seed. Returns: Generator: An torch.Generator object. Example:: >>> g_cpu = torch.Generator() >>> g_cpu.manual_seed(2147483647) """) add_docstr(torch.Generator.initial_seed, r""" Generator.initial_seed() -> int Returns the initial seed for generating random numbers. Example:: >>> g_cpu = torch.Generator() >>> g_cpu.initial_seed() 2147483647 """) add_docstr(torch.Generator.seed, r""" Generator.seed() -> int Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator. Example:: >>> g_cpu = torch.Generator() >>> g_cpu.seed() 1516516984916 """) add_docstr(torch.Generator.device, r""" Generator.device -> device Gets the current device of the generator. Example:: >>> g_cpu = torch.Generator() >>> g_cpu.device device(type='cpu') """) add_docstr(torch.searchsorted, r""" searchsorted(sorted_sequence, values, out_int32=False, right=False, out=None) -> Tensor Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the corresponding values in :attr:`values` were inserted before the indices, the order of the corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved. Return a new tensor with the same size as :attr:`values`. If :attr:`right` is False (default), then the left boundary of :attr:`sorted_sequence` is closed. More formally, the returned index satisfies the following rules: .. list-table:: :widths: 12 10 78 :header-rows: 1 * - :attr:`sorted_sequence` - :attr:`right` - *returned index satisfies* * - 1-D - False - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]`` * - 1-D - True - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]`` * - N-D - False - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]`` * - N-D - True - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]`` Args: sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost* dimension. values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. Default value is False, i.e. default output data type is torch.int64. right (bool, optional): if False, return the first suitable location that is found. If True, return the last such index. If no suitable index found, return 0 for non-numerical value (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence` (one pass the last index of the *innermost* dimension). In other words, if False, gets the lower bound index for each value in :attr:`values` on the corresponding *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper bound index instead. Default value is False. out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided. .. note:: If your use case is always 1-D sorted sequence, :func:`torch.bucketize` is preferred, because it has fewer dimension checks resulting in slightly better performance. Example:: >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]]) >>> sorted_sequence tensor([[ 1, 3, 5, 7, 9], [ 2, 4, 6, 8, 10]]) >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]]) >>> values tensor([[3, 6, 9], [3, 6, 9]]) >>> torch.searchsorted(sorted_sequence, values) tensor([[1, 3, 4], [1, 2, 4]]) >>> torch.searchsorted(sorted_sequence, values, right=True) tensor([[2, 3, 5], [1, 3, 4]]) >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9]) >>> sorted_sequence_1d tensor([1, 3, 5, 7, 9]) >>> torch.searchsorted(sorted_sequence_1d, values) tensor([[1, 3, 4], [1, 3, 4]]) """) add_docstr(torch.bucketize, r""" bucketize(input, boundaries, out_int32=False, right=False, out=None) -> Tensor Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size as :attr:`input`. If :attr:`right` is False (default), then the left boundary is closed. More formally, the returned index satisfies the following rules: .. list-table:: :widths: 15 85 :header-rows: 1 * - :attr:`right` - *returned index satisfies* * - False - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]`` * - True - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]`` Args: input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). boundaries (Tensor): 1-D tensor, must contain a monotonically increasing sequence. out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. Default value is False, i.e. default output data type is torch.int64. right (bool, optional): if False, return the first suitable location that is found. If True, return the last such index. If no suitable index found, return 0 for non-numerical value (eg. nan, inf) or the size of :attr:`boundaries` (one pass the last index). In other words, if False, gets the lower bound index for each value in :attr:`input` from :attr:`boundaries`. If True, gets the upper bound index instead. Default value is False. out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided. Example:: >>> boundaries = torch.tensor([1, 3, 5, 7, 9]) >>> boundaries tensor([1, 3, 5, 7, 9]) >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]]) >>> v tensor([[3, 6, 9], [3, 6, 9]]) >>> torch.bucketize(v, boundaries) tensor([[1, 3, 4], [1, 3, 4]]) >>> torch.bucketize(v, boundaries, right=True) tensor([[2, 3, 5], [2, 3, 5]]) """)

[ "[email protected]" ]

[email protected]

efaf5827b686a2a2c8b12a2e327f2178fa269f5c

7954d761dde104a9d977006c514ff976a9c88444

/backend/menu/migrations/0001_initial.py

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crowdbotics-apps/firebase-25585

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# Generated by Django 2.2.19 on 2021-04-09 10:26 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('delivery_user_profile', '0001_initial'), ] operations = [ migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('image', models.URLField()), ('icon', models.URLField()), ], ), migrations.CreateModel( name='Country', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('prefix', models.CharField(max_length=8)), ('flag', models.URLField()), ], ), migrations.CreateModel( name='Item', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('image', models.URLField()), ('category', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='item_category', to='menu.Category')), ], ), migrations.CreateModel( name='Review', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rating', models.FloatField()), ('review_text', models.TextField()), ('timestamp_created', models.DateTimeField(auto_now_add=True)), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='review_item', to='menu.Item')), ('profile', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='review_profile', to='delivery_user_profile.Profile')), ], ), migrations.CreateModel( name='ItemVariant', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('price', models.FloatField()), ('image', models.URLField()), ('country', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='itemvariant_country', to='menu.Country')), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='itemvariant_item', to='menu.Item')), ], ), ]

[ "[email protected]" ]

[email protected]

2973c8a04aa45789fe2dd63d8482dcf76c80e95b

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/fairing/constants/constants.py

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karthikrajkumar/fairing

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refs/heads/master

2020-06-24T01:11:10.950976

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TEMP_TAR_GZ_FILENAME = '/tmp/fairing.layer.tar.gz' DEFAULT_IMAGE_NAME = 'fairing-job' DEFAULT_BASE_IMAGE = 'gcr.io/kubeflow-images-public/fairing:dev' DEFAULT_REGISTRY = 'index.docker.io' DEFAULT_DEST_PREFIX = '/app/' DEFAULT_CONTEXT_FILENAME = '/tmp/fairing.context.tar.gz' DEFAULT_GENERATED_DOCKERFILE_FILENAME = '/tmp/Dockerfile' GOOGLE_CREDS_ENV = 'GOOGLE_APPLICATION_CREDENTIALS' GCP_CREDS_SECRET_NAME = 'user-gcp-sa' AWS_CREDS_SECRET_NAME = 'aws-secret' DEFAULT_USER_AGENT = 'kubeflow-fairing/{VERSION}' # Job Constants JOB_DEFAULT_NAME = 'fairing-job-' JOB_DEPLOPYER_TYPE = 'job' # Serving Constants SERVING_DEPLOPYER_TYPE = 'serving' #TFJob Constants TF_JOB_GROUP = "kubeflow.org" TF_JOB_KIND = "TFJob" TF_JOB_PLURAL = "tfjobs" TF_JOB_VERSION = "v1beta2" TF_JOB_DEFAULT_NAME = 'fairing-tfjob-' TF_JOB_DEPLOYER_TYPE = 'tfjob' # KFServing constants KFSERVING_GROUP = "serving.kubeflow.org" KFSERVING_KIND = "KFService" KFSERVING_PLURAL = "kfservices" KFSERVING_VERSION = "v1alpha1" KFSERVING_DEFAULT_NAME = 'fairing-kfserving-' KFSERVING_DEPLOYER_TYPE = 'kfservice' KFSERVING_CONTAINER_NAME = 'user-container'

[ "[email protected]" ]

[email protected]

a0321890fdf0babae23c4b46e7dca8a0e7afbf90

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/tools/ci_build/github/apple/c/assemble_c_pod_package.py

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NervanaSystems/onnxruntime

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2,687

py

#!/usr/bin/env python3 # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import argparse import pathlib import shutil import sys _script_dir = pathlib.Path(__file__).parent.resolve(strict=True) sys.path.append(str(_script_dir.parent)) from package_assembly_utils import ( # noqa: E402 copy_repo_relative_to_dir, gen_file_from_template, load_framework_info) def parse_args(): parser = argparse.ArgumentParser(description=""" Assembles the files for the C/C++ pod package in a staging directory. This directory can be validated (e.g., with `pod lib lint`) and then zipped to create a package for release. """) parser.add_argument("--staging-dir", type=pathlib.Path, default=pathlib.Path("./onnxruntime-mobile-c-staging"), help="Path to the staging directory for the C/C++ pod files.") parser.add_argument("--pod-version", required=True, help="C/C++ pod version.") parser.add_argument("--framework-info-file", type=pathlib.Path, required=True, help="Path to the framework_info.json file containing additional values for the podspec. " "This file should be generated by CMake in the build directory.") parser.add_argument("--framework-dir", type=pathlib.Path, required=True, help="Path to the onnxruntime.framework directory to include in the pod.") return parser.parse_args() def main(): args = parse_args() framework_info = load_framework_info(args.framework_info_file.resolve()) staging_dir = args.staging_dir.resolve() print(f"Assembling files in staging directory: {staging_dir}") if staging_dir.exists(): print("Warning: staging directory already exists", file=sys.stderr) # copy the necessary files to the staging directory framework_dir = args.framework_dir.resolve() shutil.copytree(framework_dir, staging_dir / framework_dir.name, dirs_exist_ok=True) copy_repo_relative_to_dir(["LICENSE"], staging_dir) # generate the podspec file from the template variable_substitutions = { "VERSION": args.pod_version, "IOS_DEPLOYMENT_TARGET": framework_info["IOS_DEPLOYMENT_TARGET"], "WEAK_FRAMEWORK": framework_info["WEAK_FRAMEWORK"], "LICENSE_FILE": '"LICENSE"', } podspec_template = _script_dir / "onnxruntime-mobile-c.podspec.template" podspec = staging_dir / "onnxruntime-mobile-c.podspec" gen_file_from_template(podspec_template, podspec, variable_substitutions) return 0 if __name__ == "__main__": sys.exit(main())

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