def match_by_id(items): """If the items are tagged with a MusicBrainz album ID, returns an info dict for the corresponding album. Otherwise, returns None. """ # Is there a consensus on the MB album ID? albumids = [item.mb_albumid for item in items if item.mb_albumid] if not albumids: log.debug('No album IDs found.') return None # If all album IDs are equal, look up the album. if bool(reduce(lambda x,y: x if x==y else (), albumids)): albumid = albumids[0] log.debug('Searching for discovered album ID: ' + albumid) return hooks._album_for_id(albumid) else: log.debug('No album ID consensus.') return None
def tag_album(items, timid=False, search_artist=None, search_album=None, search_id=None): """Bundles together the functionality used to infer tags for a set of items comprised by an album. Returns everything relevant: - The current artist. - The current album. - A list of (distance, items, info) tuples where info is a dictionary containing the inferred tags and items is a reordered version of the input items list. The candidates are sorted by distance (i.e., best match first). - A recommendation, one of RECOMMEND_STRONG, RECOMMEND_MEDIUM, or RECOMMEND_NONE; indicating that the first candidate is very likely, it is somewhat likely, or no conclusion could be reached. If search_artist and search_album or search_id are provided, then they are used as search terms in place of the current metadata. May raise an AutotagError if existing metadata is insufficient. """ # Get current metadata. cur_artist, cur_album, artist_consensus = current_metadata(items) log.debug('Tagging %s - %s' % (cur_artist, cur_album)) # The output result tuples (keyed by MB album ID). out_tuples = {} # Try to find album indicated by MusicBrainz IDs. if search_id: log.debug('Searching for album ID: ' + search_id) id_info = hooks._album_for_id(search_id) else: id_info = match_by_id(items) if id_info: validate_candidate(items, out_tuples, id_info) rec = recommendation(out_tuples.values()) log.debug('Album ID match recommendation is ' + str(rec)) if out_tuples and not timid: # If we have a very good MBID match, return immediately. # Otherwise, this match will compete against metadata-based # matches. if rec == RECOMMEND_STRONG: log.debug('ID match.') return cur_artist, cur_album, out_tuples.values(), rec # If searching by ID, don't continue to metadata search. if search_id is not None: if out_tuples: return cur_artist, cur_album, out_tuples.values(), rec else: return cur_artist, cur_album, [], RECOMMEND_NONE # Search terms. if not (search_artist and search_album): # No explicit search terms -- use current metadata. search_artist, search_album = cur_artist, cur_album log.debug(u'Search terms: %s - %s' % (search_artist, search_album)) # Is this album likely to be a "various artist" release? va_likely = ((not artist_consensus) or (search_artist.lower() in VA_ARTISTS) or any(item.comp for item in items)) log.debug(u'Album might be VA: %s' % str(va_likely)) # Get the results from the data sources. candidates = hooks._album_candidates(items, search_artist, search_album, va_likely) # Get the distance to each candidate. log.debug(u'Evaluating %i candidates.' % len(candidates)) for info in candidates: validate_candidate(items, out_tuples, info) # Sort by distance. out_tuples = out_tuples.values() out_tuples.sort() rec = recommendation(out_tuples) return cur_artist, cur_album, out_tuples, rec
def tag_album(items, timid=False, search_artist=None, search_album=None, search_id=None): """Bundles together the functionality used to infer tags for a set of items comprised by an album. Returns everything relevant: - The current artist. - The current album. - A list of (distance, items, info) tuples where info is a dictionary containing the inferred tags and items is a reordered version of the input items list. The candidates are sorted by distance (i.e., best match first). - A recommendation, one of RECOMMEND_STRONG, RECOMMEND_MEDIUM, or RECOMMEND_NONE; indicating that the first candidate is very likely, it is somewhat likely, or no conclusion could be reached. If search_artist and search_album or search_id are provided, then they are used as search terms in place of the current metadata. May raise an AutotagError if existing metadata is insufficient. """ # Get current metadata. cur_artist, cur_album, artist_consensus = current_metadata(items) log.debug('Tagging %s - %s' % (cur_artist, cur_album)) # The output result (distance, AlbumInfo) tuples (keyed by MB album # ID). candidates = {} # Try to find album indicated by MusicBrainz IDs. if search_id: log.debug('Searching for album ID: ' + search_id) id_info = hooks._album_for_id(search_id) else: id_info = match_by_id(items) if id_info: validate_candidate(items, candidates, id_info) rec = recommendation(candidates.values()) log.debug('Album ID match recommendation is ' + str(rec)) if candidates and not timid: # If we have a very good MBID match, return immediately. # Otherwise, this match will compete against metadata-based # matches. if rec == RECOMMEND_STRONG: log.debug('ID match.') return cur_artist, cur_album, candidates.values(), rec # If searching by ID, don't continue to metadata search. if search_id is not None: if candidates: return cur_artist, cur_album, candidates.values(), rec else: return cur_artist, cur_album, [], RECOMMEND_NONE # Search terms. if not (search_artist and search_album): # No explicit search terms -- use current metadata. search_artist, search_album = cur_artist, cur_album log.debug(u'Search terms: %s - %s' % (search_artist, search_album)) # Is this album likely to be a "various artist" release? va_likely = ((not artist_consensus) or (search_artist.lower() in VA_ARTISTS) or any(item.comp for item in items)) log.debug(u'Album might be VA: %s' % str(va_likely)) # Get the results from the data sources. search_cands = hooks._album_candidates(items, search_artist, search_album, va_likely) log.debug(u'Evaluating %i candidates.' % len(search_cands)) for info in search_cands: validate_candidate(items, candidates, info) # Sort and get the recommendation. candidates = sorted(candidates.itervalues()) rec = recommendation(candidates) return cur_artist, cur_album, candidates, rec