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refactor tag_album a little (still needs more decomposition)

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This commit is contained in:
Adrian Sampson 2010-09-16 11:08:50 -07:00
parent 4d978f3541
commit 22b4dd44f9
1 changed files with 45 additions and 34 deletions
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79
beets/autotag/__init__.py
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@ -347,6 +347,33 @@ def match_by_id(items):
# other IDs (i.e., track and artist) in case the album tag isn't
# present, but that event seems very unlikely.
def recommendation(results):
"""Given a sorted list of result tuples, returns a recommendation
flag (RECOMMEND_STRONG, RECOMMEND_MEDIUM, RECOMMEND_NONE) based
on the results' distances.
"""
if not results:
# No candidates: no recommendation.
rec = RECOMMEND_NONE
else:
min_dist = results[0][0]
if min_dist < STRONG_REC_THRESH:
# Strong recommendation level.
rec = RECOMMEND_STRONG
elif len(results) == 1:
# Only a single candidate. Medium recommendation.
rec = RECOMMEND_MEDIUM
elif min_dist <= MEDIUM_REC_THRESH:
# Medium recommendation level.
rec = RECOMMEND_MEDIUM
elif results[1][0] - min_dist >= REC_GAP_THRESH:
# Gap between first two candidates is large.
rec = RECOMMEND_MEDIUM
else:
# No conclusion.
rec = RECOMMEND_NONE
return rec
def tag_album(items, search_artist=None, search_album=None):
"""Bundles together the functionality used to infer tags for a
set of items comprised by an album. Returns everything relevant:
@ -367,8 +394,8 @@ def tag_album(items, search_artist=None, search_album=None):
# Get current metadata.
cur_artist, cur_album = current_metadata(items)
# The output list of result tuples:
dist_ordered_cands = []
# The output result tuples (keyed by MB album ID).
out_tuples = {}
# Try to find album indicated by MusicBrainz IDs.
id_info = match_by_id(items)
@ -376,11 +403,11 @@ def tag_album(items, search_artist=None, search_album=None):
ordered = order_items(items, id_info['tracks'])
if ordered:
dist = distance(ordered, id_info)
dist_ordered_cands.append((dist, ordered, id_info))
out_tuples[id_info['album_id']] = dist, ordered, id_info
# If we have a very good MBID match, don't bother
# continuing.
if dist < STRONG_REC_THRESH:
return (cur_artist, cur_album, dist_ordered_cands,
return (cur_artist, cur_album, out_tuples.values(),
RECOMMEND_STRONG)
# Otherwise, this match will compete against metadata-based
# matches.
@ -390,17 +417,21 @@ def tag_album(items, search_artist=None, search_album=None):
# No explicit search terms -- use current metadata.
search_artist, search_album = cur_artist, cur_album
# Get candidate metadata.
# Get candidate metadata from search.
if not search_artist or not search_album:
raise InsufficientMetadataError()
candidates = mb.match_album(search_artist, search_album, len(items))
candidates = candidates[:MAX_CANDIDATES]
# Get candidates from plugins.
# candidates += plugins.candidates(items)
# Get the distance to each candidate.
for info in _first_n(candidates, MAX_CANDIDATES):
# Don't duplicate the MusicBrainz match.
if id_info and id_info['album_id'] == info['album_id']:
for info in candidates:
# Don't duplicate.
if info['album_id'] in out_tuples:
continue
# Make sure the album has the correct number of tracks.
if len(items) != len(info['tracks']):
continue
@ -413,31 +444,11 @@ def tag_album(items, search_artist=None, search_album=None):
# Get the change distance.
dist = distance(ordered, info)
dist_ordered_cands.append((dist, ordered, info))
out_tuples[info['album_id']] = dist, ordered, info
# Sort by distance.
dist_ordered_cands.sort()
out_tuples = out_tuples.values()
out_tuples.sort()
# Make a recommendation.
if not dist_ordered_cands:
# No candidates: no recommendation.
rec = RECOMMEND_NONE
else:
min_dist = dist_ordered_cands[0][0]
if min_dist < STRONG_REC_THRESH:
# Strong recommendation level.
rec = RECOMMEND_STRONG
elif len(dist_ordered_cands) == 1:
# Only a single candidate. Medium recommendation.
rec = RECOMMEND_MEDIUM
elif min_dist <= MEDIUM_REC_THRESH:
# Medium recommendation level.
rec = RECOMMEND_MEDIUM
elif dist_ordered_cands[1][0] - min_dist >= REC_GAP_THRESH:
# Gap between first two candidates is large.
rec = RECOMMEND_MEDIUM
else:
# No conclusion.
rec = RECOMMEND_NONE
return cur_artist, cur_album, dist_ordered_cands, rec
rec = recommendation(out_tuples)
return cur_artist, cur_album, out_tuples, rec