def test_findbestmatch(self):
no_match = SimMatchTest(similarity=-1, name='no_match')
best_match = find_best_match(self.candidates, no_match)
self.assertEqual(best_match.result.name, 'b')
self.assertEqual(best_match.similarity, 0.75)
self.assertEqual(best_match.num_results, 4)
def _match_to_track(self, tracks, threshold=0):
# multiple matches -- calculate similarities to each of them
def candidates():
for track in tracks:
yield self.metadata.compare_to_track(track,
self.comparison_weights)
no_match = SimMatchTrack(similarity=-1,
releasegroup=None,
release=None,
track=None)
best_match = find_best_match(candidates, no_match)
if best_match.similarity < threshold:
return None
else:
track_id = best_match.result.track['id']
release_group_id, release_id, node = None, None, None
acoustid = best_match.result.track.get('acoustid', None)
if best_match.result.release:
release_group_id = best_match.result.releasegroup['id']
release_id = best_match.result.release['id']
elif 'title' in best_match.result.track:
node = best_match.result.track
return (track_id, release_group_id, release_id, acoustid, node)
def test_findbestmatch(self):
no_match = SimMatchTest(similarity=-1, name='no_match')
best_match = find_best_match(self.candidates, no_match)
self.assertEqual(best_match.result.name, 'b')
self.assertEqual(best_match.similarity, 0.75)
self.assertEqual(best_match.num_results, 4)
def _match_files(files,
tracks,
unmatched_files,
threshold=0,
use_events_iter=False):
"""Match files to tracks on this album, based on metadata similarity or recordingid."""
if use_events_iter:
# TODO: get rid of this completely at some point
events_iter = process_events_iter
else:
def _events_iter(seq):
return seq
events_iter = _events_iter
tracks_cache = defaultdict(lambda: None)
def build_tracks_cache():
for track in tracks:
tm_recordingid = track.orig_metadata['musicbrainz_recordingid']
tm_tracknumber = track.orig_metadata['tracknumber']
tm_discnumber = track.orig_metadata['discnumber']
for tup in ((tm_recordingid, tm_tracknumber,
tm_discnumber), (tm_recordingid, tm_tracknumber),
(tm_recordingid, )):
tracks_cache[tup] = track
SimMatchAlbum = namedtuple('SimMatchAlbum', 'similarity track')
no_match = SimMatchAlbum(similarity=-1, track=unmatched_files)
for file in list(files):
if file.state == File.REMOVED:
continue
# if we have a recordingid to match against, use that in priority
recid = file.match_recordingid or file.metadata[
'musicbrainz_recordingid']
if recid and mbid_validate(recid):
if not tracks_cache:
build_tracks_cache()
tracknumber = file.metadata['tracknumber']
discnumber = file.metadata['discnumber']
track = (tracks_cache[(recid, tracknumber, discnumber)]
or tracks_cache[(recid, tracknumber)]
or tracks_cache[(recid, )])
if track:
yield (file, track)
continue
# try to match by similarity
def candidates():
for track in events_iter(tracks):
similarity = track.metadata.compare(file.orig_metadata)
if similarity >= threshold:
yield SimMatchAlbum(similarity=similarity, track=track)
best_match = find_best_match(candidates, no_match)
yield (file, best_match.result.track)
def test_findbestmatch_nomatch(self):
self.test_values = []
no_match = SimMatchTest(similarity=-1, name='no_match')
best_match = find_best_match(self.candidates, no_match)
self.assertEqual(best_match.result.name, 'no_match')
self.assertEqual(best_match.similarity, -1)
def test_findbestmatch_nomatch(self):
self.test_values = []
no_match = SimMatchTest(similarity=-1, name='no_match')
best_match = find_best_match(self.candidates, no_match)
self.assertEqual(best_match.result.name, 'no_match')
self.assertEqual(best_match.similarity, -1)
self.assertEqual(best_match.num_results, 0)
def _match_to_release(self, releases, threshold=0):
# multiple matches -- calculate similarities to each of them
def candidates():
for release in releases:
match = self.metadata.compare_to_release(release, Cluster.comparison_weights)
if match.similarity >= threshold:
yield match
no_match = SimMatchRelease(similarity=-1, release=None)
best_match = find_best_match(candidates, no_match)
return best_match.result.release
def _match_to_album(self, releases, threshold=0):
# multiple matches -- calculate similarities to each of them
def candidates():
for release in releases:
yield self.metadata.compare_to_release(release, Cluster.comparison_weights)
no_match = SimMatchRelease(similarity=-1, release=None)
best_match = find_best_match(candidates, no_match)
if best_match.similarity < threshold:
return None
else:
return best_match.result.release['id']
def _match_files(self, files, recordingid=None, threshold=0):
"""Match files to tracks on this album, based on metadata similarity or recordingid."""
tracks_cache = defaultdict(lambda: None)
def build_tracks_cache():
for track in self.tracks:
tm_recordingid = track.orig_metadata['musicbrainz_recordingid']
tm_tracknumber = track.orig_metadata['tracknumber']
tm_discnumber = track.orig_metadata['discnumber']
for tup in (
(tm_recordingid, tm_tracknumber, tm_discnumber),
(tm_recordingid, tm_tracknumber),
(tm_recordingid, )):
tracks_cache[tup] = track
SimMatchAlbum = namedtuple('SimMatchAlbum', 'similarity track')
for file in list(files):
if file.state == File.REMOVED:
continue
# if we have a recordingid to match against, use that in priority
recid = recordingid or file.metadata['musicbrainz_recordingid']
if recid and mbid_validate(recid):
if not tracks_cache:
build_tracks_cache()
tracknumber = file.metadata['tracknumber']
discnumber = file.metadata['discnumber']
track = (tracks_cache[(recid, tracknumber, discnumber)]
or tracks_cache[(recid, tracknumber)]
or tracks_cache[(recid, )])
if track:
yield (file, track)
continue
# try to match by similarity
def candidates():
for track in self.tracks:
yield SimMatchAlbum(
similarity=track.metadata.compare(file.orig_metadata),
track=track
)
QtCore.QCoreApplication.processEvents()
no_match = SimMatchAlbum(similarity=-1, track=self.unmatched_files)
best_match = find_best_match(candidates, no_match)
if best_match.similarity < threshold:
yield (file, no_match.track)
else:
yield (file, best_match.result.track)
def _match_files(self, files, recordingid=None, threshold=0):
"""Match files to tracks on this album, based on metadata similarity or recordingid."""
tracks_cache = defaultdict(lambda: None)
def build_tracks_cache():
for track in self.tracks:
tm_recordingid = track.orig_metadata['musicbrainz_recordingid']
tm_tracknumber = track.orig_metadata['tracknumber']
tm_discnumber = track.orig_metadata['discnumber']
for tup in (
(tm_recordingid, tm_tracknumber, tm_discnumber),
(tm_recordingid, tm_tracknumber),
(tm_recordingid, )):
tracks_cache[tup] = track
SimMatchAlbum = namedtuple('SimMatchAlbum', 'similarity track')
for file in list(files):
if file.state == File.REMOVED:
continue
# if we have a recordingid to match against, use that in priority
recid = recordingid or file.metadata['musicbrainz_recordingid']
if recid and mbid_validate(recid):
if not tracks_cache:
build_tracks_cache()
tracknumber = file.metadata['tracknumber']
discnumber = file.metadata['discnumber']
track = (tracks_cache[(recid, tracknumber, discnumber)]
or tracks_cache[(recid, tracknumber)]
or tracks_cache[(recid, )])
if track:
yield (file, track)
continue
# try to match by similarity
def candidates():
for track in self.tracks:
yield SimMatchAlbum(
similarity=track.metadata.compare(file.orig_metadata),
track=track
)
no_match = SimMatchAlbum(similarity=-1, track=self.unmatched_files)
best_match = find_best_match(candidates, no_match)
if best_match.similarity < threshold:
yield (file, no_match.track)
else:
yield (file, best_match.result.track)
def _match_to_track(self, tracks, threshold=0):
# multiple matches -- calculate similarities to each of them
def candidates():
for track in tracks:
yield self.metadata.compare_to_track(track, self.comparison_weights)
no_match = SimMatchTrack(similarity=-1, releasegroup=None, release=None, track=None)
best_match = find_best_match(candidates, no_match)
if best_match.similarity < threshold:
return None
else:
track_id = best_match.result.track['id']
release_group_id, release_id, node = None, None, None
if best_match.result.release:
release_group_id = best_match.result.releasegroup['id']
release_id = best_match.result.release['id']
elif 'title' in best_match.result.track:
node = best_match.result.track
return (track_id, release_group_id, release_id, node)
