def _get_noterest(self):
"""Used internally by get() to cache and retrieve results from the
noterest.NoteRestIndexer."""
if 'noterest' not in self._analyses:
self._analyses['noterest'] = noterest.NoteRestIndexer(
self._get_m21_nrc_objs_no_tied()).run()
return self._analyses['noterest']
def all_melodies(file):
file = music21.converter.parse(file)
notes = noterest.NoteRestIndexer(file).run()
h_ints = interval.HorizontalIntervalIndexer(notes).run()
voices = ['0', '1', '2', '3']
settings = {'n': 4, 'vertical': voices}
ngrams = new_ngram.NewNGramIndexer([h_ints], settings).run()
combined = pandas.concat([notes, ngrams], axis=1)
voices_list = []
for voice in voices:
voice_dict = {}
for melody in ngrams['new_ngram.NewNGramIndexer'][voice].dropna(
).tolist():
condition = combined.loc[:, 'new_ngram.NewNGramIndexer'][
voice] == melody
location = combined.loc[:, 'noterest.NoteRestIndexer'][voice]
if melody not in voice_dict:
voice_dict[melody] = location[condition].tolist()
voices_list.append(voice_dict)
melo_dict = {}
for dictionary in voices_list:
for entry in dictionary:
if entry in melo_dict:
melo_dict[entry].extend(dictionary[entry])
else:
melo_dict[entry] = dictionary[entry]
return melo_dict
def test_noterest_indexer_3(self):
# When there are a bunch of notes
expected = pandas.DataFrame(
{'0': pandas.Series([u'C4' for _ in range(10)])})
test_score = pandas.DataFrame(
{'0': pandas.Series([note.Note('C4') for i in range(10)])})
nr_indexer = noterest.NoteRestIndexer(test_score)
actual = nr_indexer.run()['noterest.NoteRestIndexer']
self.assertTrue(actual.equals(expected))
def test_noterest_indexer_1(self):
# When the parts are empty
expected = pandas.DataFrame({
'0': pandas.Series(),
'1': pandas.Series()
})
nr_indexer = noterest.NoteRestIndexer(expected)
actual = nr_indexer.run()['noterest.NoteRestIndexer']
self.assertTrue(actual.equals(expected))
def from_melody(file, melody):
file = music21.converter.parse(file)
notes = noterest.NoteRestIndexer(file).run()
h_ints = interval.HorizontalIntervalIndexer(notes).run()
voices = ['0', '1', '2', '3']
settings = {'n': 4, 'vertical': voices}
ngrams = new_ngram.NewNGramIndexer([h_ints], settings).run()
combined = pandas.concat([notes, ngrams], axis=1)
all_voices = []
for voice in voices:
condition = combined.loc[:,
'new_ngram.NewNGramIndexer'][voice] == melody
location = combined.loc[:, 'noterest.NoteRestIndexer'][voice]
all_voices.extend(location[condition].tolist())
return all_voices
def find_permutations(file, motif):
file = music21.converter.parse(file)
notes = noterest.NoteRestIndexer(file).run()
h_ints = interval.HorizontalIntervalIndexer(notes).run()
voices = ['0', '1', '2', '3']
settings = {'n': 4, 'vertical': voices}
ngrams = new_ngram.NewNGramIndexer([h_ints], settings).run()
perms1 = _permute(motif)
perms = []
for motif in perms1:
perms.append(' '.join(motif))
perms = list(set(perms))
for motif in perms:
condition = ngrams['new_ngram.NewNGramIndexer'] == motif
print(notes[condition].dropna(how='all'))
def main():
piece_path = "/home/amor/Code/vizitka/vis/tests/corpus/Kyrie.krn"
# piece_path = "/home/amor/Code/vizitka/vis/tests/corpus/bach.xml"
# piece_path = "/home/amor/Code/vizitka/vis/tests/corpus/bwv603.xml"
# piece_path = '/home/amor/Code/vizitka/vis/tests/corpus/Reimenschnieder/1-026900B_.xml'
#piece_path = '/home/amor/Code/vizitka/vis/tests/corpus/Jos2308.mei'
# piece_path = '/home/amor/Code/vizitka/vis/tests/corpus/Sanctus.krn'
ind_piece = IndexedPiece(piece_path)
test_piece = ind_piece._import_score()
test_parts = test_piece.parts
# bwv603 = converter.parse(os.path.join(VIS_PATH, 'tests', 'corpus/bwv603.xml'))
# test_part = [bwv603.parts[0], bwv603.parts[1], bwv603.parts[2], bwv603.parts[3]]
setts = {'quality': True, 'simple or compound': 'simple'}
horiz_setts = {'quality': False, 'simple or compound': 'compound'}
t0 = time.time()
actual = noterest.NoteRestIndexer(test_parts).run()
# filter_setts = {'quarterLength': 2.0, 'method':None}
# filtered_results = offset.FilterByOffsetIndexer(actual, filter_setts).run()
# pdb.set_trace()
dur_ind = meter.DurationIndexer(test_parts).run()
bs_ind = meter.NoteBeatStrengthIndexer(test_parts).run()
horiz = interval.HorizontalIntervalIndexer(actual, horiz_setts).run()
# ind_piece._analyses['noterest'] = actual
# h_df = ind_piece._get_h_ints(settings=horiz_setts)
vert_ints = interval.IntervalIndexer(actual, setts).run()
dissonances = dissonance.DissonanceIndexer(
[bs_ind, dur_ind, horiz, vert_ints]).run()
t1 = time.time()
print('Time taken to run all indexers: ')
print(t1 - t0)
pdb.set_trace()
'n': 3,
'continuer': 'P1',
'horizontal': 'lowest',
'vertical': 'all',
'terminator': ['Rest'],
'open-ended': False,
'brackets': False
}
w = 6 # w is for window
ends = []
for number, piece_path in enumerate(pieces):
ind_piece = IndexedPiece(piece_path)
piece = ind_piece._import_score()
parts = piece.parts
nr = noterest.NoteRestIndexer(parts).run()
dr = meter.DurationIndexer(parts).run()
ms = meter.MeasureIndexer(parts).run()
bs = meter.NoteBeatStrengthIndexer(parts).run()
hz = interval.HorizontalIntervalIndexer(nr, h_setts).run()
hz2 = interval.HorizontalIntervalIndexer(nr, h_setts2).run()
vt = interval.IntervalIndexer(nr, v_setts).run()
# ng = new_ngram.NewNGramIndexer((vt, hz2), n_setts).run()
ds = dissonance.DissonanceIndexer([bs, dr, hz, vt]).run()
av = active_voices.ActiveVoicesIndexer(nr).run()
av_sa = active_voices.ActiveVoicesIndexer(nr, {'show_all': True}).run()
voice_dr_means = []
# Attack-density analysis each voice. The whole-piece analysis is probably what matters most though.
for x in range(len(nr.columns)):
mask = nr.iloc[:, x].dropna()