def draw_plagiarism(lo_ngram, hi_ngram, code_gen, measurements, training_data):
SP.print('Loading training data...')
td = load_pickle(training_data)
songs = td[1]
# Strip names
songs = [c for (n, c) in songs]
SP.print('Flattening %d songs...' % len(songs))
tokens = flatten(flatten(flatten(songs)))
tokens = np.array(tokens, dtype=np.uint16)
SP.print('Loading samples...')
data = load_pickle(measurements)
stats = data[code_gen]
gen = stats[False]
seqs = list(gen.values())
seqs = [s[0] for s in seqs]
n_samples = 1000
plag_ratios = {}
for ngram in range(lo_ngram, hi_ngram):
SP.header('FINDING MATCHES FOR NGRAMS OF LENGTH %d' % ngram)
samples = [sample_seq(seqs, ngram) for _ in range(n_samples)]
n_matches = find_samples(tokens, samples)
frac = n_matches / n_samples
SP.print('%d samples matches, %.2f%%.' % (n_matches, 100 * frac))
SP.leave()
plag_ratios[ngram] = frac
print(plag_ratios)
def percussive_samples(mod):
subsongs = linearize_subsongs(mod, 1)
rows = flatten(r for (_, r) in subsongs)
volumes = [header.volume for header in mod.sample_headers]
notes = list(rows_to_mod_notes(rows, volumes))
return {sample for (sample, p) in sample_props(mod, notes).items()
if p.is_percussive}
def notes_to_midi_file(notes, midi_file, midi_mapping):
SP.header('MIDI MAPPING', '%d samples', len(midi_mapping))
SP.print('sample midi base dur vol')
fmt = '%6d %4d %4d %3d %3.2f'
for sample_idx, midi_def in midi_mapping.items():
SP.print(fmt, (sample_idx,) + tuple(midi_def))
SP.leave()
notes_per_channel = sort_groupby(notes, lambda n: n.col_idx)
notes_per_channel = [list(grp) for (_, grp) in notes_per_channel]
notes_per_channel = [
list(mod_notes_to_midi_notes(notes, midi_mapping))
for notes in notes_per_channel]
notes = sorted(flatten(notes_per_channel))
SP.print('Produced %d midi notes (on/offs).' % len(notes))
# Group by column (9 for drums)
note_groups = groupby(notes, lambda el: el[0])
tracks = [MidiTrack(list(midi_notes_to_track(channel, note_group)))
for (channel, note_group) in note_groups]
midi = MidiFile(type = 1)
midi.tracks = tracks
midi.save(midi_file)
def load_data_from_disk(corpus_path, kb_limit):
index = load_index(corpus_path)
mods = [
mod for mod in index.values()
if (mod.n_channels == 4 and mod.format == 'MOD'
and mod.kb_size <= kb_limit)
]
file_paths = [corpus_path / mod.genre / mod.fname for mod in mods]
patterns = [mod_file_to_patterns(path) for path in file_paths]
patterns = flatten(patterns)
patterns = [p for p in patterns if good_pattern(p)]
return np.array(patterns, dtype=np.int8)
def rows_to_mod_notes(rows, volumes):
return flatten([column_to_mod_notes(rows, i, volumes) for i in range(4)])
def upload_caches(connection, corpus_path):
caches = [corpus_path.glob(f'*.{ext}') for ext in ['pickle', 'npy']]
caches = flatten(caches) + [corpus_path / 'index']
files = [(c, c.name) for c in caches]
upload_files(connection, files)
def upload_code(connection, sftp):
dirs = [Path(d) for d in ['musicgen', 'tools']]
for dir in dirs:
remote_mkdir_safe(sftp, dir)
files = flatten([[(src, d) for src in d.glob('*.py')] for d in dirs])
upload_files(connection, files)
def main():
args = docopt(__doc__, version='MOD Melody Extractor 1.0')
# Argument parsing
SP.enabled = args['--verbose']
input_file = args['<input-mod>']
output_file = args['<output-mod>']
max_distance = int(args['--max-distance'])
min_length = int(args['--min-length'])
min_unique = int(args['--min-unique'])
max_repeat = int(args['--max-repeat'])
mu_factor = float(args['--mu-factor'])
mu_threshold = int(args['--mu-threshold'])
trailer = int(args['--trailer'])
transpose = not args['--no-transpose']
# Load mod
mod = load_file(input_file)
rows = linearize_rows(mod)
# Extract and filter melodies
melodies = flatten(
extract_sample_groups(rows, col_idx, max_distance, mu_factor,
mu_threshold) for col_idx in range(4))
melodies = [
melody for (melody, msg) in melodies
if is_melody(melody, min_length, min_unique, max_repeat)
]
if transpose:
melodies = [move_to_c(melody) for melody in melodies]
melodies = [
remove_ending_silence(melody)
for melody in filter_duplicate_melodies(melodies)
]
SP.header('%d MELODIES' % len(melodies))
for melody in melodies:
for cell in melody:
SP.print(cell_to_string(cell))
SP.print('')
SP.leave()
melodies = [add_trailer(melody, trailer) for melody in melodies]
if not melodies:
fmt = 'Sorry, found no melodies in "%s"!'
print(fmt % args.input_module.name)
exit(1)
cells = flatten(melodies)
rows = [[c, ZERO_CELL, ZERO_CELL, ZERO_CELL] for c in cells]
patterns = list(rows_to_patterns(rows))
n_patterns = len(patterns)
pattern_table = list(range(n_patterns)) + [0] * (128 - n_patterns)
mod_out = dict(title=mod.title,
sample_headers=mod.sample_headers,
n_orders=n_patterns,
restart_pos=0,
pattern_table=bytearray(pattern_table),
initials='M.K.'.encode('utf-8'),
patterns=patterns,
samples=mod.samples)
save_file(output_file, mod_out)
