def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
histogram, lim = feats.rhythm_histogram(events, args.resolution)
for i in xrange(args.resolution):
print histogram[i],
print " "
def find_best_note_distribution(self, args):
best_file_name = None
best_score = 9000000000
for filename in args.infile:
if best_file_name is None: # Avoids returning empty values
best_file_name = filename
with open(filename, 'rb') as handler:
a = sc.Score().load(handler)
event_list = feats.event_list(a.data)
if len(event_list) > 0: # Avoids empty scores
(max_range, mean_range, std_range) = \
feats.relative_range(event_list)
if max_range < best_score:
best_score = max_range
best_file_name = filename
print best_file_name
def find_best_note_distribution(self, args):
best_file_name = None
best_score = 9000000000
for filename in args.infile:
if best_file_name is None: # Avoids returning empty values
best_file_name = filename
with open(filename, 'rb') as handler:
a = sc.Score().load(handler)
event_list = feats.event_list(a.data)
if len(event_list) > 0: # Avoids empty scores
(max_range, mean_range, std_range) = \
feats.relative_range(event_list)
if max_range < best_score:
best_score = max_range
best_file_name = filename
print best_file_name
def find_best_note_distribution(self, args):
best_file_name = None
best_score = 9000000000
for filename in args.infile:
if best_file_name is None: # Avoids returning empty values
best_file_name = filename
with open(filename, 'rb') as handler:
a = sc.Score().load(handler)
event_list = feats.event_list(a.data)
if len(event_list) > 0: # Avoids empty scores
rhythm_histogram = feats.rhythm_histogram(event_list)[0]
e = scipy.stats.entropy(rhythm_histogram)
if e < best_score:
best_score = e
best_file_name = filename
print best_file_name
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
histogram = feats.interval_histogram(events, args.fold,\
args.time_tolerance, args.duration)
for i in xrange(args.fold):
print histogram[i],
if args.statistics is True:
h = numpy.array(histogram)
print numpy.mean(h),
print numpy.std(h),
print numpy.sum(numpy.array([h[i] * numpy.log2(h[i])\
for i in xrange(len(h))\
if h[i] > 0])),
for i in xrange(4):
m = numpy.argmax(h)
print m,
h[m] = 0
print " "
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
histogram = feats.interval_histogram(events, args.fold,\
args.time_tolerance, args.duration)
for i in xrange(args.fold):
print histogram[i],
if args.statistics is True:
h = numpy.array(histogram)
print numpy.mean(h),
print numpy.std(h),
print numpy.sum(numpy.array([h[i] * numpy.log2(h[i])\
for i in xrange(len(h))\
if h[i] > 0])),
for i in xrange(4):
m = numpy.argmax(h)
print m,
h[m] = 0
print " "
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
(maxRange, meanRange, devRange) = feats.relative_range(events,\
args.time_tolerance, args.duration)
print maxRange, meanRange, devRange
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
(dMean, dDev, dMin, dMax) = feats.note_density(events)
print dMean, dDev, dMin, dMax
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
(dMean, dDev, dMin, dMax) = feats.note_density(events)
print dMean, dDev, dMin, dMax
def run(self, args):
s = score.Score().load(args.infile)
events = feats.event_list(s.data)
(maxRange, meanRange, devRange) = feats.relative_range(events,\
args.time_tolerance, args.duration)
print maxRange, meanRange, devRange