def plot_narmour_feature(prob_model, min_pitch, max_pitch, feature_name, folder, reference_note=None):
if feature_name is not None: return
max_interval= max_pitch - min_pitch
max_interval= 12
prob= [[None]*2*(max_interval+1) for i in xrange(2*max_interval+2)]
if feature_name is not None: code= [[None]*2*(max_interval+1) for i in xrange(2*max_interval+2)]
for i1_length in xrange(2*max_interval+2):
for i2_length in xrange(2*max_interval+2):
prob[i1_length][i2_length]= prob_model.get_interval_prob(i1_length-max_interval-1, i2_length-max_interval-1, feature_name=feature_name)
if reference_note is not None:
prob[i1_length][i2_length]= prob[i1_length][i2_length]*prob_model.notes_distr[Note(reference_note.pitch+i1_length+i2_length).get_canonical_note()]
if feature_name is not None:
#code[i1_length][i2_length]= i1_length -max_interval-1 + i2_length -max_interval-1
code[i1_length][i2_length]= get_interval_features(i1_length-max_interval-1, i2_length-max_interval-1)[feature_name]
x= range(2*max_interval+2)
y= range(2*max_interval+2)
#if feature_name is None:
# for reference_note in xrange(0, 12):
# print reference_note
# for i1_length in xrange(2*max_interval+2):
# for i2_length in xrange(2*max_interval+2):
# prob[i1_length][i2_length]= prob_model.get_interval_prob(i1_length-max_interval-1, i2_length-max_interval-1, feature_name=feature_name)
# prob[i1_length][i2_length]= prob[i1_length][i2_length]*prob_model.notes_distr[Note(reference_note+i1_length+i2_length).get_canonical_note()]
# prob_fname= os.path.join(folder, 'narmour_%s_%s.png' % (reference_note, feature_name))
# _do_plot(x, y, prob, prob_fname, max_interval)
if feature_name == 'all': import ipdb;ipdb.set_trace()
if feature_name is None: feature_name= 'all'
if reference_note is not None:
prob_fname= os.path.join(folder, 'narmour_%s_%s.png' % (reference_note, feature_name))
else:
prob_fname= os.path.join(folder, 'narmour_%s.png' % feature_name)
_do_plot(x, y, prob, prob_fname, max_interval, reference_note=reference_note)
def get_interval_prob(self, i1_length, i2_length, feature_name=None):
features= get_interval_features(i1_length, i2_length)
return self.get_features_prob(features, feature_name)
