def test_tonic_identification():
pitch = numpy.array(json.load(open(os.path.join(
"sample_data", "cab08727-d5c2-4fda-9d96-d107915a85ec.json"),
'r'))['pitch'])
tonic_identifier = TonicLastNote()
tonic, pitch, pitch_chunks, pitch_distribution, stable_pitches = \
tonic_identifier.identify(pitch)
saved_tonic = json.load(open(os.path.join(
'sample_data', 'cab08727-d5c2-4fda-9d96-d107915a85ec_tonic.json'),
'r'))
assert (numpy.isclose(tonic['value'], saved_tonic['value']))
def _stable_pitches_to_notes(self, stable_pitches_hz,
theoretical_intervals, tonic_hz):
stable_pitches_cent = Converter.hz_to_cent(stable_pitches_hz, tonic_hz)
# Finding nearest theoretical values of each stable pitch, identify the
# name of this value and write to output
stable_notes = {} # Defining output (return) object
for stable_pitch_cent, stable_pitch_hz in zip(stable_pitches_cent,
stable_pitches_hz):
note_cent = TonicLastNote.find_nearest(
theoretical_intervals.values(), stable_pitch_cent)
if abs(stable_pitch_cent - note_cent) < self.pitch_threshold:
for key, val in theoretical_intervals.iteritems():
if val == note_cent:
theoretical_pitch = Converter.cent_to_hz(
note_cent, tonic_hz)
stable_notes[key] = {
"performed_interval": {"value": stable_pitch_cent,
"unit": "cent"},
"theoretical_interval": {"value": note_cent,
"unit": "cent"},
"theoretical_pitch": {"value": theoretical_pitch,
"unit": "cent"},
"stable_pitch": {"value": stable_pitch_hz,
"unit": "Hz"}}
break
return stable_notes
