def test_intervals_to_samples(self):
"""Check that an interval set is sampled properly, with boundaries
conditions and out-of-range values.
"""
labels = list('abc')
intervals = np.array([(n, n + 1.0) for n in range(len(labels))])
expected_times = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5]
expected_labels = ['a', 'a', 'b', 'b', 'c', 'c']
result = util.intervals_to_samples(intervals,
labels,
offset=0,
sample_size=0.5,
fill_value='N')
self.assertEqual(result[0], expected_times)
self.assertEqual(result[1], expected_labels)
expected_times = [0.25, 0.75, 1.25, 1.75, 2.25, 2.75]
expected_labels = ['a', 'a', 'b', 'b', 'c', 'c']
result = util.intervals_to_samples(intervals,
labels,
offset=0.25,
sample_size=0.5,
fill_value='N')
self.assertEqual(result[0], expected_times)
self.assertEqual(result[1], expected_labels)
def transform_annotation(self, ann, duration):
'''Apply the structure agreement transformation.
Parameters
----------
ann : jams.Annotation
The segment annotation
duration : number > 0
The target duration
Returns
-------
data : dict
data['agree'] : np.ndarray, shape=(n, n), dtype=bool
'''
intervals, values = ann.to_interval_values()
intervals, values = adjust_intervals(intervals,
values,
t_min=0,
t_max=duration)
# Re-index the labels
ids, _ = index_labels(values)
rate = float(self.hop_length) / self.sr
# Sample segment labels on our frame grid
_, labels = intervals_to_samples(intervals, ids, sample_size=rate)
# Make the agreement matrix
return {'agree': np.equal.outer(labels, labels)}
def midi_to_trajectory(des_timebase, onsets, offsets, pitches):
hop = des_timebase[2] - des_timebase[1]
intervals = np.concatenate([np.array(onsets)[:, None], np.array(offsets)[:, None]], axis=1)
timebase, midipitches = intervals_to_samples(intervals, list(pitches),
offset=des_timebase[0], sample_size=hop, fill_value=0)
return np.array(timebase), np.array(midipitches)
def test_intervals_to_samples():
"""Check that an interval set is sampled properly, with boundaries
conditions and out-of-range values.
"""
labels = list('abc')
intervals = np.array([(n, n + 1.0) for n in range(len(labels))])
expected_times = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5]
expected_labels = ['a', 'a', 'b', 'b', 'c', 'c']
result = util.intervals_to_samples(
intervals, labels, offset=0, sample_size=0.5, fill_value='N')
assert result[0] == expected_times
assert result[1] == expected_labels
expected_times = [0.25, 0.75, 1.25, 1.75, 2.25, 2.75]
expected_labels = ['a', 'a', 'b', 'b', 'c', 'c']
result = util.intervals_to_samples(
intervals, labels, offset=0.25, sample_size=0.5, fill_value='N')
assert result[0] == expected_times
assert result[1] == expected_labels