def testFromQuantizedNoteSequenceWithQuantizedVelocity(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(60, 100, 0.0, 4.0), (64, 100, 0.0, 3.0), (67, 127, 1.0, 2.0)])
quantized_sequence = sequences_lib.quantize_note_sequence_absolute(
self.note_sequence, steps_per_second=100)
performance = list(performance_lib.Performance(
quantized_sequence, num_velocity_bins=16))
pe = performance_lib.PerformanceEvent
expected_performance = [
pe(pe.VELOCITY, 13),
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.VELOCITY, 16),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
]
self.assertEqual(expected_performance, performance)
def testPerformanceNoteDensitySequence(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_density_sequence = [
4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 0.0]
density_sequence = performance_lib.performance_note_density_sequence(
performance, window_size_seconds=1.0)
self.assertEqual(expected_density_sequence, density_sequence)
def testSetLengthAddSteps(self):
performance = performance_lib.Performance(steps_per_second=100)
performance.set_length(50)
self.assertEqual(50, performance.num_steps)
self.assertListEqual([0], performance.steps)
pe = performance_lib.PerformanceEvent
perf_events = [pe(pe.TIME_SHIFT, 50)]
self.assertEqual(perf_events, list(performance))
performance.set_length(150)
self.assertEqual(150, performance.num_steps)
self.assertListEqual([0, 100], performance.steps)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.TIME_SHIFT, 100),
pe(pe.TIME_SHIFT, 50),
]
self.assertEqual(perf_events, list(performance))
performance.set_length(200)
self.assertEqual(200, performance.num_steps)
self.assertListEqual([0, 100], performance.steps)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.TIME_SHIFT, 100),
pe(pe.TIME_SHIFT, 100),
]
self.assertEqual(perf_events, list(performance))
def testToSequenceWithRepeatedNotes(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
]
for event in perf_events:
performance.append(event)
performance_ns = performance.to_sequence()
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(60, 100, 0.0, 2.0), (64, 100, 0.0, 2.0), (60, 100, 1.0, 2.0)])
# Make comparison easier by sorting.
performance_ns.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.note_sequence.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.assertEqual(self.note_sequence, performance_ns)
def testFromQuantizedNoteSequence(self):
testing_lib.add_track_to_sequence(self.note_sequence, 0,
[(60, 100, 0.0, 4.0),
(64, 100, 0.0, 3.0),
(67, 100, 1.0, 2.0)])
quantized_sequence = sequences_lib.quantize_note_sequence_absolute(
self.note_sequence, steps_per_second=100)
performance = performance_lib.Performance(quantized_sequence)
self.assertEqual(100, performance.steps_per_second)
pe = performance_lib.PerformanceEvent
expected_performance = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
]
self.assertEqual(expected_performance, list(performance))
def testExtract(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_density_sequence = [
4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 0.0
]
density_sequence = self.control.extract(performance)
self.assertEqual(expected_density_sequence, density_sequence)
def testSteps(self):
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
]
performance = performance_lib.Performance(steps_per_second=100)
for event in perf_events:
performance.append(event)
self.assertListEqual([0, 0, 0, 100, 100], performance.steps)
performance = performance_lib.Performance(steps_per_second=100,
start_step=100)
for event in perf_events:
performance.append(event)
self.assertListEqual([100, 100, 100, 200, 200], performance.steps)
def testToSequence(self):
testing_lib.add_track_to_sequence(
self.note_sequence, 0,
[(60, 100, 0.0, 4.0), (64, 100, 0.0, 3.0), (67, 100, 1.0, 2.0)])
quantized_sequence = sequences_lib.quantize_note_sequence_absolute(
self.note_sequence, steps_per_second=100)
performance = performance_lib.Performance(quantized_sequence)
performance_ns = performance.to_sequence()
# Make comparison easier by sorting.
performance_ns.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.note_sequence.notes.sort(key=lambda n: (n.start_time, n.pitch))
self.assertEqual(self.note_sequence, performance_ns)
def testPerformancePitchHistogramSequence(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_histogram_sequence = [
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0.375, 0, 0, 0.375, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[0.0, 0, 0, 0, 1.0, 0, 0, 0.0, 0, 0, 0, 0],
[1.0 / 12.0] * 12
]
histogram_sequence = performance_lib.performance_pitch_histogram_sequence(
performance, window_size_seconds=1.0, prior_count=0)
self.assertEqual(expected_histogram_sequence, histogram_sequence)
def testSetLengthRemoveSteps(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 67),
]
for event in perf_events:
performance.append(event)
performance.set_length(200)
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 100),
pe(pe.NOTE_OFF, 64),
pe(pe.NOTE_ON, 67),
]
self.assertEqual(perf_events, list(performance))
performance.set_length(50)
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.TIME_SHIFT, 50),
]
self.assertEqual(perf_events, list(performance))
def testExtract(self):
performance = performance_lib.Performance(steps_per_second=100)
pe = performance_lib.PerformanceEvent
perf_events = [
pe(pe.NOTE_ON, 60),
pe(pe.NOTE_ON, 64),
pe(pe.NOTE_ON, 67),
pe(pe.TIME_SHIFT, 50),
pe(pe.NOTE_OFF, 60),
pe(pe.NOTE_OFF, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 67),
pe(pe.NOTE_ON, 64),
pe(pe.TIME_SHIFT, 25),
pe(pe.NOTE_OFF, 64)
]
for event in perf_events:
performance.append(event)
expected_histogram_sequence = [
[0.5, 0, 0, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0],
[0.5, 0, 0, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0],
[0.5, 0, 0, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0],
[0.5, 0, 0, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.25, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.25, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.25, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.25, 0, 0, 0.0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
histogram_sequence = self.control.extract(performance)
self.assertEqual(expected_histogram_sequence, histogram_sequence)
