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""" assert_fn.py """
import numpy as np
def assert_notes_almost_equal(actual_notes, predicted_notes, delta=5e-3):
"""
Asserts that the given lists of Note instances are equal up to a small
floating-point tolerance, similar to `assertAlmostEqual` of `unittest`.
Tolerance is 5e-3 by default, which is 5 ms for 100 ticks-per-second.
"""
assert len(actual_notes) == len(predicted_notes)
for actual_note, predicted_note in zip(actual_notes, predicted_notes):
assert abs(actual_note.onset - predicted_note.onset) < delta
assert abs(actual_note.offset - predicted_note.offset) < delta
assert actual_note.pitch == predicted_note.pitch
if actual_note.is_drum is False and predicted_note.is_drum is False:
assert actual_note.program == predicted_note.program
assert actual_note.is_drum == predicted_note.is_drum
assert actual_note.velocity == predicted_note.velocity
def assert_note_events_almost_equal(actual_note_events,
predicted_note_events,
ignore_time=False,
ignore_activity=True,
delta=5.1e-3):
"""
Asserts that the given lists of Note instances are equal up to a small
floating-point tolerance, similar to `assertAlmostEqual` of `unittest`.
Tolerance is 5e-3 by default, which is 5 ms for 100 ticks-per-second.
If `ignore_time` is True, then the time field is ignored. (useful for
comparing tie note events, default is False)
If `ignore_activity` is True, then the activity field is ignored (default
is True).
"""
assert len(actual_note_events) == len(predicted_note_events)
for j, (actual_note_event,
predicted_note_event) in enumerate(zip(actual_note_events, predicted_note_events)):
if ignore_time is False:
assert abs(actual_note_event.time - predicted_note_event.time) <= delta
assert actual_note_event.is_drum == predicted_note_event.is_drum
if actual_note_event.is_drum is False and predicted_note_event.is_drum is False:
assert actual_note_event.program == predicted_note_event.program
assert actual_note_event.pitch == predicted_note_event.pitch
assert actual_note_event.velocity == predicted_note_event.velocity
if ignore_activity is False:
assert actual_note_event.activity == predicted_note_event.activity
def assert_track_metrics_score1(metrics) -> None:
for k, v in metrics.items():
if np.isnan(v) is False:
assert v == 1.0
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