def test_add_line_element(self, tonic: str, scale_type: str,
cantus_firmus: List[str],
counterpoint_specifications: Dict[str, Any],
rules: Dict[str, Any], steps: List[Tuple[int,
int]],
expected_positions: List[int],
expected_current_measure_durations: List[int],
expected_current_motion_start: LineElement,
expected_is_last_element_consonant: bool,
expected_roll: np.ndarray) -> None:
"""Test `add_line_element` method."""
piece = Piece(tonic,
scale_type,
cantus_firmus,
counterpoint_specifications,
rules,
rendering_params={})
for movement, duration in steps:
piece.add_line_element(movement, duration)
positions = [
x.scale_element.position_in_semitones for x in piece.counterpoint
]
assert positions == expected_positions
assert piece.current_measure_durations == expected_current_measure_durations
assert piece.current_motion_start_element == expected_current_motion_start
assert piece.is_last_element_consonant == expected_is_last_element_consonant
np.testing.assert_equal(piece.piano_roll, expected_roll)
def test_create_lilypond_file_from_piece(path_to_tmp_file: str, piece: Piece,
all_steps: List[Tuple[int, int]],
expected: str) -> None:
"""Test `create_lilypond_file_from_piece` function."""
for movement, duration in all_steps:
piece.add_line_element(movement, duration)
create_lilypond_file_from_piece(piece, path_to_tmp_file)
with open(path_to_tmp_file) as in_file:
result = in_file.read()
assert result == expected
def main() -> None:
"""Parse CLI arguments, train agent, and test it."""
cli_args = parse_cli_args()
default_config_path = 'configs/default_config.yml'
default_config_path = resource_filename(__name__, default_config_path)
config_path = cli_args.config_path or default_config_path
with open(config_path) as config_file:
settings = yaml.safe_load(config_file)
results_dir = settings['piece']['rendering_params']['dir']
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
piece = Piece(**settings['piece'])
env = CounterpointEnv(piece, **settings['environment'])
best_action_sequences = optimize_with_monte_carlo_beam_search(
env, **settings['agent'])
env.verbose = True
for i_episode, action_sequence in enumerate(best_action_sequences):
print(f"\nPiece #{i_episode}:")
env.reset()
for action in action_sequence:
observation, reward, done, info = env.step(action)
env.render()
print(f"Reward is {reward}.")
def test_create_events_from_piece(path_to_tmp_file: str, piece: Piece,
all_steps: List[Tuple[int, int]],
measure_in_seconds: int, volume: float,
row_number: int, expected: str) -> None:
"""Test `create_events_from_piece` function."""
for movement, duration in all_steps:
piece.add_line_element(movement, duration)
create_events_from_piece(piece,
path_to_tmp_file,
measure_in_seconds=measure_in_seconds,
cantus_firmus_timbre='default_timbre',
counterpoint_timbre='default_timbre',
volume=volume)
with open(path_to_tmp_file) as in_file:
for i in range(row_number):
in_file.readline()
result = in_file.readline()
assert result == expected
def test_create_midi_from_piece(path_to_tmp_file: str, piece: Piece,
all_steps: List[Tuple[int, int]],
instrument_number: int, note_number: int,
expected: Dict[str, float]) -> None:
"""Test `create_midi_from_piece` function."""
for movement, duration in all_steps:
piece.add_line_element(movement, duration)
create_midi_from_piece(piece,
path_to_tmp_file,
measure_in_seconds=1,
cantus_firmus_instrument=0,
counterpoint_instrument=0,
velocity=100)
midi_data = pretty_midi.PrettyMIDI(path_to_tmp_file)
instrument = midi_data.instruments[instrument_number]
midi_note = instrument.notes[note_number]
result = {
'pitch': midi_note.pitch,
'start': midi_note.start,
'end': midi_note.end
}
assert result == expected
def test_check_validity(self, tonic: str, scale_type: str,
cantus_firmus: List[str],
counterpoint_specifications: Dict[str, Any],
rules: Dict[str,
Any], previous_steps: List[Tuple[int,
int]],
candidate_steps: List[Tuple[int, int]],
expected: List[bool]) -> None:
"""Test `check_validity` method."""
piece = Piece(tonic,
scale_type,
cantus_firmus,
counterpoint_specifications,
rules,
rendering_params={})
for movement, duration in previous_steps:
piece.add_line_element(movement, duration)
result = [
piece.check_validity(movement, duration)
for movement, duration in candidate_steps
]
assert result == expected
def test_improper_initialization(self, tonic: str, scale_type: str,
cantus_firmus: List[str],
counterpoint_specifications: Dict[str,
Any],
rules: Dict[str,
Any], match: str) -> None:
"""Test that initialization with invalid values is prohibited."""
with pytest.raises(ValueError, match=match):
_ = Piece(tonic,
scale_type,
cantus_firmus,
counterpoint_specifications,
rules,
rendering_params={})
def test_initialization(self, tonic: str, scale_type: str,
cantus_firmus: List[str],
counterpoint_specifications: Dict[str, Any],
rules: Dict[str, Any], rng: Tuple[int, int],
roll: np.ndarray) -> None:
"""Test that initialization with valid values works as expected."""
piece = Piece(tonic,
scale_type,
cantus_firmus,
counterpoint_specifications,
rules,
rendering_params={})
assert piece.current_measure_durations == []
assert (piece.lowest_row_to_show, piece.highest_row_to_show) == rng
np.testing.assert_equal(piece.piano_roll, roll)
def test_reset(self, tonic: str, scale_type: str, cantus_firmus: List[str],
counterpoint_specifications: Dict[str, Any],
rules: Dict[str, Any], steps: List[Tuple[int, int]],
expected_roll: np.ndarray) -> None:
"""Test `reset` method."""
piece = Piece(tonic,
scale_type,
cantus_firmus,
counterpoint_specifications,
rules,
rendering_params={})
for movement, duration in steps:
piece.add_line_element(movement, duration)
piece.reset()
assert piece.past_movements == []
assert piece.current_time_in_eighths == 8
np.testing.assert_equal(piece.piano_roll, expected_roll)
create_wav_from_events)
@pytest.mark.parametrize(
"piece, all_steps, instrument_number, note_number, expected",
[
(
# `piece`
Piece(tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'G4',
'end_note': 'C5',
'lowest_note': 'C4',
'highest_note': 'C6',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}),
# `all_steps`,
[(2, 4), [2, 8], [-1, 1]],
# `instrument_number`
1,
# `note_number`
3,
# `expected`
{
class TestCounterpointEnv:
"""Tests for `CounterpointEnv` class."""
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8],
# `expected`
np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
),
]
)
def test_observation(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper observation."""
for action in actions:
observation, reward, done, info = env.step(action)
assert not done
np.testing.assert_equal(observation, expected)
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': [
'rhythmic_pattern_validity',
'rearticulation_stability',
'consonance_on_strong_beat',
'resolution_of_suspended_dissonance',
],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[13, 15],
# `expected`
[6, 11]
),
]
)
def test_info(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper info about next actions."""
for action in actions:
observation, reward, done, info = env.step(action)
result = info['next_actions']
assert result == expected
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15, 9],
# `expected`
0
),
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {4: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15, 9],
# `expected`
1
),
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'C4', 'C3', 'C4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['absence_of_large_intervals'],
'params': {
'absence_of_large_intervals': {
'max_n_semitones': 7
}
}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 12],
# `expected`
-100
),
]
)
def test_reward(
self, env: CounterpointEnv, actions: List[int], expected: float
) -> None:
"""Test that `step` method returns proper reward."""
for action in actions:
observation, reward, done, info = env.step(action)
assert done
assert round(reward, 4) == expected
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15],
# `expected`
np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
),
]
)
def test_reset(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test `reset` method."""
for action in actions:
env.step(action)
observation = env.reset()
np.testing.assert_equal(observation, expected)
assert env.piece.current_time_in_eighths == 8