def from_complex_scale(
cls,
name: str,
original_scale: tuple,
intonations_per_scale_degree: tuple,
octave_of_first_pitch: int = 0,
tempo_estimation_method: str = "essentia",
) -> "Transcription":
root = cls._get_root("{}.svl".format(name))
frequency_range = root[0][0].attrib["minimum"], root[0][0].attrib[
"maximum"]
data = cls._filter_data_from_root(root)
frequencies = tuple(map(operator.itemgetter(0), data))
pitch_transcriber = ComplexScaleTranscriber(
original_scale, intonations_per_scale_degree)
octavater = ji.r(1, 1).register(octave_of_first_pitch)
pitches = tuple(octavater + pitch
for pitch in pitch_transcriber(frequencies))
new_data = tuple(
(pitch, ) + tone[1:] for pitch, tone in zip(pitches, data))
melody = cls._convert_data2melody(new_data, name,
tempo_estimation_method)
return cls(name, tuple(melody), frequency_range)
def mk_loop_cadence_with_tuk(loopsize: int,
meter: metre.Metre) -> old.JICadence:
size = meter.size
loop_amount = size // loopsize
rest = size % loopsize
harmony0 = ji.JIHarmony([ji.r(3, 2)])
harmony1 = ji.JIHarmony([ji.r(1, 1)])
basic_cadence = [old.Chord(harmony0, 0.5)
] + [old.Chord(harmony1, 1) for i in range(loopsize - 1)]
basic_cadence.append(old.Chord(harmony1, 0.5))
print(old.JICadence(basic_cadence).duration)
cadence = [basic_cadence for i in range(loop_amount)]
cadence = functools.reduce(operator.add, cadence)
if rest:
cadence.append(old.Chord(harmony0, rest))
return old.JICadence(cadence)
def mk_combination_pitches(comb: int, octave: ji.JIPitch):
pitches = (tuple(
ji.r(functools.reduce(operator.mul, com), 1)
for com in itertools.combinations((3, 5, 7, 9), comb)), )
pitches += (tuple(p.inverse().normalize(2) + octave
for p in pitches[0]), )
return (tuple(p.normalize(2) + octave for p in pitches[0]), pitches[1])
def mk_empty_attack(
duration: float,
volume: float,
frequency: float = 35,
hammer_noise: float = 3,
impedance: float = 0.3,
cutoff: float = 0.3,
q_factor: float = 5,
string_length: float = 0.8,
strike_point: float = 1 / 2,
hammer_hard_piano: float = 1,
hammer_hard_mezzo: float = 1.5,
hammer_hard_forte: float = 2,
blooming_energy: float = 0,
) -> midiplug.PyteqTone:
"""Helps making percussive sounds with Pianoteq."""
return midiplug.PyteqTone(
ji.JIPitch(ji.r(1, 1), multiply=frequency),
duration,
duration,
volume=volume,
hammer_noise=hammer_noise,
impedance=impedance,
cutoff=cutoff,
q_factor=q_factor,
string_length=string_length,
strike_point=strike_point,
hammer_hard_piano=hammer_hard_piano,
hammer_hard_mezzo=hammer_hard_mezzo,
hammer_hard_forte=hammer_hard_forte,
blooming_energy=blooming_energy,
)
def test_cut_up_by_time(self):
t0 = old.Tone(ji.r(1, 1), rhy.Unit(2))
t1 = old.Tone(ji.r(2, 1), rhy.Unit(2))
t2 = old.Tone(ji.r(1, 1), rhy.Unit(1))
r0 = old.Rest(1)
melody0 = old.Melody([t0, t1, t1, t0, t1])
melody1 = old.Melody([t1, t1, t0])
melody2 = old.Melody([r0, t1, t1, t0])
melody3 = old.Melody([t2, t1, t1, t0])
melody4 = old.Melody([t1, t1, t2])
self.assertEqual(melody0.cut_up_by_time(2, 8), melody1)
self.assertEqual(melody0.cut_up_by_time(1, 8), melody2)
self.assertEqual(melody0.cut_up_by_time(1, 8, add_earlier=True), melody3)
self.assertEqual(melody0.cut_up_by_time(2, 7, hard_cut=True), melody4)
self.assertEqual(melody0.cut_up_by_time(2, 7, hard_cut=False), melody1)
def test_cut_up_by_idx(self):
poly0 = old.Polyphon(
(
old.Melody([old.Tone(ji.r(1, 1), 2), old.Tone(ji.r(1, 1), 3)]),
old.Melody([old.Tone(ji.r(3, 2), 3), old.Tone(ji.r(3, 2), 2)]),
old.Melody([old.Tone(ji.r(4, 3), 1), old.Tone(ji.r(4, 3), 2)]),
)
)
poly0_cut = poly0.cut_up_by_idx(2, 1)
poly0_cut_expected = old.Polyphon(
(
old.Melody([old.Tone(ji.r(1, 1), 1), old.Tone(ji.r(1, 1), 1)]),
old.Melody([old.Tone(ji.r(3, 2), 2)]),
old.Melody([old.Tone(ji.r(4, 3), 2)]),
)
)
self.assertEqual(poly0_cut, poly0_cut_expected)
def __mk_siter_gong_simplified():
def mk_combination_pitches(comb: int, octave: ji.JIPitch):
pitches = (tuple(
ji.r(functools.reduce(operator.mul, com), 1)
for com in itertools.combinations((3, 5, 7, 9), comb)
if tuple(sorted(com)) != (3, 9)), )
pitches += (tuple(p.inverse().normalize(2) + octave
for p in pitches[0]), )
return tuple(p.normalize(2) + octave for p in pitches[0]), pitches[1]
def mk_re():
return sound.PyteqEngine(preset='"Cimbalom hard"')
pitch_gong_plus = ji.r(3 * 5 * 7, 1).normalize(2) + ji.r(1, 4)
pitch_gong_minus = pitch_gong_plus.inverse().normalize(2) + ji.r(1, 4)
pitches_tong = mk_combination_pitches(2, ji.r(1, 2))
pitches = (
pitches_tong[0] + (pitch_gong_plus, ),
pitches_tong[1] + (pitch_gong_minus, ),
)
pitch2notation = tuple(
instruments.mk_p2n(p, idx) for idx, p in enumerate(pitches))
pitch2notation = instruments.combine_p2n(*pitch2notation)
notation_styles = tuple(
notation.MelodicLineStyle("Large", label, False, True, False)
for label in ("+", "-"))
vertical_line_metrical = notation.VerticalLine(1.6, "", 1)
vertical_line_compound = notation.VerticalLine(0.9, "", 1)
vertical_line_unit = notation.VerticalLine(0.15, "", 1)
vertical_line_style = notation.VerticalLineStyle(vertical_line_metrical,
vertical_line_compound,
vertical_line_unit)
re = mk_re()
render_engines = (re, re)
return instruments.Instrument(
"Siter_gong",
pitch2notation,
notation_styles,
render_engines,
vertical_line_style,
)
def __call__(self, mode):
"""Return real pitch depending on the corresponding mode.
This pitch is in no specific octave yet.
"""
primes = self.__key(mode)
if not mode.gender:
primes = tuple(reversed(primes))
return ji.r(primes[0], primes[1])
def mk_loop_cadence(loopsize: int, meter: metre.Metre) -> old.JICadence:
size = meter.size
loop_amount = size // loopsize
rest = size % loopsize
harmony = ji.JIHarmony([ji.r(3, 2)])
cadence = [old.Chord(harmony, loopsize) for i in range(loop_amount)]
if rest:
cadence.append(old.Chord(harmony, rest))
return old.JICadence(cadence)
def find_high_pitch(primes0: tuple, primes1: tuple,
mode: modes.Mode) -> ji.JIPitch:
if primes0[0] == primes1[0]:
if mode.gender:
p = ji.r(primes0[0], primes0[1] * primes1[1])
else:
p = ji.r(primes0[0], 1)
elif primes0[1] == primes1[1]:
if mode.gender:
p = ji.r(1, primes0[1])
else:
p = ji.r(primes0[0] * primes1[0], primes0[1])
else:
msg = "Unknown combination of primes: {0} and {1}.".format(
primes0, primes1)
raise ValueError(msg)
return p.normalize() + ji.r(4, 1)
def _make_transcription(
self,
starting_scale_degree: int,
starting_intonation: int,
cent_distances: float,
) -> tuple:
pitches = [(starting_scale_degree, starting_intonation, 0)]
fitness = 0
for distance in cent_distances:
last_scale_degree, last_intonation, last_octave = pitches[-1]
adapted_distance = (
distance +
self._deviation_from_ideal_scale_degree_per_intonation[
last_scale_degree][last_intonation])
closest_item = tools.find_closest_item(
adapted_distance,
self._distance_to_other_scale_degrees_per_scale_degree[
last_scale_degree],
key=operator.itemgetter(1),
)
new_scale_degree = closest_item[0][0]
new_octave = last_octave + closest_item[0][1]
last_pitch = self._intonations_per_scale_degree[last_scale_degree][
last_intonation]
last_pitch += ji.r(1, 1).register(last_octave)
octavater = ji.r(1, 1).register(new_octave)
possible_intonations = tuple(
intonation + octavater for intonation in
self._intonations_per_scale_degree[new_scale_degree])
last_pitch_cents = last_pitch.cents
distance_per_intonation = tuple(into.cents - last_pitch_cents
for into in possible_intonations)
new_intonation = tools.find_closest_index(distance,
distance_per_intonation)
fitness += distance_per_intonation[new_intonation]
pitches.append((new_scale_degree, new_intonation, new_octave))
return tuple(pitches), fitness
def apply(self, melody) -> tuple:
length_melody = len(melody)
distribution = []
while len(distribution) < length_melody:
try:
distr = next(self.pattern)
except StopIteration:
self.mk_pattern()
distr = next(self.pattern)
distribution.append(distr)
melodies = [[type(melody[0])(None, 0, melody[0].duration)]
for i in range(self.amount_voices)]
pitchchange_cylce = itertools.cycle([0, 0, 0, 1, 0, 1, 0, 2, 0, 0])
d = melody[0].delay
first = True
for tone, distr in zip(melody, distribution):
hits = 1
for i, dis in enumerate(distr):
if dis == 1:
next_pitchcycle = next(pitchchange_cylce)
if next_pitchcycle == 0:
newpitch = tone.pitch.set_val_border(1)
newpitch += ji.r(hits, 1)
elif next_pitchcycle == 1:
newpitch = tone.pitch.copy()
else:
newpitch = tone.pitch.set_val_border(2).scalar(hits)
newpitch = newpitch.set_val_border(1).normalize(2)
newpitch = newpitch + ji.r(2, 1)
if first is True:
melodies[i][0].pitch = newpitch
first = False
else:
melodies[i].append(
type(tone)(newpitch, d, tone.duration))
hits += 1
d += tone.delay
for m in melodies:
for p0, p1 in zip(m, m[1:]):
p0.delay = p1.delay - p0.delay
m[-1].delay = m[-1].duration
return tuple(old.JIMelody(mel) for mel in melodies)
def mk_pitch_depending_on_gender(self,
primes0: tuple = None,
primes1: tuple = None,
gender: bool = None):
if gender is None:
gender = self.harmonic_gender
if primes0:
p0 = functools.reduce(operator.mul, primes0)
else:
p0 = 1
if primes1:
p1 = functools.reduce(operator.mul, primes1)
else:
p1 = 1
if gender:
return ji.r(p0, p1)
else:
return ji.r(p1, p0)
def make(name: str = "TWO", gender=False, group=0, sub_group0=1):
return (segments.Chord(
"{}_Bell0".format(name),
ambitus_maker=ambitus.SymmetricalRanges(ji.r(4, 1), ji.r(2, 1),
ji.r(5, 4)),
group=(group, sub_group0, 0),
chord=harmony.find_harmony(name="A", idx=0, gender=gender),
gender=gender,
n_bars=1,
duration_per_bar=10,
start=0,
dynamic_range_of_voices=(0.95, 1),
voices_entry_delay_per_voice=(0, 0.15, 0.225),
anticipation_time=5,
overlaying_time=0,
pteq_engine_per_voice=(
pteq.mk_trippy_bell_pte(
fxp='"pbIII/fxp/Bells_no_stretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
pteq.mk_trippy_bell_pte(
fxp='"pbIII/fxp/Bells_no_stretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
pteq.mk_trippy_bell_pte(
fxp='"pbIII/fxp/Bells_no_stretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
),
speech_init_attributes={},
include_glitter=False,
include_diva=False,
include_natural_radio=False,
include_percussion=False,
), )
class ToneSetTest(unittest.TestCase):
p0 = ji.r(5, 4)
p1 = ji.r(3, 2)
p2 = ji.r(1, 1)
p3 = ji.r(6, 5)
p4 = ji.r(7, 4)
p5 = ji.r(9, 8)
t0 = old.Tone(p0, rhy.Unit(1))
t1 = old.Tone(p1, rhy.Unit(1))
t2 = old.Tone(p2, rhy.Unit(1))
t3 = old.Tone(p3, rhy.Unit(1))
t3 = old.Tone(p3, rhy.Unit(1))
t4 = old.Tone(p4, rhy.Unit(1))
t5 = old.Tone(p5, rhy.Unit(1))
t0_set = old.Tone(p0, rhy.Unit(0), rhy.Unit(1))
t1_set = old.Tone(p1, rhy.Unit(1), rhy.Unit(1))
t2_set = old.Tone(p2, rhy.Unit(2), rhy.Unit(1))
t3_set = old.Tone(p3, rhy.Unit(3), rhy.Unit(1))
t4_set = old.Tone(p4, rhy.Unit(4), rhy.Unit(1))
t5_set = old.Tone(p5, rhy.Unit(5), rhy.Unit(1))
t6_set = old.Tone(p5, rhy.Unit(1), rhy.Unit(5))
mel0 = old.Melody([t0, t1, t2, t3, t4, t5])
mel1 = old.Melody([old.Rest(rhy.Unit(1)), t1, t2, t3, t4, t5])
mel2 = old.Melody([t0, t1])
set0 = old.ToneSet([t0_set, t1_set, t2_set, t3_set, t4_set, t5_set])
set1 = old.ToneSet([t1_set, t2_set, t3_set, t4_set, t5_set])
set2 = old.ToneSet([t1_set, t6_set, t2_set])
def test_constructor(self):
self.assertEqual(old.ToneSet.from_melody(ToneSetTest.mel0), ToneSetTest.set0)
def test_converter(self):
self.assertEqual(ToneSetTest.mel0, ToneSetTest.set0.convert2melody())
self.assertEqual(ToneSetTest.mel1, ToneSetTest.set1.convert2melody())
def test_pop_by(self):
popped = ToneSetTest.set0.copy().pop_by_pitch(ToneSetTest.p0, ToneSetTest.p1)
self.assertEqual(ToneSetTest.mel2, popped.convert2melody())
popped = ToneSetTest.set0.copy().pop_by_start(rhy.Unit(0), rhy.Unit(1))
self.assertEqual(ToneSetTest.mel2, popped.convert2melody())
def test_pop_by_time(self):
for t in self.set0.pop_by_time(1):
self.assertEqual(t, self.t1_set)
for t in self.set0.pop_by_time(1.5):
self.assertEqual(t, self.t1_set)
test_set0 = self.set2.pop_by_time(1.5)
test_set_compare0 = old.ToneSet([self.t1_set, self.t6_set])
test_set1 = self.set2.pop_by_time(2.7)
test_set_compare1 = old.ToneSet([self.t2_set, self.t6_set])
self.assertEqual(test_set0, test_set_compare0)
self.assertEqual(test_set1, test_set_compare1)
def test_pop_by_correct_dur_and_delay(self):
poped_by = self.set0.pop_by_pitch(self.p0, self.p5)
melody = poped_by.convert2melody()
self.assertEqual(melody[0].delay, rhy.Unit(5))
self.assertEqual(melody[0].duration, rhy.Unit(1))
def find_common_harmonics(p0: ji.JIPitch,
p1: ji.JIPitch,
gender: bool = True,
border: bool = MAX_HARMONIC) -> tuple:
"""Find all common (sub-)harmonics between two pitches.
If gender is True the function return common harmonics.
If gender is False the function return common subharmonics.
border declares the highest partial that shall be inspected.
Return tuple containing CommonHarmonic objects.
"""
if not p0.is_empty and not p1.is_empty:
harmonics = tuple(ji.r(b + 1, 1) for b in range(border))
if not gender:
harmonics = tuple(p.inverse() for p in harmonics)
harmonics_per_pitch = tuple(
tuple(p + h for h in harmonics) for p in (p0, p1))
authentic_harmonics = list(
(h, idx0, harmonics_per_pitch[1].index(h), True)
for idx0, h in enumerate(harmonics_per_pitch[0])
if h in harmonics_per_pitch[1])
normalized_authentic_harmonics = tuple(h[0].normalize()
for h in authentic_harmonics)
normalized_harmonics_per_pitch = tuple(
tuple(p.normalize() for p in har) for har in harmonics_per_pitch)
octaves_per_harmonic = tuple(
tuple(p.octave for p in har) for har in harmonics_per_pitch)
unauthentic_harmonics = []
for har0_idx, har0 in enumerate(normalized_harmonics_per_pitch[0]):
if har0 not in normalized_authentic_harmonics:
if har0 in normalized_harmonics_per_pitch[1]:
har1_idx = normalized_harmonics_per_pitch[1].index(har0)
oc = tuple(octaves[idx]
for octaves, idx in zip(octaves_per_harmonic, (
har0_idx, har1_idx)))
unauthentic_harmonics.append((har0, ) + oc + (False, ))
return tuple(
CommonHarmonic(h[0], (h[1], h[2]), gender, h[3])
for h in authentic_harmonics + unauthentic_harmonics)
else:
return tuple([])
def mk_pitches(inverse=False):
pitches0 = tuple(ji.r(p**2, 1) for p in (3, 5, 7))
pitches0 += (ji.r(3**3, 1), )
pitches1 = tuple(ji.r(p, 1) for p in (9, 19, 5, 11, 3, 13, 7))
pitches2 = tuple(ji.r(1, 1) for p in (1, ))
octaves = (ji.r(1, 2), ji.r(1, 1), ji.r(2, 1))
pitches = (pitches0, pitches1, pitches2)
if inverse:
pitches = tuple(tuple(p.inverse() for p in pi) for pi in pitches)
return functools.reduce(
operator.add,
tuple(
tuple(p.normalize(2) + o for p in pi)
for pi, o in zip(pitches, octaves)),
)
def mk_mdc_gong_and_tong(cadence, timeflow) -> tuple:
cadence_gong, cadence_tong = [], []
for chord in cadence:
hg, ht = [], []
for p in chord.pitch:
if p < ji.r(1, 2):
hg.append(p)
else:
ht.append(p)
cadence_gong.append(old.Chord(ji.JIHarmony(hg), chord.delay))
cadence_tong.append(old.Chord(ji.JIHarmony(ht), chord.delay))
cadences = tuple(
old.JICadence(c).discard_rests()
for c in (cadence_gong, cadence_tong))
return tuple(
MDC.mk_mdc_by_cadence(c, timeflow,
Score.TIME_LV_PER_INSTRUMENT[0], False)
for c in cadences)
def _adjust_scale(instrument: int, scale: tuple) -> tuple:
if instrument == "violin":
octave = 0
elif instrument == "viola":
octave = 0
elif instrument == "cello":
octave = -1
else:
raise NotImplementedError(instrument)
adapted_scale = []
for idx, pitch in enumerate(scale):
registered_pitch = pitch.register(octave)
if not idx and instrument == "viola":
registered_pitch -= ji.r(2, 1)
adapted_scale.append(registered_pitch)
return tuple(adapted_scale)
def find_normalize_dependent_combinations(
self,
primes,
maxlevel,
gender,
compound,
compound_asymmetrical,
three_compound,
allow_stacking,
normalize,
additional_intervals,
):
# find all intervals:
intervals = ji.JIHarmony(
Tree.find_intervals(
primes,
maxlevel,
gender,
compound,
compound_asymmetrical,
three_compound,
))
for additional in additional_intervals:
intervals.add(additional)
intervals = [
i.set_val_border(1).normalize(normalize) for i in intervals
]
self.intervals = intervals
comb_intervals = [
inter for inter in intervals if inter != ji.r(1, 1, val_border=2)
]
if allow_stacking is True:
combinations = tuple(
itertools.combinations_with_replacement(comb_intervals, 2))
else:
combinations = tuple(itertools.combinations(comb_intervals, 2))
valid_combinations = []
for interval in intervals:
valid = []
for comb in combinations:
if (comb[0] + comb[1]).normalize(normalize) == interval:
valid.append(comb)
valid_combinations.append(valid)
return valid_combinations
def from_complex_scale(
cls,
svl_path: str,
sf_path: str,
complex_scale_transcriber: ComplexScaleTranscriber,
time_transcriber: TimeTranscriber,
octave_of_first_pitch: int = 0,
ratio2pitchclass_dict: dict = None,
) -> "Transcription":
root = cls._get_root(svl_path)
frequency_range = root[0][0].attrib["minimum"], root[0][0].attrib["maximum"]
data = cls._filter_data_from_root(root)
frequencies = tuple(map(operator.itemgetter(0), data))
octavater = ji.r(1, 1).register(octave_of_first_pitch)
pitches = tuple(
octavater + pitch for pitch in complex_scale_transcriber(frequencies)
)
new_data = tuple((pitch,) + tone[1:] for pitch, tone in zip(pitches, data))
melody, metre, spread_metrical_loop, tempo = time_transcriber(sf_path, new_data)
if isinstance(metre, abjad.TimeSignature):
bars = tuple(
metre
for i in range(
int(
math.ceil(
melody.duration
/ fractions.Fraction(metre.numerator, metre.denominator)
)
)
)
)
else:
bars = metre
return cls(
melody,
bars,
frequency_range,
ratio2pitchclass_dict,
float(tempo),
spread_metrical_loop,
)
def __call__(self, frequencies: tuple) -> tuple:
cent_distances = tuple(
mel.SimplePitch.hz2ct(f0, f1)
for f0, f1 in zip(frequencies, frequencies[1:]))
starting_scale_degree = self._detect_starting_scale_degree(
cent_distances)
transcription_and_fitness_pairs = []
for n, intonation in enumerate(
self._intonations_per_scale_degree[starting_scale_degree]):
transcription_and_fitness_pairs.append(
self._make_transcription(starting_scale_degree, n,
cent_distances))
best = min(transcription_and_fitness_pairs,
key=operator.itemgetter(1))[0]
# convert abstract data to actual pitch objects
return tuple(self._intonations_per_scale_degree[data[0]][data[1]] +
ji.r(1, 1).register(data[2]) for data in best)
def _adapt_left_hand(left: lily.NOventLine, vm) -> None:
tw.swap_duration(9, 10, F(1, 4), left)
tw.swap_duration(20, 21, F(1, 4), left)
tw.swap_duration(30, 31, F(1, 4), left)
tw.swap_duration(40, 41, F(1, 4), left)
for n in (6, 8, 10, 16, 18, 21, 27, 29, 31, 37, 39, 41):
left[n].pitch = [left[n].pitch[0].register(keyboard.SYMBOLIC_GONG_OCTAVE)]
left[n].pedal = attachments.Pedal(False)
left[n].volume = 0.43
left[n].ottava = attachments.Ottava(-1)
tw.split(41, left, F(1, 4), F(1, 4))
tw.split(31, left, F(1, 4), F(1, 4))
tw.split(21, left, F(1, 4), F(1, 4))
tw.split(10, left, F(1, 4), F(1, 4))
tw.swap_duration(20, 19, F(1, 16), left)
tw.crop(3, left, F(1, 16))
left[3].pitch = [ji.r(7, 8)]
left[10].pitch = [
ji.r(35, 24).register(keyboard.SYMBOLIC_GONG_OCTAVE),
ji.r(35, 128),
]
left[10].ottava = attachments.Ottava(-1)
left[10].arpeggio = None
tw.add_kenong(12, left, ji.r(14, 9))
left[13].pitch = [
ji.r(64, 63).register(keyboard.SYMBOLIC_GONG_OCTAVE),
ji.r(64, 63),
ji.r(128, 189),
]
tw.crop(15, left, F(1, 16))
left[16].pitch = [ji.r(7, 8)]
def find_best_fret_for_pitch(self, pitch):
octave = 0
comp = ji.r(2, 1)
while pitch.float >= comp.float:
pitch -= comp
octave += 1
factor = pitch.float
closest = bisect.bisect_right(self.division_floats, factor)
possible_solutions = []
for c in [closest - 1, closest, closest + 1]:
try:
possible_solutions.append(self.division_floats[c])
except IndexError:
pass
hof = crosstrainer.MultiDimensionalRating(size=1, fitness=[-1])
for pos in possible_solutions:
if pos > factor:
diff = pos / factor
else:
diff = factor / pos
hof.append(pos, diff)
return MonochordFret(self.division_floats.index(hof._items[0]), octave)
def add_acciaccatura(
nth_event: int,
pitch: ji.JIPitch,
novent_line: lily.NOventLine,
add_glissando: bool = False,
use_artifical_harmonic: bool = False,
) -> None:
if use_artifical_harmonic:
abjad_note = abjad.Chord(
_get_artifical_harmonic_pitches(pitch),
abjad.Duration(1, 8),
)
abjad.tweak(abjad_note.note_heads[1]).style = "harmonic"
pitch = pitch + ji.r(4, 1)
else:
abjad_note = abjad.Note(
lily.convert2abjad_pitch(pitch, globals_.RATIO2PITCHCLASS),
abjad.Duration(1, 8),
)
novent_line[nth_event].acciaccatura = attachments.Acciaccatura(
[pitch], abjad_note, add_glissando)
from mu.mel import ji
CONCERT_PITCH = 260
PRIMES = [2, 3, 5, 7, 9, 11]
PITCHES_POSITIVE = []
PITCHES_NEGATIVE = []
for prime in PRIMES:
p0 = ji.r(prime, 1).normalize(2)
p1 = ji.r(1, prime).normalize(2)
if prime == 2:
p0 += ji.r(2, 1)
p1 += ji.r(2, 1)
p2 = p1 + ji.r(4, 1)
p3 = p0 + ji.r(4, 1)
PITCHES_POSITIVE.append(p0)
PITCHES_POSITIVE.append(p2)
PITCHES_NEGATIVE.append(p1)
PITCHES_NEGATIVE.append(p3)
if __name__ == "__main__":
import pyteqNew as pyteq
for idx, pitches in enumerate([PITCHES_POSITIVE, PITCHES_NEGATIVE]):
if idx < 1:
name = "Positive"
else:
name = "Negative"
for p_idx, pitch in enumerate(sorted(pitches)):
local_name = name + str(p_idx)
def make(name: str = "ONE", gender=False, group=0, sub_group0=1):
return (
segments.Silence(name="{}_start_silence".format(name), duration=18),
segments.Chord(
"{}_Speech0".format(name),
ambitus_maker=ambitus.SymmetricalRanges(ji.r(7, 1), ji.r(2, 1), ji.r(4, 3)),
group=(group, sub_group0, 0),
chord=harmony.find_harmony(name="A", gender=gender),
gender=gender,
n_bars=1,
duration_per_bar=34,
start=0,
dynamic_range_of_voices=(0.8, 1),
anticipation_time=2.25,
overlaying_time=3.25,
speech_init_attributes={
"speech0": {
"start": 0,
"duration": 35,
"sound_engine": speech.BrokenRadio(
(globals.SAM_SPEECH_SLICED_DERRIDA_KAFKA.path,),
duration=32,
volume=0.42,
activity_lv_per_effect={
"original": 8,
"harmonizer": 6,
"filter": 10,
"noise": 10,
"lorenz": 10,
"chenlee": 6,
},
level_per_effect={
"original": infit.Gaussian(0.4, 0.05),
"filter": infit.Gaussian(10.2, 2.25),
"harmonizer": infit.Gaussian(0.7, 0.15),
"chenlee": infit.Gaussian(0.1, 0.02),
"lorenz": infit.Gaussian(0.4, 0.02),
"noise": infit.Gaussian(0.32, 0.04),
},
transpo_maker=infit.Uniform(0.14, 0.8),
filter_freq_maker=infit.Gaussian(110, 50),
filter_q_maker=infit.Gaussian(5, 1),
curve=interpolations.InterpolationLine(
[
interpolations.FloatInterpolationEvent(0.25, 0.01),
interpolations.FloatInterpolationEvent(0.8, 1),
interpolations.FloatInterpolationEvent(0.3, 1),
interpolations.FloatInterpolationEvent(0, 0.1),
]
),
),
},
"speech1": {
"start": 0.1,
"duration": 35,
"sound_engine": speech.BrokenRadio(
(globals.SAM_SPEECH_SLICED_DERRIDA_KAFKA.path,),
duration=32,
volume=0.3,
activity_lv_per_effect={
"original": 6,
"harmonizer": 4,
"filter": 9,
"noise": 9,
"lorenz": 8,
"chenlee": 7,
},
level_per_effect={
"original": infit.Gaussian(0.4, 0.05),
"filter": infit.Gaussian(10.2, 2.25),
"harmonizer": infit.Gaussian(0.7, 0.15),
"chenlee": infit.Gaussian(0.1, 0.02),
"lorenz": infit.Gaussian(0.4, 0.02),
"noise": infit.Gaussian(0.32, 0.04),
},
transpo_maker=infit.Uniform(1.14, 1.5),
filter_freq_maker=infit.Gaussian(200, 50),
filter_q_maker=infit.Gaussian(5, 1),
curve=interpolations.InterpolationLine(
[
interpolations.FloatInterpolationEvent(0.3, 0.01),
interpolations.FloatInterpolationEvent(0.8, 1),
interpolations.FloatInterpolationEvent(0.3, 1),
interpolations.FloatInterpolationEvent(0, 0.1),
]
),
),
},
"speech2": {
"start": 0.075,
"duration": 35,
"sound_engine": speech.BrokenRadio(
(globals.SAM_SPEECH_SLICED_DERRIDA_KAFKA.path,),
duration=32,
volume=0.34,
activity_lv_per_effect={
"original": 6,
"harmonizer": 4,
"filter": 9,
"noise": 9,
"lorenz": 8,
"chenlee": 7,
},
level_per_effect={
"original": infit.Gaussian(0.4, 0.05),
"filter": infit.Gaussian(10.2, 2.25),
"harmonizer": infit.Gaussian(0.7, 0.15),
"chenlee": infit.Gaussian(0.1, 0.02),
"lorenz": infit.Gaussian(0.4, 0.02),
"noise": infit.Gaussian(0.32, 0.04),
},
transpo_maker=infit.Uniform(1.14, 1.5),
filter_freq_maker=infit.Gaussian(200, 50),
filter_q_maker=infit.Gaussian(5, 1),
curve=interpolations.InterpolationLine(
[
interpolations.FloatInterpolationEvent(0.4, 0.01),
interpolations.FloatInterpolationEvent(0.8, 1),
interpolations.FloatInterpolationEvent(0.3, 1),
interpolations.FloatInterpolationEvent(0, 0.1),
]
),
),
},
},
include_glitter=False,
include_diva=False,
include_natural_radio=False,
include_percussion=False,
include_voices=False,
),
)
def get_from_func_pitch(func) -> ji.JIPitch:
p = func(mode)
if p.set_val_border(2).primes == (3,): # 3 * 9 doesn't exist in current tuning
return p.normalize(2) - ji.r(2, 1)
else:
return mel.TheEmptyPitch
def make(name: str = "TWO", gender=False, group=0, sub_group0=1):
return (
segments.FreeStyleCP(
"{}_0".format(name),
ambitus_maker=ambitus.SymmetricalRanges(ji.r(5, 1), ji.r(9, 4),
ji.r(16, 11)),
decision_type="activity",
energy_per_voice=(9, 7, 7),
weight_range=(4, 10),
metrical_numbers=(12, 9, 12),
silence_decider_per_voice=(
infit.ActivityLevel(2),
infit.ActivityLevel(1),
infit.ActivityLevel(2),
),
group=(group, sub_group0, 0),
start_harmony=harmony.find_harmony("A",
True,
0,
tuple([]),
gender=gender),
gender=gender,
n_bars=2,
duration_per_bar=13,
start=0,
dynamic_range_of_voices=(0.2, 0.5),
anticipation_time=0.01,
overlaying_time=0.25,
cp_add_dissonant_pitches_to_nth_voice=(True, True, True),
glitter_include_dissonant_pitches=True,
pteq_engine_per_voice=(
pteq.mk_super_soft_trippy_pte(
empty_attack_dynamic_maker=infit.Value(0.2),
fxp='"pbIII/fxp/VibraphoneV-BHumanizednostretching.fxp"',
preset=None,
sustain_pedal=0,
),
pteq.mk_dreamy_pte(
# modulator=(ornamentations.SoftLineGlissandoMaker(),),
convert_dissonant_tones2glissandi=True,
empty_attack_dynamic_maker=infit.Value(0.2),
),
pteq.mk_dreamy_pte(
empty_attack_dynamic_maker=infit.Value(0.2),
convert_dissonant_tones2glissandi=True,
),
),
speech_init_attributes={},
percussion_engine_per_voice=(
percussion.Rhythmizer(
voice_meters2occupy=(0, ),
chord=infit.Cycle((
harmony.find_harmony(name="A", gender=gender),
harmony.find_harmony(name="C", gender=gender),
)),
sample_maker=infit.Cycle((
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_BIG_AGGRESSIVE),
pitch_factor=infit.Uniform(2, 6),
resonance_filter_bandwidth=infit.Uniform(0.4, 2),
resonance_filter_octave=infit.Cycle(
(2, 4, 3, 4, 1, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_MIDDLE_AGGRESSIVE),
pitch_factor=infit.Uniform(2, 5),
resonance_filter_bandwidth=infit.Uniform(0.5, 2),
resonance_filter_octave=infit.Cycle(
(2, 3, 4, 1, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
)),
likelihood_range=(0.5, 0.1),
volume_range=(0.1, 0.5),
ignore_beats_occupied_by_voice=False,
),
percussion.Rhythmizer(
voice_meters2occupy=(1, ),
chord=infit.Cycle((
harmony.find_harmony(name="A", gender=gender),
harmony.find_harmony(name="C", gender=gender),
)),
sample_maker=infit.Cycle((
percussion.ResonanceSample(
path=infit.Cycle(globals.SAM_CYMBALS_BIG_CLOSE),
pitch_factor=infit.Gaussian(3, 5),
resonance_filter_bandwidth=infit.Gaussian(
0.8, 0.5),
resonance_filter_octave=infit.Cycle((2, )),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.3),
glissando_offset=0,
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_MIDDLE_CLOSE_LOUD),
pitch_factor=infit.Gaussian(3, 4),
resonance_filter_bandwidth=infit.Uniform(0.5, 1),
resonance_filter_octave=infit.Cycle((2, )),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=0,
glissando_size=infit.Gaussian(1, 0.1),
),
)),
likelihood_range=(0.6, 0.1),
volume_range=(0.1, 0.8),
ignore_beats_occupied_by_voice=False,
),
percussion.Rhythmizer(
voice_meters2occupy=(0, 1, 2),
chord=infit.Cycle((harmony.find_harmony(name="B",
gender=gender), )),
sample_maker=infit.Cycle((
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((2, 4, 8, 4)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_LOW_LOW_CLOSE_HAND),
frequency=globals.SAM_KENDANG_LOW_LOW_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((16, 8, 4, 8, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((8, 4, 2, 4)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_LOW_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_LOW_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((8, 16, 8, 4, 8, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
)),
likelihood_range=(0.1, 0.5),
volume_range=(0.1, 0.5),
ignore_beats_occupied_by_voice=False,
),
),
include_glitter=True,
include_diva=False,
include_natural_radio=True,
include_percussion=True,
voices_overlaying_time=5,
radio_samples=(
globals.SAM_RADIO_ITALY[-1],
globals.SAM_RADIO_BIELEFELD[-1],
),
radio_n_changes=8,
radio_average_volume=0.1,
radio_shadow_time=0.01,
radio_min_volume=0.825,
),
segments.Chord(
"{}_Bell2".format(name),
ambitus_maker=ambitus.SymmetricalRanges(ji.r(12, 1), ji.r(2, 1),
ji.r(15, 8)),
group=(group, sub_group0, 1),
chord=harmony.find_harmony(name="A", idx=0, gender=gender),
gender=gender,
n_bars=1,
duration_per_bar=8,
start=4,
dynamic_range_of_voices=(0.7, 0.9),
anticipation_time=0.2,
overlaying_time=1.25,
glitter_modulater_per_voice=("randomh", "randomh", "randomh"),
pteq_engine_per_voice=(
pteq.mk_bright_bell(
fxp='"pbIII/fxp/Bells_no_stretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
pteq.mk_bright_bell(
fxp='"pbIII/fxp/GlockenspielHumanizednostretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
pteq.mk_bright_bell(
fxp='"pbIII/fxp/GlockenspielHumanizednostretching.fxp"',
preset=None,
empty_attack_dynamic_maker=infit.Value(0),
),
),
# speech_init_attributes={
# # "speech0": {
# # "start": 0.3,
# # "duration": 12.5,
# # "sound_engine": speech.Sampler(
# # globals.SAM_RADIO_CAROLINA[1], volume=0.6
# # ),
# # },
# # "speech2": {
# # "start": -0.3,
# # "duration": 12.5,
# # "sound_engine": speech.Sampler(
# # globals.SAM_RADIO_ROEHRENRADIO_CLOSE_MITTELWELLE[-1], volume=0.6
# # ),
# # },
# },
include_glitter=False,
include_diva=False,
include_natural_radio=False,
include_percussion=False,
),
segments.FreeStyleCP(
"{}_1".format(name),
ambitus_maker=ambitus.SymmetricalRanges(ji.r(5, 1), ji.r(9, 4),
ji.r(16, 11)),
decision_type="activity",
energy_per_voice=(9, 6, 7),
weight_range=(4, 10),
metrical_numbers=(13, 9, 13),
silence_decider_per_voice=(
infit.ActivityLevel(2),
infit.ActivityLevel(1),
infit.ActivityLevel(2),
),
group=(group, sub_group0, 1),
start_harmony=harmony.find_harmony("A",
True,
0,
tuple([]),
gender=gender),
gender=gender,
n_bars=2,
duration_per_bar=15,
start=-3.8,
dynamic_range_of_voices=(0.2, 0.4),
anticipation_time=0.01,
overlaying_time=0.25,
cp_add_dissonant_pitches_to_nth_voice=(True, True, True),
glitter_include_dissonant_pitches=True,
pteq_engine_per_voice=(
pteq.mk_super_soft_trippy_pte(
empty_attack_dynamic_maker=infit.Value(0.2),
fxp='"pbIII/fxp/VibraphoneV-BHumanizednostretching.fxp"',
preset=None,
sustain_pedal=0,
),
pteq.mk_dreamy_pte(
# modulator=(ornamentations.SoftLineGlissandoMaker(),),
convert_dissonant_tones2glissandi=True,
empty_attack_dynamic_maker=infit.Value(0.2),
),
pteq.mk_dreamy_pte(
empty_attack_dynamic_maker=infit.Value(0.2),
convert_dissonant_tones2glissandi=True,
),
),
speech_init_attributes={},
percussion_engine_per_voice=(
percussion.Rhythmizer(
voice_meters2occupy=(0, ),
chord=infit.Cycle((
harmony.find_harmony(name="A", gender=gender),
harmony.find_harmony(name="C", gender=gender),
)),
sample_maker=infit.Cycle((
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_BIG_AGGRESSIVE),
pitch_factor=infit.Uniform(2, 6),
resonance_filter_bandwidth=infit.Uniform(0.4, 2),
resonance_filter_octave=infit.Cycle(
(2, 4, 3, 4, 1, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_MIDDLE_AGGRESSIVE),
pitch_factor=infit.Uniform(2, 5),
resonance_filter_bandwidth=infit.Uniform(0.5, 2),
resonance_filter_octave=infit.Cycle(
(2, 3, 4, 1, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
)),
likelihood_range=(0.5, 0.1),
volume_range=(0.1, 0.5),
ignore_beats_occupied_by_voice=False,
),
percussion.Rhythmizer(
voice_meters2occupy=(1, ),
chord=infit.Cycle((
harmony.find_harmony(name="A", gender=gender),
harmony.find_harmony(name="C", gender=gender),
)),
sample_maker=infit.Cycle((
percussion.ResonanceSample(
path=infit.Cycle(globals.SAM_CYMBALS_BIG_CLOSE),
pitch_factor=infit.Gaussian(3, 5),
resonance_filter_bandwidth=infit.Gaussian(
0.8, 0.5),
resonance_filter_octave=infit.Cycle((2, )),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.3),
glissando_offset=0,
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.ResonanceSample(
path=infit.Cycle(
globals.SAM_CYMBALS_MIDDLE_CLOSE_LOUD),
pitch_factor=infit.Gaussian(3, 4),
resonance_filter_bandwidth=infit.Uniform(0.5, 1),
resonance_filter_octave=infit.Cycle((2, )),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=0,
glissando_size=infit.Gaussian(1, 0.1),
),
)),
likelihood_range=(0.6, 0.1),
volume_range=(0.1, 0.8),
ignore_beats_occupied_by_voice=False,
),
percussion.Rhythmizer(
voice_meters2occupy=(0, 1, 2),
chord=infit.Cycle((harmony.find_harmony(name="B",
gender=gender), )),
sample_maker=infit.Cycle((
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((2, 4, 8, 4)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_LOW_LOW_CLOSE_HAND),
frequency=globals.SAM_KENDANG_LOW_LOW_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((16, 8, 4, 8, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_HIGH_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((8, 4, 2, 4)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
percussion.Sample(
path=infit.Cycle(
globals.SAM_KENDANG_HIGH_LOW_CLOSE_HAND),
frequency=globals.SAM_KENDANG_HIGH_LOW_CLOSE_HAND.
information["frequency"],
pitch_factor=infit.Cycle((8, 16, 8, 4, 8, 2)),
glissando_direction=True,
glissando_duration=infit.Uniform(0.001, 0.2),
glissando_offset=infit.Uniform(0, 0.1),
glissando_size=infit.Gaussian(1, 0.2),
),
)),
likelihood_range=(0.1, 0.5),
volume_range=(0.1, 0.5),
ignore_beats_occupied_by_voice=False,
),
),
include_glitter=True,
include_diva=False,
include_natural_radio=True,
include_percussion=True,
voices_overlaying_time=5,
radio_samples=(
globals.SAM_RADIO_ITALY[-1],
globals.SAM_RADIO_BIELEFELD[-1],
),
radio_n_changes=8,
radio_average_volume=0.1,
radio_shadow_time=0.01,
radio_min_volume=0.825,
),
)
def _help_tonality_flux(
scale_degree: int,
slice0: breads.Slice,
slice1: breads.Slice,
p0: ji.JIPitch,
p1: ji.JIPitch,
available_pitches_per_tone: tuple,
# harmonicity_border for intervals in parallel movement
harmonicity_border: float = ji.r(7, 6).harmonicity_simplified_barlow,
# minimal harmonic closeness for intervals in counter movement
min_closeness: float = 0.75,
maximum_octave_difference: tuple = (1, 1),
get_available_pitches_from_adapted_instrument=None,
) -> None:
movement_direction = slice0.melody_pitch < slice1.melody_pitch
# (1) find pitches for microtonal parallel or counter movement of voices
parallel_candidates = []
counter_candidates = []
for sd in range(7):
if sd != scale_degree:
avp0, avp1 = tuple(
tuple(p for p in avp
if globals_.PITCH2SCALE_DEGREE[p] == sd)
for avp in available_pitches_per_tone)
intervals0, intervals1 = tuple(
tuple((main_pitch - side_pitch).normalize()
for side_pitch in avp)
for main_pitch, avp in ((p0, avp0), (p1, avp1)))
for it0, it1 in itertools.product(intervals0, intervals1):
relevant_pitch0 = avp0[intervals0.index(it0)]
relevant_pitch1 = avp1[intervals1.index(it1)]
rp_movement_direction = relevant_pitch0 < relevant_pitch1
if it0 == it1 or it0 == it1.inverse().normalize():
harmonicity = it0.harmonicity_simplified_barlow
if harmonicity >= harmonicity_border:
parallel_candidates.append(
((relevant_pitch0, relevant_pitch1),
harmonicity))
if movement_direction != rp_movement_direction:
closeness0 = globals_.CLOSENESS_FROM_PX_TO_PY[p0][
relevant_pitch0]
closeness1 = globals_.CLOSENESS_FROM_PX_TO_PY[p1][
relevant_pitch1]
tests = (closeness0 > min_closeness,
closeness1 > min_closeness)
if all(tests):
counter_candidates.append((
(relevant_pitch0, relevant_pitch1),
closeness0 + closeness1,
))
if parallel_candidates:
hp0, hp1 = max(parallel_candidates, key=operator.itemgetter(1))[0]
elif counter_candidates:
hp0, hp1 = max(counter_candidates, key=operator.itemgetter(1))[0]
else:
hp0, hp1 = (max(
((hp, globals_.CLOSENESS_FROM_PX_TO_PY[mp][hp]) for hp in avp
if globals_.PITCH2SCALE_DEGREE[hp] != scale_degree),
key=operator.itemgetter(1),
)[0] for mp, avp in zip((p0, p1), available_pitches_per_tone))
registered_hp0, registered_hp1 = VerseMaker._register_tonality_flux_pitch(
slice0.melody_pitch,
slice1.melody_pitch,
hp0,
hp1,
get_available_pitches_from_adapted_instrument,
maximum_octave_difference,
)
slice0.harmonic_pitch = registered_hp0
slice1.harmonic_pitch = registered_hp1
