khodowany
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from __future__ import annotations
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import dataclasses
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import fractions
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import functools
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import heapq
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import generator.fretboard
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import generator.model
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import generator.rhythm
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import generator.scales.selection
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import generator.solo.placement
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import generator.solo.planning
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import generator.solo.types
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BEAM_WIDTH = 64
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CELL_LIMIT = 32
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class SearchExhausted(ValueError):
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pass
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@dataclasses.dataclass(frozen=True)
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class Cell:
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start: fractions.Fraction
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end: fractions.Fraction
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patterns: tuple[tuple[str, tuple[tuple[fractions.Fraction, fractions.Fraction], ...]], ...]
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motif: generator.solo.types.Motif | None = None
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@dataclasses.dataclass(frozen=True)
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class Candidate:
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notes: tuple[generator.solo.types.Note, ...]
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source: str
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treatment: str
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rank: tuple
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def rhythm_patterns(library: generator.rhythm.Library, meter: str, length: fractions.Fraction, activity: fractions.Fraction):
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result = {}
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for snippet in library.for_meter(meter):
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for index, bar in enumerate(snippet.bars):
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spans = []
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for event in bar.events:
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if not event.sounding:
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continue
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if event.continuation:
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if spans and spans[-1][1] == event.onset:
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spans[-1] = (spans[-1][0], event.onset + event.duration)
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else:
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# A continuation without its attack is not an independent cell.
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spans = []
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break
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else:
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spans.append((event.onset, event.onset + event.duration))
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pattern = tuple(spans)
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if all(end <= length for _, end in pattern) and len(pattern) <= 4:
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result.setdefault(pattern, f'rhythm {snippet.id}:{index + 1}')
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ranked = sorted(result.items(), key=lambda item: (abs(len(item[0]) - length * activity), -sum(end - start for start, end in item[0]), item[1]))
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return tuple((identifier, pattern) for pattern, identifier in ranked[:8])
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def cells(plan, measures, rhythms):
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result, start, index = [], fractions.Fraction(), 0
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boundaries = [fractions.Fraction()]
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for measure in measures:
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boundaries.append(boundaries[-1] + generator.model.fraction(measure['duration']))
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start = plan.start
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music_end = plan.end - plan.release
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arrival_fits = not plan.target or plan.motif.duration < music_end - start or any(plan.target.earliest <= start + n.start <= plan.target.latest for n in plan.motif.notes)
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if plan.motif.duration <= music_end - start and arrival_fits:
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motif = plan.motif
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spans = tuple((n.start, n.end) for n in motif.notes)
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patterns = [(motif.id, spans)]
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if plan.relationship == 'variation' and spans[-1][1] + fractions.Fraction(1, 2) <= motif.duration:
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patterns.insert(0, (motif.id + ':displaced', tuple((a + fractions.Fraction(1, 2), b + fractions.Fraction(1, 2)) for a, b in spans)))
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result.append(Cell(start, start + motif.duration, tuple(patterns), motif))
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start += motif.duration
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elif plan.motif.notes and plan.motif.duration > music_end - start:
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# Fragmentation is an explicit transformation, never a truncated quotation.
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available = (plan.target.earliest if plan.target else music_end) - start
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notes = tuple(n for n in plan.motif.notes if n.end <= available)
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if notes:
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motif = dataclasses.replace(plan.motif, notes=notes, duration=notes[-1].end)
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result.append(Cell(start, start + motif.duration, ((motif.id + ':fragment', tuple((n.start, n.end) for n in notes)),), motif))
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start += motif.duration
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while start < music_end:
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index = next(i for i in range(len(measures)) if boundaries[i] <= start < boundaries[i + 1])
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end = min(music_end, boundaries[index + 1])
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patterns = rhythm_patterns(rhythms, measures[index]['time_signature'], end - start, plan.activity)
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# These primitives are deliberate alternatives, independent of catalog counts.
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patterns += (('hold', ((fractions.Fraction(), end - start),)), ('space', ()))
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if plan.target and start <= plan.target.latest < end:
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onset = max(start, plan.target.earliest) - start
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patterns = (('arrival_and_release', ((onset, min(end - start, onset + 1)),)), *patterns)
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result.append(Cell(start, end, patterns))
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start = end
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result.append(Cell(music_end, plan.end, (('planned_release', ()),)))
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return tuple(result)
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@functools.lru_cache(maxsize=2048)
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def scale_options(scale, allowed, low, high):
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return tuple(generator.solo.types.scale_pitch(scale, index) for index in range(-21, 28) if low <= generator.solo.types.scale_pitch(scale, index).number() <= high and generator.solo.types.scale_pitch(scale, index).number() % 12 in allowed and generator.fretboard.positions_for_pitch(fractions.Fraction(generator.solo.types.scale_pitch(scale, index).number())))
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def valid_notes(notes, choices):
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return all(n.pitch.number() % 12 in generator.solo.types.permitted(choices, n.start, n.end) for n in notes)
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def options(choices, start, end, register):
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scale = next((c.scale for c in choices if c.span.start <= start < c.span.end), None)
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allowed = generator.solo.types.permitted(choices, start, end)
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return scale_options(scale, allowed, max(41, register[0] - 7), min(84, register[1] + 5)) if scale and allowed else ()
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def target_ok(notes, target):
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return target is None or any(target.earliest <= n.start <= target.latest and n.pitch.number() % 12 in target.pitches for n in notes)
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def thematic_paths(cell, spans, choices, plan):
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states = [(0, ())]
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for index, (start, end) in enumerate(spans):
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following = []
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for cost, path in states:
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available = options(choices, start, end, plan.register)
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changing_ending = plan.relationship == 'answer' and index == len(spans) - 1
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if changing_ending:
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chord = next((c.span.chord for c in choices if c.span.start <= start < c.span.end), None)
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if chord:
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available = tuple(p for p in available if p.number() % 12 in generator.solo.planning.target_pitches(chord, False))
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for pitch in available:
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reference = cell.motif.notes[index]
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expected = cell.motif.contour()[index - 1] if index else 0
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motion = pitch.number() - path[-1].pitch.number() if path else 0
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# Repeated notes are identity, not missing sequence transitions.
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mismatch = int(bool(index) and ((expected == 0) != (motion == 0) or expected * motion < 0))
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note = generator.solo.types.Note(start, end, pitch, reference.sources, 'motif')
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following.append((cost + (0 if changing_ending else 8 * mismatch + abs(motion - expected)), (*path, note)))
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states = sorted(following, key=lambda item: (item[0], tuple(n.pitch for n in item[1])))[:BEAM_WIDTH]
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if not states:
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break
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return states
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def adapt_path(pitches, spans, choices, register):
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"""Connect actual pitches across collections without carrying a stale degree index."""
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states = [(0, ())]
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for index, ((start, end), intended) in enumerate(zip(spans, pitches)):
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available = options(choices, start, end, register)
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following = []
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for cost, path in states:
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desired = path[-1].pitch.number() + intended.number() - pitches[index - 1].number() if path else intended.number()
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for pitch in sorted(available, key=lambda p: (abs(p.number() - desired), p))[:2]:
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following.append((cost + abs(pitch.number() - desired), (*path, generator.solo.types.Note(start, end, pitch, role='adapted_path'))))
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states = sorted(following, key=lambda item: (item[0], tuple(n.pitch for n in item[1])))[:8]
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return [notes for _, notes in states]
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def sequence_paths(spans, choices, plan, sequences):
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if not spans:
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return [((), 'space')]
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first_scale = next((c.scale for c in choices if c.span.start <= spans[0][0] < c.span.end), None)
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if first_scale is None:
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return []
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paths = []
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allowed = tuple(generator.solo.types.permitted(choices, start, end) for start, end in spans)
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changing = any(classes != allowed[0] for classes in allowed)
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sequence_options = sequences.connecting(len(spans))[:16]
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offsets = [(s.offsets(), 'sequence ' + s.id) for s in sequence_options]
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# Single notes and explicit repetitions do not pretend to be catalog paths.
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offsets.append(((0,) * len(spans), 'single' if len(spans) == 1 else 'repeated_note'))
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for steps, identifier in offsets:
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for target_index in range(-14, 21):
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start_index = target_index - steps[-1]
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pitches = tuple(generator.solo.types.scale_pitch(first_scale, start_index + step) for step in steps)
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if not all(max(41, plan.register[0] - 7) <= p.number() <= min(84, plan.register[1] + 5) for p in pitches):
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continue
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if all(pitch.number() % 12 in classes for pitch, classes in zip(pitches, allowed)):
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notes = tuple(generator.solo.types.Note(start, end, pitch, role='path') for (start, end), pitch in zip(spans, pitches))
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paths.append((notes, identifier))
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elif changing and not paths and steps == offsets[0][0]:
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paths.extend((notes, 'harmonic_adaptation of ' + identifier) for notes in adapt_path(pitches, spans, choices, plan.register))
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return paths
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def candidates(cell, plan, choices, sequences):
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result = []
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target = plan.target if plan.target and cell.start <= plan.target.latest < cell.end else None
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for identifier, pattern in cell.patterns:
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spans = tuple((cell.start + start, cell.start + end) for start, end in pattern)
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active = tuple(i for i, (start, end) in enumerate(spans) if not any(c.scale is None and c.span.start < end and c.span.end > start for c in choices))
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local_cell = cell
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if len(active) != len(spans):
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spans = tuple(spans[i] for i in active)
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identifier += ':harmonic_fragment'
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if cell.motif:
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local_cell = dataclasses.replace(cell, motif=dataclasses.replace(cell.motif, notes=tuple(cell.motif.notes[i] for i in active)))
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paths = []
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if local_cell.motif and spans:
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if plan.treatment == 'quote' and ':fragment' not in identifier and ':harmonic_fragment' not in identifier and ':displaced' not in identifier:
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for octaves in (0, 1, -1, 2, -2):
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notes = tuple(generator.solo.types.Note(start, end, dataclasses.replace(source.pitch, octave=source.pitch.octave + octaves), source.sources, 'quote') for (start, end), source in zip(spans, local_cell.motif.notes))
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if valid_notes(notes, choices):
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paths.append((notes, f'quote octave {octaves:+}', 0))
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for mismatch, notes in thematic_paths(local_cell, spans, choices, plan):
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treatment = 'fragment' if 'fragment' in identifier else 'rhythmic_displacement' if ':displaced' in identifier else 'changed_ending' if plan.relationship == 'answer' else 'harmonic_adaptation'
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paths.append((notes, treatment, 1 + mismatch))
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else:
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paths = [(notes, name, 0) for notes, name in sequence_paths(spans, choices, plan, sequences)]
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for notes, treatment, identity in paths:
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if not target_ok(notes, target):
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continue
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notes = tuple(dataclasses.replace(n, role='target' if target and target.earliest <= n.start <= target.latest and n.pitch.number() % 12 in target.pitches else n.role) for n in notes)
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register = 16 * sum(max(0, plan.register[0] - n.pitch.number(), n.pitch.number() - plan.register[1]) for n in notes)
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if notes:
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register += abs(max(n.pitch.number() for n in notes) - sum(plan.register) // 2)
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density = abs(len(notes) - (cell.end - cell.start) * plan.activity)
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contour = sum(max(0, abs(b.pitch.number() - a.pitch.number()) - 5) for a, b in zip(notes, notes[1:]))
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if not cell.motif and len({n.pitch.number() for n in notes}) == 1:
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contour += max(0, len(notes) - 2)
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rank = (identity, register, density if not cell.motif else 0, contour)
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result.append(Candidate(notes, identifier, treatment, rank))
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# A no-harmony passage is intentional silence, not a failed pitch search.
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if not any(c.span.chord and c.span.start < cell.end and c.span.end > cell.start for c in choices):
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result = [Candidate((), 'no_harmony', 'space', (0, 0, 0, 0))]
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return sorted(result, key=lambda c: (c.rank, c.source, c.treatment, tuple(n.pitch for n in c.notes)))
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def compose(plan, measures, choices, sequences, rhythms, tempo, previous=None):
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states = [((0, 0, 0, 0, 0, 0, 0), (), previous)]
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for cell in cells(plan, measures, rhythms):
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pool = candidates(cell, plan, choices, sequences)
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following, extensions = [], {}
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for cost, decisions, last in states:
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def extension_rank(candidate):
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jump = abs(candidate.notes[0].pitch.number() - last.pitch.number()) if candidate.notes and last else 0
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return candidate.rank, jump, candidate.source, candidate.treatment, tuple(n.pitch for n in candidate.notes)
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key = last.pitch.number() if last else None
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if key not in extensions:
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extensions[key] = heapq.nsmallest(CELL_LIMIT, pool, key=extension_rank)
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for candidate in extensions[key]:
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placed = generator.solo.placement.place(candidate.notes, tempo, last)
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if placed is None:
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continue
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comfort, notes = placed
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jump = abs(notes[0].pitch.number() - last.pitch.number()) if notes and last else 0
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monotony = int(bool(notes and decisions and decisions[-1].notes) and tuple(n.pitch for n in notes) == tuple(n.pitch for n in decisions[-1].notes) and cell.motif is None)
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local = (*candidate.rank, jump, comfort, monotony)
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total = tuple(a + b for a, b in zip(cost, local))
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decision = generator.solo.types.Decision(cell.start, cell.end, candidate.source, candidate.treatment, notes)
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following.append((total, (*decisions, decision), notes[-1] if notes else last))
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if not following:
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raise SearchExhausted(f'{plan.id}, beats {cell.start + 1}-{cell.end + 1}: bounded solo search exhausted; no feasible phrase extension among {BEAM_WIDTH} states / {CELL_LIMIT} extensions (not proof that the passage is unplayable)')
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following.sort(key=lambda item: (item[0], tuple((d.source, d.treatment, tuple((n.pitch, n.position) for n in d.notes)) for d in item[1])))
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# Retain distinct endings for lookahead instead of 64 copies of one path.
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unique = {}
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for item in following:
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last = item[2]
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key = (last.pitch, last.position, last.end) if last else None
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unique.setdefault(key, item)
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states = list(unique.values())[:BEAM_WIDTH]
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_, decisions, _ = states[0]
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return generator.solo.types.Phrase(plan, decisions)
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Reference in New Issue
Block a user