mirror of
https://github.com/kennethreitz/interpretations.git
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kennethreitz
00fc35de19
Every track: tuple-unpacked scale degrees, small local helpers (rest_bars, chord_bars, play_phrase), data-driven drum patterns and phrase tuples, sparse-event dicts, explicit velocity lists for fades, dead code removed. Net -1,415 lines across 25 files. Adds .fingerprint.py, a verification harness that hashes every audible parameter of a score (notes, voicings, velocities, bends, drum hits, LFO automation, part settings). All 25 tracks fingerprint identical to their pre-refactor baselines, stored in .fingerprints/. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
108 lines
2.9 KiB
Python
108 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""Fingerprint a track's score — every audible parameter, deterministically.
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Usage:
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uv run python .fingerprint.py tracks/foo.py # print sha256
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uv run python .fingerprint.py tracks/foo.py --dump # print full JSON
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Used to verify refactors are audio-identical: fingerprint before and after.
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"""
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import hashlib
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import json
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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from play import load_score
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def _scalar(v):
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if isinstance(v, float):
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return round(v, 10)
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return v
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def _tone(t):
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"""Serialize a Tone / Chord / _DrumTone / None to plain data."""
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if t is None:
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return None
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if hasattr(t, "tones"): # Chord
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return {"chord": [_tone(x) for x in t.tones]}
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if hasattr(t, "sound"): # _DrumTone
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return {"drum": str(t.sound)}
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d = {"name": t.name, "octave": t.octave}
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if getattr(t, "system_name", None):
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d["system_name"] = t.system_name
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freq = getattr(t, "_frequency", None)
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if freq is not None:
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d["frequency"] = _scalar(freq)
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sys_ = getattr(t, "_system", None)
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if sys_ is not None:
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d["system"] = repr(sys_)
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return d
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def fingerprint(path):
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score, mod = load_score(path)
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data = {
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"time_signature": str(score.time_signature),
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"bpm": score.bpm,
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"system": score.system,
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"temperament": score.temperament,
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"reference_pitch": score.reference_pitch,
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"parts": {},
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}
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for name, part in score.parts.items():
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pd = {}
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for k, v in sorted(vars(part).items()):
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if k in ("notes", "_drum_hits", "_automation", "_fretboard"):
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continue
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if k == "synth_kw":
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pd[k] = {kk: _scalar(vv) for kk, vv in sorted(v.items())} if v else {}
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continue
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pd[k] = _scalar(v)
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pd["notes"] = [
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{
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"tone": _tone(n.tone),
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"duration": _scalar(float(getattr(n.duration, "value", n.duration))),
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"velocity": n.velocity,
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"bend": _scalar(n.bend),
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"bend_type": n.bend_type,
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"lyric": n.lyric,
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"articulation": n.articulation,
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"hold": n._hold,
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}
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for n in part.notes
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]
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pd["drum_hits"] = [
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json.loads(json.dumps(h, default=repr)) for h in part._drum_hits
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]
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pd["automation"] = [
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[_scalar(beat), {k: _scalar(v) for k, v in sorted(vals.items())}]
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for beat, vals in part._automation
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]
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data["parts"][name] = pd
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return data
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def main():
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path = sys.argv[1]
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data = fingerprint(path)
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blob = json.dumps(data, sort_keys=True, default=repr)
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if "--dump" in sys.argv:
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print(json.dumps(data, indent=1, sort_keys=True, default=repr))
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else:
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print(hashlib.sha256(blob.encode()).hexdigest())
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if __name__ == "__main__":
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main()
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