velocity_profile.py
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| 1 | """muse velocity-profile — dynamic range analysis for a MIDI track. |
| 2 | |
| 3 | Shows the velocity distribution of a MIDI track — peak, average, RMS, |
| 4 | and a per-velocity-bucket histogram. Reveals the dynamic character of |
| 5 | a composition: is it always forte? Does it have a wide dynamic range? |
| 6 | Are some bars particularly loud or soft? |
| 7 | |
| 8 | Usage:: |
| 9 | |
| 10 | muse velocity-profile tracks/melody.mid |
| 11 | muse velocity-profile tracks/piano.mid --commit HEAD~5 |
| 12 | muse velocity-profile tracks/drums.mid --by-bar |
| 13 | muse velocity-profile tracks/melody.mid --json |
| 14 | |
| 15 | Output:: |
| 16 | |
| 17 | Velocity profile: tracks/melody.mid — cb4afaed |
| 18 | Notes: 23 · Range: 48–96 · Mean: 78.3 · RMS: 79.1 |
| 19 | |
| 20 | ppp ( 1–15) │ │ 0 |
| 21 | pp (16–31) │ │ 0 |
| 22 | p (32–47) │ │ 0 |
| 23 | mp (48–63) │████ │ 2 ( 8.7%) |
| 24 | mf (64–79) │████████████████████████ │ 12 (52.2%) |
| 25 | f (80–95) │████████████ │ 8 (34.8%) |
| 26 | ff (96–111) │██ │ 1 ( 4.3%) |
| 27 | fff (112–127)│ │ 0 |
| 28 | |
| 29 | Dynamic character: mf–f (moderate-loud) |
| 30 | """ |
| 31 | |
| 32 | import argparse |
| 33 | import json |
| 34 | import logging |
| 35 | import math |
| 36 | import pathlib |
| 37 | import sys |
| 38 | |
| 39 | from muse.core.types import short_id |
| 40 | from muse.core.errors import ExitCode |
| 41 | from muse.core.repo import require_repo |
| 42 | from muse.core.refs import read_current_branch |
| 43 | from muse.core.commits import resolve_commit_ref |
| 44 | from muse.plugins.midi._query import ( |
| 45 | |
| 46 | NoteInfo, |
| 47 | load_track, |
| 48 | load_track_from_workdir, |
| 49 | notes_by_bar, |
| 50 | ) |
| 51 | |
| 52 | type _IntMap = dict[str, int] |
| 53 | |
| 54 | logger = logging.getLogger(__name__) |
| 55 | |
| 56 | _DYNAMIC_LEVELS: list[tuple[str, int, int]] = [ |
| 57 | ("ppp", 1, 15), |
| 58 | ("pp", 16, 31), |
| 59 | ("p", 32, 47), |
| 60 | ("mp", 48, 63), |
| 61 | ("mf", 64, 79), |
| 62 | ("f", 80, 95), |
| 63 | ("ff", 96, 111), |
| 64 | ("fff", 112, 127), |
| 65 | ] |
| 66 | _BAR_WIDTH = 32 # histogram bar chars |
| 67 | |
| 68 | def _velocity_level(velocity: int) -> str: |
| 69 | for name, lo, hi in _DYNAMIC_LEVELS: |
| 70 | if lo <= velocity <= hi: |
| 71 | return name |
| 72 | return "fff" |
| 73 | |
| 74 | def _rms(values: list[int]) -> float: |
| 75 | if not values: |
| 76 | return 0.0 |
| 77 | return math.sqrt(sum(v * v for v in values) / len(values)) |
| 78 | |
| 79 | def _read_branch(root: pathlib.Path) -> str: |
| 80 | return read_current_branch(root) |
| 81 | |
| 82 | def register(subparsers: "argparse._SubParsersAction[argparse.ArgumentParser]") -> None: |
| 83 | """Register the velocity-profile subcommand.""" |
| 84 | parser = subparsers.add_parser("velocity-profile", help="Analyse the dynamic range and velocity distribution of a MIDI track.", description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) |
| 85 | parser.add_argument("track", metavar="TRACK", help="Workspace-relative path to a .mid file.") |
| 86 | parser.add_argument("--commit", "-c", metavar="REF", default=None, dest="ref", help="Analyse a historical snapshot instead of the working tree.") |
| 87 | parser.add_argument("--by-bar", "-b", action="store_true", help="Show per-bar average velocity instead of the overall histogram.") |
| 88 | parser.add_argument("--json", action="store_true", dest="as_json", help="Emit results as JSON.") |
| 89 | parser.set_defaults(func=run) |
| 90 | |
| 91 | def run(args: argparse.Namespace) -> None: |
| 92 | """Analyse the dynamic range and velocity distribution of a MIDI track. |
| 93 | |
| 94 | ``muse velocity-profile`` shows peak, average, and RMS velocity, plus |
| 95 | a histogram of notes by dynamic level (ppp through fff). |
| 96 | |
| 97 | Use ``--by-bar`` to see per-bar average velocity — useful for spotting |
| 98 | which sections of a composition are louder or softer. |
| 99 | |
| 100 | Use ``--commit`` to analyse a historical snapshot. Use ``--json`` for |
| 101 | agent-readable output. |
| 102 | |
| 103 | This is fundamentally impossible in Git: Git has no model of what the |
| 104 | MIDI velocity values in a binary file mean. Muse stores notes as |
| 105 | structured semantic data, enabling musical dynamics analysis at any |
| 106 | point in history. |
| 107 | """ |
| 108 | track: str = args.track |
| 109 | ref: str | None = args.ref |
| 110 | by_bar: bool = args.by_bar |
| 111 | as_json: bool = args.as_json |
| 112 | |
| 113 | root = require_repo() |
| 114 | |
| 115 | result: tuple[list[NoteInfo], int] | None |
| 116 | commit_label = "working tree" |
| 117 | |
| 118 | if ref is not None: |
| 119 | branch = _read_branch(root) |
| 120 | commit = resolve_commit_ref(root, branch, ref) |
| 121 | if commit is None: |
| 122 | print(f"❌ Commit '{ref}' not found.", file=sys.stderr) |
| 123 | raise SystemExit(ExitCode.USER_ERROR) |
| 124 | result = load_track(root, commit.commit_id, track) |
| 125 | commit_label = short_id(commit.commit_id, strip=True) |
| 126 | else: |
| 127 | result = load_track_from_workdir(root, track) |
| 128 | |
| 129 | if result is None: |
| 130 | print(f"❌ Track '{track}' not found or not a valid MIDI file.", file=sys.stderr) |
| 131 | raise SystemExit(ExitCode.USER_ERROR) |
| 132 | |
| 133 | note_list, _tpb = result |
| 134 | |
| 135 | if not note_list: |
| 136 | print(f" (no notes found in '{track}')") |
| 137 | return |
| 138 | |
| 139 | velocities = [n.velocity for n in note_list] |
| 140 | v_min = min(velocities) |
| 141 | v_max = max(velocities) |
| 142 | v_mean = sum(velocities) / len(velocities) |
| 143 | v_rms = _rms(velocities) |
| 144 | |
| 145 | # Dynamic level counts. |
| 146 | level_counts: _IntMap = {name: 0 for name, _, _ in _DYNAMIC_LEVELS} |
| 147 | for v in velocities: |
| 148 | level_counts[_velocity_level(v)] += 1 |
| 149 | |
| 150 | if as_json: |
| 151 | if by_bar: |
| 152 | bars = notes_by_bar(note_list) |
| 153 | bar_data = [ |
| 154 | { |
| 155 | "bar": bar_num, |
| 156 | "mean_velocity": round(sum(n.velocity for n in bar_notes) / len(bar_notes), 1), |
| 157 | "note_count": len(bar_notes), |
| 158 | } |
| 159 | for bar_num, bar_notes in sorted(bars.items()) |
| 160 | ] |
| 161 | print(json.dumps( |
| 162 | {"track": track, "commit": commit_label, "by_bar": bar_data} |
| 163 | )) |
| 164 | else: |
| 165 | print(json.dumps( |
| 166 | { |
| 167 | "track": track, |
| 168 | "commit": commit_label, |
| 169 | "notes": len(note_list), |
| 170 | "min": v_min, "max": v_max, |
| 171 | "mean": round(v_mean, 1), "rms": round(v_rms, 1), |
| 172 | "histogram": {k: v for k, v in level_counts.items()}, |
| 173 | }, |
| 174 | )) |
| 175 | return |
| 176 | |
| 177 | print(f"\nVelocity profile: {track} — {commit_label}") |
| 178 | print( |
| 179 | f"Notes: {len(note_list)} · Range: {v_min}–{v_max}" |
| 180 | f" · Mean: {v_mean:.1f} · RMS: {v_rms:.1f}" |
| 181 | ) |
| 182 | print("") |
| 183 | |
| 184 | if by_bar: |
| 185 | bars = notes_by_bar(note_list) |
| 186 | for bar_num, bar_notes in sorted(bars.items()): |
| 187 | bar_vels = [n.velocity for n in bar_notes] |
| 188 | bar_mean = sum(bar_vels) / len(bar_vels) |
| 189 | bar_len = min(int(bar_mean / 127 * _BAR_WIDTH), _BAR_WIDTH) |
| 190 | print( |
| 191 | f" bar {bar_num:>4} {'█' * bar_len:<{_BAR_WIDTH}} " |
| 192 | f"avg={bar_mean:>5.1f} ({len(bar_notes)} notes)" |
| 193 | ) |
| 194 | return |
| 195 | |
| 196 | total = max(len(velocities), 1) |
| 197 | for name, lo, hi in _DYNAMIC_LEVELS: |
| 198 | count = level_counts[name] |
| 199 | bar_len = min(int(count / total * _BAR_WIDTH), _BAR_WIDTH) |
| 200 | pct = count / total * 100 |
| 201 | print( |
| 202 | f" {name:<4}({lo:>3}–{hi:>3}) │{'█' * bar_len:<{_BAR_WIDTH}}│" |
| 203 | f" {count:>4} ({pct:>5.1f}%)" |
| 204 | ) |
| 205 | |
| 206 | # Dominant dynamic level. |
| 207 | dominant = max(level_counts, key=lambda k: level_counts[k]) |
| 208 | print(f"\nDynamic character: {dominant}") |