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Add per-factor analysis metrics

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- Melodic: violations, max violation, avg movement
- Contrary motion: steps with contrary, percentage
- DCA: avg voices changing, all voices change count/percent
- Hamiltonian: unique nodes, coverage percentage
- Target range: start/end cents, achieved percentage
- CLI: --stats now shows all metrics automatically
This commit is contained in:
Michael Winter 2026-03-15 10:48:06 +01:00
parent 16ecb192d1
commit 34a6ebfabd
2 changed files with 169 additions and 35 deletions
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203
src/analyze.py
View file

@ -2,7 +2,6 @@
import argparse
import json
import statistics
from pathlib import Path
@ -14,7 +13,11 @@ def analyze_chords(
Args:
chords: List of chords, each chord is a list of pitch dicts
config: Optional config with target_octaves, melodic_max, max_path
config: Optional config with:
- target_range_octaves: target octaves (default: 2.0)
- melodic_threshold_max: max cents per voice movement (default: 300)
- max_path: path length (default: 50)
- graph_nodes: total nodes in graph (optional, for Hamiltonian coverage)
Returns:
Dict with analysis metrics
@ -25,26 +28,71 @@ def analyze_chords(
target_octaves = config.get("target_range_octaves", 2.0)
melodic_max = config.get("melodic_threshold_max", 300)
max_path = config.get("max_path", 50)
graph_nodes = config.get("graph_nodes", None)
# Basic info
num_chords = len(chords)
num_voices = len(chords[0]) if chords else 0
num_steps = num_chords - 1 if num_chords > 0 else 0
# Melodic violations
violations = 0
# ========== Melodic Threshold ==========
melodic_violations = 0
max_violation = 0
voice_changes = 0
for i in range(1, num_chords):
for v in range(num_voices):
diff = abs(chords[i][v]["cents"] - chords[i - 1][v]["cents"])
if diff > 0:
voice_changes += 1
if diff > melodic_max:
violations += 1
max_violation = max(max_violation, diff)
total_movement = 0
max_movement = 0
# Target range
target_octaves = config.get("target_range_octaves", 2.0)
# ========== Contrary Motion ==========
contrary_motion_steps = 0
# ========== DCA (Voice Changes) ==========
voice_changes_per_step = []
all_voices_change_count = 0
# ========== Hamiltonian ==========
unique_nodes = set()
node_hashes = []
for i in range(1, num_chords):
cent_diffs = []
voices_changed = 0
for v in range(num_voices):
curr_cents = chords[i][v]["cents"]
prev_cents = chords[i - 1][v]["cents"]
diff = curr_cents - prev_cents
cent_diffs.append(diff)
# Melodic
abs_diff = abs(diff)
total_movement += abs_diff
max_movement = max(max_movement, abs_diff)
if abs_diff > melodic_max:
melodic_violations += 1
max_violation = max(max_violation, abs_diff)
# DCA
if abs_diff > 0:
voices_changed += 1
# Track unique nodes
node_hash = tuple(
tuple(p["hs_array"]) for p in chords[i]
) # Convert lists to tuples for hashing
unique_nodes.add(node_hash)
node_hashes.append(node_hash)
# Contrary motion: sorted_diffs[0] < 0 and sorted_diffs[-1] > 0
if len(cent_diffs) >= 2:
sorted_diffs = sorted(cent_diffs)
if sorted_diffs[0] < 0 and sorted_diffs[-1] > 0:
contrary_motion_steps += 1
# DCA: all voices change
voice_changes_per_step.append(voices_changed)
if voices_changed == num_voices:
all_voices_change_count += 1
# ========== Target Range ==========
target_cents = target_octaves * 1200
if chords:
@ -55,19 +103,52 @@ def analyze_chords(
else:
start_avg = end_avg = actual_cents = target_percent = 0
# ========== DCA Summary ==========
avg_voice_changes = (
sum(voice_changes_per_step) / len(voice_changes_per_step)
if voice_changes_per_step
else 0
)
pct_all_change = (
(all_voices_change_count / len(voice_changes_per_step)) * 100
if voice_changes_per_step
else 0
)
# ========== Hamiltonian Coverage ==========
hamiltonian_coverage = (
(len(unique_nodes) / graph_nodes * 100) if graph_nodes else None
)
return {
"num_chords": num_chords,
"num_voices": num_voices,
"num_steps": num_steps,
# Melodic
"melodic_max": melodic_max,
"violations": violations,
"max_violation": max_violation,
"voice_changes": voice_changes,
"start_avg_cents": start_avg,
"end_avg_cents": end_avg,
"target_cents": target_cents,
"actual_cents": actual_cents,
"target_percent": target_percent,
"melodic_violations": melodic_violations,
"melodic_max_violation": max_violation,
"melodic_avg_movement": total_movement / num_steps if num_steps > 0 else 0,
"melodic_max_movement": max_movement,
# Contrary Motion
"contrary_motion_steps": contrary_motion_steps,
"contrary_motion_percent": (
(contrary_motion_steps / num_steps * 100) if num_steps > 0 else 0
),
# DCA
"dca_avg_voice_changes": avg_voice_changes,
"dca_all_voices_change_count": all_voices_change_count,
"dca_all_voices_change_percent": pct_all_change,
# Hamiltonian
"hamiltonian_unique_nodes": len(unique_nodes),
"hamiltonian_coverage": hamiltonian_coverage,
# Target Range
"target_octaves": target_octaves,
"target_cents": target_cents,
"target_start_cents": start_avg,
"target_end_cents": end_avg,
"target_actual_cents": actual_cents,
"target_percent": target_percent,
}
@ -75,28 +156,49 @@ def format_analysis(metrics: dict) -> str:
"""Format analysis metrics as readable output."""
lines = [
"=== Analysis ===",
f"Path length: {metrics['num_chords']} chords, {metrics['num_voices']} voices",
f"Path: {metrics['num_chords']} chords, {metrics['num_steps']} steps, {metrics['num_voices']} voices",
"",
"--- Melodic Threshold ---",
f"Max allowed: {metrics['melodic_max']} cents",
f"Violations: {metrics['violations']}",
f"Max violation: {metrics['max_violation']:.0f} cents",
f"Voice changes: {metrics['voice_changes']}",
f"Violations: {metrics['melodic_violations']}",
f"Max violation: {metrics['melodic_max_violation']:.0f} cents",
f"Avg movement: {metrics['melodic_avg_movement']:.1f} cents",
f"Max movement: {metrics['melodic_max_movement']:.0f} cents",
"",
"--- Target Range ---",
f"Target: {metrics['target_octaves']} octaves ({metrics['target_cents']:.0f} cents)",
f"Start avg: {metrics['start_avg_cents']:.0f} cents",
f"End avg: {metrics['end_avg_cents']:.0f} cents",
f"Achieved: {metrics['actual_cents']:.0f} cents ({metrics['target_percent']:.1f}%)",
"--- Contrary Motion ---",
f"Steps with contrary: {metrics['contrary_motion_steps']}",
f"Percentage: {metrics['contrary_motion_percent']:.1f}%",
"",
"--- DCA (Voice Changes) ---",
f"Avg voices changing: {metrics['dca_avg_voice_changes']:.2f} / {metrics['num_voices']}",
f"All voices change: {metrics['dca_all_voices_change_count']} steps ({metrics['dca_all_voices_change_percent']:.1f}%)",
"",
"--- Hamiltonian ---",
f"Unique nodes: {metrics['hamiltonian_unique_nodes']}",
]
if metrics["hamiltonian_coverage"] is not None:
lines.append(f"Coverage: {metrics['hamiltonian_coverage']:.1f}%")
lines.extend(
[
"",
"--- Target Range ---",
f"Target: {metrics['target_octaves']} octaves ({metrics['target_cents']:.0f} cents)",
f"Start: {metrics['target_start_cents']:.0f} cents",
f"End: {metrics['target_end_cents']:.0f} cents",
f"Achieved: {metrics['target_actual_cents']:.0f} cents ({metrics['target_percent']:.1f}%)",
]
)
return "\n".join(lines)
def analyze_file(file_path: str | Path) -> dict:
def analyze_file(file_path: str | Path, config: dict | None = None) -> dict:
"""Load and analyze a chord file."""
with open(file_path) as f:
chords = json.load(f)
return analyze_chords(chords)
return analyze_chords(chords, config)
def main():
@ -112,6 +214,30 @@ def main():
action="store_true",
help="Output raw JSON instead of formatted text",
)
parser.add_argument(
"--target-range",
type=float,
default=2.0,
help="Target range in octaves (default: 2.0)",
)
parser.add_argument(
"--melodic-max",
type=int,
default=300,
help="Max melodic threshold in cents (default: 300)",
)
parser.add_argument(
"--max-path",
type=int,
default=50,
help="Max path length (default: 50)",
)
parser.add_argument(
"--graph-nodes",
type=int,
default=None,
help="Total nodes in graph (for Hamiltonian coverage)",
)
args = parser.parse_args()
file_path = Path(args.file)
@ -119,7 +245,14 @@ def main():
print(f"Error: File not found: {file_path}")
return 1
metrics = analyze_file(file_path)
config = {
"target_range_octaves": args.target_range,
"melodic_threshold_max": args.melodic_max,
"max_path": args.max_path,
"graph_nodes": args.graph_nodes,
}
metrics = analyze_file(file_path, config)
if args.json:
print(json.dumps(metrics, indent=2))

1
src/io.py
View file

@ -479,6 +479,7 @@ def main():
"melodic_threshold_max": args.melodic_max,
"target_range_octaves": args.target_range,
"max_path": args.max_path,
"graph_nodes": graph.number_of_nodes() if graph else None,
}
# Load the chords from the output file
import json