128 lines
4.2 KiB
Python
128 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""Stats for actuator/valve-like outputs (Type 3)."""
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import argparse
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import csv
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import gzip
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import json
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from pathlib import Path
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from typing import Dict, List
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def parse_args():
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base_dir = Path(__file__).resolve().parent
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parser = argparse.ArgumentParser(description="Actuator/valve stats.")
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parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
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parser.add_argument("--reference", default=str(base_dir / "config.json"))
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parser.add_argument("--features", default="", help="comma-separated list")
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parser.add_argument("--config", default=str(base_dir / "config.json"))
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parser.add_argument("--out", default=str(base_dir / "results" / "actuator_stats.json"))
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parser.add_argument("--max-rows", type=int, default=200000)
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return parser.parse_args()
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def resolve_reference_glob(ref_arg: str) -> str:
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ref_path = Path(ref_arg)
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if ref_path.suffix == ".json":
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cfg = json.loads(ref_path.read_text(encoding="utf-8"))
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data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
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if not data_glob:
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raise SystemExit("reference config has no data_glob/data_path")
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combined = ref_path.parent / data_glob
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if "*" in str(combined) or "?" in str(combined):
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return str(combined)
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return str(combined.resolve())
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return str(ref_path)
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def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
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vals = {c: [] for c in cols}
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opener = gzip.open if str(path).endswith(".gz") else open
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with opener(path, "rt", newline="") as fh:
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reader = csv.DictReader(fh)
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for i, row in enumerate(reader):
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for c in cols:
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try:
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vals[c].append(float(row[c]))
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except Exception:
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pass
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if max_rows > 0 and i + 1 >= max_rows:
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break
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return vals
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def spike_stats(series: List[float]):
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if not series:
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return {
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"unique_ratio": None,
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"top1_mass": None,
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"top3_mass": None,
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"median_dwell": None,
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}
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n = len(series)
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# discretize by rounding
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rounded = [round(v, 2) for v in series]
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counts = {}
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for v in rounded:
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counts[v] = counts.get(v, 0) + 1
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unique_ratio = len(counts) / n
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top = sorted(counts.values(), reverse=True)
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top1_mass = top[0] / n if top else None
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top3_mass = sum(top[:3]) / n if len(top) >= 3 else top1_mass
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# dwell length
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dwells = []
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current = rounded[0]
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dwell = 1
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for v in rounded[1:]:
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if v == current:
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dwell += 1
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else:
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dwells.append(dwell)
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current = v
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dwell = 1
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dwells.append(dwell)
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dwells.sort()
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median_dwell = dwells[len(dwells) // 2] if dwells else None
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return {
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"unique_ratio": unique_ratio,
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"top1_mass": top1_mass,
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"top3_mass": top3_mass,
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"median_dwell": median_dwell,
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}
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def main():
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args = parse_args()
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features = [f.strip() for f in args.features.split(",") if f.strip()]
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if not features and Path(args.config).exists():
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cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
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features = cfg.get("type3_features", []) or []
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if not features:
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raise SystemExit("no features specified for actuator_stats")
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gen_vals = read_series(Path(args.generated), features, args.max_rows)
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ref_glob = resolve_reference_glob(args.reference)
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ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
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if not ref_paths:
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raise SystemExit(f"no reference files matched: {ref_glob}")
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real_vals = {c: [] for c in features}
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for p in ref_paths:
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vals = read_series(p, features, args.max_rows)
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for c in features:
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real_vals[c].extend(vals[c])
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out = {"features": features, "generated": {}, "reference": {}}
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for c in features:
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out["generated"][c] = spike_stats(gen_vals[c])
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out["reference"][c] = spike_stats(real_vals[c])
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
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print("wrote", out_path)
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if __name__ == "__main__":
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main()
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