Download scripts/classify_probe_regimes.py from panxy1019/centeredSquare: direct link, hf CLI and curl.
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https://huggingface.co/datasets/panxy1019/centeredSquare/resolve/main/scripts/classify_probe_regimes.py
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hf download hf://datasets/panxy1019/centeredSquare/scripts/classify_probe_regimes.py
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curl -L -o classify_probe_regimes.py https://huggingface.co/datasets/panxy1019/centeredSquare/resolve/main/scripts/classify_probe_regimes.py
4.54 kB
| #!/usr/bin/env python3 | |
| import argparse | |
| import csv | |
| import math | |
| from pathlib import Path | |
| import numpy as np | |
| def read_probe_u(path: Path): | |
| rows = [] | |
| with path.open() as f: | |
| for line in f: | |
| s = line.strip() | |
| if not s or s.startswith('#'): | |
| continue | |
| parts = s.split('(') | |
| try: | |
| t = float(parts[0].strip()) | |
| except ValueError: | |
| continue | |
| ux = [] | |
| uy = [] | |
| for p in parts[1:]: | |
| vals = p.rstrip(')').strip().split() | |
| if len(vals) >= 2: | |
| ux.append(float(vals[0])) | |
| uy.append(float(vals[1])) | |
| if len(ux) >= 2: | |
| rows.append((t, ux, uy)) | |
| if not rows: | |
| raise RuntimeError(f'no probe rows in {path}') | |
| t = np.array([r[0] for r in rows], dtype=float) | |
| ux = np.array([r[1] for r in rows], dtype=float) | |
| uy = np.array([r[2] for r in rows], dtype=float) | |
| return t, ux, uy | |
| def classify_case(case: Path): | |
| probe_candidates = sorted(case.glob('postProcessing/wakeProbes/*/U')) | |
| if not probe_candidates: | |
| probe_candidates = sorted(case.glob('processor0/postProcessing/wakeProbes/*/U')) | |
| if not probe_candidates: | |
| return {'case': case.name, 'status': 'missing_probe'} | |
| t, ux_all, uy_all = read_probe_u(probe_candidates[0]) | |
| if len(t) < 16: | |
| return {'case': case.name, 'status': 'too_few_points', 'n': len(t)} | |
| half = len(t) // 2 | |
| # Probe 1 is report's primary classifier: x=8, y=2. | |
| probe_index = 1 if ux_all.shape[1] > 1 else 0 | |
| t_tail = t[half:] | |
| ux = ux_all[half:, probe_index] | |
| coeff = np.polyfit(t_tail, ux, 1) | |
| detrended = ux - np.polyval(coeff, t_tail) | |
| sigma = float(np.std(detrended)) | |
| dt = float(np.median(np.diff(t))) if len(t) > 1 else math.nan | |
| peak_st = math.nan | |
| peak_amp = 0.0 | |
| sig = 0.0 | |
| if len(detrended) > 8 and np.isfinite(dt) and dt > 0: | |
| fft = np.abs(np.fft.rfft(detrended)) | |
| freqs = np.fft.rfftfreq(len(detrended), dt) | |
| band = (freqs >= 0.05) & (freqs <= 0.30) | |
| if np.any(band): | |
| band_fft = fft[band] | |
| band_freq = freqs[band] | |
| idx = int(np.argmax(band_fft)) | |
| peak_amp = float(band_fft[idx] / len(detrended)) | |
| peak_st = float(band_freq[idx]) | |
| mean_amp = float(np.mean(band_fft) / len(detrended)) | |
| sig = peak_amp / mean_amp if mean_amp > 0 else 0.0 | |
| seg_n = 4 | |
| seg_size = max(1, len(ux) // seg_n) | |
| seg_stds = [] | |
| for s in range(seg_n): | |
| seg = ux[s*seg_size:(s+1)*seg_size] | |
| st = t_tail[s*seg_size:(s+1)*seg_size] | |
| if len(seg) < 3: | |
| seg_stds.append(float('nan')) | |
| else: | |
| c = np.polyfit(st, seg, 1) | |
| seg_stds.append(float(np.std(seg - np.polyval(c, st)))) | |
| if sigma < 1e-6: | |
| regime = 'STEADY' | |
| elif peak_amp > 1e-3 and sig > 10: | |
| regime = 'PERIODIC' | |
| elif peak_amp > 1e-4 and sig > 5: | |
| regime = 'HOPF_NEAR_ONSET' | |
| elif np.isfinite(seg_stds[0]) and seg_stds[-1] > seg_stds[0] * 1.5: | |
| regime = 'GROWING_INSTABILITY' | |
| else: | |
| regime = 'STEADY_OR_TRANSITIONAL' | |
| last_time = float(t[-1]) | |
| return { | |
| 'case': case.name, | |
| 'status': 'ok', | |
| 'n': len(t), | |
| 'last_probe_time': last_time, | |
| 'ux_tail_mean': float(np.mean(ux)), | |
| 'sigma_detrend': sigma, | |
| 'peak_St': peak_st, | |
| 'fft_significance': sig, | |
| 'seg_std_0': seg_stds[0], | |
| 'seg_std_3': seg_stds[-1], | |
| 'regime': regime, | |
| 'probe_file': str(probe_candidates[0]), | |
| } | |
| def main(): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument('run_root', type=Path) | |
| args = ap.parse_args() | |
| cases = sorted([p for p in args.run_root.glob('Re*') if p.is_dir()], key=lambda p: float(p.name[2:])) | |
| rows = [classify_case(c) for c in cases] | |
| out = args.run_root / 'probe_regime_summary.csv' | |
| fields = ['case','status','n','last_probe_time','ux_tail_mean','sigma_detrend','peak_St','fft_significance','seg_std_0','seg_std_3','regime','probe_file'] | |
| with out.open('w', newline='') as f: | |
| writer = csv.DictWriter(f, fieldnames=fields) | |
| writer.writeheader() | |
| writer.writerows(rows) | |
| print(out) | |
| for r in rows: | |
| print(f"{r.get('case')}: {r.get('regime', r.get('status'))} sigma={r.get('sigma_detrend','NA')} St={r.get('peak_St','NA')} sig={r.get('fft_significance','NA')}") | |
| if __name__ == '__main__': | |
| main() | |