File size: 4,543 Bytes
12c1d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | #!/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()
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