745a64b342
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""
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Capture le bruit de fond du détecteur sur 24h (sans source).
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Gère le débranchement/rebranchement du détecteur.
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À lancer séparément avant le moniteur :
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docker-compose run --rm detect python capture_background.py
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"""
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import numpy as np
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import time
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import json
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import os
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SAMPLE_INTERVAL = int(os.environ.get("SAMPLE_INTERVAL", "60"))
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TARGET_DURATION = int(os.environ.get("TARGET_DURATION", str(86400))) # 24h
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OUTPUT_PATH = os.environ.get("BACKGROUND_PATH", "/data/background_24h.npy")
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SNAPSHOT_PATH = os.environ.get("SNAPSHOT_PATH", "/data/background_snapshot.json")
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BG_COUNTS = np.zeros(1024, dtype=np.float64)
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BG_LIVE_TIME = 0.0
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device = None
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def save_snapshot():
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"""Save a human-readable snapshot of current background."""
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cps = BG_COUNTS.sum() / BG_LIVE_TIME if BG_LIVE_TIME > 0 else 0
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# Approximate energy calibration for RC-103: E ≈ 0.33 + 2.97*ch
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peaks = []
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max_c = BG_COUNTS.max()
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if max_c > 0:
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for i, c in enumerate(BG_COUNTS):
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if c > max_c * 0.03:
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energy = 0.33 + 2.97 * i
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peaks.append({"channel": i, "energy_kev": round(energy, 1), "counts": round(float(c), 1)})
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snapshot = {
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"elapsed_hours": round((time.time() - start) / 3600, 2),
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"live_time_s": round(BG_LIVE_TIME, 1),
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"total_counts": round(float(BG_COUNTS.sum()), 0),
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"cps": round(cps, 2),
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"top_peaks": sorted(peaks, key=lambda x: -x["counts"])[:15],
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"spectrum": [round(float(c), 1) for c in BG_COUNTS],
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}
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with open(SNAPSHOT_PATH, "w") as f:
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json.dump(snapshot, f, indent=2)
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print(f"Capture du bruit de fond pendant {TARGET_DURATION/3600:.0f}h...")
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print("Assurez-vous qu'aucune source radioactive n'est a proximite du detecteur.")
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print()
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start = time.time()
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while (time.time() - start) < TARGET_DURATION:
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time.sleep(SAMPLE_INTERVAL)
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try:
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if device is None:
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from radiacode import RadiaCode
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device = RadiaCode()
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device.spectrum_reset()
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print("Radiacode connecte.")
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spectrum = device.spectrum()
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BG_COUNTS += np.array(spectrum.counts, dtype=np.float64)
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BG_LIVE_TIME += spectrum.duration.total_seconds()
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device.spectrum_reset()
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elapsed = time.time() - start
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cps = BG_COUNTS.sum() / BG_LIVE_TIME if BG_LIVE_TIME > 0 else 0
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print(
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f"Background : {elapsed/3600:.1f}h / {TARGET_DURATION/3600:.1f}h "
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f"({BG_LIVE_TIME:.0f}s live, {BG_COUNTS.sum():.0f} coups, {cps:.1f} CPS)",
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flush=True,
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)
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save_snapshot()
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except Exception as e:
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print(f"\nErreur : {e}, reconnexion...")
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device = None
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os.makedirs(os.path.dirname(OUTPUT_PATH) if os.path.dirname(OUTPUT_PATH) else ".", exist_ok=True)
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np.save(
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OUTPUT_PATH,
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{
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"counts": BG_COUNTS,
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"duration": BG_LIVE_TIME,
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"timestamp": time.time(),
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},
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)
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print(f"\n\nBackground sauvegarde : {OUTPUT_PATH}")
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print(f" Duree live : {BG_LIVE_TIME/3600:.1f}h")
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print(f" Total coups : {BG_COUNTS.sum():.0f}")
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print(f" CPS moyen : {BG_COUNTS.sum()/BG_LIVE_TIME:.1f}") |