0847a3fc80
Root cause of Am-241 misidentification: the Radiacode 103's CsI(Tl) crystal shifts low-energy peaks upward (59.5 keV → 71.6 keV for Am-241) due to non-proportional scintillation response. The model was trained on theoretical peak positions and couldn't match the shifted real peaks. Changes: - Add inverse CsI(Tl) non-linear correction to inference pipeline (radiacode_monitor.py, web/config.py, test_detection.py) E_apparent = E_true * (1 + 0.37 * exp(-E_true/100)) Corrects channel mapping so peaks appear at theoretical energies - Fix energy calibration: DetectorConfig now uses E = 0.33 + 2.97*ch with 1023 channels, matching the real detector (was energy_min=20, skip_first_channel=True, different channel width) - Add K-escape peaks for CsI(Tl) iodine X-ray escape (E - 28.5 keV) - Add asymmetric peak shapes for low-energy tails (< 200 keV) - Add log1p normalization in dataset and inference (replaces max-norm) - Add background-subtracted training mode (subtract_background flag) - Add low-signal augmentation (0.01-5 Bq activities, 30-300s durations) - Update docker-compose.yml: batch_size=32, duration=30-300s, CSI_NONLINEAR_ALPHA/BETA env vars for detect and web - Web dashboard: apply CsI correction to displayed spectra - Various UI fixes (Chart.js width, zoom/pan, isotope lines) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
378 lines
14 KiB
Python
378 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Radiacode 103 — Identification automatique d'isotopes
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Cycle de 24h avec détection branché/débranché
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Fonctionne en Docker sur machine GPU (dev) ou RPi 4 (production)
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"""
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import numpy as np
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import torch
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import time
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import json
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import logging
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import os
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import sys
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from datetime import datetime
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from pathlib import Path
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# Configuration via variables d'environnement
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MODEL_PATH = os.environ.get("MODEL_PATH", "/models/vega_best.pt")
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ISOTOPE_INDEX_PATH = os.environ.get("ISOTOPE_INDEX_PATH", "/models/vega_isotope_index.txt")
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BACKGROUND_PATH = os.environ.get("BACKGROUND_PATH", "/data/background_24h.npy")
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LOG_DIR = Path(os.environ.get("LOG_DIR", "/logs"))
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LOG_DIR.mkdir(parents=True, exist_ok=True)
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THRESHOLD = float(os.environ.get("THRESHOLD", "0.5"))
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SAMPLE_INTERVAL = int(os.environ.get("SAMPLE_INTERVAL", "60"))
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REPORT_HOUR = int(os.environ.get("REPORT_HOUR", "0"))
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MIN_LIVE_TIME = int(os.environ.get("MIN_LIVE_TIME", "3600"))
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STATE_PATH = os.environ.get("STATE_PATH", "/data/monitor_state.json")
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CPS_LOG_PATH = os.environ.get("CPS_LOG_PATH", "/data/cps_log.jsonl")
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ENERGY_OFFSET = float(os.environ.get("ENERGY_CALIBRATION_OFFSET", "0.33"))
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ENERGY_SLOPE = float(os.environ.get("ENERGY_CALIBRATION_SLOPE", "2.97"))
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# CsI(Tl) non-linear response correction
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# CsI(Tl) produces more light per keV at low energies, shifting peaks to higher
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# apparent energies. Model: E_apparent = E_true * (1 + alpha * exp(-E_true/beta))
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# Calibrated from Am-241 (59.5 keV appears at ~71.6 keV) and K-40 (correct at 1460.8 keV).
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CSI_NONLINEAR_ALPHA = float(os.environ.get("CSI_NONLINEAR_ALPHA", "0.37"))
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CSI_NONLINEAR_BETA = float(os.environ.get("CSI_NONLINEAR_BETA", "100.0"))
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def correct_csilinear_energy(spectrum_rate, num_channels=1023):
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"""Apply inverse CsI(Tl) non-linear response correction to spectrum channels.
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CsI(Tl) has non-proportional scintillation response at low energies,
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causing peaks to appear at higher channels than their true energy position.
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This function remaps channels so that peaks appear at their theoretical
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energy positions, matching what the model was trained on.
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For each output channel j (true energy position), we find the input
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channel i (apparent energy position) where the detector actually placed
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counts for that true energy.
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Args:
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spectrum_rate: Array of 1023 channel count rates
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num_channels: Number of channels
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Returns:
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Corrected spectrum with peaks at theoretical energy positions
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"""
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alpha = CSI_NONLINEAR_ALPHA
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beta = CSI_NONLINEAR_BETA
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# For each output channel j, compute the apparent energy where
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# counts for true energy E_true(j) actually appear
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output_channels = np.arange(num_channels, dtype=np.float64)
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e_true = ENERGY_OFFSET + ENERGY_SLOPE * output_channels
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# Forward model: E_apparent = E_true * (1 + alpha * exp(-E_true / beta))
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e_apparent = e_true * (1 + alpha * np.exp(-e_true / beta))
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# Input channel where the detector placed counts for this true energy
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source_channels = (e_apparent - ENERGY_OFFSET) / ENERGY_SLOPE
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source_channels = np.clip(source_channels, 0, num_channels - 1.001)
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# Linear interpolation from source channels
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lower = np.floor(source_channels).astype(int)
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upper = np.minimum(lower + 1, num_channels - 1)
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frac = source_channels - lower
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corrected = spectrum_rate[lower] * (1 - frac) + spectrum_rate[upper] * frac
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return corrected
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# Logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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handlers=[
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logging.StreamHandler(),
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logging.FileHandler(LOG_DIR / "radiacode.log"),
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],
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)
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log = logging.getLogger(__name__)
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class RadiacodeMonitor:
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def __init__(self):
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# Charger le modèle PyTorch
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device_str = os.environ.get("VEGA_DEVICE", "cpu")
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self.torch_device = torch.device(device_str)
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log.info(f"Chargement du modèle depuis {MODEL_PATH} sur {self.torch_device}...")
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checkpoint = torch.load(MODEL_PATH, map_location=self.torch_device, weights_only=False)
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# Importer VegaModel (depuis le volume monté)
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vega_ml_path = os.environ.get("VEGA_ML_PATH", "/models/vega_ml")
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if vega_ml_path not in sys.path:
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sys.path.insert(0, vega_ml_path)
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from training.vega.model import VegaModel, VegaConfig
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from training.vega.isotope_index import IsotopeIndex
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self.model_config = VegaConfig(**checkpoint["model_config"])
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self.model = VegaModel(self.model_config)
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self.model.load_state_dict(checkpoint["model_state_dict"])
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self.model.eval()
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log.info(
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f"Modèle chargé : {self.model_config.num_isotopes} isotopes, "
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f"{self.model.count_parameters():,} paramètres"
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)
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# Charger l'index des isotopes
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self.isotope_index = IsotopeIndex.load(Path(ISOTOPE_INDEX_PATH))
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# Charger le bruit de fond de référence
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self.bg_counts = None
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self.bg_live_time = None
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bg_path = Path(BACKGROUND_PATH)
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if bg_path.exists():
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bg_data = np.load(str(bg_path), allow_pickle=True).item()
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self.bg_counts = bg_data["counts"].astype(np.float64)
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self.bg_live_time = float(bg_data["duration"])
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log.info(
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f"Background chargé : {self.bg_live_time/3600:.1f}h, "
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f"{self.bg_counts.sum():.0f} coups"
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)
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else:
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log.warning(f"Pas de fichier background : {BACKGROUND_PATH}")
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# Connexion persistante au Radiacode
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self._rc = None
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self.reconnect_backoff = 0
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# Compteurs cumulés
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self.cumulated_counts = np.zeros(1024, dtype=np.float64)
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self.cumulated_live_time = 0.0
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self.last_report_date = None
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self.connected = False
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def _connect(self):
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"""Tente d'établir une connexion persistante au Radiacode."""
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try:
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from radiacode import RadiaCode
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self._rc = RadiaCode()
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self.connected = True
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self.reconnect_backoff = 0
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log.info("Radiacode connecté")
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return True
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except Exception as e:
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self._rc = None
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self.connected = False
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self.reconnect_backoff = min(self.reconnect_backoff + 1, 10)
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log.debug(f"Détecteur non disponible (retry dans {self.reconnect_backoff} cycles) : {e}")
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return False
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def _disconnect(self):
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"""Ferme la connexion au Radiacode."""
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if self._rc is not None:
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try:
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del self._rc
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except Exception:
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pass
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self._rc = None
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self.connected = False
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def sample_once(self):
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"""Échantillonne une fois. Retourne True si succès."""
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# Établir la connexion si nécessaire
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if self._rc is None:
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if self.reconnect_backoff > 0:
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self.reconnect_backoff -= 1
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return False
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if not self._connect():
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return False
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try:
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spectrum = self._rc.spectrum()
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counts = np.array(spectrum.counts, dtype=np.float64)
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live_time = spectrum.duration.total_seconds()
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if live_time > 0 and counts.sum() > 0:
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self.cumulated_counts += counts
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self.cumulated_live_time += live_time
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self._rc.spectrum_reset()
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log.info(
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f"Échantillon : {counts.sum():.0f} coups en {live_time:.1f}s "
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f"(cumul : {self.cumulated_live_time/3600:.1f}h)"
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)
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return True
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return False
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except Exception as e:
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log.warning(f"Erreur échantillonnage, reconnexion : {e}")
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self._disconnect()
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return False
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def save_state(self):
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"""Ecrit l'etat actuel du moniteur dans un fichier JSON atomique."""
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energy_kev = [round(ENERGY_OFFSET + ENERGY_SLOPE * i, 2) for i in range(1024)]
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cps = float(self.cumulated_counts.sum() / self.cumulated_live_time) if self.cumulated_live_time > 0 else 0.0
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isotopes = []
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if self.cumulated_live_time > 0:
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rate = self.cumulated_counts / self.cumulated_live_time
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if self.bg_counts is not None and self.bg_live_time is not None:
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bg_rate = self.bg_counts[:1023] / self.bg_live_time
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net_rate = np.clip(rate[:1023] - bg_rate, 0, None)
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else:
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net_rate = rate[:1023]
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isotopes = self.run_inference(net_rate)
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state = {
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"timestamp": datetime.now().isoformat(),
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"connected": self.connected,
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"cumulated_live_time_s": round(self.cumulated_live_time, 1),
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"cumulated_live_time_h": round(self.cumulated_live_time / 3600, 2),
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"total_counts": int(self.cumulated_counts.sum()),
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"cps": round(cps, 2),
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"background_subtracted": self.bg_counts is not None,
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"isotopes_detected": isotopes,
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"energy_kev": energy_kev,
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"counts": [round(float(c), 1) for c in self.cumulated_counts],
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}
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state_path = Path(STATE_PATH)
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state_path.parent.mkdir(parents=True, exist_ok=True)
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tmp_path = state_path.with_suffix(".tmp")
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with open(tmp_path, "w") as f:
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json.dump(state, f)
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os.replace(tmp_path, state_path)
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def log_cps(self, counts, live_time):
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"""Ajoute un point CPS au journal horodaté."""
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cps = float(counts.sum() / live_time) if live_time > 0 else 0.0
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entry = {
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"ts": datetime.now().isoformat(),
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"cps": round(cps, 2),
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"live_time_s": round(live_time, 1),
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"total_counts": int(counts.sum()),
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}
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log_path = Path(CPS_LOG_PATH)
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log_path.parent.mkdir(parents=True, exist_ok=True)
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with open(log_path, "a") as f:
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f.write(json.dumps(entry) + "\n")
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def run_inference(self, spectrum_rate):
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"""Exécute l'inférence PyTorch sur le spectre cumulé."""
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if spectrum_rate.max() > 0:
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# Apply CsI(Tl) non-linear correction so peaks appear
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# at theoretical energy positions (matching training data)
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corrected = correct_csilinear_energy(spectrum_rate)
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log_spectrum = np.log1p(np.maximum(corrected, 0))
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normalized = log_spectrum / log_spectrum.max()
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else:
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return []
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tensor = torch.tensor(normalized, dtype=torch.float32).unsqueeze(0).to(self.torch_device)
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with torch.no_grad():
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logits, activities = self.model(tensor)
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probs = torch.sigmoid(logits).cpu().numpy()[0]
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activities = activities.cpu().numpy()[0] * self.model_config.max_activity_bq
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results = []
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for i in range(len(probs)):
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if probs[i] >= THRESHOLD:
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results.append(
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{
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"isotope": self.isotope_index.index_to_name(i),
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"probability": float(probs[i]),
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"activity_bq": float(activities[i]),
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}
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)
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return sorted(results, key=lambda x: -x["probability"])
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def generate_report(self):
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"""Génère le rapport quotidien."""
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if self.cumulated_live_time < MIN_LIVE_TIME:
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log.warning(
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f"Pas assez de données ({self.cumulated_live_time/3600:.1f}h < "
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f"{MIN_LIVE_TIME/3600:.1f}h minimum). Pas de rapport."
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)
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return
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rate = self.cumulated_counts / self.cumulated_live_time
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if self.bg_counts is not None and self.bg_live_time is not None:
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bg_rate = self.bg_counts[:1023] / self.bg_live_time
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net_rate = np.clip(rate[:1023] - bg_rate, 0, None)
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else:
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net_rate = rate[:1023]
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results = self.run_inference(net_rate)
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now = datetime.now()
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report = {
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"date": now.isoformat(),
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"live_time_hours": self.cumulated_live_time / 3600,
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"total_counts": int(self.cumulated_counts.sum()),
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"cps_mean": float(self.cumulated_counts.sum() / self.cumulated_live_time),
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"background_subtracted": self.bg_counts is not None,
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"isotopes_detected": results,
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}
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report_path = LOG_DIR / f"report_{now.strftime('%Y-%m-%d')}.json"
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with open(report_path, "w") as f:
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json.dump(report, f, indent=2, ensure_ascii=False)
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# Affichage
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print(f"\n{'='*50}")
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print(f" RAPPORT — {now.strftime('%d/%m/%Y')}")
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print(f"{'='*50}")
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print(f" Live time : {self.cumulated_live_time/3600:.1f}h")
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print(f" Comptages : {self.cumulated_counts.sum():.0f}")
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print(f" CPS moyen : {self.cumulated_counts.sum()/self.cumulated_live_time:.1f}")
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print(
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f" Background : {'soustrait' if self.bg_counts is not None else 'non soustrait'}"
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)
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print()
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if results:
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for r in results:
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print(
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f" {r['isotope']:>10s} : {r['probability']*100:5.1f}% — {r['activity_bq']:.1f} Bq"
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)
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else:
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print(" (background uniquement)")
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print(f"{'='*50}\n")
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log.info(f"Rapport sauvegardé : {report_path}")
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# Reset pour le cycle suivant
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self.cumulated_counts = np.zeros(1024, dtype=np.float64)
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self.cumulated_live_time = 0.0
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def run(self):
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"""Boucle principale."""
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log.info("=" * 50)
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log.info("Radiacode 103 — Moniteur d'isotopes")
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log.info("=" * 50)
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log.info(f"Modèle : {MODEL_PATH}")
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log.info(f"Device : {self.torch_device}")
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log.info(f"Isotopes : {self.isotope_index.num_isotopes}")
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log.info(
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f"Background : {'chargé' if self.bg_counts is not None else 'non disponible'}"
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)
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log.info(f"Seuil : {THRESHOLD}")
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log.info(f"Intervalle : {SAMPLE_INTERVAL}s")
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while True:
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now = datetime.now()
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if self.last_report_date != now.date() and now.hour == REPORT_HOUR:
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self.generate_report()
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self.last_report_date = now.date()
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success = self.sample_once()
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if success:
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self.save_state()
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self.log_cps(self.cumulated_counts, self.cumulated_live_time)
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else:
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self.save_state()
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time.sleep(SAMPLE_INTERVAL)
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if __name__ == "__main__":
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monitor = RadiacodeMonitor()
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monitor.run() |