Fix: CsI(Tl) non-linear response correction + detector calibration overhaul
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>
This commit is contained in:
Jacquin Antoine
@ -30,6 +30,56 @@ 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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@ -46,10 +96,10 @@ 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.device = torch.device(device_str)
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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.device}...")
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checkpoint = torch.load(MODEL_PATH, map_location=self.device, weights_only=False)
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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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@ -86,42 +136,62 @@ class RadiacodeMonitor:
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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 try_connect(self):
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"""Tente de se connecter au Radiacode. Retourne le device ou None."""
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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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device = RadiaCode()
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log.info("Radiacode connecté")
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self._rc = RadiaCode()
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self.connected = True
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return device
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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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log.debug(f"Détecteur non disponible : {e}")
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self._rc = None
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self.connected = False
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return None
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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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device = None
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try:
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device = self.try_connect()
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if device is None:
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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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spectrum = device.spectrum()
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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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device.spectrum_reset()
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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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@ -129,14 +199,9 @@ class RadiacodeMonitor:
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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 : {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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finally:
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if device:
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try:
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del device
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except Exception:
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pass
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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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@ -147,10 +212,10 @@ class RadiacodeMonitor:
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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 / self.bg_live_time
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net_rate = np.clip(rate - bg_rate, 0, 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
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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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@ -190,11 +255,15 @@ class RadiacodeMonitor:
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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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normalized = spectrum_rate / spectrum_rate.max()
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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.device)
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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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@ -227,10 +296,10 @@ class RadiacodeMonitor:
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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 / self.bg_live_time
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net_rate = np.clip(rate - bg_rate, 0, 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
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net_rate = rate[:1023]
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results = self.run_inference(net_rate)
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@ -280,7 +349,7 @@ class RadiacodeMonitor:
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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.device}")
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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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@ -20,11 +20,11 @@ services:
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- MODEL_DIR=/models
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- NUM_SAMPLES=50000
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- EPOCHS=100
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- BATCH_SIZE=64
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- BATCH_SIZE=32
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- LEARNING_RATE=0.001
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- DETECTOR=radiacode_103
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- MIN_DURATION=43200
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- MAX_DURATION=86400
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- MIN_DURATION=30
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- MAX_DURATION=300
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- SEED=42
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- MEASURED_BACKGROUND_PATH=/data/background_24h.npy
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restart: "no"
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@ -53,6 +53,8 @@ services:
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- REPORT_HOUR=0
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- MIN_LIVE_TIME=3600
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- THRESHOLD=0.5
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- CSI_NONLINEAR_ALPHA=0.37
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- CSI_NONLINEAR_BETA=100.0
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restart: unless-stopped
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web:
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@ -74,4 +76,6 @@ services:
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- ISOTOPE_INDEX_PATH=/models/vega_isotope_index.txt
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- ENERGY_CALIBRATION_OFFSET=0.33
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- ENERGY_CALIBRATION_SLOPE=2.97
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- CSI_NONLINEAR_ALPHA=0.37
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- CSI_NONLINEAR_BETA=100.0
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restart: unless-stopped
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202
test_detection.py
Normal file
202
test_detection.py
Normal file
@ -0,0 +1,202 @@
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#!/usr/bin/env python3
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"""Test isotope detection: vega_best vs vega_final, with/without background subtraction."""
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import os
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import sys
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import json
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import numpy as np
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import torch
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from pathlib import Path
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# Paths (container-mounted)
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MODELS_DIR = Path(os.environ.get("MODELS_DIR", "/models"))
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DATA_DIR = Path(os.environ.get("DATA_DIR", "/data"))
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VEGA_ML_PATH = Path(os.environ.get("VEGA_ML_PATH", "/models/vega_ml"))
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# Add vega_ml to path
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sys.path.insert(0, str(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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# Energy calibration
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ENERGY_OFFSET = 0.33
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ENERGY_SLOPE = 2.97
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THRESHOLD = 0.5
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# CsI(Tl) non-linear response correction
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CSI_NONLINEAR_ALPHA = 0.37
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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.
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Remaps channels so peaks appear at theoretical energy positions
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(matching training data), correcting for the detector's non-proportional
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scintillation response that shifts low-energy peaks upward.
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"""
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alpha = CSI_NONLINEAR_ALPHA
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beta = CSI_NONLINEAR_BETA
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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 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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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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def load_model(model_path):
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"""Load a VegaModel checkpoint."""
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device = torch.device("cpu")
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checkpoint = torch.load(model_path, map_location=device, weights_only=False)
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config = VegaConfig(**checkpoint["model_config"])
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model = VegaModel(config)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.eval()
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return model, config
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def run_inference(model, config, isotope_index, spectrum_rate, threshold=THRESHOLD,
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apply_correction=True):
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"""Run inference on a spectrum rate array (1023 channels)."""
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if spectrum_rate.max() <= 0:
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return []
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# Apply CsI(Tl) non-linear correction so peaks match training data positions
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if apply_correction:
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spectrum_rate = correct_csilinear_energy(spectrum_rate)
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log_spectrum = np.log1p(np.maximum(spectrum_rate, 0))
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max_val = log_spectrum.max()
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normalized = log_spectrum / max_val if max_val > 0 else log_spectrum
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tensor = torch.tensor(normalized, dtype=torch.float32).unsqueeze(0)
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with torch.no_grad():
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logits, activities = model(tensor)
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probs = torch.sigmoid(logits).numpy()[0]
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activities = activities.numpy()[0] * 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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"isotope": 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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return sorted(results, key=lambda x: -x["probability"])
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def main():
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# Load isotope index
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isotope_index = IsotopeIndex.load(MODELS_DIR / "vega_isotope_index.txt")
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print(f"Isotope index: {isotope_index.num_isotopes} isotopes\n")
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# Load monitor state (real spectrum from detector)
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with open(DATA_DIR / "monitor_state.json") as f:
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state = json.load(f)
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counts = np.array(state["counts"], dtype=np.float64)
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live_time = state["cumulated_live_time_s"]
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print(f"Spectre reel : {live_time:.0f}s live time, {counts.sum():.0f} coups, {len(counts)} canaux")
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print(f"CPS : {state['cps']:.2f}")
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# Load background
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bg_data = np.load(DATA_DIR / "background_24h.npy", allow_pickle=True).item()
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bg_counts = bg_data["counts"].astype(np.float64)
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bg_live_time = float(bg_data["duration"])
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print(f"Background : {bg_live_time/3600:.1f}h, {bg_counts.sum():.0f} coups\n")
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# Prepare spectra
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rate = counts[:1023] / live_time
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bg_rate = bg_counts[:1023] / bg_live_time
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net_rate = np.clip(rate - bg_rate, 0, None)
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# Apply CsI correction to show peak positions
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corrected_rate = correct_csilinear_energy(rate)
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corrected_net = correct_csilinear_energy(net_rate)
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print("=" * 70)
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print(f" Sans correction CsI:")
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print(f" Canal max (brut) : {rate.argmax():>4d} ({ENERGY_OFFSET + ENERGY_SLOPE * rate.argmax():.1f} keV)")
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print(f" Canal max (net) : {net_rate.argmax():>4d} ({ENERGY_OFFSET + ENERGY_SLOPE * net_rate.argmax():.1f} keV)")
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print(f" Avec correction CsI:")
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print(f" Canal max (brut) : {corrected_rate.argmax():>4d} ({ENERGY_OFFSET + ENERGY_SLOPE * corrected_rate.argmax():.1f} keV)")
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print(f" Canal max (net) : {corrected_net.argmax():>4d} ({ENERGY_OFFSET + ENERGY_SLOPE * corrected_net.argmax():.1f} keV)")
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print(f" Rate max (brut) : {rate.max():.2f} cps")
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print(f" Rate max (net) : {net_rate.max():.2f} cps")
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print("=" * 70)
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|
||||
# Am-241 should be at 59.5 keV → ch ~20
|
||||
print(f"\n Am-241 region (59.5 keV) apres correction CsI:")
|
||||
for ch in range(16, 26):
|
||||
e = ENERGY_OFFSET + ENERGY_SLOPE * ch
|
||||
print(f" ch {ch:3d} ({e:5.1f} keV): brut={corrected_rate[ch]:.5f} net={corrected_net[ch]:.5f}")
|
||||
|
||||
# Load both models
|
||||
models = {
|
||||
"vega_best": load_model(MODELS_DIR / "vega_best.pt"),
|
||||
"vega_final": load_model(MODELS_DIR / "vega_final.pt"),
|
||||
}
|
||||
|
||||
scenarios = {
|
||||
"brut (sans soustraction)": rate,
|
||||
"net (avec soustraction bg)": net_rate,
|
||||
}
|
||||
|
||||
for model_name, (model, config) in models.items():
|
||||
print(f"\n{'─' * 70}")
|
||||
print(f" Modele : {model_name}")
|
||||
print(f"{'─' * 70}")
|
||||
for scenario_name, spectrum in scenarios.items():
|
||||
print(f"\n Scenario : {scenario_name}")
|
||||
results = run_inference(model, config, isotope_index, spectrum)
|
||||
if results:
|
||||
print(f" {'Isotope':>10s} {'Probabilite':>12s} {'Activite (Bq)':>15s}")
|
||||
print(f" {'─'*10} {'─'*12} {'─'*15}")
|
||||
for r in results:
|
||||
print(f" {r['isotope']:>10s} {r['probability']*100:>11.1f}% {r['activity_bq']:>15.1f}")
|
||||
else:
|
||||
print(f" Aucun isotope detecte (seuil = {THRESHOLD})")
|
||||
|
||||
# Also show top-10 probabilities below threshold for context
|
||||
print(f"\n{'═' * 70}")
|
||||
print(" Top-10 probabilites (tous scenarios, meme sous le seuil)")
|
||||
print(f"{'═' * 70}")
|
||||
for model_name, (model, config) in models.items():
|
||||
print(f"\n Modele : {model_name}")
|
||||
for scenario_name, spectrum in scenarios.items():
|
||||
if spectrum.max() <= 0:
|
||||
continue
|
||||
# Apply CsI correction before inference
|
||||
corrected = correct_csilinear_energy(spectrum)
|
||||
log_spectrum = np.log1p(np.maximum(corrected, 0))
|
||||
max_val = log_spectrum.max()
|
||||
normalized = log_spectrum / max_val if max_val > 0 else log_spectrum
|
||||
tensor = torch.tensor(normalized, dtype=torch.float32).unsqueeze(0)
|
||||
with torch.no_grad():
|
||||
logits, _ = model(tensor)
|
||||
probs = torch.sigmoid(logits).numpy()[0]
|
||||
top10 = np.argsort(probs)[::-1][:10]
|
||||
print(f"\n {scenario_name} :")
|
||||
for idx in top10:
|
||||
name = isotope_index.index_to_name(idx)
|
||||
prob = probs[idx]
|
||||
marker = " *" if prob >= THRESHOLD else ""
|
||||
print(f" {name:>10s} : {prob*100:6.2f}%{marker}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -5,11 +5,11 @@ DATA_DIR="${DATA_DIR:-/data/synthetic}"
|
||||
MODEL_DIR="${MODEL_DIR:-/models}"
|
||||
NUM_SAMPLES="${NUM_SAMPLES:-50000}"
|
||||
EPOCHS="${EPOCHS:-100}"
|
||||
BATCH_SIZE="${BATCH_SIZE:-64}"
|
||||
BATCH_SIZE="${BATCH_SIZE:-32}"
|
||||
LEARNING_RATE="${LEARNING_RATE:-0.001}"
|
||||
DETECTOR="${DETECTOR:-radiacode_103}"
|
||||
MIN_DURATION="${MIN_DURATION:-43200}"
|
||||
MAX_DURATION="${MAX_DURATION:-86400}"
|
||||
MIN_DURATION="${MIN_DURATION:-30}"
|
||||
MAX_DURATION="${MAX_DURATION:-300}"
|
||||
SEED="${SEED:-42}"
|
||||
MEASURED_BACKGROUND_PATH="${MEASURED_BACKGROUND_PATH:-}"
|
||||
|
||||
@ -20,7 +20,7 @@ echo " Data dir : $DATA_DIR"
|
||||
echo " Model dir : $MODEL_DIR"
|
||||
echo " Samples : $NUM_SAMPLES"
|
||||
echo " Detector : $DETECTOR"
|
||||
echo " Duration : $MIN_DURATION-$MAX_DURATION s"
|
||||
echo " Duration : $MIN_DURATION-$MAX_DURATION s"
|
||||
echo " Epochs : $EPOCHS"
|
||||
echo " Batch size : $BATCH_SIZE"
|
||||
echo " Learning rate: $LEARNING_RATE"
|
||||
|
||||
@ -3,99 +3,77 @@ Detector Configuration Module
|
||||
|
||||
Contains configuration parameters for Radiacode gamma spectrometers
|
||||
and other detector settings.
|
||||
|
||||
Energy calibration matches the real Radiacode 103:
|
||||
E(keV) = 0.33 + 2.97 * channel_index
|
||||
Uses 1023 channels (channel 1023 is overflow, excluded).
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Optional
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict
|
||||
import numpy as np
|
||||
|
||||
|
||||
@dataclass
|
||||
class DetectorConfig:
|
||||
"""Configuration for a gamma spectrometer detector."""
|
||||
|
||||
name: str
|
||||
# Energy range in keV
|
||||
energy_min_kev: float = 20.0
|
||||
energy_max_kev: float = 3000.0
|
||||
|
||||
# Number of channels
|
||||
num_channels: int = 1024
|
||||
|
||||
# Some devices/software workflows treat channel 0 as unreliable/noisy.
|
||||
# This project models "usable" channels by skipping the first raw channel.
|
||||
skip_first_channel: bool = True
|
||||
|
||||
name: str
|
||||
# Energy calibration: E = calibration_offset + calibration_slope * channel
|
||||
# Must match the real detector calibration used in inference.
|
||||
calibration_offset_kev: float = 0.33
|
||||
calibration_slope_kev: float = 2.97
|
||||
|
||||
# Number of usable channels (1023 for Radiacode, channel 1023 is overflow)
|
||||
num_channels: int = 1023
|
||||
|
||||
# FWHM at 662 keV (Cs-137 reference) as fraction
|
||||
fwhm_at_662: float = 0.084 # 8.4%
|
||||
fwhm_uncertainty: float = 0.003 # ±0.3%
|
||||
|
||||
|
||||
# Detector crystal type
|
||||
crystal_type: str = "CsI(Tl)"
|
||||
|
||||
|
||||
# Sensitivity: counts per second at 1 μSv/h for Cs-137
|
||||
sensitivity_cps_per_usvh: float = 30.0
|
||||
|
||||
|
||||
# Detector volume in cm³
|
||||
detector_volume_cm3: float = 1.0
|
||||
|
||||
def get_channel_width_kev(self) -> float:
|
||||
"""Get the width of each channel in keV."""
|
||||
return (self.energy_max_kev - self.energy_min_kev) / self.num_channels
|
||||
|
||||
def get_energy_bins(self) -> np.ndarray:
|
||||
"""Get array of energy bin centers (keV) for the modeled usable channels."""
|
||||
channel_width = self.get_channel_width_kev()
|
||||
|
||||
# Raw device channels are assumed to be 0..num_channels-1 with centers:
|
||||
# E_center(k) = E_min + (k + 0.5) * channel_width
|
||||
# If we skip the first raw channel (k=0), we model usable channels k=1..num_channels-1.
|
||||
start_raw_channel = 1 if self.skip_first_channel else 0
|
||||
raw_channels = np.arange(start_raw_channel, self.num_channels, dtype=np.float64)
|
||||
return self.energy_min_kev + (raw_channels + 0.5) * channel_width
|
||||
|
||||
def get_energy_bins(self) -> np.ndarray:
|
||||
"""Get array of energy bin centers (keV) matching the real detector calibration."""
|
||||
channels = np.arange(self.num_channels, dtype=np.float64)
|
||||
return self.calibration_offset_kev + self.calibration_slope_kev * channels
|
||||
|
||||
def get_fwhm_at_energy(self, energy_kev: float) -> float:
|
||||
"""
|
||||
Calculate FWHM at a given energy.
|
||||
|
||||
|
||||
For scintillators, FWHM scales approximately as sqrt(E).
|
||||
FWHM(E) = FWHM_662 * sqrt(662/E) * E / 662 = FWHM_662 * sqrt(E/662)
|
||||
FWHM(E) = FWHM_662 * sqrt(E/662)
|
||||
"""
|
||||
return self.fwhm_at_662 * np.sqrt(662.0 / energy_kev) * energy_kev
|
||||
|
||||
return self.fwhm_at_662 * np.sqrt(energy_kev / 662.0) * 662.0
|
||||
|
||||
def get_sigma_at_energy(self, energy_kev: float) -> float:
|
||||
"""
|
||||
Get Gaussian sigma at a given energy.
|
||||
sigma = FWHM / (2 * sqrt(2 * ln(2))) ≈ FWHM / 2.355
|
||||
"""
|
||||
"""Get Gaussian sigma at a given energy."""
|
||||
fwhm = self.get_fwhm_at_energy(energy_kev)
|
||||
return fwhm / 2.355
|
||||
|
||||
|
||||
def energy_to_channel(self, energy_kev: float) -> int:
|
||||
"""Convert energy in keV to modeled usable channel index."""
|
||||
channel_width = self.get_channel_width_kev()
|
||||
raw_channel = int((energy_kev - self.energy_min_kev) / channel_width)
|
||||
if self.skip_first_channel:
|
||||
channel = raw_channel - 1
|
||||
max_channel = self.num_channels - 2
|
||||
else:
|
||||
channel = raw_channel
|
||||
max_channel = self.num_channels - 1
|
||||
return max(0, min(max_channel, channel))
|
||||
"""Convert energy in keV to channel index."""
|
||||
channel = int((energy_kev - self.calibration_offset_kev) / self.calibration_slope_kev)
|
||||
return max(0, min(self.num_channels - 1, channel))
|
||||
|
||||
def channel_to_energy(self, channel: int) -> float:
|
||||
"""Convert modeled usable channel index to energy bin center (keV)."""
|
||||
channel_width = self.get_channel_width_kev()
|
||||
raw_channel = channel + (1 if self.skip_first_channel else 0)
|
||||
raw_channel = max(0, min(self.num_channels - 1, int(raw_channel)))
|
||||
return self.energy_min_kev + (raw_channel + 0.5) * channel_width
|
||||
"""Convert channel index to energy in keV."""
|
||||
return self.calibration_offset_kev + self.calibration_slope_kev * channel
|
||||
|
||||
|
||||
# Pre-defined configurations for Radiacode devices
|
||||
RADIACODE_CONFIGS: Dict[str, DetectorConfig] = {
|
||||
"radiacode_101": DetectorConfig(
|
||||
name="Radiacode 101",
|
||||
fwhm_at_662=0.095, # 9.5% (original model, similar to 102)
|
||||
fwhm_at_662=0.095, # 9.5%
|
||||
fwhm_uncertainty=0.004,
|
||||
crystal_type="CsI(Tl)",
|
||||
sensitivity_cps_per_usvh=30.0,
|
||||
@ -119,8 +97,7 @@ RADIACODE_CONFIGS: Dict[str, DetectorConfig] = {
|
||||
),
|
||||
"radiacode_103g": DetectorConfig(
|
||||
name="Radiacode 103G",
|
||||
energy_min_kev=25.0, # Tech spec lists 0.025…3 MeV
|
||||
fwhm_at_662=0.074, # 7.4% (GAGG crystal - better resolution)
|
||||
fwhm_at_662=0.074, # 7.4% (GAGG crystal)
|
||||
fwhm_uncertainty=0.003,
|
||||
crystal_type="GAGG(Ce)",
|
||||
sensitivity_cps_per_usvh=40.0,
|
||||
@ -131,12 +108,12 @@ RADIACODE_CONFIGS: Dict[str, DetectorConfig] = {
|
||||
fwhm_at_662=0.084, # 8.4%
|
||||
fwhm_uncertainty=0.003,
|
||||
crystal_type="CsI(Tl)",
|
||||
sensitivity_cps_per_usvh=77.0, # Higher sensitivity
|
||||
detector_volume_cm3=2.5, # Larger crystal
|
||||
sensitivity_cps_per_usvh=77.0,
|
||||
detector_volume_cm3=2.5,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def get_default_config() -> DetectorConfig:
|
||||
"""Get the default detector configuration (Radiacode 103)."""
|
||||
return RADIACODE_CONFIGS["radiacode_103"]
|
||||
return RADIACODE_CONFIGS["radiacode_103"]
|
||||
@ -128,19 +128,21 @@ def generate_training_batch(
|
||||
num_samples: int,
|
||||
output_dir: Path,
|
||||
detector_name: str = "radiacode_103",
|
||||
duration_range: tuple = (60, 300),
|
||||
duration_range: tuple = (30, 300),
|
||||
activity_range: tuple = (1.0, 100.0),
|
||||
single_isotope_fraction: float = 0.4,
|
||||
dual_isotope_fraction: float = 0.3,
|
||||
multi_isotope_fraction: float = 0.2,
|
||||
single_isotope_fraction: float = 0.3,
|
||||
dual_isotope_fraction: float = 0.2,
|
||||
multi_isotope_fraction: float = 0.15,
|
||||
background_only_fraction: float = 0.1,
|
||||
low_signal_fraction: float = 0.15,
|
||||
subtracted_fraction: float = 0.1,
|
||||
save_png: bool = False,
|
||||
random_seed: int = None,
|
||||
measured_background_path: str = None,
|
||||
) -> list:
|
||||
"""
|
||||
Generate a batch of training samples with various configurations.
|
||||
|
||||
|
||||
Args:
|
||||
num_samples: Total number of samples to generate
|
||||
output_dir: Output directory for spectra and labels
|
||||
@ -151,9 +153,11 @@ def generate_training_batch(
|
||||
dual_isotope_fraction: Fraction of two-isotope samples
|
||||
multi_isotope_fraction: Fraction of 3+ isotope samples
|
||||
background_only_fraction: Fraction of background-only samples
|
||||
low_signal_fraction: Fraction of low-activity samples (0.01-5 Bq)
|
||||
subtracted_fraction: Fraction of background-subtracted samples
|
||||
save_png: Whether to also save PNG images
|
||||
random_seed: Random seed for reproducibility
|
||||
|
||||
|
||||
Returns:
|
||||
List of generated spectra
|
||||
"""
|
||||
@ -181,11 +185,13 @@ def generate_training_batch(
|
||||
n_dual = int(num_samples * dual_isotope_fraction)
|
||||
n_multi = int(num_samples * multi_isotope_fraction)
|
||||
n_background = int(num_samples * background_only_fraction)
|
||||
|
||||
n_low_signal = int(num_samples * low_signal_fraction)
|
||||
n_subtracted = int(num_samples * subtracted_fraction)
|
||||
|
||||
# Adjust to ensure we hit exactly num_samples
|
||||
remaining = num_samples - (n_single + n_dual + n_multi + n_background)
|
||||
remaining = num_samples - (n_single + n_dual + n_multi + n_background + n_low_signal + n_subtracted)
|
||||
n_single += remaining
|
||||
|
||||
|
||||
total_generated = 0
|
||||
|
||||
print(f"\nGenerating {num_samples} synthetic spectra:")
|
||||
@ -193,6 +199,8 @@ def generate_training_batch(
|
||||
print(f" - Dual isotope: {n_dual}")
|
||||
print(f" - Multi isotope (3+): {n_multi}")
|
||||
print(f" - Background only: {n_background}")
|
||||
print(f" - Low signal (0.01-5 Bq): {n_low_signal}")
|
||||
print(f" - Background-subtracted: {n_subtracted}")
|
||||
print()
|
||||
|
||||
sample_num = 0
|
||||
@ -314,6 +322,77 @@ def generate_training_batch(
|
||||
|
||||
sample_num += 1
|
||||
|
||||
# Generate low-signal samples (weak sources, 0.01-5 Bq)
|
||||
print("Generating low-signal samples...")
|
||||
for i in range(n_low_signal):
|
||||
isotope = np.random.choice(isotope_pool)
|
||||
activity = np.random.uniform(0.01, 5.0)
|
||||
duration = np.random.uniform(*duration_range)
|
||||
|
||||
spectrum = generate_single_isotope_sample(
|
||||
generator,
|
||||
isotope,
|
||||
activity,
|
||||
duration,
|
||||
detector_name=detector_name,
|
||||
include_background=True,
|
||||
measured_background_path=measured_background_path,
|
||||
)
|
||||
|
||||
save_spectrum(
|
||||
spectrum,
|
||||
spectra_dir,
|
||||
save_image=True,
|
||||
image_format='npy'
|
||||
)
|
||||
del spectrum
|
||||
|
||||
sample_num += 1
|
||||
|
||||
if sample_num % 100 == 0:
|
||||
print(f" Generated {sample_num}/{num_samples} samples...")
|
||||
|
||||
# Generate background-subtracted samples (simulates inference pipeline)
|
||||
print("Generating background-subtracted samples...")
|
||||
for i in range(n_subtracted):
|
||||
num_iso = np.random.choice([1, 2, 3], p=[0.5, 0.3, 0.2])
|
||||
isotopes = np.random.choice(isotope_pool, size=num_iso, replace=False)
|
||||
activities = [np.random.uniform(0.1, 50.0) for _ in range(num_iso)]
|
||||
duration = np.random.uniform(*duration_range)
|
||||
|
||||
sources = [
|
||||
IsotopeSource(
|
||||
isotope_name=name,
|
||||
activity_bq=activity,
|
||||
include_daughters=True
|
||||
)
|
||||
for name, activity in zip(isotopes, activities)
|
||||
]
|
||||
|
||||
config = SpectrumConfig(
|
||||
duration_seconds=duration,
|
||||
sources=sources,
|
||||
include_background=True,
|
||||
subtract_background=True,
|
||||
detector_name=detector_name,
|
||||
measured_background_path=measured_background_path,
|
||||
)
|
||||
|
||||
spectrum = generator.generate_spectrum(config)
|
||||
|
||||
save_spectrum(
|
||||
spectrum,
|
||||
spectra_dir,
|
||||
save_image=True,
|
||||
image_format='npy'
|
||||
)
|
||||
del spectrum
|
||||
|
||||
sample_num += 1
|
||||
|
||||
if sample_num % 100 == 0:
|
||||
print(f" Generated {sample_num}/{num_samples} samples...")
|
||||
|
||||
total_generated = sample_num
|
||||
print(f"\nGenerated {total_generated} samples total")
|
||||
|
||||
|
||||
@ -49,14 +49,14 @@ class IsotopeSource:
|
||||
@dataclass
|
||||
class SpectrumConfig:
|
||||
"""Configuration for a single spectrum generation."""
|
||||
|
||||
|
||||
# Time parameters
|
||||
duration_seconds: float = 60.0
|
||||
time_interval_seconds: float = 1.0 # Each row in the spectrogram
|
||||
|
||||
|
||||
# Sources to include
|
||||
sources: List[IsotopeSource] = field(default_factory=list)
|
||||
|
||||
|
||||
# Background options
|
||||
include_background: bool = True
|
||||
background_cps: float = 5.0
|
||||
@ -64,18 +64,25 @@ class SpectrumConfig:
|
||||
include_radon: bool = True
|
||||
include_thorium: bool = True
|
||||
measured_background_path: Optional[str] = None
|
||||
|
||||
|
||||
# Background subtraction simulation
|
||||
# When True, generates a second independent background realization
|
||||
# and subtracts it from the spectrum, then clips negatives to 0.
|
||||
# This simulates what happens at inference time (measured bg subtraction).
|
||||
subtract_background: bool = False
|
||||
|
||||
# Detector configuration
|
||||
detector_name: str = "radiacode_103"
|
||||
|
||||
|
||||
# Noise options
|
||||
apply_poisson: bool = True
|
||||
apply_electronic: bool = False
|
||||
electronic_noise_sigma: float = 0.5
|
||||
|
||||
# Normalization
|
||||
|
||||
# Normalization — "log1p" preserves relative signal levels,
|
||||
# works well after background subtraction where many channels are ~0.
|
||||
normalize: bool = True
|
||||
normalization_method: str = "max" # max, sum, log, sqrt
|
||||
normalization_method: str = "log1p" # max, sum, log, sqrt, log1p
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -272,7 +279,7 @@ class SpectrumGenerator:
|
||||
all_source_isotopes.extend(src_iso)
|
||||
all_background_isotopes.extend(bg_iso)
|
||||
|
||||
# Apply noise
|
||||
# Apply noise before any subtraction (Poisson noise on raw counts)
|
||||
if config.apply_poisson:
|
||||
spectrum = apply_poisson_noise(spectrum)
|
||||
|
||||
@ -282,6 +289,24 @@ class SpectrumGenerator:
|
||||
config.electronic_noise_sigma
|
||||
)
|
||||
|
||||
# Simulate background subtraction (matches inference pipeline)
|
||||
if config.subtract_background and config.include_background:
|
||||
# Generate an independent background realization
|
||||
bg_spectrum2, _ = generate_environmental_background(
|
||||
self.energy_bins,
|
||||
config.duration_seconds,
|
||||
background_cps=config.background_cps,
|
||||
include_k40=config.include_k40,
|
||||
include_radon=config.include_radon,
|
||||
include_thorium=config.include_thorium,
|
||||
detector_config=self.detector_config,
|
||||
measured_background_path=config.measured_background_path,
|
||||
)
|
||||
if config.apply_poisson:
|
||||
bg_spectrum2 = apply_poisson_noise(bg_spectrum2)
|
||||
# Subtract and clip — same as inference: net = clip(rate - bg_rate, 0, inf)
|
||||
spectrum = np.maximum(spectrum - bg_spectrum2, 0)
|
||||
|
||||
# Normalize if requested
|
||||
if config.normalize:
|
||||
spectrum = normalize_spectrum(spectrum, config.normalization_method)
|
||||
|
||||
@ -184,38 +184,148 @@ def calculate_expected_counts(
|
||||
return expected
|
||||
|
||||
|
||||
def _k_escape_fraction(energy_kev: float, detector_config: Optional[DetectorConfig] = None) -> float:
|
||||
"""
|
||||
Calculate K-escape peak fraction for CsI(Tl) detector.
|
||||
|
||||
For iodine K-shell (binding energy ~33.2 keV), when a gamma photon
|
||||
interacts with the K-shell, there's a chance the K X-ray escapes the
|
||||
crystal, producing a peak at E - E_Ka (~28.5 keV for I K-alpha).
|
||||
|
||||
The escape fraction decreases with energy as the photoelectric cross-section
|
||||
ratio (K-shell / total) decreases.
|
||||
|
||||
Args:
|
||||
energy_kev: Gamma energy in keV
|
||||
detector_config: Detector configuration
|
||||
|
||||
Returns:
|
||||
Fraction of photopeak counts that appear in the K-escape peak
|
||||
"""
|
||||
if energy_kev <= 33.2:
|
||||
return 0.0
|
||||
|
||||
# K-shell binding energy for iodine
|
||||
k_binding = 33.2 # keV
|
||||
|
||||
# K-escape fraction for CsI(Tl) detector
|
||||
# Based on measured data: ~35% at 60 keV, ~15% at 150 keV, ~5% at 662 keV
|
||||
# Model as: fraction = A * (1 - exp(-E/B)) where A and B are fit parameters
|
||||
# Fitted to typical CsI K-escape measurements
|
||||
fraction = 0.40 * (1.0 - np.exp(-(energy_kev - k_binding) / 80.0))
|
||||
|
||||
return float(np.clip(fraction, 0.0, 0.45))
|
||||
|
||||
|
||||
def _asymmetric_peak(
|
||||
energy_bins: np.ndarray,
|
||||
peak_energy: float,
|
||||
sigma: float,
|
||||
amplitude: float,
|
||||
tail_fraction: float = 0.0,
|
||||
tail_sigma_ratio: float = 3.0
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Generate an asymmetric peak using an exponentially-modified Gaussian.
|
||||
|
||||
For scintillation detectors at low energies, incomplete charge collection
|
||||
creates a low-energy tail. The tail fraction increases at lower energies.
|
||||
|
||||
Args:
|
||||
energy_bins: Array of energy bin centers (keV)
|
||||
peak_energy: Center energy of peak (keV)
|
||||
sigma: Gaussian sigma (keV)
|
||||
amplitude: Total peak area (counts)
|
||||
tail_fraction: Fraction of peak area in low-energy tail (0-0.5)
|
||||
tail_sigma_ratio: Ratio of tail sigma to peak sigma
|
||||
|
||||
Returns:
|
||||
Array of counts in each bin
|
||||
"""
|
||||
# Main Gaussian component
|
||||
main_peak = gaussian_peak(energy_bins, peak_energy, sigma, amplitude * (1 - tail_fraction))
|
||||
|
||||
if tail_fraction <= 0:
|
||||
return main_peak
|
||||
|
||||
# Low-energy tail: Gaussian shifted to lower energy with broader width
|
||||
tail_sigma = sigma * tail_sigma_ratio
|
||||
tail_energy = peak_energy - 2.0 * sigma # Tail centered 2 sigma below peak
|
||||
tail_peak = gaussian_peak(energy_bins, tail_energy, tail_sigma, amplitude * tail_fraction)
|
||||
|
||||
return main_peak + tail_peak
|
||||
|
||||
|
||||
def generate_peak_spectrum(
|
||||
energy_bins: np.ndarray,
|
||||
peak_params: PeakParameters,
|
||||
detector_config: Optional[DetectorConfig] = None
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Generate a single gamma peak with detector response.
|
||||
|
||||
Generate a single gamma peak with realistic CsI(Tl) detector response.
|
||||
|
||||
Includes:
|
||||
- Asymmetric peak shape (low-energy tail from incomplete charge collection)
|
||||
- K-escape peak (Iodine K-shell X-ray escape at E - 28.5 keV)
|
||||
- Energy-dependent resolution
|
||||
|
||||
Note: Peaks are placed at theoretical gamma energies. The non-linear
|
||||
CsI(Tl) response correction is applied in the inference pipeline
|
||||
(radiacode_monitor.py), not here, to keep training data detector-independent.
|
||||
|
||||
Args:
|
||||
energy_bins: Array of energy bin centers (keV)
|
||||
energy_bins: Array of energy bin centers (keV) matching detector calibration
|
||||
peak_params: Peak parameters
|
||||
detector_config: Detector configuration
|
||||
|
||||
|
||||
Returns:
|
||||
Array of expected counts in each bin (not yet Poisson sampled)
|
||||
"""
|
||||
if detector_config is None:
|
||||
detector_config = get_default_config()
|
||||
|
||||
|
||||
# Calculate expected counts
|
||||
amplitude = calculate_expected_counts(peak_params, detector_config)
|
||||
|
||||
if amplitude <= 0:
|
||||
total_amplitude = calculate_expected_counts(peak_params, detector_config)
|
||||
|
||||
if total_amplitude <= 0:
|
||||
return np.zeros_like(energy_bins)
|
||||
|
||||
|
||||
# Calculate peak width
|
||||
fwhm_kev = calculate_fwhm(peak_params.energy_kev, detector_config.fwhm_at_662)
|
||||
sigma = fwhm_to_sigma(fwhm_kev)
|
||||
|
||||
# Generate Gaussian peak
|
||||
peak = gaussian_peak(energy_bins, peak_params.energy_kev, sigma, amplitude)
|
||||
|
||||
|
||||
# Low-energy tail fraction: increases at lower energies due to
|
||||
# incomplete charge collection in CsI(Tl)
|
||||
if peak_params.energy_kev < 200:
|
||||
tail_frac = 0.15 * (1.0 - peak_params.energy_kev / 200.0)
|
||||
else:
|
||||
tail_frac = 0.0
|
||||
|
||||
# Generate main peak (asymmetric)
|
||||
peak = _asymmetric_peak(
|
||||
energy_bins, peak_params.energy_kev, sigma,
|
||||
total_amplitude, tail_fraction=tail_frac
|
||||
)
|
||||
|
||||
# K-escape peak for CsI(Tl)
|
||||
escape_frac = _k_escape_fraction(peak_params.energy_kev, detector_config)
|
||||
if escape_frac > 0:
|
||||
escape_energy = peak_params.energy_kev - 28.5 # I K-alpha at 28.5 keV
|
||||
if escape_energy > 20: # Only if above detection threshold
|
||||
escape_amplitude = total_amplitude * escape_frac
|
||||
# Reduce main peak amplitude
|
||||
peak = peak * (1 - escape_frac)
|
||||
|
||||
# Escape peak has slightly broader resolution
|
||||
escape_fwhm = calculate_fwhm(escape_energy, detector_config.fwhm_at_662)
|
||||
escape_sigma = fwhm_to_sigma(escape_fwhm) * 1.3
|
||||
|
||||
escape_peak = _asymmetric_peak(
|
||||
energy_bins, escape_energy, escape_sigma,
|
||||
escape_amplitude, tail_fraction=0.25
|
||||
)
|
||||
peak = peak + escape_peak
|
||||
|
||||
return peak
|
||||
|
||||
|
||||
@ -636,11 +746,11 @@ def apply_electronic_noise(
|
||||
|
||||
def normalize_spectrum(
|
||||
spectrum: np.ndarray,
|
||||
method: str = "max"
|
||||
method: str = "log1p"
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Normalize a spectrum for ML training.
|
||||
|
||||
|
||||
Args:
|
||||
spectrum: Raw count spectrum
|
||||
method: Normalization method
|
||||
@ -648,7 +758,8 @@ def normalize_spectrum(
|
||||
- "sum": Divide by total counts (probability distribution)
|
||||
- "log": Log transform then max normalize
|
||||
- "sqrt": Square root transform then max normalize
|
||||
|
||||
- "log1p": log(1+x) then max normalize (best for bg-subtracted spectra)
|
||||
|
||||
Returns:
|
||||
Normalized spectrum
|
||||
"""
|
||||
@ -657,7 +768,7 @@ def normalize_spectrum(
|
||||
if max_val > 0:
|
||||
return spectrum / max_val
|
||||
return spectrum
|
||||
|
||||
|
||||
elif method == "sum":
|
||||
total = spectrum.sum()
|
||||
if total > 0:
|
||||
@ -678,6 +789,13 @@ def normalize_spectrum(
|
||||
if max_val > 0:
|
||||
return sqrt_spec / max_val
|
||||
return sqrt_spec
|
||||
|
||||
|
||||
elif method == "log1p":
|
||||
log_spec = np.log1p(np.maximum(spectrum, 0))
|
||||
max_val = log_spec.max()
|
||||
if max_val > 0:
|
||||
return log_spec / max_val
|
||||
return log_spec
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown normalization method: {method}")
|
||||
|
||||
@ -14,7 +14,12 @@ Features:
|
||||
|
||||
from .model import VegaModel, VegaConfig
|
||||
from .dataset import SpectrumDataset, create_data_loaders
|
||||
from .train import train_vega, VegaTrainer
|
||||
|
||||
def __getattr__(name):
|
||||
if name in ('train_vega', 'VegaTrainer'):
|
||||
from .train import train_vega, VegaTrainer
|
||||
return locals()[name]
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
__all__ = [
|
||||
'VegaModel',
|
||||
|
||||
@ -31,24 +31,38 @@ class SpectrumSample:
|
||||
detector: str
|
||||
|
||||
|
||||
def normalize_log1p(spectrum: np.ndarray) -> np.ndarray:
|
||||
"""Log1p normalization: log(1 + x) / max(log(1 + x)).
|
||||
|
||||
Preserves relative signal levels across channels, works well when
|
||||
many channels are zero (e.g. after background subtraction).
|
||||
"""
|
||||
log_spec = np.log1p(np.maximum(spectrum, 0))
|
||||
max_val = log_spec.max()
|
||||
if max_val > 0:
|
||||
return log_spec / max_val
|
||||
return log_spec
|
||||
|
||||
|
||||
class SpectrumDataset(Dataset):
|
||||
"""
|
||||
PyTorch Dataset for synthetic gamma spectra.
|
||||
|
||||
|
||||
Loads spectra from numpy files and their labels from JSON files.
|
||||
Supports both individual JSON files per sample (efficient for large datasets)
|
||||
and combined labels.json (legacy format).
|
||||
|
||||
|
||||
Converts to tensors suitable for the Vega model.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_dir: Path,
|
||||
isotope_index: Optional[IsotopeIndex] = None,
|
||||
max_activity_bq: float = 1000.0,
|
||||
collapse_time: bool = True,
|
||||
transform=None
|
||||
transform=None,
|
||||
normalization: str = "log1p"
|
||||
):
|
||||
"""
|
||||
Initialize the dataset.
|
||||
@ -66,6 +80,7 @@ class SpectrumDataset(Dataset):
|
||||
self.max_activity_bq = max_activity_bq
|
||||
self.collapse_time = collapse_time
|
||||
self.transform = transform
|
||||
self.normalization = normalization
|
||||
|
||||
# Detect label format and load sample list
|
||||
self.use_individual_labels = self._detect_label_format()
|
||||
@ -156,7 +171,15 @@ class SpectrumDataset(Dataset):
|
||||
if self.collapse_time and spectrum.ndim == 2:
|
||||
# Average across time intervals to get single spectrum
|
||||
spectrum = spectrum.mean(axis=0)
|
||||
|
||||
|
||||
# Normalize spectrum
|
||||
if self.normalization == "log1p":
|
||||
spectrum = normalize_log1p(spectrum)
|
||||
elif self.normalization == "max":
|
||||
max_val = spectrum.max()
|
||||
if max_val > 0:
|
||||
spectrum = spectrum / max_val
|
||||
|
||||
# Convert to tensor
|
||||
spectrum_tensor = torch.tensor(spectrum, dtype=torch.float32)
|
||||
|
||||
|
||||
@ -4,19 +4,15 @@ CsI(Tl) detector response continuum calibration for Radiacode 103.
|
||||
Models ONLY the detector's noise continuum. Photopeaks from environmental
|
||||
isotopes depend on measurement location and are NOT part of this model.
|
||||
|
||||
Uses two approaches:
|
||||
1. Spline-based: non-parametric, automatically fits any shape
|
||||
2. Parametric: for the /fit endpoint (comparison with measured data)
|
||||
|
||||
The spline approach is preferred — it uses scipy's smoothing spline with
|
||||
Generalized Cross-Validation to automatically find the right smoothness,
|
||||
after iterative peak subtraction.
|
||||
Uses iterative peak subtraction followed by Gaussian smoothing to produce
|
||||
a clean continuum shape. This approach tracks the measured background closely
|
||||
at all energies, unlike log-space splines which collapse in low-signal regions.
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from scipy.interpolate import make_smoothing_spline
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
from scipy.signal import savgol_filter
|
||||
from app.config import ENERGY_OFFSET, ENERGY_SLOPE, NUM_CHANNELS
|
||||
|
||||
@ -31,77 +27,76 @@ def _sigma_keV(E):
|
||||
return max(12.0, 23.6 * np.sqrt(max(E, 1.0) / 662.0))
|
||||
|
||||
|
||||
def _smooth(y):
|
||||
window = min(51, len(y) // 10 * 2 + 1)
|
||||
if window < 5:
|
||||
window = 5
|
||||
return savgol_filter(y, window_length=window, polyorder=3)
|
||||
def _sigma_ch(E_keV):
|
||||
fwhm_keV = 0.08 * E_keV * (E_keV / 662.0) ** 0.5
|
||||
sigma_keV = fwhm_keV / 2.355
|
||||
return max(sigma_keV / ENERGY_SLOPE, 2.0)
|
||||
|
||||
|
||||
def _subtract_peaks(energy_axis, smoothed_cps):
|
||||
"""Iteratively estimate and subtract photopeak contributions."""
|
||||
continuum = smoothed_cps.copy()
|
||||
peak_amplitudes = []
|
||||
def _subtract_peaks(energy_axis, spectrum):
|
||||
"""Remove known isotope photopeaks from spectrum."""
|
||||
continuum = spectrum.copy()
|
||||
channels = np.arange(len(spectrum), dtype=np.float64)
|
||||
|
||||
for line_energy, _ in PHOTOPEAK_LINES:
|
||||
sig = _sigma_keV(line_energy)
|
||||
idx = np.argmin(np.abs(energy_axis - line_energy))
|
||||
n_sigma = max(int(2 * sig / 2.97), 3)
|
||||
off_lo = continuum[max(0, idx - 3 * n_sigma):max(1, idx - n_sigma)]
|
||||
off_hi = continuum[min(len(continuum), idx + n_sigma):min(len(continuum), idx + 3 * n_sigma)]
|
||||
off_peak = np.concatenate([off_lo, off_hi])
|
||||
local_bg = np.median(off_peak) if len(off_peak) > 0 else 0
|
||||
idx = int(np.argmin(np.abs(energy_axis - line_energy)))
|
||||
sig = _sigma_ch(line_energy)
|
||||
far = int(5 * sig) + 3
|
||||
|
||||
lo_start = max(0, idx - far - int(3 * sig))
|
||||
lo_end = max(0, idx - far)
|
||||
hi_start = min(len(spectrum), idx + far)
|
||||
hi_end = min(len(spectrum), idx + far + int(3 * sig))
|
||||
|
||||
baseline_regions = []
|
||||
if lo_end > lo_start:
|
||||
baseline_regions.append(continuum[lo_start:lo_end])
|
||||
if hi_end > hi_start:
|
||||
baseline_regions.append(continuum[hi_start:hi_end])
|
||||
|
||||
if not baseline_regions:
|
||||
continue
|
||||
|
||||
local_bg = float(np.median(np.concatenate(baseline_regions)))
|
||||
peak_height = continuum[idx] - local_bg
|
||||
|
||||
if peak_height > 0:
|
||||
amplitude = peak_height * sig * np.sqrt(2 * np.pi)
|
||||
gaussian = amplitude * np.exp(-0.5 * ((energy_axis - line_energy) / sig) ** 2) / (sig * np.sqrt(2 * np.pi))
|
||||
gaussian = peak_height * np.exp(-0.5 * ((channels - idx) / sig) ** 2)
|
||||
continuum -= gaussian
|
||||
continuum = np.maximum(continuum, 0)
|
||||
peak_amplitudes.append({"energy_keV": line_energy, "amplitude": float(max(0, peak_height) * sig * np.sqrt(2 * np.pi)) if peak_height > 0 else 0.0})
|
||||
|
||||
return continuum, peak_amplitudes
|
||||
return np.maximum(continuum, 0), [{"energy_keV": e, "amplitude": 0.0} for e, _ in PHOTOPEAK_LINES]
|
||||
|
||||
|
||||
def calibrate_spline(measured_cps, energy_axis):
|
||||
"""
|
||||
Fit a smoothing spline to the peak-subtracted continuum.
|
||||
Fit continuum using peak subtraction + Gaussian smoothing.
|
||||
|
||||
Uses scipy's make_smoothing_spline with GCV (Generalized Cross-Validation)
|
||||
to automatically find the optimal smoothing parameter.
|
||||
|
||||
Returns a dict with the fitted spline evaluated at all channels.
|
||||
Uses scipy's gaussian_filter1d after iterative peak subtraction,
|
||||
producing a smooth continuum that tracks the measured background
|
||||
closely at all energies including the high-energy tail.
|
||||
"""
|
||||
E = energy_axis
|
||||
y_smooth = _smooth(measured_cps)
|
||||
continuum, peak_amplitudes = _subtract_peaks(E, y_smooth)
|
||||
# Step 1: Smooth to reduce statistical noise
|
||||
window = min(51, len(measured_cps) // 10 * 2 + 1)
|
||||
if window < 5:
|
||||
window = 5
|
||||
y_smooth = savgol_filter(measured_cps, window_length=window, polyorder=3)
|
||||
|
||||
# Ensure positive values for spline fitting
|
||||
continuum = np.maximum(continuum, 0)
|
||||
# Step 2: Subtract known photopeaks
|
||||
continuum, peak_amplitudes = _subtract_peaks(energy_axis, y_smooth)
|
||||
|
||||
# Use log-space for better fit at low-signal high-energy region
|
||||
# Add small offset to avoid log(0)
|
||||
offset = continuum[continuum > 0].min() * 0.1 if (continuum > 0).any() else 1e-6
|
||||
log_continuum = np.log(continuum + offset)
|
||||
|
||||
# Fit smoothing spline in log-space (GCV auto-selects lambda)
|
||||
try:
|
||||
spline = make_smoothing_spline(E, log_continuum)
|
||||
log_fit = spline(E)
|
||||
# Convert back from log-space
|
||||
fit_cps = np.exp(log_fit) - offset
|
||||
fit_cps = np.maximum(fit_cps, 0)
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
# Step 3: Gaussian smooth for final continuum shape
|
||||
sigma = max(15, len(continuum) // 60)
|
||||
continuum_smooth = gaussian_filter1d(continuum, sigma=sigma)
|
||||
continuum_smooth = np.maximum(continuum_smooth, 0)
|
||||
|
||||
# Quality metrics
|
||||
residuals = continuum - fit_cps
|
||||
residuals = continuum - continuum_smooth
|
||||
ss_res = np.sum(residuals ** 2)
|
||||
ss_tot = np.sum((continuum - continuum.mean()) ** 2)
|
||||
r_squared = 1.0 - ss_res / ss_tot if ss_tot > 0 else 0
|
||||
|
||||
return {
|
||||
"continuum_cps": fit_cps,
|
||||
"continuum_cps": continuum_smooth,
|
||||
"peak_amplitudes": peak_amplitudes,
|
||||
"r_squared": float(r_squared),
|
||||
"residuals_rms": float(np.sqrt(np.mean(residuals ** 2))),
|
||||
@ -109,29 +104,24 @@ def calibrate_spline(measured_cps, energy_axis):
|
||||
|
||||
|
||||
def calibrate_background(measured_cps, energy_axis):
|
||||
"""
|
||||
Fit the continuum model using smoothing spline.
|
||||
Returns both spline-based fit and parameters for the /fit endpoint.
|
||||
"""
|
||||
"""Fit the continuum model using peak subtraction + Gaussian smoothing."""
|
||||
result = calibrate_spline(measured_cps, energy_axis)
|
||||
if "error" in result:
|
||||
return result
|
||||
|
||||
# The spline result is the continuum CPS array
|
||||
return {
|
||||
"params": {}, # Non-parametric model, no params
|
||||
"params": {},
|
||||
"continuum_cps": result["continuum_cps"],
|
||||
"peak_amplitudes": result["peak_amplitudes"],
|
||||
"r_squared": result["r_squared"],
|
||||
"residuals_rms": result["residuals_rms"],
|
||||
"method": "smoothing_spline_gcv",
|
||||
"method": "peak_subtract_gaussian",
|
||||
}
|
||||
|
||||
|
||||
def build_calibrated_continuum(energy_axis, total_counts, params):
|
||||
"""Build the continuum from calibrated parameters."""
|
||||
if "continuum_cps" in params:
|
||||
# Spline-based: already have the CPS array
|
||||
cps = np.array(params["continuum_cps"])
|
||||
if cps.sum() > 0:
|
||||
return cps * total_counts / cps.sum()
|
||||
@ -151,13 +141,20 @@ def load_or_calibrate():
|
||||
if _cached_result is not None:
|
||||
return _cached_result
|
||||
|
||||
# Try loading from cache file first (read-only volume is fine for reads)
|
||||
if _CALIBRATION_PATH.exists():
|
||||
try:
|
||||
with open(_CALIBRATION_PATH) as f:
|
||||
_cached_result = json.load(f)
|
||||
return _cached_result
|
||||
cached = json.load(f)
|
||||
# Invalidate if method changed
|
||||
if cached.get("method") != "peak_subtract_gaussian":
|
||||
cached = None
|
||||
except Exception:
|
||||
pass
|
||||
cached = None
|
||||
|
||||
if cached and "continuum_cps" in cached:
|
||||
_cached_result = cached
|
||||
return _cached_result
|
||||
|
||||
from app.config import BACKGROUND_PATH, BACKGROUND_SNAPSHOT_PATH
|
||||
|
||||
@ -199,10 +196,14 @@ def load_or_calibrate():
|
||||
"r_squared": result["r_squared"],
|
||||
}
|
||||
|
||||
_CALIBRATION_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
tmp = _CALIBRATION_PATH.with_suffix(".tmp")
|
||||
with open(tmp, "w") as f:
|
||||
json.dump(_cached_result, f, indent=2)
|
||||
tmp.replace(_CALIBRATION_PATH)
|
||||
# Write cache if volume is writable (may fail on read-only mounts)
|
||||
try:
|
||||
_CALIBRATION_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
tmp = _CALIBRATION_PATH.with_suffix(".tmp")
|
||||
with open(tmp, "w") as f:
|
||||
json.dump(_cached_result, f, indent=2)
|
||||
tmp.replace(_CALIBRATION_PATH)
|
||||
except OSError:
|
||||
pass # Read-only volume — in-memory cache is sufficient
|
||||
|
||||
return _cached_result
|
||||
@ -1,4 +1,5 @@
|
||||
import os
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
STATE_PATH = Path(os.environ.get("STATE_PATH", "/data/monitor_state.json"))
|
||||
@ -11,8 +12,47 @@ ISOTOPE_INDEX_PATH = Path(os.environ.get("ISOTOPE_INDEX_PATH", "/models/vega_iso
|
||||
ENERGY_OFFSET = float(os.environ.get("ENERGY_CALIBRATION_OFFSET", "0.33"))
|
||||
ENERGY_SLOPE = float(os.environ.get("ENERGY_CALIBRATION_SLOPE", "2.97"))
|
||||
NUM_CHANNELS = 1023 # Last channel (1023) is overflow bin, excluded from display
|
||||
ENERGY_MIN = 30.0 # keV - detector lower limit
|
||||
ENERGY_MAX = 3000.0 # keV - detector upper limit (3 MeV)
|
||||
|
||||
# CsI(Tl) non-linear response correction parameters
|
||||
# Matches the detector's non-proportional scintillation response
|
||||
CSI_NONLINEAR_ALPHA = float(os.environ.get("CSI_NONLINEAR_ALPHA", "0.37"))
|
||||
CSI_NONLINEAR_BETA = float(os.environ.get("CSI_NONLINEAR_BETA", "100.0"))
|
||||
|
||||
|
||||
def correct_csi_nonlinear(spectrum, num_channels=1023):
|
||||
"""Apply inverse CsI(Tl) non-linear response correction.
|
||||
|
||||
Remaps spectrum channels so peaks appear at their theoretical energy
|
||||
positions, correcting for the detector's non-proportional scintillation
|
||||
response that shifts low-energy peaks upward.
|
||||
"""
|
||||
alpha = CSI_NONLINEAR_ALPHA
|
||||
beta = CSI_NONLINEAR_BETA
|
||||
output_channels = np.arange(num_channels, dtype=np.float64)
|
||||
e_true = ENERGY_OFFSET + ENERGY_SLOPE * output_channels
|
||||
e_apparent = e_true * (1 + alpha * np.exp(-e_true / beta))
|
||||
source_channels = (e_apparent - ENERGY_OFFSET) / ENERGY_SLOPE
|
||||
source_channels = np.clip(source_channels, 0, num_channels - 1.001)
|
||||
lower = np.floor(source_channels).astype(int)
|
||||
upper = np.minimum(lower + 1, num_channels - 1)
|
||||
frac = source_channels - lower
|
||||
return spectrum[lower] * (1 - frac) + spectrum[upper] * frac
|
||||
|
||||
|
||||
def energy_axis():
|
||||
"""Generate energy axis in keV from channel numbers."""
|
||||
return [round(ENERGY_OFFSET + ENERGY_SLOPE * i, 2) for i in range(NUM_CHANNELS)]
|
||||
"""Generate energy axis in keV from channel numbers, clipped to detector range."""
|
||||
axis = [round(ENERGY_OFFSET + ENERGY_SLOPE * i, 2) for i in range(NUM_CHANNELS)]
|
||||
return [e for e in axis if ENERGY_MIN <= e <= ENERGY_MAX]
|
||||
|
||||
|
||||
def energy_mask():
|
||||
"""Return boolean mask of channels within detector energy range."""
|
||||
return [ENERGY_MIN <= ENERGY_OFFSET + ENERGY_SLOPE * i <= ENERGY_MAX for i in range(NUM_CHANNELS)]
|
||||
|
||||
|
||||
def clip_to_range(arr):
|
||||
"""Clip array to detector energy range using energy mask."""
|
||||
mask = energy_mask()
|
||||
return [arr[i] for i in range(len(arr)) if mask[i]]
|
||||
@ -1,6 +1,6 @@
|
||||
import json
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from app.config import BACKGROUND_SNAPSHOT_PATH, BACKGROUND_PATH, energy_axis, NUM_CHANNELS, ENERGY_OFFSET, ENERGY_SLOPE
|
||||
from app.config import BACKGROUND_SNAPSHOT_PATH, BACKGROUND_PATH, energy_axis, NUM_CHANNELS, ENERGY_OFFSET, ENERGY_SLOPE, clip_to_range
|
||||
from app.theoretical_bg import generate_continuum_only
|
||||
from app.noise import extract_continuum
|
||||
import numpy as np
|
||||
@ -25,8 +25,9 @@ def _load_reference():
|
||||
return None
|
||||
try:
|
||||
bg_data = np.load(str(BACKGROUND_PATH), allow_pickle=True).item()
|
||||
raw_counts = [round(float(c), 1) for c in bg_data["counts"][:NUM_CHANNELS]]
|
||||
return {
|
||||
"counts": [round(float(c), 1) for c in bg_data["counts"][:NUM_CHANNELS]],
|
||||
"counts": clip_to_range(raw_counts),
|
||||
"live_time_s": round(float(bg_data["duration"]), 1),
|
||||
}
|
||||
except Exception:
|
||||
@ -54,11 +55,12 @@ async def get_background_spectrum():
|
||||
"""Live background spectrum (from snapshot) with energy axis."""
|
||||
snapshot = _load_snapshot()
|
||||
live_time = snapshot.get("live_time_s", 0)
|
||||
raw_spectrum = snapshot.get("spectrum", [0] * 1024)[:NUM_CHANNELS]
|
||||
|
||||
return {
|
||||
"channels": list(range(NUM_CHANNELS)),
|
||||
"channels": clip_to_range(list(range(NUM_CHANNELS))),
|
||||
"energy_kev": energy_axis(),
|
||||
"counts": snapshot.get("spectrum", [0] * 1024)[:NUM_CHANNELS],
|
||||
"counts": clip_to_range(raw_spectrum),
|
||||
"live_time_s": live_time,
|
||||
"cps": snapshot.get("cps", 0),
|
||||
"top_peaks": snapshot.get("top_peaks", []),
|
||||
@ -74,7 +76,7 @@ async def get_background_reference():
|
||||
raise HTTPException(status_code=404, detail="No 24h reference background available")
|
||||
|
||||
return {
|
||||
"channels": list(range(NUM_CHANNELS)),
|
||||
"channels": clip_to_range(list(range(NUM_CHANNELS))),
|
||||
"energy_kev": energy_axis(),
|
||||
"counts": ref["counts"],
|
||||
"live_time_s": ref["live_time_s"],
|
||||
@ -84,7 +86,10 @@ async def get_background_reference():
|
||||
@router.get("/continuum")
|
||||
async def get_continuum(cps: float = 6.0, live_time_s: float = 3600.0):
|
||||
"""CsI(Tl) detector response continuum only (no photopeaks, no noise)."""
|
||||
return generate_continuum_only(cps=cps, live_time_s=live_time_s)
|
||||
raw = generate_continuum_only(cps=cps, live_time_s=live_time_s)
|
||||
raw["energy_kev"] = clip_to_range(raw["energy_kev"])
|
||||
raw["counts"] = clip_to_range(raw["counts"])
|
||||
return raw
|
||||
|
||||
|
||||
@router.get("/fit")
|
||||
@ -132,10 +137,14 @@ async def fit_background():
|
||||
# Build fitted curve at same scale as measured
|
||||
fitted_counts = build_calibrated_continuum(e_axis, measured_counts.sum(), result)
|
||||
|
||||
e_list = [round(float(E), 2) for E in e_axis]
|
||||
m_list = [round(float(c), 1) for c in measured_counts]
|
||||
f_list = [round(float(c), 1) for c in fitted_counts]
|
||||
|
||||
return {
|
||||
"energy_kev": [round(float(E), 2) for E in e_axis],
|
||||
"measured_counts": [round(float(c), 1) for c in measured_counts],
|
||||
"fitted_counts": [round(float(c), 1) for c in fitted_counts],
|
||||
"energy_kev": clip_to_range(e_list),
|
||||
"measured_counts": clip_to_range(m_list),
|
||||
"fitted_counts": clip_to_range(f_list),
|
||||
"method": result.get("method", "spline"),
|
||||
"r_squared": result["r_squared"],
|
||||
"residuals_rms": result["residuals_rms"],
|
||||
@ -174,7 +183,9 @@ async def get_background_noise():
|
||||
e_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
|
||||
continuum = extract_continuum(counts, energy_axis=e_axis)
|
||||
|
||||
e_list = [round(float(E), 2) for E in e_axis]
|
||||
c_list = [round(float(c), 1) for c in continuum]
|
||||
return {
|
||||
"energy_kev": [round(float(E), 2) for E in e_axis],
|
||||
"counts": [round(float(c), 1) for c in continuum],
|
||||
"energy_kev": clip_to_range(e_list),
|
||||
"counts": clip_to_range(c_list),
|
||||
}
|
||||
@ -1,6 +1,7 @@
|
||||
import json
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from app.config import STATE_PATH, BACKGROUND_PATH, energy_axis, NUM_CHANNELS
|
||||
from app.config import (STATE_PATH, BACKGROUND_PATH, energy_axis, NUM_CHANNELS,
|
||||
clip_to_range, correct_csi_nonlinear)
|
||||
import numpy as np
|
||||
|
||||
router = APIRouter()
|
||||
@ -8,7 +9,7 @@ router = APIRouter()
|
||||
|
||||
@router.get("/current")
|
||||
async def get_current_spectrum():
|
||||
"""Current accumulated spectrum with energy axis."""
|
||||
"""Current accumulated spectrum with energy axis (CsI-corrected)."""
|
||||
if not STATE_PATH.exists():
|
||||
raise HTTPException(status_code=503, detail="Monitor not started yet")
|
||||
|
||||
@ -18,6 +19,9 @@ async def get_current_spectrum():
|
||||
except (json.JSONDecodeError, OSError):
|
||||
raise HTTPException(status_code=503, detail="Monitor state file corrupt")
|
||||
|
||||
raw_counts = state.get("counts", [0] * 1024)[:NUM_CHANNELS]
|
||||
# Apply CsI correction so peaks appear at theoretical energy positions
|
||||
corrected_counts = correct_csi_nonlinear(np.array(raw_counts, dtype=np.float64))
|
||||
return {
|
||||
"timestamp": state.get("timestamp", ""),
|
||||
"connected": state.get("connected", False),
|
||||
@ -27,15 +31,15 @@ async def get_current_spectrum():
|
||||
"total_counts": state.get("total_counts", 0),
|
||||
"background_subtracted": state.get("background_subtracted", False),
|
||||
"isotopes_detected": state.get("isotopes_detected", []),
|
||||
"channels": list(range(NUM_CHANNELS)),
|
||||
"channels": clip_to_range(list(range(NUM_CHANNELS))),
|
||||
"energy_kev": energy_axis(),
|
||||
"counts": state.get("counts", [0] * 1024)[:NUM_CHANNELS],
|
||||
"counts": clip_to_range([round(float(c), 1) for c in corrected_counts]),
|
||||
}
|
||||
|
||||
|
||||
@router.get("/difference")
|
||||
async def get_difference_spectrum():
|
||||
"""Background-subtracted spectrum (net signal)."""
|
||||
"""Background-subtracted spectrum (net signal, CsI-corrected)."""
|
||||
if not STATE_PATH.exists():
|
||||
raise HTTPException(status_code=503, detail="Monitor not started yet")
|
||||
|
||||
@ -59,18 +63,21 @@ async def get_difference_spectrum():
|
||||
bg_live_time = float(bg_data["duration"])
|
||||
bg_rate = bg_counts / bg_live_time
|
||||
net_rate = np.clip(rate - bg_rate, 0, None)
|
||||
net_counts = net_rate * live_time
|
||||
# Apply CsI correction to net spectrum
|
||||
corrected_net = correct_csi_nonlinear(net_rate)
|
||||
net_counts = corrected_net * live_time
|
||||
bg_available = True
|
||||
else:
|
||||
net_counts = counts
|
||||
bg_available = False
|
||||
|
||||
net_counts_list = [round(float(c), 1) for c in net_counts]
|
||||
return {
|
||||
"timestamp": state.get("timestamp", ""),
|
||||
"cumulated_live_time_s": live_time,
|
||||
"background_subtracted": bg_available,
|
||||
"channels": list(range(NUM_CHANNELS)),
|
||||
"channels": clip_to_range(list(range(NUM_CHANNELS))),
|
||||
"energy_kev": energy_axis(),
|
||||
"counts": [round(float(c), 1) for c in net_counts],
|
||||
"raw_counts": state.get("counts", [])[:NUM_CHANNELS],
|
||||
"counts": clip_to_range(net_counts_list),
|
||||
"raw_counts": clip_to_range(state.get("counts", [])[:NUM_CHANNELS]),
|
||||
}
|
||||
@ -30,7 +30,7 @@ def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
|
||||
energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
|
||||
total_counts = cps * live_time_s
|
||||
|
||||
# Try calibrated spline first
|
||||
# Try calibrated spline first (fits measured background)
|
||||
continuum_cps = _get_continuum_cps()
|
||||
|
||||
if continuum_cps is not None and len(continuum_cps) == NUM_CHANNELS:
|
||||
@ -39,7 +39,7 @@ def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
|
||||
if continuum.sum() > 0:
|
||||
continuum *= total_counts / continuum.sum()
|
||||
else:
|
||||
# Fallback: simple parametric model
|
||||
# Fallback: parametric model
|
||||
continuum = _fallback_continuum(energy_axis, total_counts)
|
||||
|
||||
return {
|
||||
|
||||
@ -68,18 +68,29 @@ nav a {
|
||||
nav a:hover { background: rgba(255,255,255,0.1); }
|
||||
nav a.active { color: var(--accent-bright); border-bottom: 2px solid var(--accent-bright); }
|
||||
|
||||
main { padding: 16px; }
|
||||
main { padding: 12px 0; }
|
||||
|
||||
.tab-content { display: none; }
|
||||
.tab-content.active { display: block; }
|
||||
|
||||
.controls, .bg-stats, #isotopes-table, #peaks-table, .history-item, .chart-header {
|
||||
margin-left: 12px;
|
||||
margin-right: 12px;
|
||||
}
|
||||
|
||||
.chart-container {
|
||||
background: var(--bg-card);
|
||||
border-radius: 8px;
|
||||
padding: 12px;
|
||||
margin-bottom: 12px;
|
||||
margin: 0 12px 12px 12px;
|
||||
height: 450px;
|
||||
width: calc(100% - 24px);
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.chart-container canvas {
|
||||
display: block;
|
||||
}
|
||||
|
||||
.controls {
|
||||
|
||||
@ -4,7 +4,7 @@
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Radiacode 103 — Dashboard</title>
|
||||
<link rel="stylesheet" href="/static/css/style.css?v=3">
|
||||
<link rel="stylesheet" href="/static/css/style.css?v=8">
|
||||
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-annotation@3.0.1/dist/chartjs-plugin-annotation.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/hammerjs@2.0.8/hammer.min.js"></script>
|
||||
@ -83,12 +83,12 @@
|
||||
</section>
|
||||
</main>
|
||||
|
||||
<script src="/static/js/isotope_lines.js?v=3"></script>
|
||||
<script src="/static/js/isotope_lines.js?v=5"></script>
|
||||
<script src="/static/js/chart_pan.js?v=3"></script>
|
||||
<script src="/static/js/spectrum.js?v=9"></script>
|
||||
<script src="/static/js/spectrum.js?v=15"></script>
|
||||
<script src="/static/js/history.js?v=2"></script>
|
||||
<script src="/static/js/background.js?v=30"></script>
|
||||
<script src="/static/js/cps.js?v=7"></script>
|
||||
<script src="/static/js/background.js?v=34"></script>
|
||||
<script src="/static/js/cps.js?v=8"></script>
|
||||
<script src="/static/js/app.js?v=3"></script>
|
||||
</body>
|
||||
</html>
|
||||
@ -6,11 +6,11 @@ let bgContinuumData = null;
|
||||
document.getElementById('reset-zoom-bg')?.addEventListener('click', () => {
|
||||
if (bgChart) {
|
||||
bgChart.resetZoom();
|
||||
// After resetZoom, force the scale to full energy range
|
||||
delete bgChart._panRange;
|
||||
const firstPt = bgChart.data.datasets[0]?.data?.[0];
|
||||
const lastPt = bgChart.data.datasets[0]?.data?.[bgChart.data.datasets[0].data.length - 1];
|
||||
const fullMin = firstPt?.x ?? 0;
|
||||
const fullMax = lastPt?.x ?? 3036;
|
||||
const fullMax = lastPt?.x ?? 3000;
|
||||
bgChart.options.scales.x.min = fullMin;
|
||||
bgChart.options.scales.x.max = fullMax;
|
||||
bgChart.update();
|
||||
@ -170,12 +170,12 @@ function updateBackgroundChart(spec) {
|
||||
datasets: datasets,
|
||||
};
|
||||
|
||||
// Preserve pan range (user zoomed), but reset to full range when data refreshes
|
||||
// Preserve pan range only if user has explicitly zoomed/panned
|
||||
const panRange = bgChart?._panRange;
|
||||
const firstX = datasets[0].data[0]?.x;
|
||||
const lastX = datasets[0].data[datasets[0].data.length - 1]?.x;
|
||||
const xMin = panRange ? panRange[0] : (firstX ?? 0);
|
||||
const xMax = panRange ? panRange[1] : (lastX ?? 3036);
|
||||
const firstX = datasets[0].data[0]?.x ?? 0;
|
||||
const lastX = datasets[0].data[datasets[0].data.length - 1]?.x ?? 3000;
|
||||
const xMin = panRange ? panRange[0] : firstX;
|
||||
const xMax = panRange ? panRange[1] : lastX;
|
||||
const options = {
|
||||
responsive: true,
|
||||
maintainAspectRatio: false,
|
||||
@ -214,8 +214,8 @@ function updateBackgroundChart(spec) {
|
||||
},
|
||||
y: {
|
||||
type: showLog ? 'logarithmic' : 'linear',
|
||||
min: showLog ? 0.5 : undefined,
|
||||
title: { display: true, text: `Comptages (${showLog ? 'log' : 'lin'})`, color: '#888' },
|
||||
...(showLog ? { min: 0.9 } : {}),
|
||||
ticks: { color: '#888' },
|
||||
grid: { color: '#333' },
|
||||
}
|
||||
@ -229,7 +229,7 @@ function updateBackgroundChart(spec) {
|
||||
} else {
|
||||
bgChart = new Chart(ctx, { type: 'line', data: chartData, ...options });
|
||||
const firstX = datasets[0].data[0]?.x ?? 0;
|
||||
const lastX = datasets[0].data[datasets[0].data.length - 1]?.x ?? 3036;
|
||||
const lastX = datasets[0].data[datasets[0].data.length - 1]?.x ?? 3000;
|
||||
enablePan(bgChart, 'reset-zoom-bg', firstX, lastX);
|
||||
}
|
||||
}
|
||||
|
||||
@ -41,10 +41,9 @@ function updateCpsChart(labels, values) {
|
||||
}]
|
||||
};
|
||||
|
||||
const existingMin = cpsChart?.scales.x?.min;
|
||||
const existingMax = cpsChart?.scales.x?.max;
|
||||
const xMin = existingMin ?? labels[0];
|
||||
const xMax = existingMax ?? labels[labels.length - 1];
|
||||
const panRange = cpsChart?._panRange;
|
||||
const xMin = panRange ? panRange[0] : labels[0];
|
||||
const xMax = panRange ? panRange[1] : labels[labels.length - 1];
|
||||
const options = {
|
||||
responsive: true,
|
||||
maintainAspectRatio: false,
|
||||
@ -114,10 +113,15 @@ function updateCpsChart(labels, values) {
|
||||
}
|
||||
}
|
||||
|
||||
// Reset zoom
|
||||
// Reset zoom — restore full time range
|
||||
document.getElementById('reset-zoom-cps')?.addEventListener('click', () => {
|
||||
if (cpsChart) {
|
||||
cpsChart.resetZoom();
|
||||
delete cpsChart._panRange;
|
||||
const labels = cpsChart.data.labels;
|
||||
cpsChart.options.scales.x.min = labels[0];
|
||||
cpsChart.options.scales.x.max = labels[labels.length - 1];
|
||||
cpsChart.update();
|
||||
document.getElementById('reset-zoom-cps').style.display = 'none';
|
||||
}
|
||||
});
|
||||
@ -48,7 +48,7 @@ const ISOTOPE_LINES = [
|
||||
];
|
||||
|
||||
// Filtrer les lignes dans la plage visible du détecteur (30-3050 keV pour Radiacode 103)
|
||||
const VISIBLE_LINES = ISOTOPE_LINES.filter(l => l.energy_keV >= 30 && l.energy_keV <= 3050);
|
||||
const VISIBLE_LINES = ISOTOPE_LINES.filter(l => l.energy_keV >= 30 && l.energy_keV <= 3000);
|
||||
|
||||
// Global crosshair plugin — vertical dashed line on hover for all charts
|
||||
const CrosshairPlugin = {
|
||||
@ -72,6 +72,50 @@ const CrosshairPlugin = {
|
||||
};
|
||||
Chart.register(CrosshairPlugin);
|
||||
|
||||
// Auto-scale Y axis to visible X range
|
||||
const AutoScaleYPlugin = {
|
||||
id: 'autoScaleY',
|
||||
beforeUpdate(chart) {
|
||||
const xScale = chart.scales?.x;
|
||||
const yScale = chart.scales?.y;
|
||||
if (!xScale || !yScale || xScale.type !== 'linear') return;
|
||||
|
||||
const xMin = xScale.min;
|
||||
const xMax = xScale.max;
|
||||
if (xMin == null || xMax == null) return;
|
||||
|
||||
let yMin = Infinity;
|
||||
let yMax = -Infinity;
|
||||
let count = 0;
|
||||
|
||||
chart.data.datasets.forEach(ds => {
|
||||
for (const pt of ds.data) {
|
||||
const x = Array.isArray(pt) ? pt[0] : pt.x;
|
||||
const y = Array.isArray(pt) ? pt[1] : pt.y;
|
||||
if (x === undefined || y === undefined) continue;
|
||||
if (x >= xMin && x <= xMax) {
|
||||
if (y > 0 && y < yMin) yMin = y;
|
||||
if (y > yMax) yMax = y;
|
||||
count++;
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
if (count === 0 || yMin === Infinity) return;
|
||||
|
||||
const isLog = yScale.type === 'logarithmic';
|
||||
if (isLog) {
|
||||
chart.options.scales.y.min = Math.max(0.5, yMin * 0.7);
|
||||
chart.options.scales.y.max = yMax * 1.5;
|
||||
} else {
|
||||
const padding = (yMax - yMin) * 0.05 || 1;
|
||||
chart.options.scales.y.min = Math.max(0, yMin - padding);
|
||||
chart.options.scales.y.max = yMax + padding;
|
||||
}
|
||||
}
|
||||
};
|
||||
Chart.register(AutoScaleYPlugin);
|
||||
|
||||
// Couleurs par catégorie d'isotope
|
||||
function isotopeLineColor(isotope) {
|
||||
if (["K-40", "Bi-214", "Pb-214", "Ra-226"].includes(isotope)) return "rgba(255,152,0,0.5)"; // Uranium chain - orange
|
||||
|
||||
@ -22,22 +22,29 @@ function updateSpectrumChart(data) {
|
||||
const showBgOverlay = document.getElementById('show-bg-overlay').checked;
|
||||
const ctx = document.getElementById('spectrum-chart').getContext('2d');
|
||||
|
||||
const toData = (counts, energies) => counts.map((v, i) => ({ x: energies[i], y: v }));
|
||||
const energy = data.energy_kev;
|
||||
|
||||
const datasets = [{
|
||||
label: data.background_subtracted ? 'Spectre (background soustrait)' : 'Spectre cumulé',
|
||||
data: data.counts,
|
||||
data: toData(data.counts, energy),
|
||||
borderColor: '#4fc3f7',
|
||||
backgroundColor: 'rgba(79, 195, 247, 0.1)',
|
||||
borderWidth: 1,
|
||||
pointRadius: 0,
|
||||
fill: true,
|
||||
fill: logScale ? 'origin' : true,
|
||||
tension: 0.1,
|
||||
}];
|
||||
|
||||
// Overlay background if requested and available
|
||||
// Overlay background if requested and available, scaled to match spectrum max
|
||||
if (showBgOverlay && bgOverlayData) {
|
||||
const specMax = Math.max(...data.counts);
|
||||
const bgMax = Math.max(...bgOverlayData.counts);
|
||||
const bgScale = bgMax > 0 ? specMax / bgMax : 1;
|
||||
const bgEnergy = bgOverlayData.energy_kev || energy;
|
||||
datasets.push({
|
||||
label: 'Background',
|
||||
data: bgOverlayData.counts,
|
||||
data: bgOverlayData.counts.map((v, i) => ({ x: bgEnergy[i] ?? energy[i], y: v * bgScale })),
|
||||
borderColor: 'rgba(255, 152, 0, 0.6)',
|
||||
backgroundColor: 'rgba(255, 152, 0, 0.05)',
|
||||
borderWidth: 1,
|
||||
@ -48,7 +55,6 @@ function updateSpectrumChart(data) {
|
||||
}
|
||||
|
||||
const chartData = {
|
||||
labels: data.energy_kev,
|
||||
datasets: datasets,
|
||||
};
|
||||
|
||||
@ -58,10 +64,11 @@ function updateSpectrumChart(data) {
|
||||
annotations = buildIsotopeAnnotations(detectedOnly, (data.isotopes_detected || []).map(i => i.isotope));
|
||||
}
|
||||
|
||||
const existingMin = spectrumChart?.scales.x?.min;
|
||||
const existingMax = spectrumChart?.scales.x?.max;
|
||||
const xMin = existingMin ?? data.energy_kev[0];
|
||||
const xMax = existingMax ?? data.energy_kev[data.energy_kev.length - 1];
|
||||
const firstPt = datasets[0].data[0];
|
||||
const lastPt = datasets[0].data[datasets[0].data.length - 1];
|
||||
const panRange = spectrumChart?._panRange;
|
||||
const xMin = panRange ? panRange[0] : (firstPt?.x ?? 0);
|
||||
const xMax = panRange ? panRange[1] : (lastPt?.x ?? 3000);
|
||||
const options = {
|
||||
responsive: true,
|
||||
maintainAspectRatio: false,
|
||||
@ -73,13 +80,10 @@ function updateSpectrumChart(data) {
|
||||
enabled: true,
|
||||
mode: 'index',
|
||||
intersect: false,
|
||||
filter: (item) => item.raw != null,
|
||||
filter: (item) => item.parsed.y != null,
|
||||
callbacks: {
|
||||
title: (items) => {
|
||||
const idx = items[0].dataIndex;
|
||||
return `${data.energy_kev[idx]} keV`;
|
||||
},
|
||||
label: (item) => `${item.dataset.label}: ${item.raw.toFixed(1)} counts`
|
||||
title: (items) => `${items[0].parsed.x.toFixed(1)} keV`,
|
||||
label: (item) => `${item.dataset.label}: ${item.parsed.y.toFixed(1)} counts`
|
||||
}
|
||||
},
|
||||
annotation: {
|
||||
@ -108,8 +112,8 @@ function updateSpectrumChart(data) {
|
||||
},
|
||||
y: {
|
||||
type: logScale ? 'logarithmic' : 'linear',
|
||||
min: logScale ? 0.5 : undefined,
|
||||
title: { display: true, text: logScale ? 'Comptages (log)' : 'Comptages', color: '#888' },
|
||||
min: logScale ? 0.9 : undefined,
|
||||
ticks: { color: '#888' },
|
||||
grid: { color: '#333' },
|
||||
}
|
||||
@ -122,7 +126,12 @@ function updateSpectrumChart(data) {
|
||||
spectrumChart.update();
|
||||
} else {
|
||||
spectrumChart = new Chart(ctx, { type: 'line', data: chartData, ...options });
|
||||
enablePan(spectrumChart, 'reset-zoom-spectrum', data.energy_kev[0], data.energy_kev[data.energy_kev.length - 1]);
|
||||
const panMin = firstPt?.x ?? 0;
|
||||
const panMax = lastPt?.x ?? 3000;
|
||||
enablePan(spectrumChart, 'reset-zoom-spectrum', panMin, panMax);
|
||||
// Fix: Chart.js may read wrong canvas dimensions on first render;
|
||||
// resize on next frame ensures layout is fully computed.
|
||||
requestAnimationFrame(() => spectrumChart.resize());
|
||||
}
|
||||
}
|
||||
|
||||
@ -166,10 +175,16 @@ document.getElementById('show-bg-overlay').addEventListener('change', async (e)
|
||||
refreshSpectrum();
|
||||
});
|
||||
|
||||
// Reset zoom
|
||||
// Reset zoom — restore full energy range
|
||||
document.getElementById('reset-zoom-spectrum')?.addEventListener('click', () => {
|
||||
if (spectrumChart) {
|
||||
spectrumChart.resetZoom();
|
||||
delete spectrumChart._panRange;
|
||||
const firstPt = spectrumChart.data.datasets[0]?.data?.[0];
|
||||
const lastPt = spectrumChart.data.datasets[0]?.data?.[spectrumChart.data.datasets[0].data.length - 1];
|
||||
spectrumChart.options.scales.x.min = firstPt?.x ?? 0;
|
||||
spectrumChart.options.scales.x.max = lastPt?.x ?? 3000;
|
||||
spectrumChart.update();
|
||||
document.getElementById('reset-zoom-spectrum').style.display = 'none';
|
||||
}
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user