Dash web: crosshair, zoom/pan X, scale log/lin, continuum extraction, background resume
- Tooltip entier (intersect:false) + ligne verticale crosshair sur tous les graphes - Zoom molette/pinch sur l'axe X, pan souris, limites clamped 30-3000 keV - Toggle échelle log/linéaire onglet Background - Extraction continuum détecteur (isotope peaks subtracted + Gaussian smoothing) - Reprise snapshot précédent au démarrage capture_background.py - Suppression refs "Théorique" et "Bruit capteur" de l'interface - Plugin chartjs-plugin-zoom + hammerjs via CDN - Fix Chart constructor spread operator Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -1,139 +1,74 @@
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"""
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Theoretical natural background spectrum for CsI(Tl) detectors (Radiacode 103).
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CsI(Tl) detector response continuum for Radiacode 103.
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Shape calibrated against real Radiacode 103 background measurements.
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The CsI(Tl) crystal (1 cm³, 8.4% FWHM) produces a spectrum with:
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- A dominant low-energy hump peaking around 100-120 keV
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- Exponential decay at higher energies
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- Subtle photopeaks from natural isotopes
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Models ONLY the detector's noise continuum. Photopeaks from environmental
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isotopes depend on measurement location and are NOT included.
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Auto-calibrated from measured background using smoothing spline (GCV)
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when available. Falls back to a simple parametric model otherwise.
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"""
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import numpy as np
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from app.config import ENERGY_OFFSET, ENERGY_SLOPE, NUM_CHANNELS
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# Photopeak lines: (energy_keV, relative_weight)
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# Weights tuned so peaks are visible above local continuum at typical CPS
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NATURAL_BG_LINES = [
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(295.22, 0.10), # Pb-214
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(351.93, 0.18), # Pb-214
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(609.31, 0.15), # Bi-214
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(911.20, 0.08), # Ac-228
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(968.97, 0.05), # Ac-228
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(1120.29, 0.06), # Bi-214
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(1460.83, 0.12), # K-40
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(1764.49, 0.08), # Bi-214
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(2614.51, 0.18), # Tl-208
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]
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def _gaussian(x, center, sigma, amplitude):
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return amplitude * np.exp(-0.5 * ((x - center) / sigma) ** 2)
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def generate_theoretical_bg(cps: float = 6.0, live_time_s: float = 3600.0):
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channels = np.arange(NUM_CHANNELS, dtype=np.float64)
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energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
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total_counts = cps * live_time_s
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# ── 1. Main hump: asymmetric peak at ~105 keV ──
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# Real data: rises from ~60 at 10keV to ~280 at 100-120keV, then falls
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hump_center = 110.0
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hump = np.zeros(NUM_CHANNELS, dtype=np.float64)
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low_mask = energy_axis <= hump_center
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hump[low_mask] = _gaussian(energy_axis[low_mask], hump_center, 55.0, 1.0)
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hump[~low_mask] = _gaussian(energy_axis[~low_mask], hump_center, 50.0, 1.0)
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# ── 2. Compton continuum tail ──
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# Real data: ~136@200, ~80@250, ~44@295, ~14@400, ~5@600
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tail = 0.45 * np.exp(-energy_axis / 240) + 0.04 * np.exp(-energy_axis / 700)
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# ── 3. Low-energy noise floor ──
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noise_floor = 0.008
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# ── 4. Combine continuum ──
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continuum = hump + tail + noise_floor
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# ── 5. Photopeaks ──
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# CsI(Tl) 8.4% FWHM at 662 keV, scaling as sqrt(E)
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# sigma(E) = FWHM(E) / 2.355 = 0.084 * sqrt(E * 662) / 662 / 2.355
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# Simplified: sigma = 23.6 * sqrt(E/662) keV
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def sigma_keV(E):
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return max(12.0, 23.6 * np.sqrt(max(E, 1.0) / 662.0))
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peak_frac = 0.08 # 8% of total counts in resolved photopeaks
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total_weight = sum(w for _, w in NATURAL_BG_LINES)
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peaks = np.zeros(NUM_CHANNELS, dtype=np.float64)
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for line_energy, weight in NATURAL_BG_LINES:
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sig = sigma_keV(line_energy)
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peak_counts = total_counts * peak_frac * (weight / total_weight)
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amplitude = peak_counts / (sig * np.sqrt(2 * np.pi))
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peaks += _gaussian(energy_axis, line_energy, sig, amplitude)
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# ── 6. Combine and normalize ──
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raw = continuum + peaks / total_counts # peaks normalized later
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raw *= total_counts / raw.sum()
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# ── 7. Poisson-like noise ──
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rng = np.random.default_rng(42)
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noise = rng.normal(0, 1, NUM_CHANNELS) * np.sqrt(np.maximum(raw, 1.0)) * 0.25
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raw += noise
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# Floor at 0.9 for log scale
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spectrum = np.clip(raw, 0.9, None)
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key_lines = [
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(295.22, "Pb-214"), (351.93, "Pb-214"),
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(609.31, "Bi-214"), (911.20, "Ac-228"),
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(1120.29, "Bi-214"), (1460.83, "K-40"),
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(1764.49, "Bi-214"), (2614.51, "Tl-208"),
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]
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return {
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"energy_kev": [round(float(E), 2) for E in energy_axis],
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"counts": [round(float(c), 1) for c in spectrum],
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"cps": round(cps, 2),
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"live_time_s": round(live_time_s, 1),
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"lines": [
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{"energy_keV": E, "name": name} for E, name in key_lines
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],
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}
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def _get_continuum_cps():
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"""Try to load calibrated spline continuum from measured data."""
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try:
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from app.bg_calibration import load_or_calibrate
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calibrated = load_or_calibrate()
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if calibrated and "continuum_cps" in calibrated:
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return np.array(calibrated["continuum_cps"])
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except Exception:
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pass
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return None
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def generate_continuum_only(cps: float = 6.0, live_time_s: float = 3600.0):
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"""Generate only the CsI(Tl) continuum shape (no photopeaks, no noise).
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This matches the model used in training (generate_realistic_continuum in
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spectrum_physics.py) for direct comparison with measured backgrounds.
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"""
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"""Detector response continuum only (no photopeaks, no noise)."""
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channels = np.arange(NUM_CHANNELS, dtype=np.float64)
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energy_axis = ENERGY_OFFSET + ENERGY_SLOPE * channels
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total_counts = cps * live_time_s
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# Asymmetric hump at ~110 keV
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hump_center = 110.0
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hump = np.where(
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energy_axis <= hump_center,
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np.exp(-0.5 * ((energy_axis - hump_center) / 55.0) ** 2),
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np.exp(-0.5 * ((energy_axis - hump_center) / 50.0) ** 2),
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)
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# Try calibrated spline first
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continuum_cps = _get_continuum_cps()
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# Compton continuum tail
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tail = 0.45 * np.exp(-energy_axis / 240.0) + 0.04 * np.exp(-energy_axis / 700.0)
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# Noise floor
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noise_floor = 0.008
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continuum = hump + tail + noise_floor
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# Normalize to target total counts
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if continuum.sum() > 0 and total_counts > 0:
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continuum *= total_counts / continuum.sum()
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if continuum_cps is not None and len(continuum_cps) == NUM_CHANNELS:
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# Scale calibrated CPS to match requested total counts
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continuum = continuum_cps.copy()
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if continuum.sum() > 0:
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continuum *= total_counts / continuum.sum()
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else:
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# Fallback: simple parametric model
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continuum = _fallback_continuum(energy_axis, total_counts)
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return {
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"energy_kev": [round(float(E), 2) for E in energy_axis],
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"counts": [round(float(c), 1) for c in continuum],
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"cps": round(cps, 2),
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"live_time_s": round(live_time_s, 1),
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}
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}
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def _fallback_continuum(energy_axis, total_counts):
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"""Simple parametric fallback when no measured data available."""
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E = energy_axis
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# Asymmetric hump
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hump_center, sigma_left, tail_decay_right = 110.0, 40.0, 100.0
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left = np.exp(-0.5 * ((E - hump_center) / sigma_left) ** 2)
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right = np.exp(-(E - hump_center) / tail_decay_right)
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hump = np.where(E <= hump_center, left, right)
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# Housing absorption
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absorption = 1.0 * (1.0 - np.exp(-E / 20.0))
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# Compton tail
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compton = 0.5 / (np.maximum(E, 1.0) + 15.0) ** 1.3
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continuum = (hump + compton) * absorption
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if continuum.sum() > 0 and total_counts > 0:
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continuum *= total_counts / continuum.sum()
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return continuum
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