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>

web/app/noise.py

@@ -0,0 +1,107 @@
+"""
+Detector-agnostic continuum extraction for gamma-ray spectra.
+
+Extracts the detector's intrinsic response curve (continuum) from measured
+background data. Isotope photopeaks are subtracted, then the residual is
+smoothed to produce a clean continuum shape that reflects only the detector's
+physics — no isotope signatures.
+
+Works with any scintillator or semiconductor detector.
+"""
+
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+
+
+# Common environmental isotope lines (keV) — subtracted regardless of detector.
+_ENV_PEAKS = [
+    (241.0, 0.04),
+    (295.22, 0.1842),
+    (351.93, 0.3560),
+    (609.31, 0.4549),
+    (911.20, 0.2580),
+    (1120.29, 0.1492),
+    (1460.83, 0.1066),
+    (1764.49, 0.1531),
+    (2614.51, 0.3586),
+]
+
+_E_OFFSET = 0.33
+_E_SLOPE = 2.97
+
+
+def _sigma_ch(E_keV):
+    """Peak sigma in channels at energy E_keV (sqrt(E) resolution scaling)."""
+    fwhm_keV = 0.08 * E_keV * (E_keV / 662.0) ** 0.5
+    sigma_keV = fwhm_keV / 2.355
+    return max(sigma_keV / _E_SLOPE, 2.0)
+
+
+def _subtract_peaks(counts, energy_axis):
+    """Remove known isotope photopeaks from spectrum."""
+    continuum = counts.copy()
+    channels = np.arange(len(counts), dtype=np.float64)
+
+    for line_energy, _ in _ENV_PEAKS:
+        idx = int(np.argmin(np.abs(energy_axis - line_energy)))
+        if idx < 0 or idx >= len(counts):
+            continue
+
+        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(counts), idx + far)
+        hi_end = min(len(counts), 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:
+            gaussian = peak_height * np.exp(-0.5 * ((channels - idx) / sig) ** 2)
+            continuum -= gaussian
+
+    return np.maximum(continuum, 0)
+
+
+def extract_continuum(counts, energy_axis=None):
+    """Extract the detector's intrinsic response continuum.
+
+    Removes isotope photopeaks, then smooths with a wide Gaussian filter
+    to produce a clean curve showing only the detector's continuum shape.
+
+    Parameters
+    ----------
+    counts : array
+        Raw accumulated counts per channel.
+    energy_axis : array, optional
+        Energy axis in keV.
+
+    Returns
+    -------
+    array — smooth continuum (peak-subtracted, Gaussian-smoothed)
+    """
+    counts = np.asarray(counts, dtype=np.float64)
+    n_channels = len(counts)
+
+    if energy_axis is None:
+        energy_axis = _E_OFFSET + _E_SLOPE * np.arange(n_channels, dtype=np.float64)
+
+    continuum = _subtract_peaks(counts, energy_axis)
+
+    # Wide Gaussian smooth (sigma ~1.5% of channels ≈ 45 keV)
+    sigma = max(15, n_channels // 60)
+    continuum_smooth = gaussian_filter1d(continuum, sigma=sigma)
+    continuum_smooth = np.maximum(continuum_smooth, 0)
+
+    return continuum_smooth