Combined pr48 pr35

scripts/plot_overhead_pdg.py

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+"""
+Per-track overhead distribution overlay by particle species.
+Usage: python3 plot_overhead_pdg.py <histos_overhead.root> [output.png]
+"""
+import sys
+import uproot
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+RFILE = sys.argv[1] if len(sys.argv) > 1 else '/tmp/latest_histos_overhead.root'
+OUTFILE = sys.argv[2] if len(sys.argv) > 2 else '/tmp/overhead_pdg.png'
+
+species = [
+    ('m_overhead_dist_pdg11',   'e⁻',    '#1f77b4'),
+    ('m_overhead_dist_pdg22',   'γ',      '#ff7f0e'),
+    ('m_overhead_dist_pdg-22',  'opt-γ',  '#d62728'),
+    ('m_overhead_dist_pdg-11',  'e⁺',     '#9467bd'),
+    ('m_overhead_dist_pdg2112', 'n',      '#2ca02c'),
+    ('m_overhead_dist_pdg2212', 'p',      '#8c564b'),
+    ('m_overhead_dist_pdg211',  'π⁺',     '#e377c2'),
+    ('m_overhead_dist_pdg-211', 'π⁻',     '#bcbd22'),
+]
+
+XMIN, XMAX, SMOOTH = 2.0, 100.0, 7
+
+fig, axes = plt.subplots(1, 2, figsize=(13, 5))
+fig.patch.set_facecolor('white')
+
+with uproot.open(RFILE) as f:
+    for hname, label, color in species:
+        if hname not in f:
+            continue
+        h = f[hname]
+        vals, edges = h.to_numpy()
+        centers = (edges[:-1] + edges[1:]) / 2
+        mask = (centers >= XMIN) & (centers <= XMAX)
+        x = centers[mask]
+        y = vals[mask].astype(float)
+        total = y.sum()
+        if total == 0:
+            continue
+        y_norm = y / total
+        y_smooth = np.convolve(y_norm, np.ones(SMOOTH) / SMOOTH, mode='same')
+        n_tracks = int(vals.sum())
+        mean_us = (centers * vals).sum() / vals.sum() if vals.sum() > 0 else 0
+        full_label = f'{label} ({n_tracks/1e6:.1f}M, mean={mean_us:.0f}µs)'
+        for ax in axes:
+            ax.plot(x, y_smooth, label=full_label, color=color, linewidth=1.8)
+
+axes[0].set_xlim(XMIN, 40)
+axes[0].set_ylim(bottom=0)
+axes[0].set_xlabel('per-track overhead [µs]', fontsize=11)
+axes[0].set_ylabel('fraction of tracks (normalized)', fontsize=10)
+axes[0].set_title('Linear scale', fontsize=11)
+axes[0].legend(fontsize=8, framealpha=0.95)
+axes[0].grid(True, color='#dddddd', linewidth=0.5)
+
+axes[1].set_yscale('log')
+axes[1].set_xlim(XMIN, XMAX)
+axes[1].set_ylim(1e-6, 0.2)
+axes[1].set_xlabel('per-track overhead [µs]', fontsize=11)
+axes[1].set_ylabel('fraction of tracks (normalized)', fontsize=10)
+axes[1].set_title('Log scale', fontsize=11)
+axes[1].legend(fontsize=8, framealpha=0.95)
+axes[1].grid(True, color='#dddddd', linewidth=0.5, which='both')
+
+for ax in axes:
+    ax.set_facecolor('white')
+    for sp in ax.spines.values():
+        sp.set_edgecolor('#aaaaaa')
+
+fig.suptitle(
+    'Per-track overhead by species (normalized, smoothed, <2µs excluded)\n'
+    f'{RFILE}',
+    fontsize=10, y=1.01
+)
+plt.tight_layout()
+plt.savefig(OUTFILE, dpi=150, bbox_inches='tight', facecolor='white')
+print(f'saved {OUTFILE}')

scripts/plot_overhead_spatial.py

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+"""
+Per-track overhead spatial heatmaps (xy and zr).
+Usage: python3 plot_overhead_spatial.py <histos_overhead.root> [output.png]
+"""
+import sys
+import uproot
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+
+RFILE = sys.argv[1] if len(sys.argv) > 1 else '/tmp/latest_histos_overhead.root'
+OUTFILE = sys.argv[2] if len(sys.argv) > 2 else '/tmp/overhead_spatial.png'
+
+with uproot.open(RFILE) as f:
+    hxy = f['m_overhead_xy']
+    xy_vals, xy_xe, xy_ye = hxy.to_numpy()
+    hzr = f['m_overhead_zr']
+    zr_vals, zr_xe, zr_ye = hzr.to_numpy()
+
+# mm -> cm
+xy_xe /= 10; xy_ye /= 10
+zr_xe /= 10; zr_ye /= 10
+
+# zoom zr: z in [-500, 500] cm, r in [0, 200] cm
+zmask = (zr_xe >= -500) & (zr_xe <= 500)
+rmask = (zr_ye >= 0)    & (zr_ye <= 200)
+zi = np.where(zmask)[0]
+ri = np.where(rmask)[0]
+zr_zoom  = zr_vals[zi[0]:zi[-1], ri[0]:ri[-1]]
+zr_xe_z  = zr_xe[zi[0]:zi[-1]+1]
+zr_ye_z  = zr_ye[ri[0]:ri[-1]+1]
+
+fig, axes = plt.subplots(1, 2, figsize=(15, 6))
+fig.patch.set_facecolor('white')
+
+def heatmap(ax, xe, ye, vals, xlabel, ylabel, title):
+    vp = np.where(vals > 0, vals, np.nan)
+    vmin = np.nanpercentile(vp, 20)
+    vmax = np.nanpercentile(vp, 99.5)
+    im = ax.pcolormesh(xe, ye, vp.T, norm=LogNorm(vmin=vmin, vmax=vmax), cmap='plasma')
+    cb = plt.colorbar(im, ax=ax)
+    cb.set_label('overhead [ns·tracks]', fontsize=10)
+    ax.set_xlabel(xlabel, fontsize=11)
+    ax.set_ylabel(ylabel, fontsize=11)
+    ax.set_title(title, fontsize=12)
+    ax.set_facecolor('#f0f0f0')
+    for sp in ax.spines.values():
+        sp.set_edgecolor('#999999')
+
+heatmap(axes[0], xy_xe, xy_ye, xy_vals, 'x [cm]', 'y [cm]', 'Overhead map — xy')
+axes[0].set_aspect('equal')
+heatmap(axes[1], zr_xe_z, zr_ye_z, zr_zoom, 'z [cm]', 'r [cm]', 'Overhead map — zr (|z|<500cm, r<200cm)')
+
+fig.suptitle(
+    'Per-track overhead spatial map (track start position weighted by overhead)\n'
+    f'{RFILE}',
+    fontsize=10, y=1.01
+)
+plt.tight_layout()
+plt.savefig(OUTFILE, dpi=150, bbox_inches='tight', facecolor='white')
+print(f'saved {OUTFILE}')