backwards_ecal: drop full calorimeter hit sum plot

benchmarks/backwards_ecal/backwards_ecal.org

@@ -91,10 +91,8 @@ energies = [
     "10GeV",
     "20GeV",
 ]
-
 filter_name = [
     "MCParticles.*",
-    "*EcalEndcapNRecHits*",
     "*EcalEndcapNClusters*",
 ]
 
@@ -128,21 +126,14 @@ sigmas_rel_FWHM_cb = {}
 fractions_below = {}
 
 for ix, energy in enumerate(energies):
-    for use_clusters in [False, True]:
     energy_value = float(energy.replace("GeV", "").replace("MeV", "e-3"))
-        if use_clusters:
     clf_label = "leading cluster"
-        else:
-            clf_label = "sum all hits"
     def clf(events):
-            if use_clusters:
         return ak.drop_none(ak.max(events["EcalEndcapNClusters.energy"], axis=-1)) / energy_value
-            else:
-                return ak.sum(events["EcalEndcapNRecHits.energy"], axis=-1) / energy_value
     e_pred = clf(e_eval[energy])
 
     plt.sca(axs[ix])
-        counts, bins, patches = plt.hist(e_pred, weights=np.full_like(e_pred, 1.0 / ak.num(e_pred, axis=0)), bins=np.linspace(0.01, 1.01, 101), label=rf"$e^-$ {clf_label}", hatch=None if use_clusters else r"xxx", alpha=0.8 if use_clusters else 1.)
+    counts, bins, patches = plt.hist(e_pred, weights=np.full_like(e_pred, 1.0 / ak.num(e_pred, axis=0)), bins=np.linspace(0.01, 1.01, 101), label=rf"$e^-$ {clf_label}")
     plt.title(f"{energy}")
 
     e_over_p = (bins[1:] + bins[:-1]) / 2
@@ -156,7 +147,7 @@ for ix, energy in enumerate(energies):
         par, pcov = scipy.optimize.curve_fit(f, e_over_p[5:], counts[5:], p0=p0, maxfev=10000)
     except RuntimeError:
         par = None
-        plt.plot(e_over_p, f(e_over_p, *par), label=rf"Crystal Ball fit", color="tab:green" if use_clusters else "green", lw=0.8)
+    plt.plot(e_over_p, f(e_over_p, *par), label=rf"Crystal Ball fit", color="tab:green", lw=0.8)
 
     def summarize_fit(par):
         _, _, _, loc_cb, scale_cb = par
@@ -165,7 +156,6 @@ for ix, energy in enumerate(energies):
         f_prime = lambda x: f(x, *par) - y_max / 2
         x_plus, = scipy.optimize.root(f_prime, loc_cb + scale_cb).x
         x_minus, = scipy.optimize.root(f_prime, loc_cb - scale_cb).x
-            color = "cyan" if use_clusters else "orange"
         plt.axvline(x_minus, ls="--", lw=0.75, color=patches[0].get_facecolor(), label=r"$\mu - $FWHM")
         plt.axvline(x_plus, ls=":", lw=0.75, color=patches[0].get_facecolor(), label=r"$\mu + $FWHM")
         fwhm = (x_plus - x_minus) / loc_cb
@@ -248,23 +238,16 @@ axs_roc = np.ravel(np.array(axs_roc))
 rocs = {}
 
 for ix, energy in enumerate(energies):
-    for use_clusters in [False, True]:
     energy_value = float(energy.replace("GeV", "").replace("MeV", "e-3"))
-        if use_clusters:
     clf_label = "leading cluster"
-        else:
-            clf_label = "sum all hits"
     def clf(events):
-            if use_clusters:
         return ak.drop_none(ak.max(events["EcalEndcapNClusters.energy"], axis=-1)) / energy_value
-            else:
-                return ak.sum(events["EcalEndcapNRecHits.energy"], axis=-1) / energy_value
     e_pred = clf(e_eval[energy])
     pi_pred = clf(pi_eval[energy])
 
     for do_log, ax in [(False, axs[ix]), (True, axs_log[ix])]:
         plt.sca(ax)
-            plt.hist(e_pred, weights=np.full_like(e_pred, 1.0 / ak.num(e_pred, axis=0)), bins=np.linspace(0., 1.01, 101), label=rf"$e^-$ {clf_label}", hatch=None if use_clusters else r"xxx", alpha=0.8 if use_clusters else 1.)
+        plt.hist(e_pred, weights=np.full_like(e_pred, 1.0 / ak.num(e_pred, axis=0)), bins=np.linspace(0., 1.01, 101), label=rf"$e^-$ {clf_label}")
         plt.hist(pi_pred, weights=np.full_like(pi_pred, 1.0 / ak.num(pi_pred, axis=0)), bins=np.linspace(0., 1.01, 101), label=rf"$\pi^-$ {clf_label}", histtype="step")
         plt.title(f"{energy}")
         plt.legend()