backwards_ecal: smooth fil_between, adjust xlim

benchmarks/backwards_ecal/backwards_ecal.org

@@ -212,13 +212,17 @@ for clf_label, sigma_rel_FWHM_cb in sigmas_rel_FWHM_cb.items():
         lw=0.5,
         label=f"Functional fit: ${np.ceil(stochastic * 10) / 10:.1f}\% / \sqrt{{E}} \oplus {np.ceil(constant * 10) / 10:.1f}\%$",
     )
+xmin = np.min(energy_values)
+xmax = np.max(energy_values) * 1.05
+xs = np.arange(xmin, xmax + 0.1, 0.1)
 plt.fill_between(
-    energy_values,
-    np.sqrt((2 / np.sqrt(energy_values)) ** 2 + 1 ** 2),
-    np.sqrt((2 / np.sqrt(energy_values)) ** 2 + 3 ** 2),
-    alpha=0.2, color="black", label=r"YR requirement $2\% / \sqrt{E} \oplus (1-3)\%$",
+    xs,
+    np.sqrt((2 / np.sqrt(xs)) ** 2 + 1 ** 2),
+    np.sqrt((2 / np.sqrt(xs)) ** 2 + 3 ** 2),
+    lw=0., alpha=0.2, color="black", label=r"YR requirement $2\% / \sqrt{E} \oplus (1-3)\%$",
 )
-plt.ylim(top=10.)
+plt.xlim(0., xmax)
+plt.ylim(top=6.)
 plt.legend()
 plt.xlabel("Energy, GeV", loc="right")
 plt.ylabel(r"$\sigma_{E} / E$ derived from FWHM, %", loc="top")
@@ -296,6 +300,8 @@ for clf_label, roc in rocs.items():
         marker=".",
         label=f"{clf_label}",
     )
+xmax = np.max(energy_values) * 1.05
+plt.xlim(0., xmax)
 plt.yscale("log")
 plt.legend()
 plt.xlabel("Energy, GeV")