examples: cpr_sns: adjust n-convergence plotting

examples/cpr_sns.py

@@ -112,10 +112,11 @@ def do(W_xi=6, multin=False):
     )
 
     if multin:
+        mphi = phi[::2]
         res0 = j(
             T,
             h,
-            phi[None, :],
+            mphi[None, :],
             alpha_soc=alpha,
             eta=eta,
             L=L[:, None],
@@ -126,7 +127,7 @@ def do(W_xi=6, multin=False):
         res1 = j(
             T,
             h,
-            phi[None, :],
+            mphi[None, :],
             alpha_soc=alpha,
             eta=eta,
             L=L[:, None],
@@ -142,19 +143,19 @@ def do(W_xi=6, multin=False):
     if multin:
         Jx0_mean = np.asarray(
             [Res(*x).Jx[1:-1].sum(axis=1).mean(axis=0) for x in res0.flat]
-        ).reshape(res.shape)
+        ).reshape(res0.shape)
 
         Jx1_mean = np.asarray(
             [Res(*x).Jx[1:-1].sum(axis=1).mean(axis=0) for x in res1.flat]
-        ).reshape(res.shape)
+        ).reshape(res1.shape)
 
     fig, axs = plt.subplots(1, 2, layout="compressed")
 
     ax = axs[0]
     ax.plot(phi / pi, Jx_mean.T / abs(Jx_mean).max(axis=1))
     if multin:
-        ax.plot(phi / pi, Jx0_mean.T / abs(Jx_mean).max(axis=1), "k:")
-        ax.plot(phi / pi, Jx1_mean.T / abs(Jx_mean).max(axis=1), "k:")
+        ax.plot(mphi / pi, Jx0_mean.T / abs(Jx0_mean).max(axis=1), "k:", alpha=0.25)
+        ax.plot(mphi / pi, Jx1_mean.T / abs(Jx1_mean).max(axis=1), "k:")
     ax.set_xlabel(r"$\varphi / \pi$")
     ax.set_ylabel(r"$I / I_{\mathrm{max}}$")
     ax.legend(L / xi, title=r"$L/\xi$", loc="lower right")
@@ -167,7 +168,7 @@ def do(W_xi=6, multin=False):
     ax = axs[1]
     ax.plot(L / xi, 100 * eff(Jx_mean))
     if multin:
-        ax.plot(L / xi, 100 * eff(Jx0_mean), "k:")
+        ax.plot(L / xi, 100 * eff(Jx0_mean), "k:", alpha=0.25)
         ax.plot(L / xi, 100 * eff(Jx1_mean), "k:")
     ax.set_xlabel(r"$L / \xi$")
     ax.set_ylabel(r"$\eta$  [%]")