Add function for plotting average bias correction

plot_results.py

@@ -365,18 +365,33 @@ def plot_bias_correction(k=[[0, 0]], file_indexes=[0], i_channel=None,
     return
 
 
-def plot_one_over_n():
-    # TODO: Make it beautiful
-    averages = []
-    k_x = 0
-    k_y = k_x
-    for i_file in range(len(file_names)):
+def plot_average_bias_correction(file_indexes=[0], i_channel=None,
+                                 fig_name=None):
+    average_corrections = []
+    for i_file in file_indexes:
+        if i_channel is None:
             bias_correction = np.abs(np.imag(
-            transformed_input_means[i_file, k_x, k_y, :]
-            - means[i_file, k_x, k_y, :]))
-        averages.append(np.mean(bias_correction))
-    plt.plot(1/np.array([2, 4, 8, 16]), averages, "x-")
+                transformed_input_means[i_file, :, :, :]
+                - means[i_file, :, :, :]))
+        else:
+            bias_correction = np.abs(np.real(
+                transformed_input_means[i_file, i_channel, :, :, :]
+                - means[i_file, i_channel, :, :, :]))
+        average_corrections.append(np.mean(bias_correction))
+    ax = plt.gca()
+    ax.yaxis.set_major_formatter(mtick.FormatStrFormatter("%.2e"))
+    ax.tick_params(axis="x", which="minor", bottom=False)
+    plt.title("Average bias correction over all $\\nu$ and $k$")
+    plt.loglog(n_worm_samples[file_indexes], average_corrections, "x-")
+    plt.xticks(n_worm_samples[file_indexes], [str(n) for n in n_worm_samples])
+    plt.xlabel("n_worm_samples")
+    plt.ylabel(r"$|f(\hat{x})-\hat{y}_{\mathrm{jackknife}}|$", fontsize=12)
+    plt.grid(which="major")
+    if fig_name is None:
         plt.show()
+    else:
+        plt.savefig(fig_name, dpi=200, bbox_inches="tight")
+    return
 
 
 def plot_sigma0():