Ensure consistent spacing between plot and legends + cleanup

Author: jerrymhuang

bayesflow/diagnostics/plots/pairs_posterior.py

@@ -118,47 +118,6 @@ def pairs_posterior(
             g, plot_data, color=post_color, color2=prior_color, fontsize=legend_fontsize, show_single_legend=False
         )
 
-        # target_handle = plt.Line2D(
-        #     [0], [0],
-        #     color=target_color,
-        #     linestyle="--",
-        #     marker="x",
-        #     label="Targets",
-        # )
-        #
-        # for ax in g.axes.flat:
-        #     if getattr(ax, "legend_", None) is not None:
-        #         ax.legend_.remove()
-        #
-        # diag_ax = g.axes[0, 0]
-        # # Collect histogram legend handles
-        # # hist_handles = getattr(diag_ax, '_legend_handles', [])
-        #
-        # hist_handles, hist_labels = diag_ax.get_legend_handles_labels()
-        #
-        # handles = []
-        # labels = []
-        #
-        # for handle, label in zip(hist_handles, hist_labels):
-        #
-        #     if label != "Targets":
-        #         handles.append(handle)
-        #         labels.append(label)
-        #
-        # handles.append(target_handle)
-        # labels.append(target_handle.get_label())
-        #
-        # # handles = hist_handles + [target_handle]
-        # # labels = hist_labels + [target_handle.get_label()]  # safer to refresh labels
-        #
-        # g.figure.legend(
-        #     handles=handles,
-        #     labels=labels,
-        #     loc="center right",
-        #     frameon=False,
-        #     fontsize=legend_fontsize
-        # )
-
     return g
 
 

bayesflow/diagnostics/plots/pairs_samples.py

@@ -194,33 +194,22 @@ def _pairs_samples(
     return g
 
 
-# create a histogram plot on a twin y axis
-# this ensures that the y scaling of the diagonal plots
-# in independent of the y scaling of the off-diagonal plots
 def histplot_twinx(x, **kwargs):
-    # Create a twin axis
-    # ax2 = plt.gca().twinx()
-
-    label = kwargs.pop("labels", None)
-    color = kwargs.get("colors", None)
-
-    ax = plt.gca()
+    """
+    # create a histogram plot on a twin y axis
+    # this ensures that the y scaling of the diagonal plots
+    # in independent of the y scaling of the off-diagonal plots
 
+    Parameters
+    ----------
+    x : np.ndarray
+        Data to be plotted.
+    """
     # create a histogram on the twin axis
     sns.histplot(x, legend=False, **kwargs)
 
-    # if label is not None:
-    #     legend_artist = Patch(color=color, label=label)
-    #     # Store the artist for later
-    #     if not hasattr(ax, '_legend_handles'):
-    #         ax._legend_handles = []
-    #     ax._legend_handles.append(legend_artist)
-
     # make the twin axis invisible
     plt.gca().spines["right"].set_visible(False)
     plt.gca().spines["top"].set_visible(False)
-    # ax2.set_ylabel("")
-    # ax2.set_yticks([])
-    # ax2.set_yticklabels([])
 
     return None

bayesflow/utils/plot_utils.py

@@ -383,12 +383,13 @@ def create_legends(
         handles.append(target_handle)
         labels.append(target_label)
 
+    # If there are more than one dataset to plot,
     if len(handles) > 1 or show_single_legend:
         g.figure.legend(
             handles=handles,
             labels=labels,
-            loc="center right",
-            bbox_to_anchor=(1.2, 0.5),
+            loc="center left",
+            bbox_to_anchor=(1, 0.5),
             frameon=False,
             fontsize=fontsize,
         )