make sure that plots work with only one parameter

bayesflow/diagnostics.py

@@ -147,12 +147,13 @@ def plot_recovery(
     if fig_size is None:
         fig_size = (int(4 * n_col), int(4 * n_row))
     f, axarr = plt.subplots(n_row, n_col, figsize=fig_size)
+
     # turn axarr into 1D list
+    axarr = np.atleast_1d(axarr)
     if n_col > 1 or n_row > 1:
         axarr_it = axarr.flat
     else:
-        # for 1x1, axarr is not a list -> turn it into one for use with enumerate
-        axarr_it = [axarr]
+        axarr_it = axarr
 
     for i, ax in enumerate(axarr_it):
         if i >= n_params:
@@ -337,12 +338,13 @@ def plot_z_score_contraction(
     if fig_size is None:
         fig_size = (int(4 * n_col), int(4 * n_row))
     f, axarr = plt.subplots(n_row, n_col, figsize=fig_size)
+
     # turn axarr into 1D list
+    axarr = np.atleast_1d(axarr)
     if n_col > 1 or n_row > 1:
         axarr_it = axarr.flat
     else:
-        # for 1x1, axarr is not a list -> turn it into one for use with enumerate
-        axarr_it = [axarr]
+        axarr_it = axarr
 
     # Loop and plot
     for i, ax in enumerate(axarr_it):
@@ -480,6 +482,7 @@ def plot_sbc_ecdf(
 
         # Initialize figure
         f, ax = plt.subplots(n_row, n_col, figsize=fig_size)
+        ax = np.atleast_1d(ax)
 
     # Plot individual ecdf of parameters
     for j in range(ranks.shape[-1]):
@@ -657,7 +660,8 @@ def plot_sbc_histograms(
     if fig_size is None:
         fig_size = (int(5 * n_col), int(5 * n_row))
     f, axarr = plt.subplots(n_row, n_col, figsize=fig_size)
-
+    axarr = np.atleast_1d(axarr)
+
     # Compute ranks (using broadcasting)
     ranks = np.sum(post_samples < prior_samples[:, np.newaxis, :], axis=1)