Changed plotting behaviour in MCMC and nested sampling plot related f…

…unctions
VChristiaens · Mar 26, 2024 · dbb6614 · dbb6614
1 parent ed00bb9
commit dbb6614
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Showing 3 changed files with 8 additions and 8 deletions.
diff --git a/vip_hci/fm/negfc_fmerit.py b/vip_hci/fm/negfc_fmerit.py
@@ -893,7 +893,8 @@ def get_mu_and_sigma(
 
     indices = get_annular_wedge(pca_res, radius_int, 2 * fwhm, wedge=wedge)
     yy, xx = indices
-    indices_inv = get_annular_wedge(pca_res_inv, radius_int, 2 * fwhm, wedge=wedge)
+    indices_inv = get_annular_wedge(pca_res_inv, radius_int, 2 * fwhm,
+                                    wedge=wedge)
     yyi, xxi = indices_inv
     all_res = np.concatenate((pca_res[yy, xx], pca_res_inv[yyi, xxi]))
     mu = np.mean(all_res)

diff --git a/vip_hci/fm/negfc_mcmc.py b/vip_hci/fm/negfc_mcmc.py
@@ -1162,8 +1162,6 @@ def show_walk_plot(chain, save=False, output_dir='', **kwargs):
     if save:
         plt.savefig(output_dir+'walk_plot.pdf')
         plt.close(fig)
-    else:
-        plt.show()
 
 
 def show_corner_plot(chain, burnin=0.5, save=False, output_dir='', **kwargs):
@@ -1219,8 +1217,6 @@ def show_corner_plot(chain, burnin=0.5, save=False, output_dir='', **kwargs):
     if save:
         plt.savefig(output_dir+'corner_plot.pdf')
         plt.close(fig)
-    else:
-        plt.show()
 
 
 def confidence(isamples, cfd=68.27, bins=100, gaussian_fit=False, weights=None,

diff --git a/vip_hci/fm/negfc_nested.py b/vip_hci/fm/negfc_nested.py
@@ -391,17 +391,20 @@ def nested_sampling_results(ns_object, burnin=0.4, bins=None, cfd=68.27,
         plt.ylabel('weights')
         plt.vlines(indburnin, res.weights.min(), res.weights.max(),
                    linestyles='dotted')
+        if save:
+            plt.savefig(output_dir+'Nested_results.pdf')
         if plot:
             plt.show()
 
-        plt.savefig(output_dir+'Nested_results.pdf')
-
         print("\nWalk plots before the burnin")
         show_walk_plot(np.expand_dims(res.samples, axis=0))
         if burnin > 0:
             print("\nWalk plots after the burnin")
             show_walk_plot(np.expand_dims(res.samples[indburnin:], axis=0))
-        plt.savefig(output_dir+'Nested_walk_plots.pdf')
+        if save:
+            plt.savefig(output_dir+'Nested_walk_plots.pdf')
+        if plot:
+            plt.show()
 
     mean, cov = nestle.mean_and_cov(res.samples[indburnin:],
                                     res.weights[indburnin:])