Make sure that the valid models are selected for the scaling array too so it...

Make sure that the valid models are selected for the scaling array too so it is the same size as the likelihood array.

pcigale/analysis_modules/pdf_analysis/workers.py

@@ -186,6 +186,9 @@ def analysis(idx, obs):
         # If all the models are valid, it is much more efficient to use a slice
         if likelihood.size == wlikely[0].size:
             wlikely = slice(None, None)
+        likelihood = likelihood[wlikely]
+        scaling_l = scaling[wlikely]
+
         gbl_results.bayes.weight[idx] = np.nansum(likelihood)
 
         # We compute the weighted average and standard deviation using the
@@ -197,7 +200,7 @@ def analysis(idx, obs):
             else:
                 _ = lambda x: x
             values = _(gbl_models.intprop[prop][wz])
-            mean, std = weighted_param(values[wlikely], likelihood[wlikely])
+            mean, std = weighted_param(values[wlikely], likelihood)
             gbl_results.bayes.intmean[prop][idx] = mean
             gbl_results.bayes.interror[prop][idx] = std
             if gbl_models.conf['analysis_params']['save_chi2'] is True:
@@ -210,8 +213,8 @@ def analysis(idx, obs):
             else:
                 _ = lambda x: x
             values = _(gbl_models.extprop[prop][wz])
-            mean, std = weighted_param(values[wlikely] * scaling * corr_dz,
-                           likelihood[wlikely])
+            mean, std = weighted_param(values[wlikely] * scaling_l * corr_dz,
+                           likelihood)
             gbl_results.bayes.extmean[prop][idx] = mean
             gbl_results.bayes.exterror[prop][idx] = std
             if gbl_models.conf['analysis_params']['save_chi2'] is True: