The standardisation of error bars must be done on the final estimates, not on...

The standardisation of error bars must be done on the final estimates, not on the estimates for a single block.

pcigale/analysis_modules/pdf_analysis/workers.py

@@ -185,10 +185,8 @@ def analysis(idx, obs):
             if variable.endswith('_log'):
                 variable = variable[:-4]
                 _ = np.log10
-                maxstd = lambda mean, std: max(0.02, std)
             else:
                 _ = lambda x: x
-                maxstd = lambda mean, std: max(0.05 * mean, std)
 
             if variable in gbl_results.bayes.massproportional:
                 values = _(gbl_models.properties[i, wz] * scaling * corr_dz)
@@ -197,7 +195,7 @@ def analysis(idx, obs):
 
             mean, std = weighted_param(values, likelihood)
             gbl_results.bayes.means[idx, i] = mean
-            gbl_results.bayes.errors[idx, i] = maxstd(mean, std)
+            gbl_results.bayes.errors[idx, i] = std
             if gbl_models.conf['analysis_params']['save_chi2'] is True:
                 save_chi2(obs, variable, gbl_models, chi2, values)
         best_idx_z = np.nanargmin(chi2)

pcigale/managers/results.py

@@ -66,10 +66,6 @@ class BayesResultsManager(object):
                     for result in results)):
             raise TypeError("A list of BayesResultsManager is required.")
 
-        if len(results) == 1:
-            results[0]._weights = None
-            return results[0]
-
         means = np.array([result.means for result in results])
         errors = np.array([result.errors for result in results])
         weights = np.array([result.weights for result in results])[..., None]
@@ -91,6 +87,14 @@ class BayesResultsManager(object):
                                           (means-merged.means)**2), axis=0) /
                                    sumweights)
 
+        for i, variable in enumerate(merged.propertiesnames):
+            if variable.endswith('_log'):
+                maxstd = lambda mean, std: max(0.02, std)
+            else:
+                maxstd = lambda mean, std: max(0.05 * mean, std)
+            merged.errors[:, i] = maxstd(merged.means[:, i],
+                                         merged.errors[:, i])
+
         return merged
 
     @property