When estimating a parameter in log, these were not scaled appropriately and...

When estimating a parameter in log, these were not scaled appropriately and taken in log when saving the related χ²

CHANGELOG.md

@@ -6,6 +6,9 @@
 
 ### Changed
 ### Fixed
+- To estimate parameters in log, pcigale determines which variables end with the "_log" string and removed it to compute the models. However in some circumstances, it was overzealous. This has been fixed. (Médéric Boquien)
+- When estimating a parameter in log, these were not scaled appropriately and taken in log when saving the related χ². (Médéric Boquien)
+
 ### Optimised
 - Prior to version 0.7.0, we needed to maintain the list of redshifts for all the computed models. Past 0.7.0 we just infer the redshift from a list unique redshifts. This means that we can now discard the list of redshifts for all the models and only keep the list of unique redshifts. This saves ~8 MB of memory for every 10⁶ models. the models should be computed slightly faster but it is in the measurement noise. (Médéric Boquien)
 

pcigale/analysis_modules/pdf_analysis/utils.py

@@ -83,7 +83,7 @@ def save_pdf(obsid, name, model_variable, likelihood):
         table.write(OUT_DIR + "{}_{}_pdf.fits".format(obsid, name))
 
 
-def save_chi2(obsid, name, model_variable, chi2):
+def _save_chi2(obsid, name, model_variable, chi2):
     """Save the best reduced χ² versus an analysed variable
 
     Parameters
@@ -103,6 +103,37 @@ def save_chi2(obsid, name, model_variable, chi2):
     table.write(OUT_DIR + "{}_{}_chi2.fits".format(obsid, name))
 
 
+def save_chi2(obsid, names, mass_proportional, model_variables, scaling, chi2):
+    """Save the best reduced χ² versus analysed variables
+
+    Parameters
+    ----------
+    obsid: string
+        Name of the object. Used to prepend the output file name
+    name: list of strings
+        Analysed variables names
+    model_variables: array
+        Values of the model variables
+    scaling: array
+        Scaling factors of the models
+    chi2:
+        Reduced χ²
+    """
+    for i, name in enumerate(names):
+        if name.endswith('_log'):
+            if name[:-4] in mass_proportional:
+                model_variable = np.log10(model_variables[:, i] * scaling)
+            else:
+                model_variable = np.log10(model_variables[:, i])
+        else:
+            if name in mass_proportional:
+                model_variable = model_variables[:, i] * scaling
+            else:
+                model_variable = model_variables
+
+        _save_chi2(obsid, name, model_variable, chi2)
+
+
 def save_table_analysis(filename, obsid, analysed_variables, analysed_averages,
                         analysed_std):
     """Save the estimated values derived from the analysis of the PDF

pcigale/analysis_modules/pdf_analysis/workers.py

@@ -287,13 +287,9 @@ def analysis(idx, obs):
         if gbl_save['best_sed']:
             save_best_sed(obs['id'], sed, scaling[best_index_z])
         if gbl_save['chi2']:
-            for i, variable in enumerate(gbl_analysed_variables):
-                if variable in sed.mass_proportional_info:
-                    save_chi2(obs['id'], variable, gbl_model_variables[wz, i] *
-                              scaling, chi2 / (nobs - 1))
-                else:
-                    save_chi2(obs['id'], variable, gbl_model_variables[wz, i],
-                              chi2 / (nobs - 1))
+            save_chi2(obs['id'], gbl_analysed_variables,
+                      sed.mass_proportional_info, gbl_model_variables[wz, :],
+                      scaling, chi2 / (nobs - 1))
         if gbl_save['pdf']:
             for i, variable in enumerate(gbl_analysed_variables):
                 if variable in sed.mass_proportional_info: