Move the simple reduced χ² analysis to a specific function and call this...

Move the simple reduced χ² analysis to a specific function and call this function. It helps keeping the code more readable.

pcigale/analysis_modules/pdf_analysis/__init__.py

@@ -35,7 +35,7 @@ import numpy as np
 
 from ...utils import read_table
 from .. import AnalysisModule, complete_obs_table
-from .utils import save_table_analysis, save_table_best
+from .utils import save_table_analysis, save_table_best, analyse_chi2
 from ...warehouse import SedWarehouse
 from ...data import Database
 from .workers import sed as worker_sed
@@ -231,17 +231,7 @@ class PdfAnalysis(AnalysisModule):
                          initargs=initargs) as pool:
                 pool.starmap(worker_analysis, enumerate(obs_table))
 
-        val_best_chi2_red = np.ctypeslib.as_array(best_chi2_red[0])
-        verylow_redchi2 = sum(chi2_red < TOLERANCE 
-                              for chi2_red in val_best_chi2_red)/n_obs*100.
-        low_redchi2 = sum(chi2_red < 0.5 
-                              for chi2_red in val_best_chi2_red)/n_obs*100.
-        # If low values of reduced chi^2, it means that the data are overfitted
-        # Errors might be under-estimated or not enough valid data.
-        if verylow_redchi2 > 10. or low_redchi2 > 20.:
-            print("\nWarning: "
-              "{} % of the objects have chi^2_red ~ 0. and {} % chi^2_red < 0.5"
-               .format(np.round(verylow_redchi2,1), np.round(low_redchi2,1)))
+        analyse_chi2(best_chi2_red)
 
         print("\nSaving results...")
 

pcigale/analysis_modules/pdf_analysis/utils.py

@@ -244,3 +244,20 @@ def dchi2_over_ds2(s):
     func = dchi2_over_ds_data - dchi2_over_ds_lim
 
     return func
+
+def analyse_chi2(chi2):
+    """Function to analyse the best chi^2 and find out whether what fraction of
+    objects seem to be overconstrainted.
+
+    Parameters
+    ----------
+    chi2: RawArray
+        Contains the reduced chi^2
+
+    """
+    chi2_red = np.ctypeslib.as_array(chi2[0])
+    # If low values of reduced chi^2, it means that the data are overfitted
+    # Errors might be under-estimated or not enough valid data.
+    print("\n{}% of the objects have chi^2_red~0 and {}% chi^2_red<0.5"
+        .format(np.round((chi2_red < 1e-12).sum()/chi2_red.size, 1),
+                np.round((chi2_red < 0.5).sum()/chi2_red.size, 1)))