write idea chi2 infos propagation, chi2 calculation not possible in h…

…ist_hooks due to axes concatenation with categories

hbt/config/hist_hooks.py

@@ -872,6 +872,7 @@ def LO_primitive_funtion(values, a, b, c, d, e, f):
 
             pcovs = []
             # minuit_pcovs = []
+            chi2_infos = {}
 
             # loop over bins and calculate the morphed value
             for cat_ in range(len(model_hists[0].axes[0].centers)):
@@ -1017,6 +1018,7 @@ def LO_primitive_funtion(values, a, b, c, d, e, f):
         morphed_variances_correct_categorization = morphed_variances[np.argsort(np.argsort(model_hists[0].axes[0]))]
 
         # insert the new hist into the hists dict
+        new_proc.x.chi2_infos = chi2_infos
         hist[new_proc] = new_hist
 
         # insert values into the new histogram

hbt/plotting/morphing_tests.py

@@ -837,9 +837,14 @@ def plot_3d_morphing(
             # morphed histogram? The error of the bin value of the non-morphed histogram?
             # might depend on which method is chosen above.
 
-            # TODO: implement my solution: calculate the chi2 for the morphed histograms for each point
-            # in the hist_hooks and propagate the chi2 values to the plotting function as auxiliary data
-            # in the process instance from the hists dictionary
+            # TODO: implement my solution: propagate the points used for the fit from the hist_hooks
+            # then calculate the chi2 for each point with the corresponding guidance points
+            # BUT IS IT EVEN NECESSARY?
+
+            # -> for which fit do we want the chi 2? fit all the points in the histogram (= different coupling values)
+            # without caring about how they were created? or only the points that were used for the fit of the histogram
+            # at this specific coupling constant value (=points used for the morphing)?
+
 
             chi2 = np.sum((hists_to_plot[vbf_point["name"]].values() - non_morphed_hists["name"].values())**2 /
                 hists_to_plot[vbf_point["name"]].variances())