Fixing the grouping and the thcovmat flag

n3fit/src/n3fit/scripts/n3fit_exec.py

@@ -147,10 +147,13 @@ def from_yaml(cls, o, *args, **kwargs):
             N3FIT_FIXED_CONFIG['actions_'].extend((training_action, validation_action))
         #Theorycovmat flags and defaults
         N3FIT_FIXED_CONFIG['theory_covmat_flag'] = False
+        N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting'] = False
+        N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = False
         if (thconfig:=file_content.get('theorycovmatconfig')) is not None:
-            N3FIT_FIXED_CONFIG['theory_covmat_flag'] = True
             N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting'] = thconfig.get('use_thcovmat_in_fitting', True) 
-            N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = thconfig.get('use_thcovmat_in_sampling', True)  
+            N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = thconfig.get('use_thcovmat_in_sampling', True) 
+            if N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] or N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting']:
+                N3FIT_FIXED_CONFIG['theory_covmat_flag'] = True
             N3FIT_FIXED_CONFIG['use_user_uncertainties'] = thconfig.get('use_user_uncertainties', False) 
             N3FIT_FIXED_CONFIG['use_scalevar_uncertainties'] = thconfig.get('use_scalevar_uncertainties', True) 
         #Sampling flags

validphys2/src/validphys/config.py

@@ -1584,11 +1584,12 @@ def load_default_data_grouping(self, spec):
         # somebody will want to add to this at some point e.g for th. uncertainties
         allowed = {
             "standard_report": "experiment",
+            "thcovmat_fit": "ALL"
         }
         return allowed[spec]
 
     def produce_processed_data_grouping(
-        self, data_grouping=None, data_grouping_recorded_spec_=None
+        self, use_thcovmat_in_fitting, use_thcovmat_in_sampling, data_grouping=None, data_grouping_recorded_spec_=None
     ):
         """Process the data_grouping key from the runcard, or lockfile. If
         `data_grouping_recorded_spec_` is present then its value is taken, and
@@ -1602,6 +1603,8 @@ def produce_processed_data_grouping(
         if data_grouping is None:
             # fallback to old default behaviour, but still record to lockfile
             data_grouping = self.parse_data_grouping("standard_report")
+            if use_thcovmat_in_fitting or use_thcovmat_in_sampling:
+                data_grouping = self.parse_data_grouping("thcovmat_fit")
         if data_grouping_recorded_spec_ is not None:
             return data_grouping_recorded_spec_[data_grouping]
         return self.load_default_data_grouping(data_grouping)
@@ -1653,17 +1656,6 @@ def produce_group_dataset_inputs_by_metadata(
             for name, group in res.items()
         ]
 
-    def produce_group_dataset_inputs_by_fitting_group(
-        self, data_input, theory_covmat_flag
-    ):
-        """
-        Groups datasets all together in a group called ALL if the theory covariance matrix
-        is used in the fit, otherwise it groups them by experiment.
-        """
-        if theory_covmat_flag:
-            return self.produce_group_dataset_inputs_by_metadata(data_input, "ALL")
-        return self.produce_group_dataset_inputs_by_metadata(data_input, "experiment")
-
     def produce_fivetheories(self, point_prescription):
         if point_prescription == "5bar point":
             return "bar"

validphys2/src/validphys/n3fit_data.py

@@ -311,7 +311,7 @@ def fitting_data_dict(
     return dict_out
 
 
-exps_fitting_data_dict = collect("fitting_data_dict", ("group_dataset_inputs_by_fitting_group",))
+exps_fitting_data_dict = collect("fitting_data_dict", ("group_dataset_inputs_by_metadata",))
 
 
 def replica_nnseed_fitting_data_dict(replica, exps_fitting_data_dict, replica_nnseed):