get rid of 'use_user_uncertainties' flag

doc/sphinx/source/tutorials/general_th_covmat.rst

@@ -42,7 +42,6 @@ Instructions
     ############################################################################
     theorycovmatconfig:
       use_scalevar_uncertainties: False
-      use_user_uncertainties: True
       user_covmat_path: "IeGM9CY8RxGcb5r6bIEYlQ==/topthcovmat.csv"
       use_thcovmat_in_sampling: True
       use_thcovmat_in_fitting: True
@@ -58,15 +57,15 @@ generation). The default is ``True`` for both.
       be avoided unless you know what you are doing.
 
 
-4. The flag ``use_scalevar_uncertainties``  corresponds to whether or not a
+1. The flag ``use_scalevar_uncertainties``  corresponds to whether or not a
    theory covmat is to be constructed from a pointprescription with instructions
    inside ``theorycovmatconfig``. Usually in cases where a custom covmat is
    being loaded, this will be set to ``False``.
 
-5. Make sure that datasets are grouped under one big experiment called "BIGEXP",
+2. Make sure that datasets are grouped under one big experiment called "BIGEXP",
    just like in :ref:`vptheorycov-index`.
 
-6. For an example runcard, see `here <https://github.com/NNPDF/nnpdf/tree/master/validphys2/examples/fit_with_user_covmat.yaml.>`_
+3. For an example runcard, see `here <https://github.com/NNPDF/nnpdf/tree/master/validphys2/examples/fit_with_user_covmat.yaml.>`_
 
 
 Including both scale variation uncertainties and user uncertainties

n3fit/src/n3fit/scripts/n3fit_exec.py

@@ -165,9 +165,6 @@ def from_yaml(cls, o, *args, **kwargs):
             N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = thconfig.get(
                 'use_thcovmat_in_sampling', 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
             )

validphys2/src/validphys/config.py

@@ -783,7 +783,7 @@ def produce_loaded_theory_covmat(
         self,
         output_path,
         data_input,
-        use_user_uncertainties=False,
+        user_covmat_path=None,
         use_scalevar_uncertainties=True,
         use_thcovmat_in_sampling=False,
         use_thcovmat_in_fitting=False,
@@ -796,7 +796,7 @@ def produce_loaded_theory_covmat(
             return 0.0
         # Load correct file according to how the thcovmat was generated by vp-setupfit
         generic_path = "datacuts_theory_theorycovmatconfig_theory_covmat_custom.csv"
-        if use_user_uncertainties is True:
+        if user_covmat_path is not None:
             if use_scalevar_uncertainties is True:
                 generic_path = "datacuts_theory_theorycovmatconfig_total_theory_covmat.csv"
             else:
@@ -1154,15 +1154,13 @@ def parse_lumi_channel(self, ch: str):
     def produce_all_lumi_channels(self):
         return {"lumi_channels": self.parse_lumi_channels(list(LUMI_CHANNELS))}
 
-    def produce_loaded_user_covmat_path(
-        self, user_covmat_path: str = "", use_user_uncertainties: bool = False
-    ):
+    def produce_loaded_user_covmat_path(self, user_covmat_path: str = ""):
         """
         Path to the user covmat provided by user_covmat_path in the runcard.
         If no path is provided, returns None.
         For use in theorycovariance.construction.user_covmat.
         """
-        if not use_user_uncertainties:
+        if user_covmat_path is None:
             return None
         else:
             l = self.loader
@@ -1171,7 +1169,7 @@ def produce_loaded_user_covmat_path(
 
     @configparser.explicit_node
     def produce_nnfit_theory_covmat(
-        self, inclusive_use_scalevar_uncertainties, use_user_uncertainties: bool = False
+        self, inclusive_use_scalevar_uncertainties, user_covmat_path: str = None
     ):
         """
         Return the theory covariance matrix used in the fit.
@@ -1180,7 +1178,7 @@ def produce_nnfit_theory_covmat(
         the tables directory.
         """
         if inclusive_use_scalevar_uncertainties:
-            if use_user_uncertainties:
+            if user_covmat_path is not None:
                 # Both scalevar and user uncertainties
                 from validphys.theorycovariance.construction import total_theory_covmat_fitting
 
@@ -1190,7 +1188,7 @@ def produce_nnfit_theory_covmat(
                 from validphys.theorycovariance.construction import theory_covmat_custom
 
                 f = theory_covmat_custom
-        elif use_user_uncertainties:
+        elif user_covmat_path is not None:
             # Only user uncertainties
             from validphys.theorycovariance.construction import user_covmat_fitting