remove 'use_scalevar_uncertainties' variable

doc/sphinx/source/tutorials/general_th_covmat.rst

@@ -41,7 +41,6 @@ Instructions
 
     ############################################################################
     theorycovmatconfig:
-      use_scalevar_uncertainties: False
       user_covmat_path: "IeGM9CY8RxGcb5r6bIEYlQ==/topthcovmat.csv"
       use_thcovmat_in_sampling: True
       use_thcovmat_in_fitting: True
@@ -56,16 +55,10 @@ generation). The default is ``True`` for both.
       Changing either of these to ``False`` will affect the fit outcome and should
       be avoided unless you know what you are doing.
 
-
-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``.
-
-2. Make sure that datasets are grouped under one big experiment called "BIGEXP",
+1. Make sure that datasets are grouped under one big experiment called "BIGEXP",
    just like in :ref:`vptheorycov-index`.
 
-3. For an example runcard, see `here <https://github.com/NNPDF/nnpdf/tree/master/validphys2/examples/fit_with_user_covmat.yaml.>`_
+2. 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_scalevar_uncertainties'] = thconfig.get(
-                'use_scalevar_uncertainties', True
-            )
         # Fitting flag
         file_content.update(N3FIT_FIXED_CONFIG)
         return cls(file_content, *args, **kwargs)

validphys2/src/validphys/config.py

@@ -213,16 +213,6 @@ def produce_replicas(self, nreplica: int):
     def parse_point_prescriptions(self, point_prescriptions):
         return NSList(point_prescriptions, nskey="point_prescription")
 
-    def produce_inclusive_use_scalevar_uncertainties(
-        self, use_scalevar_uncertainties: bool = False, point_prescriptions: (list, None) = None
-    ):
-        """Whether to use a scale variation uncertainty theory covmat.
-        Checks whether a point prescription is included in the runcard and if so
-        assumes scale uncertainties are to be used."""
-        if not use_scalevar_uncertainties and point_prescriptions is not None:
-            use_scalevar_uncertainties = True
-        return use_scalevar_uncertainties
-
     # TODO: load fit config from here
     @element_of("fits")
     @_id_with_label
@@ -784,7 +774,7 @@ def produce_loaded_theory_covmat(
         output_path,
         data_input,
         user_covmat_path=None,
-        use_scalevar_uncertainties=True,
+        point_prescriptions=None,
         use_thcovmat_in_sampling=False,
         use_thcovmat_in_fitting=False,
     ):
@@ -797,7 +787,7 @@ def produce_loaded_theory_covmat(
         # Load correct file according to how the thcovmat was generated by vp-setupfit
         generic_path = "datacuts_theory_theorycovmatconfig_theory_covmat_custom.csv"
         if user_covmat_path is not None:
-            if use_scalevar_uncertainties is True:
+            if point_prescriptions is not None:
                 generic_path = "datacuts_theory_theorycovmatconfig_total_theory_covmat.csv"
             else:
                 generic_path = "datacuts_theory_theorycovmatconfig_user_covmat.csv"
@@ -1169,15 +1159,15 @@ def produce_loaded_user_covmat_path(self, user_covmat_path: str = ""):
 
     @configparser.explicit_node
     def produce_nnfit_theory_covmat(
-        self, inclusive_use_scalevar_uncertainties, user_covmat_path: str = None
+        self, point_prescriptions: list = None, user_covmat_path: str = None
     ):
         """
         Return the theory covariance matrix used in the fit.
 
         This function is only used in vp-setupfit to store the necessary covmats as .csv files in
         the tables directory.
         """
-        if inclusive_use_scalevar_uncertainties:
+        if point_prescriptions is not None:
             if user_covmat_path is not None:
                 # Both scalevar and user uncertainties
                 from validphys.theorycovariance.construction import total_theory_covmat_fitting