allow to pass point_prescription instead of jus tpoint_prescriptions

n3fit/runcards/examples/nnpdfpol20_dis_nlo_mhou.yml

@@ -78,7 +78,7 @@ theory:
   theoryid: 41_100_010
 
 theorycovmatconfig:
-  point_prescription: '7 point'
+  point_prescriptions: ['7 point']
   pdf: NNPDFpol11_100
   unpolarized_bc: NNPDF40_nlo_pch_as_01180
   use_thcovmat_in_fitting: true

validphys2/examples/data_theory_comparison_w_sv.yaml

@@ -6,7 +6,7 @@ meta:
 use_cuts: "internal"
 
 theoryid: 717 # define the central theory
-point_prescription: "3 point"
+point_prescriptions: "3 point"
 use_theorycovmat: true
 
 use_pdferr: true

validphys2/examples/theory_covariance/covariance_matrix_plots_3pt.yaml

@@ -7,7 +7,7 @@ default_theory:
    - theoryid: 163
 
 theoryid: 163
-point_prescription: '3 point'
+point_prescriptions: '3 point'
 
 
 

validphys2/examples/theory_covariance/covariance_matrix_plots_9pt_reduced.yaml

@@ -7,7 +7,7 @@ default_theory:
    - theoryid: 163
 
 theoryid: 163
-point_prescription: '9 point'
+point_prescriptions: '9 point'
 
 
 dataspecs:

validphys2/examples/theory_covariance/validation_report_3pt.yaml

@@ -47,7 +47,7 @@ theoryconfig:  # For generating theory covariance matrix
    theoryid: 163
 
 
-   point_prescription: '3 point'
+   point_prescriptions: ['3 point']
 
    use_cuts: fromfit
    fit: 190315_ern_nlo_central_163_global

validphys2/examples/theory_covariance/validation_report_9pt.yaml

@@ -46,7 +46,7 @@ theoryconfig:  # For generating theory covariance matrix
 
    theoryid: 163
 
-   point_prescription: '9 point'
+   point_prescriptions: ['9 point']
 
    use_cuts: fromfit
    fit: 190315_ern_nlo_central_163_global

validphys2/src/validphys/config.py

@@ -214,12 +214,17 @@ 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: (str, None) = None
+        self,
+        use_scalevar_uncertainties: bool = False,
+        point_prescriptions: (list, None) = None,
+        point_prescription: (str, 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):
+        if (not use_scalevar_uncertainties) and (
+            point_prescriptions is not None or point_prescription is not None
+        ):
             use_scalevar_uncertainties = True
         return use_scalevar_uncertainties
 
@@ -1631,7 +1636,9 @@ def produce_group_dataset_inputs_by_experiment(self, data_input):
     def produce_group_dataset_inputs_by_process(self, data_input):
         return self.produce_group_dataset_inputs_by_metadata(data_input, "nnpdf31_process")
 
-    def produce_point_prescriptions_theoryids(self, theoryid, point_prescriptions):
+    def produce_point_prescriptions_theoryids(
+        self, theoryid, point_prescription, point_prescriptions=None
+    ):
         """Produces a list of theoryids given a theoryid at central scales and a point
         prescription. The options for the latter are '3 point', '5 point', '5bar point', '7 point'
         and '9 point'. Note that these are defined in arXiv:1906.10698. This hard codes the
@@ -1657,6 +1664,10 @@ def produce_point_prescriptions_theoryids(self, theoryid, point_prescriptions):
         )
 
         pp_thids = {}
+
+        if point_prescriptions == None:
+            point_prescriptions = [point_prescription]
+
         for pp in point_prescriptions:
             try:
                 scales = pp_scales_dict[pp]