Implementing Juan's comments

Author: achiefa

n3fit/runcards/examples/Basic_runcard_pc_covmat.yml

@@ -33,10 +33,10 @@ datacuts:
 ################################################################################
 # NNLO QCD TRN evolution
 theory:
-  theoryid: 708
+  theoryid: 40_000_000
 
 theorycovmatconfig:
-  point_prescriptions: ["9 point", "power corrections"]
+  point_prescriptions: ["power corrections"]
   pc_parameters:
     H2p: {yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.0], nodes: [0.0, 0.001, 0.01, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
     H2d: {yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.0], nodes: [0.0, 0.001, 0.01, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}

n3fit/src/n3fit/performfit.py

@@ -41,7 +41,7 @@ def performfit(
     debug=False,
     maxcores=None,
     double_precision=False,
-    parallel_models=False,
+    parallel_models=True,
 ):
     """
     This action will (upon having read a validcard) process a full PDF fit

validphys2/src/validphys/config.py

@@ -1323,17 +1323,21 @@ def produce_nnfit_theory_covmat(
             f = user_covmat_fitting
 
         return f
-    
-    def produce_mult_factor_user_covmat(self, mult_factor: float = None, user_covmat_path: str = None):
+
+    def produce_mult_factor_user_covmat(
+        self, mult_factor: float = 1.0, user_covmat_path: str = None
+    ):
         """
         Multiplicative factors for the user covmat provided by mult_factors_user_covmat in the runcard.
         If no factors are provided, returns None.
         For use in theorycovariance.construction.user_covmat.
         """
         # Check that if mult_factors is provided, user_covmat_paths is also provided
         if mult_factor is not None and user_covmat_path is None:
-            raise ConfigError("If mult_factors is provided, user_covmat_paths must also be provided.")
-            
+            raise ConfigError(
+                "If mult_factors is provided, user_covmat_paths must also be provided."
+            )
+
         if mult_factor is None:
             return 1.0 if user_covmat_path is not None else None
         else:
@@ -1945,18 +1949,7 @@ def produce_filter_data(
         if not fakedata:
             return validphys.filters.filter_real_data
         else:
-            # TODO we don't want to sample from the theory covmat for L1 data,
-            # but we do want to use the theory covmat for L2 data
-            if theorycovmatconfig is not None and theorycovmatconfig.get(
-                "use_thcovmat_in_fakedata_sampling"
-            ):
-                # NOTE: By the time we run theory covmat closure tests,
-                # hopefully the generation of pseudodata will be done in python.
-                raise ConfigError(
-                    "Generating L1 closure test data which samples from the theory "
-                    "covariance matrix has not been implemented yet."
-                )
-            elif inconsistent_fakedata:
+            if inconsistent_fakedata:
                 log.info("Using filter for inconsistent closure data")
                 return validphys.filters.filter_inconsistent_closure_data_by_experiment
 
@@ -1986,14 +1979,14 @@ def produce_total_phi_data(self, fitthcovmat):
 
     @configparser.explicit_node
     def produce_covs_pt_prescrip(self, point_prescription):
-        if point_prescription != 'power corrections':
-            from validphys.theorycovariance.construction import covs_pt_prescrip_mhou
-
-            f = covs_pt_prescrip_mhou
-        else:
+        if point_prescription == 'power corrections':
             from validphys.theorycovariance.construction import covs_pt_prescrip_pc
 
             f = covs_pt_prescrip_pc
+        else:
+            from validphys.theorycovariance.construction import covs_pt_prescrip_mhou
+
+            f = covs_pt_prescrip_mhou
 
         return f
 

validphys2/src/validphys/theorycovariance/construction.py

@@ -15,7 +15,6 @@
 
 pass
 from validphys.checks import check_pc_parameters
-from validphys.core import PDF
 from validphys.results import results, results_central
 from validphys.theorycovariance.higher_twist_functions import compute_deltas_pc
 from validphys.theorycovariance.theorycovarianceutils import (
@@ -227,34 +226,31 @@ def covmat_n3lo_ad(name1, name2, deltas1, deltas2):
 def covmat_power_corrections(deltas1, deltas2):
     """Returns the the theory covariance sub-matrix for power
     corrections. The two arguments `deltas1` and `deltas2` contain
-    the shifts for the firs and second experiment, respectively.
+    the shifts for the first and second experiment, respectively.
 
     The shifts are given in this form:
     ```
-    deltas1 = {shift1_label: array1_of_shifts1,
-               shift2_label: array1_of_shifts2,
-               shift3_label: array1_of_shifts3,
-               ...}
-    deltas2 = {shift1_label: array2_of_shifts1,
-               shift2_label: array2_of_shifts2,
-               shift3_label: array2_of_shifts3,
-               ...}
+    deltas1 = [array1_of_shifts1,
+               array1_of_shifts2,
+               array1_of_shifts3,
+               ...]
+    deltas2 = [array2_of_shifts1,
+               array2_of_shifts2,
+               array2_of_shifts3,
+               ...]
     ```
     The sub-matrix is computed using the 5-point prescription, thus
 
       s = array1_of_shifts1 X array2_of_shifts1 + array1_of_shifts2 X array2_of_shifts2 + ...
 
-    where `X` is the outer product.
+    where ``X`` is the outer product.
     """
-    # Check that `deltas1` and `deltas2` have the same shifts
-    if deltas1.keys() != deltas2.keys():
-        raise RuntimeError('The two dictionaries do not contain the same shifts.')
 
-    size1 = next(iter(deltas1.values())).size
-    size2 = next(iter(deltas2.values())).size
+    size1 = deltas1[0].size
+    size2 = deltas2[0].size
     s = np.zeros(shape=(size1, size2))
-    for shift in deltas1.keys():
-        s += np.outer(deltas1[shift], deltas2[shift])
+    for shift1, shift2 in zip(deltas1, deltas2):
+        s += np.outer(shift1, shift2)
     return s
 
 
@@ -367,12 +363,7 @@ def covs_pt_prescrip_mhou(combine_by_type, point_prescription):
 
 @check_pc_parameters
 def covs_pt_prescrip_pc(
-    combine_by_type,
-    point_prescription,
-    pdf: PDF,
-    pc_parameters,
-    pc_included_procs,
-    pc_excluded_exps,
+    combine_by_type, point_prescription, pdf, pc_parameters, pc_included_procs, pc_excluded_exps
 ):
     """Produces the sub-matrices of the theory covariance matrix for power
     corrections. Sub-matrices correspond to applying power corrected shifts
@@ -399,6 +390,11 @@ def covs_pt_prescrip_pc(
             if not (is_excluded_exp or is_included_proc):
                 deltas1 = compute_deltas_pc(data_spec1, pdf, pc_parameters)
                 deltas2 = compute_deltas_pc(data_spec2, pdf, pc_parameters)
+
+                # Convert deltas1 and deltas2 to the format expected by compute_covs_pt_prescrip
+                deltas1 = [np.array(deltas1[shift]) for shift in sorted(deltas1.keys())]
+                deltas2 = [np.array(deltas2[shift]) for shift in sorted(deltas2.keys())]
+
                 s = compute_covs_pt_prescrip(
                     point_prescription, exp_name1, deltas1, exp_name2, deltas2
                 )