removed pdf covmat changes

NNPDF · Jun 9, 2023 · e309d4a · e309d4a
1 parent a6f0b15
commit e309d4a
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Showing 2 changed files with 5 additions and 89 deletions.
diff --git a/validphys2/src/validphys/closuretest/multiclosure.py b/validphys2/src/validphys/closuretest/multiclosure.py
@@ -42,7 +42,7 @@
 @check_use_t0
 @check_t0pdfset_matches_multiclosure_law
 def internal_multiclosure_dataset_loader(
-    dataset, fits_pdf, multiclosure_underlyinglaw, fits, t0_covmat_from_systematics_pdferr_considered
+    dataset, fits_pdf, multiclosure_underlyinglaw, fits, t0_covmat_from_systematics
 ):
     """Internal function for loading multiple theory predictions for a given
     dataset and a single covariance matrix using underlying law as t0 PDF,
@@ -92,10 +92,10 @@ def internal_multiclosure_dataset_loader(
         multiclosure_underlyinglaw, dataset
     )
 
-    sqrt_covmat = la.cholesky(t0_covmat_from_systematics_pdferr_considered, lower=True)
+    sqrt_covmat = la.cholesky(t0_covmat_from_systematics, lower=True)
     # TODO: support covmat reg and theory covariance matrix
     # possibly make this a named tuple
-    return (fits_dataset_predictions, fits_underlying_predictions, t0_covmat_from_systematics_pdferr_considered, sqrt_covmat)
+    return (fits_dataset_predictions, fits_underlying_predictions, t0_covmat_from_systematics, sqrt_covmat)
 
 
 
@@ -109,15 +109,15 @@ def internal_multiclosure_data_loader(
     fits_pdf,
     multiclosure_underlyinglaw,
     fits,
-    dataset_inputs_t0_covmat_from_systematics_pdferr_considered,
+    dataset_inputs_t0_covmat_from_systematics,
 ):
     """Like `internal_multiclosure_dataset_loader` except for all data"""
     return internal_multiclosure_dataset_loader(
         data,
         fits_pdf,
         multiclosure_underlyinglaw,
         fits,
-        dataset_inputs_t0_covmat_from_systematics_pdferr_considered,
+        dataset_inputs_t0_covmat_from_systematics,
     )
 
 

diff --git a/validphys2/src/validphys/covmats.py b/validphys2/src/validphys/covmats.py
@@ -303,48 +303,6 @@ def t0_covmat_from_systematics(
         _central_values=dataset_t0_predictions
     )
 
-def t0_covmat_from_systematics_pdferr_considered(
-    loaded_commondata_with_cuts,
-    *,
-    dataset_input,
-    pdf,
-    dataset,
-    covmat_combi=False,
-    pdf_err=False,
-    use_weights_in_covmat=True,
-    norm_threshold=None,
-    dataset_t0_predictions
-):
-    """Like :py:func:`t0_covmat_from_systematics` except allows for the
-    PDF error covariance matrix to be summed to it.
-    """
-    if covmat_combi:
-        th = ThPredictionsResult.from_convolution(pdf, dataset)
-        pdf_cov = np.cov(th.error_members, rowvar=True)
-
-        return pdf_cov + covmat_from_systematics(
-                                                    loaded_commondata_with_cuts,
-                                                    dataset_input,
-                                                    use_weights_in_covmat,
-                                                    norm_threshold=norm_threshold,
-                                                    _central_values=dataset_t0_predictions
-                                                )
-
-    elif pdf_err:
-        th = ThPredictionsResult.from_convolution(pdf, dataset)
-        pdf_cov = np.cov(th.error_members, rowvar=True)
-        regularized_pdf_covmat = regularize_covmat(pdf_cov)
-        return regularized_pdf_covmat
-
-    else:
-        return covmat_from_systematics(
-                                        loaded_commondata_with_cuts,
-                                        dataset_input,
-                                        use_weights_in_covmat,
-                                        norm_threshold=norm_threshold,
-                                        _central_values=dataset_t0_predictions
-                                    )
-
 
 dataset_inputs_t0_predictions = collect("dataset_t0_predictions", ("data",))
 
@@ -430,48 +388,6 @@ def dataset_inputs_t0_covmat_from_systematics_pdferr_considered(
         )
 
 
-def dataset_inputs_t0_total_covmat_separate(
-    dataset_inputs_t0_exp_covmat_separate,
-    loaded_theory_covmat
-):
-    """
-    Function to compute the covmat to be used for the sampling by make_replica.
-    In this case the t0 prescription is used for the experimental covmat and the multiplicative 
-    errors are separated. Moreover, the theory covmat is added to experimental covmat.  
-    """
-    covmat = dataset_inputs_t0_exp_covmat_separate
-    covmat += loaded_theory_covmat
-    return covmat
-
-def dataset_inputs_t0_exp_covmat_separate(
-    dataset_inputs_loaded_cd_with_cuts,
-    *,
-    data_input,
-    use_weights_in_covmat=True,
-    norm_threshold=None,
-    dataset_inputs_t0_predictions,
-):
-    """
-    Function to compute the covmat to be used for the sampling by make_replica.
-    In this case the t0 prescription is used for the experimental covmat and the multiplicative 
-    errors are separated.
-    """
-    covmat = generate_exp_covmat(dataset_inputs_loaded_cd_with_cuts, data_input, use_weights_in_covmat, norm_threshold, dataset_inputs_t0_predictions , True)
-    return covmat
-
-def dataset_inputs_total_covmat_separate(
-    dataset_inputs_exp_covmat_separate,
-    loaded_theory_covmat,
-):
-    """
-    Function to compute the covmat to be used for the sampling by make_replica.
-    In this case the t0 prescription is not used for the experimental covmat and the multiplicative 
-    errors are separated. Moreover, the theory covmat is added to experimental covmat.
-    """
-    covmat = dataset_inputs_exp_covmat_separate
-    covmat += loaded_theory_covmat
-    return covmat
-
 def dataset_inputs_exp_covmat_separate(
     dataset_inputs_loaded_cd_with_cuts,
     *,