reactivate use_t0_sampling

doc/sphinx/source/n3fit/runcard_detailed.rst

@@ -58,6 +58,34 @@ to fix it such that it is the same for all replicas with ``same_trvl_per_replica
 
 .. _preprocessing-label:
 
+
+Monte Carlo replica data
+------------------------
+The uncertainties on the PDFs are determined trough a Monte Carlo sampling procedure
+where each pseudodata replica is generated by adding some noise to the central value
+of the data points. 
+In order to generate the pseudodata, the user can set the following parameter in the runcard:
+
+.. code-block:: yaml
+
+    genrep: true
+
+The noise is generated by sampling from a normal distribution
+that is centred around zero. There are two options for the covariance matrix
+of the noise: the experimental covariance matrix (default)
+or the t0 covariance matrix.
+
+In order to use the t0-covariance matrix in the pseudodata generation, it is enough 
+to set 
+
+.. code-block:: yaml
+
+    use_t0_sampling: true
+
+in the runcard.
+
+
+
 Preprocessing
 -------------
 The behaviour of the preprocessing in the ``n3fit`` code is controlled, as in the old ``nnfit`` code, through the ``fitting:basis`` parameter of the nnpdf runcard.

n3fit/runcards/examples/nnpdf40-like.yml

@@ -109,7 +109,9 @@ theory:
 trvlseed: 591866982
 nnseed: 945709987
 mcseed: 519562661
+
 genrep: true
+use_t0_sampling: true
 
 ################################################################################
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer

validphys2/src/validphys/config.py

@@ -798,25 +798,52 @@ def produce_sep_mult(self, separate_multiplicative=False):
 
     @configparser.explicit_node
     def produce_dataset_inputs_sampling_covmat(
-        self, sep_mult=False, use_thcovmat_in_sampling=False
+        self, sep_mult=False, use_thcovmat_in_sampling=False, use_t0_sampling=False
     ):
         """
-        Produces the correct covmat to be used in make_replica according
-        to some options: whether to include the theory covmat and whether to
+        Produces the correct MC replica method sampling covmat to be used in
+        make_replica according to some options: whether to sample using a t0
+        covariance matrix, include the theory covmat and whether to
         separate the multiplcative errors.
+
+        Parameters
+        ----------
+        sep_mult : bool
+            Whether to separate the multiplicative errors.
+        use_thcovmat_in_sampling : bool
+            Whether to include the theory covariance matrix.
+        use_t0_sampling : bool
+            Whether to sample using a t0 covariance matrix.
+
+        Returns
+        -------
+        Callable
         """
         from validphys import covmats
 
-        if use_thcovmat_in_sampling:
-            if sep_mult:
-                return covmats.dataset_inputs_total_covmat_separate
+        if use_t0_sampling:
+            if use_thcovmat_in_sampling:
+                if sep_mult:
+                    return covmats.dataset_inputs_t0_total_covmat_separate
+                else:
+                    return covmats.dataset_inputs_t0_total_covmat
             else:
-                return covmats.dataset_inputs_total_covmat
+                if sep_mult:
+                    return covmats.dataset_inputs_t0_exp_covmat_separate
+                else:
+                    return covmats.dataset_inputs_t0_exp_covmat
+
         else:
-            if sep_mult:
-                return covmats.dataset_inputs_exp_covmat_separate
+            if use_thcovmat_in_sampling:
+                if sep_mult:
+                    return covmats.dataset_inputs_total_covmat_separate
+                else:
+                    return covmats.dataset_inputs_total_covmat
             else:
-                return covmats.dataset_inputs_exp_covmat
+                if sep_mult:
+                    return covmats.dataset_inputs_exp_covmat_separate
+                else:
+                    return covmats.dataset_inputs_exp_covmat
 
     def produce_loaded_theory_covmat(
         self,