minor changes

src/eko/evolution_operator/beam_function.py

@@ -80,16 +80,12 @@ class SCET_I(Operator):
         order in alpha_s and L
     """
 
-    log_label = "Matching"
-    # complete list of possible matching operators labels
+    log_label = "Scet_I"
     full_labels = br.scet_labels
 
     def __init__(self, config, managers, order):
-        # dummy values for nf and q2
-        nf=5
-        q2=1
-        super().__init__(config, managers, Segment(q2, q2, nf))
-        # this is the order (QCD, L) of the scet kernel
+        super().__init__(config, managers, Segment(origin=1, target=1, nf=5))
+        # order (alpha_s, L) of the SCET kernel
         self.order_scet = order
 
     @property

src/eko/member.py

@@ -310,44 +310,7 @@ def to_flavor_basis_tensor(self, qed: bool):
                     ] += out_weight * (op.error * in_weight)
         return value_tensor, error_tensor
 
-    # def to_flavor_basis_tensor_scet(self, qed: bool):
-    #         """Convert the computations into an rank 4 tensor.
-
-    #         A sparse tensor defined with dot-notation (e.g. ``S.g``) is converted
-    #         to a plain rank-4 array over flavor operator space and momentum
-    #         fraction operator space.
-
-    #         """
-
-    #         len_pids = len(br.flavor_basis_pids)
-    #         len_xgrid = list(self.op_members.values())[0].value.shape[0]
-    #         # dimension will be pids^2 * xgrid^2
-    #         value_tensor = np.zeros((len_pids, len_xgrid, len_pids, len_xgrid))
-    #         error_tensor = value_tensor.copy()
-
-    #         for name, op in self.op_members.items():
-    #             
-    #             in_pids = (np.asarray(br.flavor_basis_names)==name.input).astype(int)
-    #             out_pids = (np.asarray(br.flavor_basis_names)==name.target).astype(int)
-
-    #             for out_idx, out_weight in enumerate(out_pids):
-    #                 for in_idx, in_weight in enumerate(in_pids):
-    #                     # keep the outer index to the left as we're multiplying from the right
-    #                     value_tensor[
-    #                         out_idx,  # output pid (position)
-    #                         :,  # output momentum fraction
-    #                         in_idx,  # input pid (position)
-    #                         :,  # input momentum fraction
-    #                     ] += out_weight * (op.value * in_weight)
-    #                     error_tensor[
-    #                         out_idx,  # output pid (position)
-    #                         :,  # output momentum fraction
-    #                         in_idx,  # input pid (position)
-    #                         :,  # input momentum fraction
-    #                     ] += out_weight * (op.error * in_weight)
-
-    #         return value_tensor, error_tensor
-
+
     def to_flavor_basis_tensor_scet(self):
             """Convert the computations into an rank 4 tensor.
 
@@ -364,7 +327,7 @@ def to_flavor_basis_tensor_scet(self):
 
 
             for name, op in self.op_members.items():
-
+                # np.asarray might not be necessary
                 in_idx = np.where(np.asarray(br.flavor_basis_names)==name.input)[0][0]
                 out_idx = np.where(np.asarray(br.flavor_basis_names)==name.target)[0][0]
 
@@ -380,6 +343,8 @@ def to_flavor_basis_tensor_scet(self):
                     in_idx,  # input pid (position)
                     :,  # input momentum fraction
                 ] += op.error 
+
+            # error can be removed (ongoing PR)
             return value_tensor, error_tensor