more fixes :)

columnflow/production/normalization.py

@@ -176,9 +176,9 @@ def normalization_weights(self: Producer, events: ak.Array, **kwargs) -> ak.Arra
     attribute. When py:attr`allow_stitching` is set to True, the sum of event weights is computed
     for all datasets with a leaf process contained in the leaf processes of the
     py:attr:`dataset_inst`. For stitching, the process_id needs to be reconstructed for each leaf
-    process on a per event basis. Moreover, when stitching is enabled, the an additional normalization
+    process on a per event basis. Moreover, when stitching is enabled, an additional normalization
     weight is computed for the inclusive dataset only and stored in a column named
-    py:attr:`weight_name`_inclusive_only. This weight resembles the normalization weight for the
+    `<weight_name>_inclusive_only`. This weight resembles the normalization weight for the
     inclusive dataset, as if it were unstitched and should therefore only be applied, when using the
     inclusive dataset as a standalone dataset.
 
@@ -203,9 +203,9 @@ def normalization_weights(self: Producer, events: ak.Array, **kwargs) -> ak.Arra
     events = set_ak_column(events, self.weight_name, norm_weight, value_type=np.float32)
 
     # If we are stitching, we also compute the inclusive weight for debugging purposes
-    if self.allow_stitching and self.dataset_inst == self.get_inclusive_dataset():
+    if self.allow_stitching and self.dataset_inst == self.inclusive_dataset:
         incl_norm_weight = events.mc_weight * self.inclusive_weight
-        events = set_ak_column(events, f"{self.weight_name}_inclusive_only", incl_norm_weight, value_type=np.float32)
+        events = set_ak_column(events, self.weight_name_incl, incl_norm_weight, value_type=np.float32)
     return events
 
 
@@ -296,7 +296,7 @@ def normalization_weights_setup(
     # create a event weight lookup table
     process_weight_table = sp.sparse.lil_matrix((1, max_id + 1), dtype=np.float32)
     if self.allow_stitching and self.get_xsecs_from_inclusive_dataset:
-        inclusive_dataset = self.get_inclusive_dataset()
+        inclusive_dataset = self.inclusive_dataset
         logger.info(f"using inclusive dataset {inclusive_dataset.name} for cross section lookup")
 
         # get the branching ratios from the inclusive sample
@@ -342,11 +342,13 @@ def normalization_weights_init(self: Producer) -> None:
     self.produces.add(self.weight_name)
     if self.allow_stitching:
         self.stitching_datasets = self.get_stitching_datasets()
+        self.inclusive_dataset = self.get_inclusive_dataset()
     else:
         self.stitching_datasets = [self.dataset_inst]
 
-    if self.allow_stitching and self.dataset_inst == self.get_inclusive_dataset():
-        self.produces.add(f"{self.weight_name}_inclusive_only")
+    if self.allow_stitching and self.dataset_inst == self.inclusive_dataset:
+        self.weight_name_incl = f"{self.weight_name}_inclusive_only"
+        self.produces.add(self.weight_name_incl)
 
 
 stitched_normalization_weights = normalization_weights.derive(