Merge branch 'main' of github.com:brightway-lca/bw_timex

Author: TimoDiepers

bw_timex/dynamic_biosphere_builder.py

@@ -131,6 +131,9 @@ def build_dynamic_biosphere_matrix(
             A sparse matrix with the dimensions (bio_flows at a specific time step) x (processes).
         """
 
+        lci_dict = {}
+        temporal_market_lci_dict = {}
+        
         for row in self.timeline.itertuples():
             idx = row.time_mapped_producer
             if from_timeline:
@@ -145,6 +148,8 @@ def build_dynamic_biosphere_matrix(
                 time,
             ) = self.activity_time_mapping_dict.reversed()[idx]
 
+            
+
             if idx in self.node_id_collection_dict["temporalized_processes"]:
 
                 time_in_datetime = convert_date_string_to_datetime(
@@ -204,6 +209,7 @@ def build_dynamic_biosphere_matrix(
                             col=process_col_index,
                             amount=amount,
                         )
+            
             elif idx in self.node_id_collection_dict["temporal_markets"]:
                 (
                     (original_db, original_code),
@@ -215,11 +221,24 @@ def build_dynamic_biosphere_matrix(
                 else:
                     demand = self.demand_from_technosphere(idx, process_col_index)
 
+                
                 if demand:
-                    self.lca_obj.redo_lci(demand)
-                    # aggregated biosphere flows of background supply chain emissions.
-                    # Rows are bioflows.
-                    aggregated_inventory = self.lca_obj.inventory.sum(axis=1)
+                    for act, amount in demand.items():
+                        # check if lci already calculated for this activity
+                        if not act in lci_dict.keys():
+                            self.lca_obj.redo_lci({act: 1})
+                            #  biosphere flows by activity of background supply chain emissions.
+                            # Rows are bioflows. Columns are activities.
+                            # save for reuse in dict
+                            lci_dict[act] = self.lca_obj.inventory
+                        # add lci of both background activities of the temporal market and save total lci
+                        if idx not in temporal_market_lci_dict.keys():
+                            temporal_market_lci_dict[idx] = lci_dict[act] * amount
+                        else:
+                            temporal_market_lci_dict[idx] += lci_dict[act] * amount
+                        
+
+                    aggregated_inventory = temporal_market_lci_dict[idx].sum(axis=1)
 
                     for row_idx, amount in enumerate(aggregated_inventory.A1):
                         bioflow = self.lca_obj.dicts.biosphere.reversed[row_idx]
@@ -254,7 +273,7 @@ def build_dynamic_biosphere_matrix(
         dynamic_biomatrix = sp.coo_matrix((self.values, (self.rows, self.cols)), shape)
         self.dynamic_biomatrix = dynamic_biomatrix.tocsr()
 
-        return self.dynamic_biomatrix
+        return self.dynamic_biomatrix, temporal_market_lci_dict
 
     def demand_from_timeline(self, row, original_db):
         """
@@ -278,13 +297,9 @@ def demand_from_timeline(self, row, original_db):
         """
         demand = {}
         for db, amount in row.interpolation_weights.items():
-            [(timed_act_id, _)] = [
-                (act, db_name)
-                for (act, db_name) in self.interdatabase_activity_mapping[
-                    (row.producer, original_db)
-                ]
-                if db == db_name
-            ]
+            timed_act_id = self.interdatabase_activity_mapping.find_match(
+                row.producer, db
+            )
             demand[timed_act_id] = amount
         return demand
 

bw_timex/timex_lca.py

@@ -595,6 +595,8 @@ def calculate_dynamic_inventory(
         None
             calculates the dynamic inventory and stores it in the attribute
             `dynamic_inventory` as a matrix and in `dynamic_inventory_df` as a DataFrame.
+            Also calculates and stores the lci of the temporal markets in the attribute 
+            self.temporal_market_lcis for use in contribution analysis of the background processes.
 
         See also
         --------
@@ -624,7 +626,7 @@ def calculate_dynamic_inventory(
             self.interdatabase_activity_mapping,
             from_timeline=from_timeline,
         )
-        self.dynamic_biomatrix = (
+        self.dynamic_biomatrix, self.temporal_market_lcis = (
             self.dynamic_biosphere_builder.build_dynamic_biosphere_matrix(
                 from_timeline=from_timeline
             )
@@ -1176,7 +1178,7 @@ def collect_temporalized_processes_from_timeline(self) -> None:
 
         for producer, time_mapped_producer in unique_producers:
             if (
-                bd.get_activity(producer)["database"]
+                self.nodes_dict[producer]["database"]
                 in self.database_date_dict_static_only.keys()
             ):
                 temporal_market_ids.add(time_mapped_producer)