Speeding up writing output (#841)

src/troute-network/troute/nhd_io.py

@@ -2289,31 +2289,51 @@ def create_mask(ids):
 
         merge_flowveldepth_reset = merge_flowveldepth_reset.dropna(subset=['nex'])
         merge_flowveldepth_reset['nex'] = merge_flowveldepth_reset['nex'].astype(int)
-        # Define custom aggregation functions
-        def sum_q_columns(group):
-            q_columns = [col for col in group.columns if col[1] == 'q']
-            return group[q_columns].sum()
-
-        def custom_v(group):
-            v_columns = [col for col in group.columns if col[1] == 'v']
-            v_values = group[v_columns]
-            if len(v_values) > 1:
-                return pd.Series({col: np.nan for col in v_values.columns})
-            else:
-                return v_values.iloc[0]
+
+        def optimized_v_data(merge_flowveldepth_reset, v_columns):
+            # Ensure v_columns is a list
+            if isinstance(v_columns, tuple):
+                v_columns = [v_columns]
+
+            # Calculate the size of each group by 'nex', to find out the duplicate
+            group_sizes = merge_flowveldepth_reset.groupby('nex').size()
+
+            unique_nex = group_sizes[group_sizes == 1].index
+            non_unique_nex = group_sizes[group_sizes > 1].index
+            filtered_unique_df = merge_flowveldepth_reset[merge_flowveldepth_reset['nex'].isin(unique_nex)]
+            filtered_non_unique_df = merge_flowveldepth_reset[merge_flowveldepth_reset['nex'].isin(non_unique_nex)]
+            filtered_non_unique_df.loc[:, v_columns] = np.nan
+            filtered_non_unique_df = filtered_non_unique_df.drop_duplicates(subset=['nex'])
+
+            combined_df = pd.concat([filtered_unique_df, filtered_non_unique_df])
+            v_data = combined_df.set_index('nex')[v_columns]
+
+            return v_data
+
+
+        def optimized_agg(merge_flowveldepth_reset):
+            # Identify columns for each operation
+            q_columns = [col for col in merge_flowveldepth_reset.columns if col[1] == 'q']
+            v_columns = [col for col in merge_flowveldepth_reset.columns if col[1] == 'v']
+            d_columns = [col for col in merge_flowveldepth_reset.columns if col[1] == 'd']
+
+            # Sum of flowrate columns
+            sum_q = merge_flowveldepth_reset.groupby('nex')[q_columns].sum()
+
+            # Handle velocity in a way that if there are 2 segments coming into one put NaN value
+            v_data = optimized_v_data(merge_flowveldepth_reset, v_columns) 
+
+            # Average depth columns 
+            avg_d = merge_flowveldepth_reset.groupby('nex')[d_columns].mean()
 
-        def avg_d_columns(group):
-            d_columns = [col for col in group.columns if col[1] == 'd']
-            return group[d_columns].mean()
+            # Combine all results into a single DataFrame
+            all_nex_data = pd.concat([sum_q, v_data, avg_d], axis=1)
 
-        # Apply the groupby with the custom aggregation functions
-        def custom_agg(group):
-            result = pd.concat([sum_q_columns(group), custom_v(group), avg_d_columns(group)])
-            return result 
+            return all_nex_data
 
-        all_nex_data = merge_flowveldepth_reset.groupby('nex').apply(custom_agg)
+        all_nex_data = optimized_agg(merge_flowveldepth_reset)
         all_nex_data.index = all_nex_data.index.rename('featureID')
-        all_nex_data['Type'] = 'nex'
+        all_nex_data['Type'] = 'nex'        
         # Set the new 'Type' column as an index
         all_nex_data = all_nex_data.set_index('Type', append=True)