update app.py

app.py

@@ -694,45 +694,46 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                 progress_bar.progress(progress_percentage)
                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
     
-    # if aggregated_results:
-    #     result_df = pd.DataFrame(aggregated_results)
-    #     if aggregation_period.lower() == 'daily':
-    #         aggregated_output = result_df.groupby('Location Name').agg({
-    #             'Latitude': 'first' if shape_type.lower() == 'point' else None,
-    #             'Longitude': 'first' if shape_type.lower() == 'point' else None,
-    #             'Start Date': 'first',
-    #             'End Date': 'first',
-    #             'Calculated Value': 'mean'
-    #         }).reset_index()
-    #         # Remove None columns (for polygons)
-    #         aggregated_output = aggregated_output[[col for col in aggregated_output.columns if col is not None]]
-    #         aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
-    #         return aggregated_output.to_dict(orient='records')
-    #     else:
-    #         return result_df.to_dict(orient='records')
+    if aggregated_results:
+        result_df = pd.DataFrame(aggregated_results)
+        if aggregation_period.lower() == 'daily':
+            aggregated_output = result_df.groupby('Location Name').agg({
+                'Latitude': 'first' if shape_type.lower() == 'point' else None,
+                'Longitude': 'first' if shape_type.lower() == 'point' else None,
+                'Start Date': 'first',
+                'End Date': 'first',
+                'Calculated Value': 'mean'
+            }).reset_index()
+            # Remove None columns (for polygons)
+            aggregated_output = aggregated_output[[col for col in aggregated_output.columns if col is not None]]
+            aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+            return aggregated_output.to_dict(orient='records')
+        else:
+            return result_df.to_dict(orient='records')
     
-    # return []
+    return []
     
     if aggregated_results:
-            result_df = pd.DataFrame(aggregated_results)
-            if aggregation_period.lower() == 'daily':
-                # Define aggregation dictionary based on shape_type
-                agg_dict = {
-                    'Start Date': 'first',
-                    'End Date': 'first',
-                    'Calculated Value': 'mean'
-                }
-                if shape_type.lower() == 'point':
-                    agg_dict['Latitude'] = 'first'
-                    agg_dict['Longitude'] = 'first'
+        result_df = pd.DataFrame(aggregated_results)
+        if aggregation_period.lower() == 'daily':
+            # Define aggregation dictionary based on shape_type
+            agg_dict = {
+                'Start Date': 'first',
+                'End Date': 'first',
+                'Calculated Value': 'mean'
+            }
+            if shape_type.lower() == 'point':
+                agg_dict['Latitude'] = 'first'
+                agg_dict['Longitude'] = 'first'
                 
-                aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
-                aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
-                return aggregated_output.to_dict(orient='records')
-            else:
-                return result_df.to_dict(orient='records')
+            aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
+            aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+            return aggregated_output.to_dict(orient='records')
+        else:
+            return result_df.to_dict(orient='records')
         
-        return []
+    return []
+    
 # When the user clicks the process button, start the calculation
 if st.button(f"Calculate ({index_choice})"):
     if file_upload is not None: