update app.py

app.py

@@ -757,13 +757,114 @@ def calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_
 #             st.warning("Please upload a file.")
 
 
+# def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, index_choice, reducer_choice, shape_type, aggregation_period, custom_formula=""):
+#     aggregated_results = []
+    
+#     # Check if the index_choice is 'custom formula' and the custom formula is empty
+#     if index_choice.lower() == 'custom_formula' and not custom_formula:
+#         st.error("Custom formula cannot be empty. Please provide a formula.")
+#         return aggregated_results  # Return early to avoid further processing
+    
+#     # Initialize progress bar
+#     total_steps = len(locations_df)
+#     progress_bar = st.progress(0)
+#     progress_text = st.empty()
+    
+#     with st.spinner('Processing data...'):
+#         if shape_type.lower() == "point":
+#             for idx, row in locations_df.iterrows():
+#                 # Check if the latitude and longitude columns exist and have values
+#                 latitude = row.get('latitude')
+#                 longitude = row.get('longitude')
+#                 if pd.isna(latitude) or pd.isna(longitude):
+#                     st.warning(f"Skipping location {idx} with missing latitude or longitude")
+#                     continue
+                
+#                 location_name = row.get('name', f"Location_{idx}")
+#                 roi = ee.Geometry.Point([longitude, latitude])
+                
+#                 collection = ee.ImageCollection(dataset_id) \
+#                     .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
+#                     .filterBounds(roi)
+                
+#                 # Aggregate data based on the selected period
+#                 if aggregation_period.lower() == 'daily':
+#                     collection = aggregate_data_daily(collection)
+#                 elif aggregation_period.lower() == 'weekly':
+#                     collection = aggregate_data_weekly(collection)
+#                 elif aggregation_period.lower() == 'monthly':
+#                     collection = aggregate_data_monthly(collection, start_date_str, end_date_str)
+#                 elif aggregation_period.lower() == 'yearly':
+#                     collection = aggregate_data_yearly(collection)
+                
+#                 # Process each image in the collection
+#                 image_list = collection.toList(collection.size())
+#                 for i in range(image_list.size().getInfo()):
+#                     image = ee.Image(image_list.get(i))
+                    
+#                     # Define timestamp and period label based on aggregation period
+#                     if aggregation_period.lower() == 'daily':
+#                         timestamp = image.get('day')
+#                         period_label = 'Date'
+#                     elif aggregation_period.lower() == 'weekly':
+#                         timestamp = image.get('week_start')
+#                         period_label = 'Week'
+#                     elif aggregation_period.lower() == 'monthly':
+#                         timestamp = image.get('month')
+#                         period_label = 'Month'
+#                     elif aggregation_period.lower() == 'yearly':
+#                         timestamp = image.get('year')
+#                         period_label = 'Year'
+                    
+#                     # Format the timestamp based on aggregation period
+#                     if aggregation_period.lower() == 'monthly':
+#                         date = ee.Date(timestamp).format('YYYY-MM').getInfo()  # Month-wise format
+#                     elif aggregation_period.lower() == 'weekly':
+#                         date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()  # Week-wise format (week start)
+#                     elif aggregation_period.lower() == 'yearly':
+#                         date = ee.Date(timestamp).format('YYYY').getInfo()  # Year-wise format
+#                     else:  # Daily
+#                         date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()  # Daily format
+                    
+#                     # Calculate the index for each period
+#                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
+                    
+#                     try:
+#                         index_value = index_image.reduceRegion(
+#                             reducer=get_reducer(reducer_choice),
+#                             geometry=roi,
+#                             scale=30
+#                         ).get(index_image.bandNames().get(0))
+                        
+#                         calculated_value = index_value.getInfo()
+                        
+#                         # Append the results if valid
+#                         if isinstance(calculated_value, (int, float)):
+#                             aggregated_results.append({
+#                                 'Location Name': location_name,
+#                                 'Latitude': latitude,
+#                                 'Longitude': longitude,
+#                                 period_label: date,  # Use dynamic period label (Date, Week, Month, Year)
+#                                 'Start Date': start_date_str,
+#                                 'End Date': end_date_str,
+#                                 'Calculated Value': calculated_value
+#                             })
+#                         else:
+#                             st.warning(f"Skipping invalid value for {location_name} on {date}")
+#                     except Exception as e:
+#                         st.error(f"Error retrieving value for {location_name}: {e}")
+                
+#                 # Update progress bar
+#                 progress_percentage = (idx + 1) / total_steps
+#                 progress_bar.progress(progress_percentage)
+#                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
 def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, index_choice, reducer_choice, shape_type, aggregation_period, custom_formula=""):
     aggregated_results = []
     
     # Check if the index_choice is 'custom formula' and the custom formula is empty
     if index_choice.lower() == 'custom_formula' and not custom_formula:
         st.error("Custom formula cannot be empty. Please provide a formula.")
-        return aggregated_results  # Return early to avoid further processing
+        return aggregated_results
     
     # Initialize progress bar
     total_steps = len(locations_df)
@@ -773,7 +874,6 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     with st.spinner('Processing data...'):
         if shape_type.lower() == "point":
             for idx, row in locations_df.iterrows():
-                # Check if the latitude and longitude columns exist and have values
                 latitude = row.get('latitude')
                 longitude = row.get('longitude')
                 if pd.isna(latitude) or pd.isna(longitude):
@@ -818,13 +918,13 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                     
                     # Format the timestamp based on aggregation period
                     if aggregation_period.lower() == 'monthly':
-                        date = ee.Date(timestamp).format('YYYY-MM').getInfo()  # Month-wise format
+                        date = ee.Date(timestamp).format('YYYY-MM').getInfo()
                     elif aggregation_period.lower() == 'weekly':
-                        date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()  # Week-wise format (week start)
+                        date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
                     elif aggregation_period.lower() == 'yearly':
-                        date = ee.Date(timestamp).format('YYYY').getInfo()  # Year-wise format
+                        date = ee.Date(timestamp).format('YYYY').getInfo()
                     else:  # Daily
-                        date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()  # Daily format
+                        date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
                     
                     # Calculate the index for each period
                     index_image = calculate_index_for_period(image, roi, index_choice, reducer_choice, custom_formula)
@@ -838,13 +938,12 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                         
                         calculated_value = index_value.getInfo()
                         
-                        # Append the results if valid
                         if isinstance(calculated_value, (int, float)):
                             aggregated_results.append({
                                 'Location Name': location_name,
                                 'Latitude': latitude,
                                 'Longitude': longitude,
-                                period_label: date,  # Use dynamic period label (Date, Week, Month, Year)
+                                period_label: date,
                                 'Start Date': start_date_str,
                                 'End Date': end_date_str,
                                 'Calculated Value': calculated_value
@@ -854,7 +953,6 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
                     except Exception as e:
                         st.error(f"Error retrieving value for {location_name}: {e}")
                 
-                # Update progress bar
                 progress_percentage = (idx + 1) / total_steps
                 progress_bar.progress(progress_percentage)
                 progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")