update app.py

app.py

@@ -108,6 +108,16 @@ end_date = st.date_input("End Date", value=pd.to_datetime('2020-12-31'))
 start_date_str = start_date.strftime('%Y-%m-%d')
 end_date_str = end_date.strftime('%Y-%m-%d')
 
+# Frequency selection
+frequency = st.selectbox("Select Frequency for Date Incrementation", 
+                         ["Daily", "Weekly", "Monthly", "Yearly", "Custom Number of Days"])
+
+# Custom days input if "Custom Number of Days" is selected
+if frequency == "Custom Number of Days":
+    custom_days = st.number_input("Enter number of days", min_value=1, value=1)
+else:
+    custom_days = None  # Default to None when custom days aren't required
+
 # Initialize session state for storing results if not already done
 if 'results' not in st.session_state:
     st.session_state.results = []
@@ -173,47 +183,6 @@ def calculate_custom_formula(image, geometry, formula):
     )
     return result.get('Custom Index')
 
-# Function to calculate the frequency-specific aggregation
-def aggregate_by_frequency(results, frequency):
-    # Create an empty list to store the aggregated results
-    aggregated_results = []
-
-    # Aggregate based on the frequency selected
-    if frequency == 'Daily':
-        for result in results:
-            # Add daily aggregation logic
-            date = result['Date'].date()
-            aggregated_results.append({**result, 'Aggregated Date': date})
-            
-    elif frequency == 'Weekly':
-        for result in results:
-            # Aggregate by week: calculate the start of the week (Monday)
-            date = result['Date']
-            week_start = date - timedelta(days=date.weekday())  # Get the start of the week
-            aggregated_results.append({**result, 'Aggregated Date': week_start})
-
-    elif frequency == 'Monthly':
-        for result in results:
-            # Aggregate by month
-            date = result['Date']
-            month_start = date.replace(day=1)  # Set the date to the first of the month
-            aggregated_results.append({**result, 'Aggregated Date': month_start})
-
-    elif frequency == 'Yearly':
-        for result in results:
-            # Aggregate by year
-            date = result['Date']
-            year_start = date.replace(month=1, day=1)  # Set the date to the first of the year
-            aggregated_results.append({**result, 'Aggregated Date': year_start})
-
-    return aggregated_results
-
-# Select frequency for aggregation (Daily, Weekly, Monthly, Yearly)
-frequency = st.selectbox(
-    "Select Frequency for Aggregation",
-    ['Daily', 'Weekly', 'Monthly', 'Yearly']
-)
-
 # Process each point for index calculation
 if file_upload:
     locations_df = None  # Initialize locations_df to None
@@ -265,6 +234,11 @@ if file_upload:
         # Store the map in session_state
         st.session_state.map_data = m
 
+            
+    # Loop through the selected frequency and increment the date
+    while current_date <= end_date:
+        current_date_str = current_date.strftime('%Y-%m-%d')
+        
     # Store results with dates for aggregation
     results_with_dates = []
     for idx, row in locations_df.iterrows():
@@ -307,31 +281,47 @@ if file_upload:
                 result = calculate_custom_formula(image, roi, custom_formula)
 
             if result is not None:
-                # Store the result along with the date (use the image acquisition date)
-                acquisition_date = image.get('system:time_start').getInfo()
-                date = datetime.utcfromtimestamp(acquisition_date / 1000)  # Convert to datetime
                 calculated_value = result.getInfo()  # Get the numeric value
 
                 # Add the date and calculated value to the results list
-                results_with_dates.append({
+                st.session_state.results.append({
                     'Location Name': location_name,
                     'Latitude': latitude,
                     'Longitude': longitude,
                     'Date': date,
                     'Calculated Value': calculated_value
                 })
-
-    # Aggregate the results based on the selected frequency
-    aggregated_results = aggregate_by_frequency(results_with_dates, frequency)
-
-    # Convert the aggregated results to a DataFrame
-    agg_df = pd.DataFrame(aggregated_results)
-
-    # Optionally, allow user to download the results as a CSV file
-    filename = f"{main_selection}_{sub_selection}_{start_date.strftime('%Y/%m/%d')}_{end_date.strftime('%Y/%m/%d')}_{shape_type}_{frequency}.csv"
-    st.download_button(
-        label="Download aggregated results as CSV",
-        data=agg_df.to_csv(index=False).encode('utf-8'),
-        file_name=filename,
-        mime='text/csv'
-    )
+        
+        # Increment the current date based on the selected frequency
+        if frequency == "Daily":
+            current_date += timedelta(days=1)
+        elif frequency == "Weekly":
+            current_date += timedelta(weeks=1)
+        elif frequency == "Monthly":
+            current_date += timedelta(days=30)  # Approximation
+        elif frequency == "Yearly":
+            current_date += timedelta(days=365)  # Approximation
+        elif frequency == "Custom Number of Days" and custom_days is not None:
+            current_date += timedelta(days=custom_days)
+
+# After processing, show the results
+if st.session_state.results:
+    # Convert the results to a DataFrame for better visualization
+    result_df = pd.DataFrame(st.session_state.results)
+
+    # Show the results in a table format
+    st.write("Processed Results Table:")
+    st.dataframe(result_df[['Location Name', 'Latitude', 'Longitude', 'Date', 'Calculated Value']])
+
+    # Generate the dynamic filename
+    filename = f"{main_selection}_{sub_selection}_{start_date.strftime('%Y/%m/%d')}_{end_date.strftime('%Y/%m/%d')}_{shape_type}.csv"
+
+    # Convert results to DataFrame for download
+    if st.session_state.results:
+        result_df = pd.DataFrame(st.session_state.results)
+        st.download_button(
+            label="Download results as CSV",
+            data=result_df.to_csv(index=False).encode('utf-8'),
+            file_name=filename,
+            mime='text/csv'
+        )