update app.py

app.py

@@ -106,34 +106,59 @@ def convert_to_ee_geometry(geometry):
     else:
         raise ValueError("Unsupported geometry input type. Supported types are Shapely, GeoJSON, and KML.")
 
-# Function to calculate custom formula
+# Function to calculate custom formula with dynamic scale handling and normalization
 def calculate_custom_formula(image, geometry, selected_bands, custom_formula, reducer_choice, dataset_id, user_scale=None):
     try:
-        # Determine the scale: Use user-defined scale if provided, otherwise use dataset's native resolution
-        default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
+        # Fetch the nominal scales of the selected bands
+        band_scales = []
+        for band in selected_bands:
+            band_scale = image.select(band).projection().nominalScale().getInfo()
+            band_scales.append(band_scale)
+        
+        # Determine the finest (smallest) scale among the selected bands
+        default_scale = min(band_scales) if band_scales else 30  # Default to 30m if no bands are found
+        
+        # Use user-defined scale if provided, otherwise use the finest scale
         scale = user_scale if user_scale is not None else default_scale
-        band_values = {}
-        band_names = image.bandNames().getInfo()
+        
+        # Rescale all bands to the chosen scale
+        rescaled_bands = {}
         for band in selected_bands:
-            if band not in band_names:
-                raise ValueError(f"Band '{band}' not found in the dataset.")
-            band_values[band] = image.select(band)
-        reducer = get_reducer(reducer_choice)
+            band_image = image.select(band)
+            band_scale = band_image.projection().nominalScale().getInfo()
+            if band_scale != scale:
+                # Resample the band to match the target scale
+                rescaled_band = band_image.resample('bilinear').reproject(
+                    crs=band_image.projection().crs(),
+                    scale=scale
+                )
+                rescaled_bands[band] = rescaled_band
+            else:
+                rescaled_bands[band] = band_image
+        
+        # Validate and extract band values
         reduced_values = {}
+        reducer = get_reducer(reducer_choice)
         for band in selected_bands:
-            value = band_values[band].reduceRegion(
+            value = rescaled_bands[band].reduceRegion(
                 reducer=reducer,
                 geometry=geometry,
-                scale=scale
+                scale=scale  # Use the determined scale here
             ).get(band).getInfo()
             reduced_values[band] = float(value if value is not None else 0)
+        
+        # Evaluate the custom formula
         formula = custom_formula
         for band in selected_bands:
             formula = formula.replace(band, str(reduced_values[band]))
         result = eval(formula, {"__builtins__": {}}, reduced_values)
+        
+        # Validate the result
         if not isinstance(result, (int, float)):
             raise ValueError("Formula did not result in a numeric value.")
+        
         return ee.Image.constant(result).rename('custom_result')
+    
     except ZeroDivisionError:
         st.error("Error: Division by zero in the formula.")
         return ee.Image(0).rename('custom_result').set('error', 'Division by zero')
@@ -438,7 +463,6 @@ elif imagery_base == "Custom Input":
 if not data:
     st.error("No valid dataset available. Please check your inputs.")
     st.stop()
-
 st.markdown("<hr><h5><b>{}</b></h5>".format(imagery_base), unsafe_allow_html=True)
 main_selection = st.selectbox(f"Select {imagery_base} Dataset Category", list(data.keys()))
 sub_selection = None
@@ -450,7 +474,6 @@ if main_selection:
         st.write(f"You selected: {main_selection} -> {sub_options[sub_selection]}")
         st.write(f"Dataset ID: {sub_selection}")
         dataset_id = sub_selection
-
 st.markdown("<hr><h5><b>Earth Engine Index Calculator</b></h5>", unsafe_allow_html=True)
 if main_selection and sub_selection:
     dataset_bands = data[main_selection]["bands"].get(sub_selection, [])
@@ -493,7 +516,6 @@ if main_selection and sub_selection:
             st.warning("Please enter a custom formula to proceed.")
             st.stop()
         st.write(f"Custom Formula: {custom_formula}")
-
 reducer_choice = st.selectbox(
     "Select Reducer (e.g, mean , sum , median , min , max , count)",
     ['mean', 'sum', 'median', 'min', 'max', 'count'],
@@ -503,7 +525,6 @@ start_date = st.date_input("Start Date", value=pd.to_datetime('2024-11-01'))
 end_date = st.date_input("End Date", value=pd.to_datetime('2024-12-01'))
 start_date_str = start_date.strftime('%Y-%m-%d')
 end_date_str = end_date.strftime('%Y-%m-%d')
-
 if imagery_base == "Sentinel" and "Sentinel-2" in sub_options[sub_selection]:
     st.markdown("<h5>Cloud Filtering</h5>", unsafe_allow_html=True)
     tile_cloud_threshold = st.slider(
@@ -522,7 +543,6 @@ if imagery_base == "Sentinel" and "Sentinel-2" in sub_options[sub_selection]:
         step=5,
         help="Individual pixels with cloud coverage exceeding this threshold will be masked."
     )
-
 aggregation_period = st.selectbox(
     "Select Aggregation Period (e.g, Custom(Start Date to End Date) , Daily , Weekly , Monthly , Yearly)",
     ["Custom (Start Date to End Date)", "Daily", "Weekly", "Monthly", "Yearly"],
@@ -544,7 +564,6 @@ elif shape_type.lower() == "polygon":
         value=True,
         help="Check to include pixels on the polygon boundary; uncheck to exclude them."
     )
-
 st.markdown("<h5>Calculation Scale</h5>", unsafe_allow_html=True)
 default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
 user_scale = st.number_input(
@@ -553,7 +572,6 @@ user_scale = st.number_input(
     value=float(default_scale),
     help=f"Default scale for this dataset is {default_scale} meters. Adjust if needed."
 )
-
 file_upload = st.file_uploader(f"Upload your {shape_type} data (CSV, GeoJSON, KML)", type=["csv", "geojson", "kml"])
 locations_df = pd.DataFrame()
 original_lat_col = None
@@ -672,7 +690,6 @@ if file_upload is not None:
                     m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
             st.write("Map of Uploaded Polygons:")
             m.to_streamlit()
-
 if st.button(f"Calculate {custom_formula}"):
     if not locations_df.empty:
         with st.spinner("Processing Data..."):