Fix DEM and Slope

app.py

@@ -187,21 +187,33 @@ def get_wayback_data():
 
         layer_data = []
         for layer in layers:
-            title = layer.find("ows:Title", ns)
-            resource = layer.find("wmts:ResourceURL", ns)  # Tile URL template
+            title_element = layer.find("ows:Title", ns)
+            resource_element = layer.find("wmts:ResourceURL", ns)
 
-            title_text = title.text if title is not None else "N/A"
-            url_template = resource.get("template") if resource is not None else "N/A"
+            # Skip layer if essential elements are missing
+            if title_element is None or resource_element is None:
+                continue
+            
+            title_text = title_element.text
+            url_template = resource_element.get("template")
 
             layer_data.append({"Title": title_text, "ResourceURL_Template": url_template})
 
         wayback_df = pd.DataFrame(layer_data)
+
+        if wayback_df.empty:
+            print("Warning: No valid Wayback layers were found in the XML data.")
+            return wayback_df
+
         wayback_df["date"] = pd.to_datetime(wayback_df["Title"].str.extract(r"(\d{4}-\d{2}-\d{2})").squeeze(), errors="coerce")
+        # Drop rows where a date could not be parsed
+        wayback_df.dropna(subset=['date'], inplace=True)
+        
         wayback_df.set_index("date", inplace=True)
         wayback_df.sort_index(ascending=False, inplace=True) # Sort with the latest first
         return wayback_df
 
-    except (requests.exceptions.RequestException, ET.ParseError, Exception) as e:
+    except (requests.exceptions.RequestException, ET.ParseError, KeyError) as e:
         print(f"Could not fetch or parse Wayback data: {e}")
         return pd.DataFrame() # Return empty dataframe on failure
 
@@ -244,6 +256,7 @@ def get_dem_slope_maps(ee_geometry, map_center, zoom=12, wayback_url=None, wayba
 
     slope_map_html = "<div>No Slope data available for this area.</div>"
     try:
+        dem_layer = ee.Image("USGS/SRTMGL1_003").resample("bilinear").reproject(crs="EPSG:4326", scale=30)
         slope_layer = ee.Terrain.slope(dem_layer).clip(ee_geometry)
         slope_vis_params = {"min": 0, "max": 60, "palette": ['#00FF00', '#FFFF00', '#FFA500', '#FF0000']}
         slope_map.addLayer(slope_layer, slope_vis_params, "Slope")
@@ -395,84 +408,7 @@ def process_and_display(file_obj, url_str, buffer_m, progress=gr.Progress()):
     return m._repr_html_(), None, stats_df, dem_html, slope_html, geometry_json, buffer_geometry_json
 
 
-def calculate_indices(
-    geometry_json, buffer_geometry_json, veg_indices, evi_vars, date_range,
-    min_year, max_year, progress=gr.Progress()
-):
-    """Calculates vegetation indices based on user inputs."""
-    one_time_setup()
-    
-    if not all([geometry_json, buffer_geometry_json, veg_indices]):
-        return "Please process a file and select at least one index first.", None, None, None
-
-    try:
-        # Recreate GDFs from JSON
-        geometry_gdf = gpd.read_file(geometry_json)
-        buffer_geometry_gdf = gpd.read_file(buffer_json)
-
-        # Convert to EE geometry
-        ee_geometry = ee.Geometry(json.loads(geometry_gdf.to_crs(4326).to_json())['features'][0]['geometry'])
-        buffer_ee_geometry = ee.Geometry(json.loads(buffer_geometry_gdf.to_crs(4326).to_json())['features'][0]['geometry'])
-        
-        # Date ranges
-        start_day, start_month = date_range[0].day, date_range[0].month
-        end_day, end_month = date_range[1].day, date_range[1].month
-        dates = [
-            (f"{year}-{start_month:02d}-{start_day:02d}", f"{year}-{end_month:02d}-{end_day:02d}")
-            for year in range(min_year, max_year + 1)
-        ]
-
-        # GEE processing
-        collection = (
-            ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
-            .select(
-                ["B2", "B3", "B4", "B8", "MSK_CLDPRB"],
-                ["Blue", "Green", "Red", "NIR", "MSK_CLDPRB"]
-            )
-            .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', evi_vars))
-        )
-
-        result_rows = []
-        for i, (start_date, end_date) in enumerate(dates):
-            progress((i + 1) / len(dates), desc=f"Processing {start_date} to {end_date}")
-            filtered_collection = collection.filterDate(start_date, end_date).filterBounds(ee_geometry)
-            if filtered_collection.size().getInfo() == 0:
-                continue
-
-            row = {'daterange': f"{start_date.split('-')[0]}"}
-            for veg_index in veg_indices:
-                mosaic = filtered_collection.qualityMosaic(veg_index)
-                
-                mean_val = mosaic.reduceRegion(reducer=ee.Reducer.mean(), geometry=ee_geometry, scale=10, maxPixels=1e9).get(veg_index).getInfo()
-                buffer_mean_val = mosaic.reduceRegion(reducer=ee.Reducer.mean(), geometry=buffer_ee_geometry, scale=10, maxPixels=1e9).get(veg_index).getInfo()
-
-                row[veg_index] = mean_val
-                row[f"{veg_index}_buffer"] = buffer_mean_val
-                row[f"{veg_index}_ratio"] = (mean_val / buffer_mean_val) if buffer_mean_val else np.nan
-            result_rows.append(row)
-
-        if not result_rows:
-            return "No satellite imagery found for the selected dates.", None, None, None
-
-        result_df = pd.DataFrame(result_rows).set_index('daterange')
-        result_df = result_df.round(3)
-
-        # Create plots
-        plots = []
-        for veg_index in veg_indices:
-            plot_cols = [col for col in [veg_index, f"{veg_index}_buffer", f"{veg_index}_ratio"] if col in result_df.columns]
-            plot_df = result_df[plot_cols].dropna()
-            if not plot_df.empty:
-                fig = px.line(plot_df, x=plot_df.index, y=plot_df.columns, markers=True, title=f"{veg_index} Time Series")
-                fig.update_layout(xaxis_title="Year", yaxis_title="Index Value")
-                plots.append(fig)
-
-        return None, result_df, plots, "Calculation complete."
 
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-        return f"An error occurred during calculation: {e}", None, None, None
 
 def calculate_indices(
     geometry_json, buffer_geometry_json, veg_indices, evi_vars, date_range,
@@ -487,7 +423,6 @@ def calculate_indices(
     try:
         # Recreate GDFs from JSON
         geometry_gdf = gpd.read_file(geometry_json)
-        # CORRECTED LINE: Use the correct variable name defined in the function signature
         buffer_geometry_gdf = gpd.read_file(buffer_geometry_json)
 
         # Convert to EE geometry
@@ -519,7 +454,9 @@ def calculate_indices(
             if filtered_collection.size().getInfo() == 0:
                 continue
 
-            row = {'daterange': f"{start_date.split('-')[0]}"}
+            year_val = int(start_date.split('-')[0])
+            row = {'Year': year_val, 'Date Range': f"{start_date}_to_{end_date}"}
+
             for veg_index in veg_indices:
                 mosaic = filtered_collection.qualityMosaic(veg_index)
                 
@@ -540,11 +477,15 @@ def calculate_indices(
         # Create plots
         plots = []
         for veg_index in veg_indices:
-            plot_cols = [col for col in [veg_index, f"{veg_index}_buffer", f"{veg_index}_ratio"] if col in result_df.columns]
-            plot_df = result_df[plot_cols].dropna()
+             plot_cols = [veg_index, f"{veg_index}_buffer", f"{veg_index}_ratio"]
+            existing_plot_cols = [col for col in plot_cols if col in result_df.columns]
+            
+            plot_df = result_df[['Year'] + existing_plot_cols].dropna()
             if not plot_df.empty:
                 fig = px.line(plot_df, x=plot_df.index, y=plot_df.columns, markers=True, title=f"{veg_index} Time Series")
                 fig.update_layout(xaxis_title="Year", yaxis_title="Index Value")
+                # Ensure x-axis ticks are whole numbers for years
+                fig.update_xaxes(dtick=1)
                 plots.append(fig)
 
         return None, result_df, plots, "Calculation complete."