add RGB variants

app.py

@@ -147,7 +147,8 @@ def process_date(daterange, satellite, veg_indices):
             bucket[f"mosaic_{veg_index}"] = mosaic_veg_index            
             
         # Get median mosaic
-        bucket["mosaic_visual"] = collection.qualityMosaic("NDVI")
+        bucket["mosaic_visual_max_ndvi"] = collection.qualityMosaic("NDVI")
+        bucket["mosaic_visual_median"] = collection.median()
 
         if satellite == "COPERNICUS/S2_SR_HARMONIZED":
             cloud_mask_probability = fc["features"][0]["properties"]["MSK_CLDPRB"] / 100
@@ -216,13 +217,14 @@ for satellite in satellites:
     satellite_selected[satellite] = st.checkbox(satellite, value=True, disabled=True)
 
 # Date range input
-jan_1 = pd.to_datetime("2024/01/01", format="%Y/%m/%d")
-dec_31 = pd.to_datetime("2024/12/31", format="%Y/%m/%d")
-nov_15 = pd.to_datetime("2024/11/15", format="%Y/%m/%d")
-dec_15 = pd.to_datetime("2024/12/15", format="%Y/%m/%d")
+max_year = datetime.now().year
+jan_1 = pd.to_datetime(f"{max_year}/01/01", format="%Y/%m/%d")
+dec_31 = pd.to_datetime(f"{max_year}/12/31", format="%Y/%m/%d")
+nov_15 = pd.to_datetime(f"{max_year}/11/15", format="%Y/%m/%d")
+dec_15 = pd.to_datetime(f"{max_year}/12/15", format="%Y/%m/%d")
 input_daterange = st.date_input("Date Range (Ignore year. App will compute indices for all possible years)", (nov_15, dec_15), jan_1, dec_31)
 min_year = int(st.number_input("Minimum Year", value=2010, min_value=2010, step=1))
-max_year = int(st.number_input("Maximum Year", value=datetime.now().year, min_value=2010, step=1))
+max_year = int(st.number_input("Maximum Year", value=max_year, min_value=2010, step=1))
 
 # Input: GeoJSON/KML file
 input_file = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml", "shp"])
@@ -385,22 +387,23 @@ if "result" in st.session_state:
                 m.add_child(colormap)
                 m.to_streamlit()
 
-    st.write("<h3><div style='text-align: center;'>RGB</div></h3>", unsafe_allow_html=True)
-    cols = st.columns(2)
-    for col, daterange_str in zip(cols, [year_1, year_2]):
-        start_date, end_date = daterange_str_to_dates(daterange_str)
-        mid_date = start_date + (end_date - start_date) / 2
-        esri_date = min(wayback_mapping.keys(), key=lambda x: abs(pd.to_datetime(x) - mid_date))
-        with col:
-            m = gee_folium.Map()
-            visual_mosaic = result_df.loc[daterange_str, "mosaic_visual"]
-            # visual_layer = gee_folium.ee_tile_layer(mosaic, {"bands": ["R", "G", "B"], "min": min_all, "max": max_all})
-            
-            m.add_layer(
-                visual_mosaic.select(["R", "G", "B"])
-            )
-            add_geometry_to_maps([m])
-            m.to_streamlit()
+    for name, key in zip(["RGB (Max NDVI Mosaic)", "RGB (Median Mosaic)"], ["mosaic_visual_max_ndvi", "mosaic_visual_median"]):
+        st.write(f"<h3><div style='text-align: center;'>{name}</div></h3>", unsafe_allow_html=True)
+        cols = st.columns(2)
+        for col, daterange_str in zip(cols, [year_1, year_2]):
+            start_date, end_date = daterange_str_to_dates(daterange_str)
+            mid_date = start_date + (end_date - start_date) / 2
+            esri_date = min(wayback_mapping.keys(), key=lambda x: abs(pd.to_datetime(x) - mid_date))
+            with col:
+                m = gee_folium.Map()
+                visual_mosaic = result_df.loc[daterange_str, key]
+                # visual_layer = gee_folium.ee_tile_layer(mosaic, {"bands": ["R", "G", "B"], "min": min_all, "max": max_all})
+                
+                m.add_layer(
+                    visual_mosaic.select(["R", "G", "B"])
+                )
+                add_geometry_to_maps([m])
+                m.to_streamlit()
         
     st.write("<h3><div style='text-align: center;'>Esri RGB Imagery</div></h3>", unsafe_allow_html=True)
     cols = st.columns(2)