modify pages

pages/1_🌲_Japan_Vegetation_Cover.py

@@ -220,7 +220,6 @@ def get_saturday(in_date):
 
 
 def app():
-
     st.title("Japan Vegetation Cover Fraction")
     st.markdown(
         """**Introduction:** This interactive dashboard is designed for visualizing Japan Fractional Vegetation Cover at town block levels. 
@@ -231,100 +230,14 @@ def app():
     """
     )
 
-    with st.expander("See a demo"):
-        st.image("https://i.imgur.com/Z3dk6Tr.gif")
+    prefecture = st.selectbox("Prefecture", ["Tokyo", "Kanagawa", "Chiba", "Saitama"])
+    
+    # Load GeoJSON data
+    gdf = gpd.read_file(f'https://github.com/kunifujiwara/data/blob/master/frac_veg/FRAC_VEG_{prefecture}.geojson')
 
-    row1_col1, row1_col2, row1_col3, row1_col4, row1_col5 = st.columns(
-        [0.6, 0.8, 0.6, 1.4, 2]
-    )
-    with row1_col1:
-        frequency = st.selectbox("Monthly/weekly data", ["Monthly", "Weekly"])
-    with row1_col2:
-        types = ["Current month data", "Historical data"]
-        if frequency == "Weekly":
-            types.remove("Current month data")
-        cur_hist = st.selectbox(
-            "Current/historical data",
-            types,
-        )
-    with row1_col3:
-        if frequency == "Monthly":
-            scale = st.selectbox(
-                "Scale", ["National", "State", "Metro", "County"], index=3
-            )
-        else:
-            scale = st.selectbox("Scale", ["National", "Metro"], index=1)
-
-    gdf = get_geom_data(scale.lower())
-
-    if frequency == "Weekly":
-        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
-        weeks = get_weeks(inventory_df)
-        with row1_col1:
-            selected_date = st.date_input("Select a date", value=weeks[-1])
-            saturday = get_saturday(selected_date)
-            selected_period = saturday.strftime("%-m/%-d/%Y")
-            if saturday not in weeks:
-                st.error(
-                    "The selected date is not available in the data. Please select a date between {} and {}".format(
-                        weeks[0], weeks[-1]
-                    )
-                )
-                selected_period = weeks[-1].strftime("%-m/%-d/%Y")
-        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
-        inventory_df = filter_weekly_inventory(inventory_df, selected_period)
-
-    if frequency == "Monthly":
-        if cur_hist == "Current month data":
-            inventory_df = get_inventory_data(
-                data_links["monthly_current"][scale.lower()]
-            )
-            selected_period = get_periods(inventory_df)[0]
-        else:
-            with row1_col2:
-                inventory_df = get_inventory_data(
-                    data_links["monthly_historical"][scale.lower()]
-                )
-                start_year, end_year = get_start_end_year(inventory_df)
-                periods = get_periods(inventory_df)
-                with st.expander("Select year and month", True):
-                    selected_year = st.slider(
-                        "Year",
-                        start_year,
-                        end_year,
-                        value=start_year,
-                        step=1,
-                    )
-                    selected_month = st.slider(
-                        "Month",
-                        min_value=1,
-                        max_value=12,
-                        value=int(periods[0][-2:]),
-                        step=1,
-                    )
-                selected_period = str(selected_year) + str(selected_month).zfill(2)
-                if selected_period not in periods:
-                    st.error("Data not available for selected year and month")
-                    selected_period = periods[0]
-                inventory_df = inventory_df[
-                    inventory_df["month_date_yyyymm"] == int(selected_period)
-                ]
-
-    data_cols = get_data_columns(inventory_df, scale.lower(), frequency.lower())
-
-    with row1_col4:
-        selected_col = st.selectbox("Attribute", data_cols)
-    with row1_col5:
-        show_desc = st.checkbox("Show attribute description")
-        if show_desc:
-            try:
-                label, desc = get_data_dict(selected_col.strip())
-                markdown = f"""
-                **{label}**: {desc}
-                """
-                st.markdown(markdown)
-            except:
-                st.warning("No description available for selected attribute")
+    # Select attribute to visualize
+    attributes = gdf.select_dtypes(include=[float, int]).columns.tolist()
+    selected_attribute = st.selectbox("Select attribute to visualize", attributes)
 
     row2_col1, row2_col2, row2_col3, row2_col4, row2_col5, row2_col6 = st.columns(
         [0.6, 0.68, 0.7, 0.7, 1.5, 0.8]
@@ -349,134 +262,56 @@ def app():
         else:
             elev_scale = 1
 
-    gdf = join_attributes(gdf, inventory_df, scale.lower())
-    gdf_null = select_null(gdf, selected_col)
-    gdf = select_non_null(gdf, selected_col)
-    gdf = gdf.sort_values(by=selected_col, ascending=True)
-
-    colors = cm.get_palette(palette, n_colors)
-    colors = [hex_to_rgb(c) for c in colors]
-
-    for i, ind in enumerate(gdf.index):
-        index = int(i / (len(gdf) / len(colors)))
-        if index >= len(colors):
-            index = len(colors) - 1
-        gdf.loc[ind, "R"] = colors[index][0]
-        gdf.loc[ind, "G"] = colors[index][1]
-        gdf.loc[ind, "B"] = colors[index][2]
-
-    initial_view_state = pdk.ViewState(
-        latitude=40,
-        longitude=-100,
-        zoom=3,
-        max_zoom=16,
-        pitch=0,
-        bearing=0,
-        height=900,
-        width=None,
-    )
+    # Create color map
+    color_scale = cm.LinearColormap(colors=cm.get_palette(palette, n_colors), vmin=gdf[selected_attribute].min(), vmax=gdf[selected_attribute].max())
+    gdf['color'] = gdf[selected_attribute].apply(lambda x: color_scale(x))
 
-    min_value = gdf[selected_col].min()
-    max_value = gdf[selected_col].max()
-    color = "color"
-    # color_exp = f"[({selected_col}-{min_value})/({max_value}-{min_value})*255, 0, 0]"
-    color_exp = f"[R, G, B]"
+    # Convert hex colors to RGB
+    gdf['color'] = gdf['color'].apply(lambda x: [int(x[1:3], 16), int(x[3:5], 16), int(x[5:7], 16)])
 
-    geojson = pdk.Layer(
+    # Create PyDeck layer
+    layer = pdk.Layer(
         "GeoJsonLayer",
         gdf,
         pickable=True,
-        opacity=0.5,
+        opacity=0.8,
         stroked=True,
         filled=True,
         extruded=show_3d,
         wireframe=True,
-        get_elevation=f"{selected_col}",
+        get_elevation=selected_attribute if show_3d else None,
         elevation_scale=elev_scale,
-        # get_fill_color="color",
-        get_fill_color=color_exp,
+        get_fill_color="color",
         get_line_color=[0, 0, 0],
         get_line_width=2,
         line_width_min_pixels=1,
     )
 
-    geojson_null = pdk.Layer(
-        "GeoJsonLayer",
-        gdf_null,
-        pickable=True,
-        opacity=0.2,
-        stroked=True,
-        filled=True,
-        extruded=False,
-        wireframe=True,
-        # get_elevation="properties.ALAND/100000",
-        # get_fill_color="color",
-        get_fill_color=[200, 200, 200],
-        get_line_color=[0, 0, 0],
-        get_line_width=2,
-        line_width_min_pixels=1,
+    # Set initial view state
+    view_state = pdk.ViewState(
+        latitude=gdf.geometry.centroid.y.mean(),
+        longitude=gdf.geometry.centroid.x.mean(),
+        zoom=9,
+        pitch=45 if show_3d else 0,
     )
 
-    # tooltip = {"text": "Name: {NAME}"}
-
-    # tooltip_value = f"<b>Value:</b> {median_listing_price}""
-    tooltip = {
-        "html": "<b>Name:</b> {NAME}<br><b>Value:</b> {"
-        + selected_col
-        + "}<br><b>Date:</b> "
-        + selected_period
-        + "",
-        "style": {"backgroundColor": "steelblue", "color": "white"},
-    }
-
-    layers = [geojson]
-    if show_nodata:
-        layers.append(geojson_null)
-
+    # Create PyDeck chart
     r = pdk.Deck(
-        layers=layers,
-        initial_view_state=initial_view_state,
-        map_style="light",
-        tooltip=tooltip,
+        layers=[layer],
+        initial_view_state=view_state,
+        map_style="mapbox://styles/mapbox/light-v9",
+        tooltip={"text": "{NAME}\n{" + selected_attribute + "}"}
     )
 
-    row3_col1, row3_col2 = st.columns([6, 1])
-
-    with row3_col1:
-        st.pydeck_chart(r)
-    with row3_col2:
-        st.write(
-            cm.create_colormap(
-                palette,
-                label=selected_col.replace("_", " ").title(),
-                width=0.2,
-                height=3,
-                orientation="vertical",
-                vmin=min_value,
-                vmax=max_value,
-                font_size=10,
-            )
-        )
-    row4_col1, row4_col2, row4_col3 = st.columns([1, 2, 3])
-    with row4_col1:
-        show_data = st.checkbox("Show raw data")
-    with row4_col2:
-        show_cols = st.multiselect("Select columns", data_cols)
-    with row4_col3:
-        show_colormaps = st.checkbox("Preview all color palettes")
-        if show_colormaps:
-            st.write(cm.plot_colormaps(return_fig=True))
-    if show_data:
-        if scale == "National":
-            st.dataframe(gdf[["NAME", "GEOID"] + show_cols])
-        elif scale == "State":
-            st.dataframe(gdf[["NAME", "STUSPS"] + show_cols])
-        elif scale == "County":
-            st.dataframe(gdf[["NAME", "STATEFP", "COUNTYFP"] + show_cols])
-        elif scale == "Metro":
-            st.dataframe(gdf[["NAME", "CBSAFP"] + show_cols])
-        elif scale == "Zip":
-            st.dataframe(gdf[["GEOID10"] + show_cols])
+    # Display the map
+    st.pydeck_chart(r)
+
+    # Display color scale
+    st.write(color_scale)
+
+    # Option to show raw data
+    if st.checkbox("Show raw data"):
+        st.write(gdf[[selected_attribute, 'NAME']])
 
 
 app()