modify pages

pages/1_🌲_Japan_Vegetation_Cover.py

@@ -2,9 +2,7 @@ import streamlit as st
 import geopandas as gpd
 import pydeck as pdk
 import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-import numpy as np
+from branca import colormap as cm
 import pathlib
 import os
 import requests
@@ -23,7 +21,7 @@ st.sidebar.info(
 st.sidebar.title("Contact")
 st.sidebar.info(
     """
-    Kunihiko Fujiwara (https://ual.sg/author/kunihiko-fujiwara/)
+    Qiusheng Wu at [wetlands.io](https://wetlands.io) | [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
     """
 )
 
@@ -77,7 +75,7 @@ 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. 
-        The data sources include [Vegetation Cover Fraction](https://zenodo.org/records/5553516) from a [Research Project](https://doi.org/10.3130/aijt.28.521), 
+        The data sources include [Vegetation Cover Fraction](https://zenodo.org/records/5553516) from a research project (https://doi.org/10.3130/aijt.28.521), 
         and [Cartographic Boundary Files](https://www.e-stat.go.jp/gis/statmap-search?page=1&type=2&aggregateUnitForBoundary=A&toukeiCode=00200521&toukeiYear=2015&serveyId=A002005212015&coordsys=1&format=shape&datum=2000) from Census of Japan 2015.
         """
     )
@@ -115,14 +113,9 @@ def app():
         else:
             elev_scale = 1
 
-    norm = mcolors.Normalize(vmin=gdf[selected_attribute].min(), vmax=gdf[selected_attribute].max())
-    cmap = plt.get_cmap(palette, n_colors)
-    
-    def get_color(value):
-        rgba = cmap(norm(value))
-        return [int(rgba[0]*255), int(rgba[1]*255), int(rgba[2]*255)]
-
-    gdf['color'] = gdf[selected_attribute].apply(get_color)
+    color_scale = cm.LinearColormap(colors=palette, vmin=gdf[selected_attribute].min(), vmax=gdf[selected_attribute].max(), n=n_colors)
+    gdf['color'] = gdf[selected_attribute].apply(lambda x: color_scale(x))
+    gdf['color'] = gdf['color'].apply(lambda x: [int(x[1:3], 16), int(x[3:5], 16), int(x[5:7], 16)])
 
     gdf_null = select_null(gdf, selected_attribute)
     gdf = select_non_null(gdf, selected_attribute)
@@ -178,12 +171,7 @@ def app():
 
     st.pydeck_chart(r)
 
-    # Display color scale
-    fig, ax = plt.subplots(figsize=(6, 1))
-    fig.subplots_adjust(bottom=0.5)
-    cbar = fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, orientation='horizontal')
-    cbar.set_label(selected_attribute)
-    st.pyplot(fig)
+    st.write(color_scale)
 
     if st.checkbox("Show raw data"):
         st.write(gdf[[selected_attribute, 'NAME']])