#!/usr/bin/env python
# coding: utf-8

# In[1]:


import geemap
import ee
import folium
import geopandas as gpd


# In[2]:


Map = geemap.Map()

esa = ee.ImageCollection("ESA/WorldCover/v100").first()
esa_vis = {'bands': ['Map']}

Map.addLayer(esa, esa_vis, "ESA Land Cover")
Map.add_legend(title="ESA Land Cover", builtin_legend='ESA_WorldCover')

Map


# In[18]:


datadir = 'C:/Users/jtrum/world_bank/data/'

# Define the path to your GeoJSON file
aoi_path = datadir + 'luanda2clean.geojson'

# Load the GeoJSON as a Feature
aoi = geemap.geojson_to_ee(aoi_path)

# Create a map
Map = geemap.Map()
#choose a different basemap
Map.add_basemap('CartoDB.DarkMatter')

# Load the ESA Land Cover data
esa = ee.ImageCollection("ESA/WorldCover/v100").first()

# Filter the data to your AOI
esa = esa.clip(aoi)

esa_vis = {'bands': ['Map']}

# Add the ESA Land Cover layer to the map
Map.addLayer(esa, esa_vis, "ESA Land Cover")

# Add a legend to the map
Map.add_legend(title="ESA Land Cover", builtin_legend='ESA_WorldCover')
#customize colors in the legend
Map.add_legend(colors=[("#FF0000",), ("#008000",), ("#0000FF",), ("#FFFF00",), ("#800080",), ("#FFA500",), ("#FFC0CB",), ("#A52A2A",), ("#808080",), ("#00FFFF",), ("#FF00FF",)])

#start the zoom at the center of the AOI
Map.centerObject(aoi, 10)



# Display the map
Map


# In[35]:


df = geemap.image_area_by_group(esa, scale=1000, denominator=1e6, decimal_places=4, verbose=True)


# In[36]:


df.dtypes


# In[ ]:





# In[37]:


df['percentage'] = df['percentage']*100

# reset the index and rename the columns to be the name of the Land Cover Type
df = df.reset_index().rename(columns={'group': 'type'})


# Create a mapping dictionary with numbers as strings
type_mapping = {
    '10': 'Trees',
    '20': 'Shrublands',
    '30': 'Grasslands',
    '40': 'Croplands',
    '50': 'Built-Up',
    '60': 'Barren',
    '70': 'Snow and Ice',
    '80': 'Water Bodies',
    '90': 'Herbaceous Wetlands',
    '95': 'Mangroves',
    '100': 'Moss and Lichen'
}

# Use the mapping dictionary to replace values in the 'type' column
df['type'] = df['type'].replace(type_mapping)
df
                                


# In[39]:


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Create a donut chart
plt.figure(figsize=(8, 8))
plt.pie(df['percentage'], labels=df['type'], autopct='%1.1f%%', startangle=90, wedgeprops=dict(width=0.4, edgecolor='w'))

# Add a circle in the center to make it a donut chart
circle = plt.Circle((0, 0), 0.3, color='white')
plt.gca().add_artist(circle)

# Set aspect ratio to equal to ensure a circular chart
plt.axis('equal')

# Add a title
plt.title('Donut Chart of Type Distribution')
plt.show;
# Show the chart