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

# To follow this tutorial, you will need to [sign up](https://datapane.com/accounts/signup/) for an account with <https://datapane.com>, then install and authenticate the `datapane` Python package. More information can be found [here](https://docs.datapane.com/tutorials/tut-getting-started). 
# 
# - `pip install datapane`
# - `datapane login`
# - `datapane ping`

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import ee 
import geemap.eefolium as geemap


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# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)

# Use an elevation dataset and terrain functions to create
# a custom visualization of topography.

# Load a global elevation image.
elev = ee.Image('USGS/GMTED2010')

# Zoom to an area of interest.
Map.setCenter(-121.069, 50.709, 6)

# Add the elevation to the map.
Map.addLayer(elev, {}, 'elev')

# Use the terrain algorithms to compute a hillshade with 8-bit values.
shade = ee.Terrain.hillshade(elev)
Map.addLayer(shade, {}, 'hillshade', False)

# Create a "sea" variable to be used for cartographic purposes
sea = elev.lte(0)
Map.addLayer(sea.mask(sea), {'palette':'000022'}, 'sea', False)

# Create a custom elevation palette from hex strings.
elevationPalette = ['006600', '002200', 'fff700', 'ab7634', 'c4d0ff', 'ffffff']
# Use these visualization parameters, customized by location.
visParams = {'min': 1, 'max': 3000, 'palette': elevationPalette}

# Create a mosaic of the sea and the elevation data
visualized = ee.ImageCollection([
  # Mask the elevation to get only land
  elev.mask(sea.Not()).visualize(**visParams),
  # Use the sea mask directly to display sea.
  sea.mask(sea).visualize(**{'palette':'000022'})
]).mosaic()

# Note that the visualization image doesn't require visualization parameters.
Map.addLayer(visualized, {}, 'elev palette', False)

# Convert the visualized elevation to HSV, first converting to [0, 1] data.
hsv = visualized.divide(255).rgbToHsv()
# Select only the hue and saturation bands.
hs = hsv.select(0, 1)
# Convert the hillshade to [0, 1] data, as expected by the HSV algorithm.
v = shade.divide(255)
# Create a visualization image by converting back to RGB from HSV.
# Note the cast to byte in order to export the image correctly.
rgb = hs.addBands(v).hsvToRgb().multiply(255).byte()
Map.addLayer(rgb, {}, 'styled')

states = ee.FeatureCollection('TIGER/2018/States')
Map.addLayer(ee.Image().paint(states, 0, 2), {}, "US States")


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# Add layer control to the map.
Map.setControlVisibility()
# Display the map.
Map


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Map.publish(name='gee_folium_map', headline='Terrain Visualization', visibility='PUBLIC', overwrite=True)