{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "<a href=\"https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/35_geemap_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
    "\n",
    "Uncomment the following line to install [geemap](https://geemap.org) if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "## Import geemap library\n",
    "\n",
    "The [geemap](https://github.com/gee-community/geemap) Python package has two plotting backends: [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium). A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that Google Colab currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, geemap will automatically use the folium plotting backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Create an interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Add Earth Engine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Earth Engine dataset\n",
    "image = ee.Image(\"USGS/SRTMGL1_003\")\n",
    "\n",
    "# Set visualization parameters.\n",
    "vis_params = {\n",
    "    \"min\": 0,\n",
    "    \"max\": 4000,\n",
    "    \"palette\": [\"006633\", \"E5FFCC\", \"662A00\", \"D8D8D8\", \"F5F5F5\"],\n",
    "}\n",
    "\n",
    "# Print the elevation of Mount Everest.\n",
    "xy = ee.Geometry.Point([86.9250, 27.9881])\n",
    "elev = image.sample(xy, 30).first().get(\"elevation\").getInfo()\n",
    "print(\"Mount Everest elevation (m):\", elev)\n",
    "\n",
    "# Add Earth Engine layers to Map\n",
    "Map.addLayer(image, vis_params, \"DEM\")\n",
    "Map.addLayer(xy, {\"color\": \"red\"}, \"Mount Everest\")\n",
    "\n",
    "# Center the map based on an Earth Engine object or coordinates (longitude, latitude)\n",
    "# Map.centerObject(xy, 4)\n",
    "Map.setCenter(86.9250, 27.9881, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "## Display the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Map"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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