{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://demo.leafmap.org/lab/index.html?path=notebooks/05_load_raster.ipynb)\n",
    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/leafmap/blob/master/docs/notebooks/05_load_raster.ipynb)\n",
    "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeos/leafmap/HEAD)\n",
    "\n",
    "**Loading local raster datasets with leafmap**\n",
    "\n",
    "Uncomment the following line to install [leafmap](https://leafmap.org) if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install leafmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "To follow this tutorial, you need to install the [leafmap](https://leafmap.org) and [xarray_leaflet](https://github.com/davidbrochart/xarray_leaflet) Python packages. Use the following conda commands to create a conda env and install packages. Note that `xarray_leaflet` does not work properly on Windows ([source](https://github.com/davidbrochart/xarray_leaflet/issues/30)). Also, the `add_raster` function is only available for the ipyleaflet plotting backend. Therefore, Google Colab is not supported. Use the binder link above instead.  \n",
    "\n",
    "- `conda create -n gee python`\n",
    "- `conda activate gee`\n",
    "- `conda install mamba -c conda-forge`\n",
    "- `mamba install leafmap xarray_leaflet -c conda-forge`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "Use the ipyleaflet plotting backend. The folium plotting backend does not support the `add_raster` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import leafmap.leafmap as leafmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "Specify input raster datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat = \"landsat.tif\"\n",
    "dem = \"dem.tif\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "Download samples raster datasets\n",
    "\n",
    "More datasets can be downloaded from https://viewer.nationalmap.gov/basic/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "url1 = \"https://opengeos.org/data/raster/landsat7.tif\"\n",
    "url2 = \"https://opengeos.org/data/raster/srtm90.tif\"\n",
    "satellite = leafmap.download_file(url1, \"landsat.tif\")\n",
    "dem = leafmap.download_file(url2, \"dem.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "Create an interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "Add local raster datasets to the map\n",
    "\n",
    "More colormap can be found at https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.add_raster(dem, colormap=\"terrain\", layer_name=\"DEM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.add_raster(landsat, bands=[5, 4, 3], layer_name=\"Landsat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "Display the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "m"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}