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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we show how to quickly display single-band image tiles for a [RasterFrame](https://rasterframes.io) data structure. Tiles are subsets of scenes, where scenes are discrete instances of Earth observation data with specified spatial extent, datetime, and coordinate reference system (CRS).\n",
    "\n",
    "# Import Libraries\n",
    "\n",
    "We start by importing the Python libraries used in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from earthai.init import *\n",
    "from IPython.display import display_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query the Earth OnDemand Catalog\n",
    "\n",
    "Next we query the Earth OnDemand catalog to obtain imagery using the `earth_ondemand.read_catalog` function. This example queries the [MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance (NBAR)](https://lpdaac.usgs.gov/products/mcd43a4v006/) product centered on a specific point with latitude/longitude coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = earth_ondemand.read_catalog(\n",
    "    geo='POINT(-110.0 44.5)',\n",
    "    start_datetime='2018-07-01',\n",
    "    end_datetime='2018-07-05',\n",
    "    collections='mcd43a4',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the Query Results into a RasterFrame\n",
    "\n",
    "Now we read the results of the Earth OnDemand query into a RasterFrame using the `spark.read.raster` function.  In this example we read in only the red and near-infrared (NIR) MODIS bands, B01 and B02. For clarity we also rename the columns **red** and **nir** using the `withColumnRenamed` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = spark.read.raster(cat, \n",
    "                       catalog_col_names=['B01','B02']) \\\n",
    "          .withColumnRenamed('B01', 'red') \\\n",
    "          .withColumnRenamed('B02', 'nir')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the Image Tiles\n",
    "\n",
    "To visualize these tiles, we can use the `select` method on the RasterFrame. As shown below, `select` shows only the top five rows of **rf**, and displays separate, single-band renderings of the **red** and **nir** tiles using the Viridis color palette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.select('red', 'nir')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view more than the top five rows, we can use the `display_html` function from the IPython.display module, which we imported above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display_html(rf.select('red','nir').showHTML(10), \n",
    "             raw=True)"
   ]
  }
 ],
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