{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Plan Position Indicator\n",
    "A Plan Position Indicator (PPI) plot is a common plot requested by radar scientists. Let's show how to create this plot using `xradar`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmweather  # noqa\n",
    "from open_radar_data import DATASETS\n",
    "\n",
    "import xradar as xd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Read in some data\n",
    "\n",
    "Fetching CfRadial1 radar data file from [open-radar-data](https://github.com/openradar/open-radar-data) repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = DATASETS.fetch(\"cfrad.20080604_002217_000_SPOL_v36_SUR.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "Read the data using the `cfradial1` engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "radar = xd.io.open_cfradial1_datatree(filename, first_dim=\"auto\")\n",
    "display(radar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "## Add georeferencing\n",
    "We can use the georeference function, or the accessor to add our georeference information!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "### Georeference Accessor\n",
    "If you prefer the accessor (`.xradar.georefence()`), this is how you would add georeference information to your radar object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "radar = radar.xradar.georeference()\n",
    "display(radar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "Please observe, that the additional coordinates `x`, `y`, `z` have been added to the dataset. This will also add `spatial_ref` CRS information on the used Azimuthal Equidistant Projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "radar[\"sweep_0\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "### Use the Function\n",
    "We can also use the function `xd.geoference.get_x_y_z_tree` function if you prefer that method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "radar = xd.georeference.get_x_y_z_tree(radar)\n",
    "display(radar[\"sweep_0\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "## Plot our Data\n",
    "\n",
    "### Plot simple PPI\n",
    "\n",
    "Now, let's create our PPI plot! We just use the newly created 2D-coordinates `x` and `y` to create a meshplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [],
   "source": [
    "radar[\"sweep_0\"][\"DBZ\"].plot(x=\"x\", y=\"y\", cmap=\"ChaseSpectral\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "### Plot PPI on map with cartopy\n",
    "\n",
    "If you have `cartopy` installed, you can easily plot on maps. We first have to extract the CRS from the dataset and to wrap it in a `cartopy.crs.Projection`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [],
   "source": [
    "proj_crs = xd.georeference.get_crs(radar[\"sweep_0\"].ds)\n",
    "cart_crs = cartopy.crs.Projection(proj_crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "Second, we create a matplotlib GeoAxes and a nice map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection=cartopy.crs.PlateCarree())\n",
    "radar[\"sweep_0\"][\"DBZ\"].plot(\n",
    "    x=\"x\",\n",
    "    y=\"y\",\n",
    "    cmap=\"ChaseSpectral\",\n",
    "    transform=cart_crs,\n",
    "    cbar_kwargs=dict(pad=0.075, shrink=0.75),\n",
    ")\n",
    "ax.coastlines()\n",
    "ax.gridlines(draw_labels=True)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}