{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9ca177b5",
   "metadata": {},
   "source": [
    "# Radar Plan Position Indicator (PPI) in 3D\n",
    "\n",
    "This is adopted from [this example](https://docs.openradarscience.org/projects/xradar/en/stable/notebooks/plot-ppi.html) to visualize sweeps of radar data as a 3D volume. The 3D visualization here help us gather insight into the 3D structure storms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8b087a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import xradar as xd\n",
    "import pyvista as pv\n",
    "import pvxarray  # Import to register accessor\n",
    "from open_radar_data import DATASETS\n",
    "\n",
    "pv.set_plot_theme(\"document\")\n",
    "pv.set_jupyter_backend(\"server\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dc11e2d-e134-47f4-b0f7-d7ccf9742822",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temporary manual mode override; waiting for pyvista release including changes from https://github.com/pyvista/pyvista/pull/5798\n",
    "\n",
    "pv.global_theme.trame.server_proxy_enabled = False\n",
    "pv.global_theme.trame.jupyter_extension_enabled = True"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ff462499",
   "metadata": {},
   "source": [
    "Fetching CfRadial1 radar data file from open-radar-data repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72e62a95",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = DATASETS.fetch(\"cfrad.20080604_002217_000_SPOL_v36_SUR.nc\")\n",
    "radar = xd.io.open_cfradial1_datatree(filename)\n",
    "radar = radar.xradar.georeference()  # Add georeferencing to plot in 3D\n",
    "list(radar.children)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "42d1af27",
   "metadata": {},
   "source": [
    "Now we need to stack all of the sweeps on top of each other to create a 3D data volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc4a1f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds_list = []\n",
    "for key in list(radar.children):\n",
    "    if \"sweep\" in key:\n",
    "        ds_list.append(radar[key].ds.drop_duplicates(dim=\"azimuth\"))\n",
    "ds = xr.concat(ds_list, dim=\"sweep\")\n",
    "ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "24717b0f",
   "metadata": {},
   "source": [
    "Use `pyvista-xarray`'s accessor to create a PyVista mesh from the data volume. This will build a curvilinear grid (`pyvista.StructuredGrid`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3a3239b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mesh = ds[\"DBZ\"].pyvista.mesh(x=\"x\", y=\"y\", z=\"z\")\n",
    "mesh"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ebd3f0c6",
   "metadata": {},
   "source": [
    "Preview the data volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e4da30",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mesh.plot(clim=(-20, 60))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f87e9d75",
   "metadata": {},
   "source": [
    "Pseudo-volume rendering with transparent contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4f3f3b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pl = pv.Plotter()\n",
    "pl.add_mesh(mesh.contour(), opacity=0.9, clim=(-20, 60), lighting=False)\n",
    "pl.show(jupyter_backend=\"static\")"
   ]
  }
 ],
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