{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Universal Format (UF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmweather  # noqa\n",
    "import xarray as xr\n",
    "from open_radar_data import DATASETS\n",
    "\n",
    "import xradar as xd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "## Download\n",
    "\n",
    "Fetching Universal Format radar data file from [open-radar-data](https://github.com/openradar/open-radar-data) repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import atexit\n",
    "from pathlib import Path\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "tmpdir_obj = TemporaryDirectory()\n",
    "atexit.register(tmpdir_obj.cleanup)  # remove even if you forget\n",
    "tmpdir = Path(tmpdir_obj.name)\n",
    "\n",
    "\n",
    "def get_temp_file(fname):\n",
    "    import gzip\n",
    "    import shutil\n",
    "\n",
    "    fnamei = Path(DATASETS.fetch(fname))\n",
    "    fnameo = tmpdir / fnamei.stem\n",
    "    with gzip.open(fnamei) as fin:\n",
    "        with open(fnameo, \"wb\") as fout:\n",
    "            shutil.copyfileobj(fin, fout)\n",
    "    return fnameo\n",
    "\n",
    "\n",
    "fname = get_temp_file(\"20110427_164233_rvp8-rel_v001_SUR.uf.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## xr.open_dataset\n",
    "\n",
    "Making use of the xarray `uf` backend. We also need to provide the group. Note, that we are using CfRadial2 group access pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(fname, group=\"sweep_0\", engine=\"uf\")\n",
    "display(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.testing.assert_almost_equal(ds.sweep_fixed_angle.values, 0.703125)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "### Plot Time vs. Azimuth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.azimuth.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "### Plot Range vs. Time\n",
    "\n",
    "We need to sort by time and specify the y-coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.DBZH.sortby(\"time\").plot(y=\"time\", cmap=\"HomeyerRainbow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "### Plot Range vs. Azimuth\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.DBZH.plot(cmap=\"HomeyerRainbow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "## backend_kwargs\n",
    "\n",
    "Beside `first_dim` there are several additional backend_kwargs for the `uf` backend, which handle different aspects of angle alignment. This comes into play, when azimuth and/or elevation arrays are not evenly spacend and other issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "help(xd.io.UFBackendEntrypoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(fname, group=\"sweep_0\", engine=\"uf\", first_dim=\"time\")\n",
    "display(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "## open_uf_datatree\n",
    "\n",
    "The same works analoguous with the datatree loader. But additionally we can provide a sweep string, number or list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17",
   "metadata": {},
   "outputs": [],
   "source": [
    "help(xd.io.open_uf_datatree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = xd.io.open_uf_datatree(fname, sweep=4)\n",
    "display(dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "### Plot Sweep Range vs. Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree[\"sweep_4\"].ds.DBZH.sortby(\"time\").plot(y=\"time\", cmap=\"HomeyerRainbow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "### Plot Sweep Range vs. Azimuth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree[\"sweep_4\"].ds.DBZH.plot(cmap=\"HomeyerRainbow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = xd.io.open_uf_datatree(fname, sweep=\"sweep_8\")\n",
    "display(dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = xd.io.open_uf_datatree(fname, sweep=[0, 1, 8])\n",
    "display(dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree[\"sweep_0\"][\"sweep_fixed_angle\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree[\"sweep_8\"][\"sweep_fixed_angle\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = xd.io.open_uf_datatree(fname)\n",
    "display(dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree[\"sweep_1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "## clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpdir_obj.cleanup()"
   ]
  }
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