{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Plotting Quantified Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from astropy import units as u\n",
    "\n",
    "import astropy_xarray  # noqa: F401\n",
    "\n",
    "# to be able to read unit attributes following the CF conventions\n",
    "# import cf_xarray.units  # must be imported before astropy_xarray\n",
    "u.set_enabled_aliases(\n",
    "    {\n",
    "        \"degK\": u.Kelvin,\n",
    "        \"K\": u.Kelvin,\n",
    "        \"degrees_north\": u.deg,\n",
    "        \"degrees_east\": u.deg,\n",
    "    },\n",
    ")\n",
    "\n",
    "xr.set_options(display_expand_data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "## load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
    "data = ds.air\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## quantify the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<strong>Note:</strong> this example uses the data provided by the <code>xarray.tutorial</code> functions. As such, the <code>units</code> attributes follow the CF conventions, which <code>astropy</code> does not understand by default. To still be able to read them, registry be aliases can be used. For more information, see <a href=\"https://github.com/xarray-contrib/cf-xarray\"><tt>cf-xarray</tt></a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "quantified = data.astropy.quantify()\n",
    "quantified"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "## work with the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_means = quantified.astropy.sel(time=\"2013\").groupby(\"time.month\").mean()\n",
    "monthly_means"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "Most operations will preserve the units but there are some which will drop them (see the [duck array integration status](https://xarray.pydata.org/en/stable/user-guide/duckarrays.html#missing-features) page). To work around that there are unit-aware versions on the `.astropy` accessor. For example, to select data use `.astropy.sel` instead of `.sel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_means.sel(\n",
    "    lat=u.Quantity(4350, \"arcmin\"),\n",
    "    lon=u.Quantity(12000, \"arcmin\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "## plot\n",
    "\n",
    "`xarray`'s plotting functions will cast the data to `numpy.ndarray`, so we need to \"dequantify\" first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_means.astropy.dequantify(format=\"unicode\").plot.imshow(col=\"month\", col_wrap=4)"
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
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