{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# plotting quantified data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to be able to read unit attributes following the CF conventions\n",
    "import cf_xarray.units  # noqa: F401  # must be imported before pint_xarray\n",
    "import xarray as xr\n",
    "\n",
    "import pint_xarray  # noqa: F401\n",
    "from pint_xarray import unit_registry as ureg\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>pint</code> does not understand by default. To still be able to read them we are using the registry provided by <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.pint.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.pint.to(\"degC\").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 `.pint` accessor. For example, to select data use `.pint.sel` instead of `.sel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_means.sel(\n",
    "    lat=ureg.Quantity(4350, \"angular_minute\"),\n",
    "    lon=ureg.Quantity(12000, \"angular_minute\"),\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.pint.dequantify(format=\"~P\").plot.imshow(col=\"month\", col_wrap=4)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}