{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"top\"></a>\n",
    "<div style=\"width:1000 px\">\n",
    "\n",
    "<div style=\"float:right; width:98 px; height:98px;\">\n",
    "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n",
    "</div>\n",
    "    \n",
    "![under construction](https://images2.minutemediacdn.com/image/upload/c_fit,f_auto,fl_lossy,q_auto,w_728/v1555999902/shape/mentalfloss/under_construction1_0.gif?itok=Pn9g_wu6)\n",
    "    \n",
    "<h1>xarray: Calculations</h1>\n",
    "<h3>Unidata AMS 2021 Student Conference</h3>\n",
    "\n",
    "<div style=\"clear:both\"></div>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "Using [xarray](http://xarray.pydata.org/en/stable/), you can perform many of the same calculations as you would with a basic NumPy array, but with the added benefit of name-based rather than position-based dimensions/axes as well as label-based lookups. In this notebook, we'll be demonstrating several of these types of calculations (except for a specific subset of calculations, called aggregations, which combine data together using some summary statistic...these get [their own training notebook](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/analysis/xarray_aggregations.ipynb)).\n",
    "\n",
    "For a full overview of xarray calculations, see [the relevant page in xarray's documentation](http://xarray.pydata.org/en/stable/computation.html).\n",
    "\n",
    "<div style=\"float:right; width:250 px\"><img src=\"../../instructors/images/xarray_data_access_screenshot.png\" alt=\"HTML repr for a basic NetCDF dataset opened with xarray\" style=\"height: 300px;\"></div>\n",
    "\n",
    "\n",
    "### Focuses\n",
    "\n",
    "- Show how ufuncs work smoothly on xarray data\n",
    "- Demonstrate pandas-like missing data calculations\n",
    "- Learn artithmetic operations take care of dimension broadcasting automatically\n",
    "- Learn how to wrap custom computations\n",
    "- Demonstrate using calculations from an external library (xskillscore, for verification statistics)\n",
    "\n",
    "### Objectives\n",
    "\n",
    "1. [...](#1.-Access-local-NetCDF-data)\n",
    "1. [...](#2.-Access-remote-OPENDAP-data)\n",
    "1. [...](#3.-Access-local-GRIB-data)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Section 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"#top\">Top</a>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Section 2\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"#top\">Top</a>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Section 3\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a href=\"#top\">Top</a>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See also\n",
    "\n",
    "There's a lot more to xarray than just calculations! Be sure to take a look at the other xarray training notebooks linked below:\n",
    "\n",
    "- [Xarray data access](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/dataAccess/xarray_data_access.ipynb)\n",
    "- [Indexing with xarray](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/analysis/xarray_indexing.ipynb)\n",
    "- [Interpolation and regridding with xarray](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/analysis/xarray_interpolation.ipynb)\n",
    "- [Xarray aggregation operations](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/analysis/xarray_aggregations.ipynb)\n",
    "- [MetPy with xarray](https://nbviewer.jupyter.org/github/Unidata/pyaos-ams-2021/blob/master/notebooks/analysis/metpy_and_xarray.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"#top\">Top</a>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pyaos-ams-2021]",
   "language": "python",
   "name": "conda-env-pyaos-ams-2021-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}