{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/distributed/dashboard/core.py:72: UserWarning: \n",
      "Port 8787 is already in use. \n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the diagnostics dashboard on a random port instead.\n",
      "  warnings.warn(\"\\n\" + msg)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33214</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:33662/status' target='_blank'>http://127.0.0.1:33662/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>2</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>2.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:33214' processes=2 threads=4, memory=2.00 GB>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path1 = '~/shared-cmip6-for-ns1000k/historical/CESM2-WACCM/r1i1p1f1/' +\\\n",
    "'so2_AERmon_CESM2-WACCM_historical_r1i1p1f1_gn_185001-201412.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks={\n",
    "    'time' : 10,  \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = xr.open_dataset(path1, chunks=chunks, use_cftime = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = dataset['so2'].isel(lev=69).mean(['lat','lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.to_netcdf('./test.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4212201240>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "da.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}